Resources, Conservation & Recycling 151 (2019) 104413
Contents lists available at ScienceDirect
Resources, Conservation & Recycling journal homepage: www.elsevier.com/locate/resconrec
Full length article
Development strategies for heavy duty electric battery vehicles: Comparison between China, EU, Japan and USA
M. Naumanena,b, , T. Uusitaloa, E. Huttunen-Saarivirtaa, R. van der Havea a b
VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, FI-02044 VTT, Finland Statistics Finland, FI-00022 Statistics Finland, Finland
A R T I C LE I N FO
A B S T R A C T
Keywords: Heavy duty vehicle Electric vehicle Battery Literature analysis Statistical methods Raw material
This paper investigates the development of heavy duty electric battery vehicles through analysing research papers and patents and identiﬁes emerging technology areas by using a generative probabilistic model, Latent Dirichlet Allocation (LDA). The focus of the analysis is on literature and patents published since 2010 up to date, summing up altogether more than 25 000 references. We divide the references into eight topics: operating an electric vehicle, its control, motor operations, thermal management, battery module, battery technologies, electric vehicle (EV) infrastructure and charging EV. Because battery technologies are of strategic importance in technological competition for companies and relate to the areal raw material base, we take a more detailed look at this ﬁeld. The results from publication analysis are presented for China, Europe, Japan and USA. The distribution of raw materials required for heavy duty vehicles shows interesting correlations with the national development strategies. China holds reserves and/or mine production for all key raw materials categories (battery, magnet and electric cabling) needed in heavy duty electric vehicles. In addition to having an extensive raw materials base, China has protected intellectual property rights in many areas thus defending the raw materials also by controlling the access to the technologies. USA has some raw material reserves and/or production among all three raw material categories also, but their global contribution is signiﬁcantly lower. Japan, with very narrow natural resource base, has very limited patenting or scientiﬁc publishing in any of the studied sub-areas of heavy duty electric vehicle development.
1. Introduction Transport accounts for a large share of fossil fuel consumption in Europe and is responsible for a quarter of European Union (EU) greenhouse gas emissions. Road transport alone contributes to one-ﬁfth of the total CO2 emissions in EU, with heavy duty vehicles, i.e., trucks, buses and coaches1, producing more than 16% of the transportation CO2 emissions worldwide (Çabukoglu et al., 2018). Electriﬁcation of road transport provides a good opportunity to improve its sustainability, provided that electricity comes from renewable energy sources (European Commission, 2017a). Indeed, sale of electric vehicles (EV) has steadily increased during the recent years parallel to the lowering cost of EV batteries. These trends are expected to continue in the near future as the cost competitiveness gap between EVs and internal combustion engine vehicles is foreseen to narrow further. Assessments of country targets, original equipment manufacturer (OEM)
announcements and scenarios on electric car deployment indicate that the electric car stock will range between 9 million and 20 million by 2020 and between 40 million and 70 million by 2025 (International Energy Agency, 2017). Battery technologies are in the core of future electric mobility. The progress in these technologies, covering the whole value chain into the product and the entire circular economy: the exploration, mining and processing of raw materials, battery design and production, use and end-of-life product management (collection, reuse and recycling), determines their overall economy and sustainability (Lebedeva et al., 2017). Most of the current battery technologies are based on lithium (lithium ion, lithium ion polymer) or nickel (nickel cadmium, nickel metal hydride) (Manzetti and Mariasiu, 2015). However, within one battery type, there may be several diﬀerent chemistry variations. As an example, lithium ion batteries typically contain graphitic carbon as an anode material, whereas multiple cathode materials are in commercial
Corresponding author. E-mail address: [email protected]
ﬁ (M. Naumanen). 1 According to European Commission (2014) HDVs are deﬁned as freight vehicles of more than 3.5 tonnes (trucks) or passenger transport vehicles of more than 8 seats (buses and coaches). (European Comission, 2014) https://doi.org/10.1016/j.resconrec.2019.104413 Received 14 February 2019; Received in revised form 26 June 2019; Accepted 15 July 2019 0921-3449/ © 2019 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
currently insuﬃcient to displace the internal combustion engine regime. Suppliers pursue proﬁt by their oﬀerings, yet fully electric vehicles have been loss-making products until today; their sales have been driven by positive expectations and active support policies from business and government. It is acknowledged that 80% of the total environmental impact of vehicles -light or heavy- throughout all life cycle are generated during the use phase (Saidani et al., 2018). Indeed, Sen et al. (2017) have carried out a lifecycle assessment comparing biodiesel, compressed natural gas, hybrid and battery electric trucks to the diesel-powered trucks in USA. According to their analysis, battery electric vehicles could reduce greenhouse gas emissions by 63% providing that electricity is generated from renewable energy sources. The battery powering therefore signiﬁcantly improves the lifecycle performance of a truck along with ambient air quality. Talebian et al. (Talebian et al., 2018) have predicted the eﬀect of all-electric trucking on greenhouse gas emission reductions in British Columbia by 2040. Their results show that all-electric trucking, when reaching the level of at least 65% of the stock, will reduce 64% of the emissions from road freight transport by 2040. Such stock share requires enforcement of strict ﬂeet emission regulations and early-market subsidies for manufacturers, customers and infrastructure developers to promote all-electric vehicles. Çabukoglu et al. (Çabukoglu et al., 2018) have investigated the electriﬁcation potential of the Swiss heavy-duty ﬂeet. They identiﬁed three conditions for a high electriﬁcation potential: 1) An exemption from maximum permissible weight regulations is needed. If electric trucks have to operate within the same legal conﬁnes as conventional trucks, the achievable CO2 avoidance does not justify the eﬀort. 2) Home bases of electric trucks require a high-capacity grid connection; currently, a minimum of 50 kW per vehicle is to be expected. 3) Truck electriﬁcation requires a supporting intra-day energy infrastructure, such as swapping stations. As demonstrated above, EU has been active in formulating development strategies for, e.g., battery technologies, even though they rely on several raw materials that primarily depend on import and policy actions, e.g., ﬂeet emission regulations, to facilitate the implementation of the recent technologies. In some other areas of the World, which may be much more abundant with respect to the several raw materials essential in electric battery vehicles, the strategies may not be narrated as clearly as in EU but are more reﬂected to concrete implementation of the strategies and the development activities. In this paper, the development strategies for heavy-duty electric battery vehicles are evaluated through patent and literature analyses and compared between four key ﬁnancial areas: China, EU, Japan and USA. These are correlated with the data on raw material resources in the corresponding areas, given that the transport electriﬁcation is highly raw-material intensive. We focused on countries that produced over 10% of the articles and patent (applications) in the ﬁeld. South Korea was the ﬁrst country not to meet this criterion. (Table 1) The research is motivated by the limited number of papers that are focussed on the heavy duty electric battery vehicles, thus we aim to bridge the identiﬁed gap in the literature. We aim to answer the following research questions: What are the central development trends related to heavy duty electric battery vehicles and the roles of the four areas (China, EU, Japan, USA)? How do the results correlate with local raw materials resources? What are the policy implications for Europe
use: lithium cobalt oxide (LCO, LiCoO2), lithium nickel cobalt aluminium oxide (NCA, LiNi0.8Co0.15Al0.05) and lithium nickel manganese cobalt oxide (NMC) in diﬀerent compositional variants (Olivetti et al., 2017). Among these electrode materials, natural graphite and cobalt are so-called Critical Raw Materials (CRM) for EU, i.e., strategically important for EU manufacturing industry, but heavily import dependent (European Commission, 2017b), whereas the recycling rate of lithium is exceptionally low, about 1% (UNEP, 2011). Besides battery solutions, another way to decarbonise heavy duty transport is through fuel cell technologies (Çabukoglu et al., 2019; Kast et al., 2017). Various types of fuel cells are available, with polymer electrolyte membrane (PEM) being widely used in fuel cell electric vehicles (Mayyas and Mann, 2019). Nevertheless, here fuel cell material solutions do not rely on CRM to such an extent as in the case of battery technologies. The main policy challenges related to battery technologies are the improvement in competitiveness, promotion of circular economy and decrease in the dependency on imported raw materials2. The Strategic Action Plan on Batteries by the European Commission (European Commission, 2018) is a response to these challenges, aiming at: 1) Securing access to raw materials from resource-rich countries outside EU; 2) Supporting European battery cells manufacturing at scale and a full competitive value chain in Europe by bringing key industry players and national and regional authorities together; 3) Strengthening industrial leadership through stepped-up EU research and innovation support to advanced and disruptive technologies in the batteries sector; 4) Developing and strengthening a highly skilled workforce all through the battery value chain; 5) Supporting the sustainability of EU battery cell manufacturing industry with the lowest environmental footprint possible; 6) Ensuring consistency with the broader enabling and regulatory framework in support of batteries and storage deployment. Nevertheless, we must remember that electriﬁcation of transport introduces changes also elsewhere in the design of vehicles and supporting infrastructure, not just in the power supply. For example, the ongoing electriﬁcation of transport has intensiﬁed the use of magnets (rich in rare earth elements, such as neodymium and dysprosium) in all vehicle types (including EVs and fuel cell vehicles) with great demand scenarios and that of cables and charging stations (copper) both in the vehicles and in the supporting infrastructure (Copper Development Association, 2017; Goodenough et al., 2018; Valero et al., 2018; X.-Y. Li et al., 2019). Several studies have been carried out regarding the impacts of electriﬁcation of transport sector (Harrison and Thiel, 2017; Wolinetz and Axsen, 2017; Dijk et al., 2016; Sen et al., 2017; Talebian et al., 2018; Çabukoglu et al., 2018). Harrison and Thiel (2017) have developed a system dynamics based market agent model to analyse passenger car powertrain technology transition within the EU until 2050. With a focus on subsidy scenarios for both infrastructure deployment and vehicle purchase, and set within the context of the EU ﬂeet emission regulations, they ﬁnd that there are important interactions between diﬀerent powertrain types and infrastructure provision. One conclusion from their study is that without stringent emission regulation targets in place for automobile manufacturers, a signiﬁcant transition towards e-mobility is unlikely to take place. Their ﬁndings also suggest that subsidies are only beneﬁcial in the earlier years of market introduction and should cover all technologies. Wolinetz and Axsen (2017) model how policy can inﬂuence the plug-in electric vehicle market evolution. With no-policy scenario they indicate that the annual plug-in electric vehicle sales will grow from 1.4% in 2020 to 7% in 2030. With strong demand-focused policies in place, 2030 market share likely ranges from 17% to 28%. These policies are purchase subsidies and charger rollout. In order to achieve 2030 market shares of over 30%, further policies are needed incentivizing or requiring carmakers to increase the availability and variety of plug-in electric vehicle makes and models. Dijk et al. (2016) have studied to what extent electric propulsion is disrupting the order in the automotive industry. Their analysis suggests that the disruptive niche of full-electric mobility is
Table 1 Countries/regions with the highest article and patent publication activity.
% of total
% of total
% of total
China European Union USA Japan South Korea
9856 2214 1961 2691 1764
51 % 11 % 10 % 14 % 9%
1261 1594 1091 199 341
21 % 26 % 18 % 3% 6%
11117 3808 3052 2890 2105
44 % 15 % 12 % 11 % 8%
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
based on the ﬁndings? Answers are sought for through literature analysis, concentrating on scientiﬁc research papers and patents, as research and patenting behavior in the automotive sector has been shown to reﬂect the actual research and development eﬀorts (van den Hoed, 2005). This study is structured as follows. Section 2 reviews the related literature, i.e. articles, patents and patent applications, as well as the methodologies. Section 3 presents the results of our analysis. Section 4 is devoted to a discussion of our analytical results, and Section 5 comprises the conclusions and some directions for further study.
(Liebesny et al., 1974), and its rich technological information is accessible online for long time periods. On the other hand, patent counts analysis per se does not reﬂect variation in technological quality or the commercialisation of inventions (Verspagen, 2007). In automotive industry, patent analysis has been used to understand the technological transformation of alternative vehicle technologies (van den Hoed, 2007; Frenken et al., 2004; Yang et al., 2013). Recent studies have developed from the mere quantiﬁcation of countries, authors or technologies and augmented the analysis with text mining (Yau et al., 2014). There are remarkable similarities between literature bibliometrics and patent bibliometrics, and they are both applicable to the same wide ranges of problems. (F. Narin, 1994) shows that there are striking similarities between literature and patent distributions of national productivity, inventor productivity, referencing cycles, citation impact and within country citation preferences. Although both academic publications and patents are textual data, there are diﬀerences between them in terms of purpose, quality and use of language. The purpose of a scientiﬁc or a business article is to communicate ﬁndings to the relevant research or business community and public. A patent is a legal document that gives the creator an exclusive right for their invention. Existing patent documents – or more speciﬁcally, their claims - and other publicly available materials cannot overlap. If something is public knowledge, it cannot be patented anymore. It follows that patent citation behavior is extremely complex because of multiple citers within the same patent: the patent examiner and the inventor/applicant. (Azagra-Caro Joaquín et al., 2011) contend that examiner citations are for the purpose of restricting patent claims, whereas inventor/applicant citations are for demonstrating prior work/ art related to the invention. (R. Li et al., 2014) found that the non-selfcitations by inventors are quite noisy and cannot indicate the linkages between science and technology and that self-citations by inventors are more appropriate for understanding the linkages. In order to guarantee the quality, peer review is usually used to ﬁlter and improve scientiﬁc articles. On the other hand, patents are reviewed by their legal requirements only. The heterogeneity between academic articles and patents enables many terms and constituents to appear in only one resource. This means that these terms and constituents do not contribute for the similarity calculation between these two realms of textual data. In order to reduce the negative impact on topic similarity calculation, shared terms and constituents could have a higher weight in it. In addition, one might link authors to observed terms and constituents in the academic literature, patents and patent applications in a speciﬁc R&D ﬁeld and hence, increase the number of linkages between these two resources.
2. Methodology 2.1. Identifying emerging technology areas and trends This study identiﬁes the rise and fall of emerging technology areas by using a generative probabilistic model, Latent Dirichlet Allocation (LDA), to discover key topics from documents (D. Blei et al., 2003; Yan and Zhou, 2015). We identify emerging topics using the abstracts of scientiﬁc articles and patents covering heavy duty electric battery vehicles. This approach facilitates identiﬁcation of emerging technology trends in the area to achieve opportunities for sustainable growth. In this context, the innovation in technological areas can be regarded as the eﬀort to continuously obtain business opportunities (Chen, 2016). Our approach is to examine both the structured and unstructured data. First, text mining is applied to the abstracts of scientiﬁc articles and patents covering heavy duty electric battery vehicles. Then, LDA, a topic modeling technique, is performed to identify the emerging topics in these articles and patents. The topic probabilities provided by the LDA are summarized by year. Many researchers have used text mining to extract valuable information based on textual data. The text-mining of unstructured data can be important when analyzing emerging technology areas (Han and So, 2015). It is a knowledge-discovery methodology that enables researchers to discern patterns and trends and possibly extract hidden knowledge. Unstructured data can include variables that represent information about the title, abstract, and claims, as well as the description of the invention (Daim et al., 2006). Analyzing topics from an unstructured speciﬁcation of articles and patents can provide meaningful information for innovation and technology opportunities in industry (Ma and Porter, 2015). The uncertainty related to technology evolution and prospects can be reduced by understanding the technology cycles. Indicators, such as patents, publications or citations, have been introduced as a major asset in technology forecasting (Watts and Porter, 1997). Patent applications, particularly in the manufacturing sector, such as automotive industry, can be eﬀectively utilised to forecast and scrutinise the trends of technological activities (Yoon and Park, 2004). Griliches (1990) argues that patent data remains a unique resource for the study of technical change. Patent documents contain lengthy and rich explanations of technical information that can be used to quantify the evolution of technologies over time (Daim et al., 2006). Patents can be used to measure the impact of R&D activities (Ernst, 1997), identiﬁcation of new business areas (Seol et al., 2011) and diﬀusion of technologies and technological trajectories (Liu and Shyu, 1997). Patent analysis can support the study of prioritisation of R&D programs (Jeon et al., 2011) and technological assessment of competitors (Francis Narin et al., 1987). The basis of a patent analysis lies in understanding the managerial implications of the results. The managerial implications of patent analysis have been drawn from multiple bodies of literature, such as technological evolution (Abernathy and Utterback, 1978). Considering patents as the source of information to learn about technological development has both beneﬁts and pitfalls. The major beneﬁt of patent data is its uniqueness, meaning that the information stored in patents may not be republished in non-patent literature, like books or articles
2.2. Bibliometric analysis Text mining techniques try to discover information from unstructured or semi-structured textual data, which is not accessible by simple statistical techniques. The bibliometric characterization on the other hand aims to assess R&D outputs’ trends and the development trends of the design research area. We calculate traditional bibliometric indicators such as total outputs, total citation and citation-based impact assessment to provide an overview of the development of heavy duty electric battery vehicles’ research and development. Combining textual mining techniques and bibliometric analysis help us discover more unseen patterns in the ﬁeld than simple bibliometric analysis. In our bibliometric analysis, we combine patent data with data from scientiﬁc articles. We considered both information sources equally valuable. However, as 76% of our material is from patents and patent applications, they are naturally emphasized. The economic value of the information that is presented in patents and patent applications may be even larger though and proves a further justiﬁcation for our approach. Studies have shown that 70–90% of the information in patents is never published anywhere else (Global Patent Sources, An Overview of 3
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Table 2 A summary of the bibliometric analysis, including databases, strings, dataset size and type of analysis. Strings searched
(“electric vehicle”;” hybrid vehicle”;” industrial vehicle”;” power vehicle”;” mining vehicle”;” heavy machine”; truck;” construction equipment”;” engineering equipment”;” heavy vehicle”; tractor;” engineering vehicle”; harvester; or bus) and “batter*”
Database Web of Science
Dataset size 6 094
Analysis Temporal and geographical distribution of articles; article types; articles in the highest impact journals; salient keywords and emerging topics Temporal and geographical distribution of publications; most widely cited patents; salient keywords and emerging topics
International Patents, 2007). Our bibliometric analysis was performed as follows. First, we searched literature related to heavy duty electric battery vehicles using the search engine Web of Sciences (core collection) and PatBase patent database. The assumption in bibliometric analysis with the text mining approach is that the high probability of occurrence of one speciﬁc term or phrase (combination of a few words) in a title or abstract of research papers or patent documents is a better indicator for its relevance to one technology area or industry. The reasons behind utilising text mining and machine learning in research paper and patent search and data retrieval in this paper are the following: patent classes are too wide to analyse a speciﬁc technology ﬁeld, and their hierarchal structure is based on technology ﬁelds rather than an application area; new emerging technologies are not assigned to a speciﬁc patent class; the inconsistency between the keywords entered by researchers, innovators and applicants may return irrelevant patents; and the keyword search strategy may not capture the contextual meaning. (Table 2) We included in our search literature, patents and patent applications available from 2010 to June 2018, by topic, using the following strings: “electric vehicle”;” hybrid vehicle”;” industrial vehicle”;” power vehicle”;” mining vehicle”;” heavy machine”; truck;” construction equipment”;” engineering equipment”;” heavy vehicle”; tractor;” engineering vehicle”; harvester; or bus together with “batter*”. Emphasis is on heavy-duty vehicles but the issue cannot be totally resolved as many of the articles and patents/applications consider both categories; however, only about 10% of the covered material has “electric vehicle”, “electric car” or “electric automobile” as a key word in it. We obtained our datasets and downloaded the full record and cited references. Pilkington et al. (2002) emphasise that a study should use clear boundaries between generic and more application speciﬁc patents. This can be achieved by using an archive of reliable keywords (Wesseling et al., 2014). However, the problem of keywords is the inconsistency of how terminologies are used by companies, researchers or attorneys. In addition, the database search is based on the match of exact wording; it is merely a means of ﬁnding phrases without contextual meanings. The analysis provides descriptive statistics such as number of publications per year; geographical distribution of publications by country (based on authors’ aﬃliations); most popular publication platforms, such as journals, books and conference proceedings. This information is directly derived from the Web of Science records. Furthermore, the analysis includes a list of salient keyword terms associated with the overall corpus; and eight topics, with a list of associated topic-speciﬁc keywords. The eight topics or clusters with their characteristic keywords and summary statistics are presented in Table 3. The presented key words have the highest approximated probability to belong to the cluster in question. That is, the term occurs in many articles and those articles share a common “bag of words” that leads LDA procedure to deem them to be related to each other. In addition, we concentrated on terms that occurred from 20 to 1000 times in the textual data. Our rationale was that if a term occurs too many times in the documents it does not show any geographical, temporal or quality diﬀerence between those. Instead, we focused on studying less frequent concepts that included the more common term, e.g. “electric vehicle charging stations”. Our focus is on changing the unstructured part of research papers and patents (e.g., title, abstract), which contains valuable technical information, to structured data (numbers) to facilitate
2.3. Latent dirichlet allocation Latent Dirichlet allocation (LDA) is a statistical process that falls into the family of topic models and is generally considered the simplest of topic models. The goal of topic modeling is to automatically discover the topics from a collection of documents. The documents themselves are observed, while the topic structure—the topics, per-document topic distributions, and the per-document per-word topic assignments— is hidden structure. The central computational problem for topic modeling is to use the observed documents to infer the hidden topic structure (D. Blei et al., 2003). Topic models are “algorithms for discovering the main themes that pervade a large and otherwise unstructured collection of documents” (D. M. Blei, 2012). We formally deﬁne a topic to be a distribution over a ﬁxed vocabulary. This statistical model reﬂects the intuition that documents exhibit multiple topics. There are a set number of topics possible for each document and within each topic there is a distribution of words used for that topic. Each document exhibits the topics in diﬀerent proportion. The distribution that is used to draw the per-document topic distributions is called a Dirichlet distribution. In the generative process for LDA, the result of the Dirichlet is used to allocate the words of the document to diﬀerent topics. A given word may be used in more than one topic, but its relative probability would vary across topics. In other words, all the documents in the collection share the same set of topics, but each document exhibits those topics in diﬀerent proportion. “Latent” refers to the fact that LDA is designed to infer the underlying topics in a document, “Dirichlet” refers to the family of probability distributions used in the estimation, and “allocation” refers to the fact that the estimation allocates words to topics. The algorithms have no information about these subjects and the articles are not label with topics or keywords. The interpretable topic distributions arise by computing the hidden structure that likely generated the observed collection of documents. The observed data are the words of each document and the hidden variables represent the latent thematic structure of the collection. That is, the topics themselves and how each document exhibits them. This interpretable hidden structure can be used to aid tasks like information retrieval, classiﬁcation, and corpus exploration. In this way, topic modeling provides an algorithmic solution to managing, organizing, and annotating large archives of texts. The researcher can observe a document and must “reverse” the generative process to understand the hidden (latent) topic structure. By using an entire set of documents (referred to as a corpus) and our assumptions regarding number of topics and the existence of a joint probability function, a researcher can generate the set of topics in the corpus. To be more precise, the LDA generates sets of words that appear together on a regular basis. The researchers then review these sets of words and provide a label to the topic they represent. Topic models can help move us “…(B)eyond an understanding” of “how texts are being said” to a broader understanding of “what is being said” (Huang et al., 2018). Speciﬁcally, we assume that K topics are associated with a collection, and that each document exhibits these topics with diﬀerent proportions. This is often a natural assumption to make because documents 4
Resources, Conservation & Recycling 151 (2019) 104413
4 520 4 428 2 044 2 727 2 578 3 863 1 685 3 536 25 381 67 89 65 98 67 75 68 73
% % % % % % % %
in a corpus tend to be heterogeneous, combining a subset of main ideas or themes that permeate the collection as a whole. The challenge is that these topics are not known in advance; the goal is to learn them from the data. Latent Dirichlet allocation (LDA) is based on seminal work in latent semantic indexing (LSI) (Deerwester et al., 1990) and probabilistic LSI (Hofmann, 1999). The relationship between these techniques is clearly described in (Steyvers and Griﬃths, 2006). LDA is also used commercially. For example, the New York Times uses LDA to recommend articles to subscribers by inferring topics from articles they have read and identifying articles with similar content (Spangher, 2015).
414 137 208 13 235 274 145 279 1 705
2.4. Temporal patterns
330 124 158 13 192 214 133 203 1 367
We pose and consider the problem of analyzing the temporal development of a document collection. This problem requires simultaneously understanding what topics are popular and drive the changes in popularity of the topics. In particular, we address the following questions: What are the key topics in a collection of documents and how did their popularity change over time? Citation analysis has also tackled the problem of ﬁnding the journals with the most impact (Garﬁeld, 2003). Though not without controversy, the impact factor uses citations to measure how important the articles within a journal are on average. In general, more citations means greater popularity. However, because of variables such as journal size and shifts in journal popularity over time, when compared with raw citation counts, the impact factor calculates a more accurate measure of the inﬂuence of a particular journal’s papers. This vein of work is similar to ours because the problem formulation presented in our work generalizes to groups of documents and authors, not just individual documents and authors. Our work bears similarity to timeline creation (Alonso et al., 2009). Timeline creation seeks to correlate real-world events with the text used in the document collections. There is an implicit assumption that as real-world events change, the text used in the documents will change as well. Words that nobody used at one time may become widely popular, e.g. words describing a new technology or new idea, the bursts seem to correspond to the rise and fall in popularity of research topics. Our method proceeds in three steps. In the ﬁrst step, we determine the key topics in the document collection via clustering. Each cluster represents a key topic. In the second step, a concise description of the key topic for each cluster is formed. And in the ﬁnal step, we visualize the temporal behavior of topics as a ﬂow through time indicating increasing or decreasing popularity. We programmed the model to highlight eight topics. To describe each of the topics, we extract the key sequences of words that have the highest weights in the topic. These sequences of words are the most important terms in deﬁning the topic. The speciﬁcity of the keyword is calculated as the ratio of the frequency of the keyword in a certain topic to the overall keyword frequency in the overall corpus (Sievert and Shirley, 2014). Based on the given keywords, we interpreted the meaning of each topic. We chose as many terms that we felt was sufﬁcient to convey a good sense of the topic’s content without presenting an overwhelming amount of information. Using the top terms allows us to reliably describe the topic. Based on the salient keywords and topics revealed by the bibliometric analysis, we performed a conceptual analysis to identify possible national development focus areas in each topic. We identiﬁed the national development strategy aspects of the economical, technological, social and environmental dimensions. We present these emphasized aspects in tables for each of the eight topics of our analysis. When we examine the temporal pattern of the terminology, we see that the terms divide about evenly as older and newer ones. However, we believe the newer ones are of higher interest for the reader and present mainly those in our summary tables. In principle, the sequence of terms follows temporal order. However, in some case we have
electric automobile, electric car, electric motor car control device, control method, control module, internal combustion engine dc dc converter, voltage battery, power electronics thermal management, air conditioning, heat exchanger energy management, lithium ion battery, negative electrode, positive electrode plug hybrid electric vehicle, plug electric vehicle, renewable energy battery box, battery module, battery case charging system, charging station, charging device, charging pile Operating EV Vehicle control Motor operations Thermal management Battery technologies EV infrastructure Battery module EV charging
3 032 3 930 1 327 2 681 1 731 2 879 1 142 2 565 19 287
Charateristic keywords Cluster
Table 3 The selected eight clusters and their summary statistics.
744 237 351 20 420 496 265 489 3 022
M. Naumanen, et al.
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
the more novel sub-areas, whereas European science publication dominance occurs more in the medium-recent to older sub-areas. Similarly to Motor Operations and in contrast to Battery Technologies topic areas, China’s dominance in patenting seem to relate to lower impact patents. However, it can be that those patents are so new that they have not managed to get citations.
deviated from this by putting similar terms close to each other even though they might have appeared in slightly diﬀerent time points. The comment “older:” refers to those terms that typically occur in older, pre-2015 texts. Finally, we plot how topic popularity varies over time. For each term, we compute the number of documents where it appears on any particular year and take an average of these. Using the average year of appearance clearly presents the changes in each term’s popularity over time. While the latent Dirichlet allocation does not take time into account when clustering the documents, this last step relates clusters to time. In our analysis, we tried to estimate the “quality” of each of the patent or article in our database. The forward citation, backward citation, and classiﬁcation of patents have been used as an indirect measure of patent quality. Many studies have used forward citations as a measure of patent value and related these to various patent characteristics. Harhoﬀ et al. (1999) demonstrated a signiﬁcant relationship between economic value and the number of patent citations. Harhoﬀ et al. (2003) observed that the number of references and the number of forward citations were positively associated with patent value. The division between higher and lower impact publications is based on the average impact/number of citations the article or patent (application) receives. The best one third is then deemed with a high impact status, the rest being deemed as lower impact ones. To estimate an article’s quality, we used SJR (SCImago Journal Rank) indicator from the SCImago Journal & Country Rank portal. It is a publicly available portal that includes the journals and country scientiﬁc indicators developed from the information contained in the Scopus® database (Elsevier B.V.). Citation data is drawn from over 34 100 titles from more than 5 000 international publishers and country performance metrics from 239 countries worldwide. SCImago is a research group from the Consejo Superior de Investigaciones Cientíﬁcas (CSIC), University of Granada, Extremadura, Carlos III (Madrid) and Alcalá de Henares, dedicated to information analysis, representation and retrieval by means of visualisation techniques. SCImago Journal Rank (SJR indicator) accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from. A journal's SJR is a numeric value indicating the average number of weighted citations received during a selected year per document published in that journal during the previous three years.
3.2. Controlling EV In the area of control technologies for EV’s, we ﬁnd also that China is the overall dominant region in patenting activity. Within this technological space, we see that the Europe area is dominant in science publications. China’s patenting dominance is mostly concentrated in the medium-recent sub-areas both in higher and lower impact patents. Among the most novel sub-areas (hydraulic braking and regenerative power generation), Europe dominates science publication whereas China dominates patenting. In the other two most novel subareas of electric motor control and prediction methods, no region dominates either science publishing or patenting. When looking at high impact articles and patents, range extender, electronic control unit and travel distance (145, 105 and 104 documents) are characteristic for the older publications whereas charge discharge control and integrated control (55 and 47 documents) characterize newer high impact publications. Other controlling operations, such as drive, speed, torque, travel/cruise and transmission controls (113, 89, 80, 60 and 51 documents, respectively) are characteristic for new lower impact publications. In addition, braking energy recovery and driving safety (56 and 47 documents) belong to this category. In general, China dominates patenting in all these areas. Some characteristic terms for older lower impact publications are power transmission and engine control (231 and 118 documents), where China is the leading patenting nation, and range of electric vehicle (180 documents). (Table 5) 3.3. Motor operations Motor operations is a technology area where there are few high impact publications. Other than that, China is dominating patenting activity in this space. When looking at high impact articles and patents, bidirectional dc dc converter, battery dc dc converter and converter topology (85, 50 and 39 documents) are characteristic for the older publications whereas ac power supply and threshold voltage (34 and 20 documents) characterize newer high impact publications. Some characteristic terms for older lower impact publications are ac dc converter, low voltage battery and soft switching (115, 110 and 72 documents). European countries are dominating the scientiﬁc articles for the low voltage battery sub-area. This technology is intended to improve the safety of electric vehicles. Newer lower impact publications are characterized in terms like power electronics, zero voltage switching and low voltage dc dc converter (180, 85 and 32 documents). Again, China dominates patenting in these areas. Europe and the USA are the dominant science publishing regions, particularly in the sub-areas related to voltage. (Table 6)
3. Results 3.1. Operating EV When looking at the overall publishing landscape within the Operating EV area, it is easily observed that China is the dominant patenting country in most of its sub-areas. China also dominates science publication activity in a number of sub-areas. Examples of such subareas are truck chassis (69 documents) and electric scooter (36 documents). When looking at high impact articles and patents, fork and forklift trucks (184 and 154 documents) are characteristic for the older publications whereas electric bus, remote control, easy use and intelligent electric vehicle (50, 145, 81 and 32 documents, respectively) characterize newer high impact publications. Some characteristic terms for older lower impact publications are data storage and data collection (59 and 44 documents), where China and the USA are the leading patenting nations, and battery monitoring (89 documents). Newer lower impact publications are characterized in terms like wireless communication, intelligent control and improve eﬃciency (183, 88 and 80 documents). China dominates patenting in all these areas. (Table 4) The leading region in terms of dominating science publication is the EU, dominating ﬁve sub-areas. When considering the relative newness of the sub-areas, it stands out that the dominance of Chinese patenting has been particularly built up in the medium to short term, that is, in
3.4. Thermal management In general, this area is marked by having relatively many “highquality” sub-areas as compared to the areas. Once again, China is observed as the only dominant region when it comes to patenting of subareas. A notable exception is that patenting in air conditioning is dominated by the United States. Japan has dominance in science publishing related to coolant liquids and maximum temperature (24 and 30 documents). Terms air conditioning, temperature sensor and heat dissipation (311, 283 and 260 documents) characterize older high impact publications. As said, patenting in the ﬁrst area is dominated by American organizations. The latter two areas are dominated by Chinese. Cooling 6
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Table 4 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Operating EV topic. Economical Technological Social
China: low price, improve eﬃciency USA: extend battery life, data collection China: improve stability, acceleration sensor, fault detection, wireless communication, wireless transmission, intelligent control; older: forklift truck, electric scooter China: ensure safety, display panel, intelligent electric vehicle, liquid crystal display, stable operation, usb interface, alarm module, easy use; older: improve safety Europe: safety device, reliable operation; older: easy maintenance USA: self locking NA
organizations. Similarly, hybrid energy storage, energy loss, exhaust emissions, crystal structure and weight cost (155, 64, 29, 26 and 25 documents) characterize newer high impact publications. China dominates patenting in all these areas. Some characteristic terms for older lower impact publications are lithium ion but also lead acid and fuel cell batteries (705, 168 and 121 documents), where China is the leading patenting nation. In fuel cell batteries, China dominates scientiﬁc articles also. Newer lower impact publications are characterized in terms like regenerative braking, soc (state of charge) estimation, electrode active material and ultracapacitor (370, 147, 110 and 104 documents). China dominates patenting in all these areas. Lithium iron phosphate, lithium manganese, lithium titanate oxide and nickel cobalt (118, 28, 27 and 25 documents) are some of the materials that typically appear in older documents, that is, in documents that date before the year 2015. On the other hand, nickel metal hydride, nickel manganese cobalt, cobalt oxide and cobalt manganese (32, 24, 23 and 21 documents, respectively) are materials that typically appear in documents from the year 2015 onwards. No region dominates patenting in the older material technologies. However, China is strong in the newer ones. Fig. 1 displays a visual presentation of the Battery Technologies topic area. X-axis shows the number of patents or articles where the particular term appears. Y-axis shows the “newness” of the particular term: if the term has appeared in a publication that date back to the years 2010–2014, the publication is deemed “old”; the newer ones are deemed “new”. Y-axis then shows the new publications’ share of all publications. The size of the bubble relates to the average impact of the publications where the terms have appeared, the bigger the bubble the higher the average impact. The colors in the Fig. 1 refer to the dominating publishing country or region. EU countries are characterized by green, China by red and United States by blue color. Grey color indicates that none of the examined countries or EU has published more than half of the documents bearing the term. Color inside the bubble indicates dominance in scientiﬁc publications whereas the border color shows dominance in patenting. In principle, the depth of the color shows whether the majority of the publications has been patents or articles. However, all of the depicted terms have appeared more often in patents than in articles. We dissect the Battery Technologies ﬁeld by 1) sub-areas with a high proportion of newly published documents, 2) large sub-areas as measured by number of documents, and 3) sub-areas with high quality
Table 5 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Controlling EV topic. Economical
China: braking energy recovery, vehicle weight Europe: development cost Japan: distance travel, cost reduced China: charge discharge control; older: battery service life, super capacitor, electric braking, remote monitoring Europe: regenerative power generation, integrated control; older: battery power consumption USA: smaller battery China: cruise/drive control, acceleration performance, driving safety, speed control; older: travel distance Europe: driving experience, user experience China: energy recycling
plate, thermal insulation and weather conditions (56, 33 and 25 documents) are some of the terms that characterize newer high impact publications – and are also ones where Europe has produces most of the scientiﬁc articles. Thermal management, cooling system, temperature control and battery thermal management (325, 233, 168 and 106 documents, respectively) are areas that are characteristic for older lower impact publications. These seem to be areas that are studied and patented actively in all regions as none can claim dominance in these areas. Some characteristic terms for newer lower impact publications are battery temperature, heat transfer and passenger cabin (333, 151 and 89 documents), where China is again the leading patenting nation. (Table 7) 3.5. Battery technologies Because battery technologies are of strategic importance in technological competition for companies and relate to the areal raw material base, we take a more detailed look at this ﬁeld. For older high impact articles and patents, characteristics terms include battery aging, cathode active material, anode active material, polymer battery, nonaqueous electrolyte, soh (state of health) battery, hydrogen storage, lithium polymer battery and manganese oxide (71, 69, 33, 45, 26, 23, 21 and 20 documents). Aside from cathode or anode active materials, polymer batteries, nonaqueous electrolyte and lithium polymer batteries, publications in these areas are dominated by Chinese
Table 6 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Motor Operations topic. Economical Technological
China: weight & volume China: recharging battery, peak eﬃciency, zero voltage switching; older: bidirectional charger, ac dc converter Europe: low voltage dc dc converter; older: low voltage battery, maximum current Japan: voltage controller, voltage converter USA: harmonic distortion, peak current, threshold voltage, dc dc boost converter; older: bidirectional battery, zero current switching, soft switching NA NA
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Table 7 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Thermal Management topic. Economical Technological
NA China: cooling eﬀect, working temperature, battery temperature, heat insulation, heat transfer; older: heat dissipation, temperature control, motor cooling Europe: thermal insulation, electric vehicle battery thermal management; older: operating temperature Japan: cool battery, coolant liquid; older: maximum temperature China: older: low temperature environment, cabin heating Europe: lower temperature, weather conditions; older: ambient temperatures USA: vehicle heating, management of electric vehicle; older: air conditioning NA
3.6. EV infrastructure
or impact as measured by publication status and patent citations. Looking at the sub-areas with a high proportion of newly published documents, we can see that Europe is the dominant region in scientiﬁc publications. Following the general trend, China is unrivaled as regards dominance of patenting activity across multiple sub-areas. For the large sub-areas as measured by number of documents, we can see that China is the dominant region in scientiﬁc publications for fuel cell battery, ultracapacitor and battery lifetime (121, 104 and 94 documents). The United States leads scientiﬁc publications for soc (state of charge) estimation. Again, China is dominating patenting activity. The only exception is the US dominance in battery lifetime related patents. Turning to the sub-areas with high quality or impact, we observe that the United States dominates scientiﬁc publications in two subareas, in battery aging and hydrogen storage. Publications in these two areas tend to have appeared before the year 2015. Europe dominates scientiﬁc publications in one sub-area, namely that of weight costs, which is also the sub-area with the highest proportion of newly published documents. This suggests that this sub-area is quite relevant. However, China dominates patenting activity in all these three areas, as well as in the majority of the other high-impact sub-areas within battery technology. (Table 8)
EV infrastructure topic area include a number of terms that relate to the market acceptance of electric vehicles, but also terms that has a lot to do with environmental friendliness and alternative energy sources. When looking at high impact articles and patents, vibration energy, piezoelectric energy and environmental pollution (145, 143 and 90 documents) are characteristic for the older publications whereas greenhouse gas emissions, solar battery, electrical grid and penetration of electric vehicles (101, 80, 37 and 29 documents) characterize newer high impact publications. Characteristic terms for older lower impact publications are plug hybrid electric and plug electric vehicles (471 and 223 documents). Newer lower impact publications are characterized with terms like smart grid, wireless power transfer, energy supply and wireless sensor networks (288, 153, 112 and 100 documents). China dominates patenting in all these areas. However, when smart grid is tied explicitly with electric vehicles, the USA becomes the dominating nation. There are a number of terms that relate to the economics of manufacturing, obtaining, owning or running an electric vehicle. These terms include running cost, cost reduction, battery cost, low manufacturing cost, total cost of ownership and operating costs (27, 54, 52, 44, 35 and 33 documents, respectively). In general, they appear in lower impact publications, perhaps in business press. (Table 9)
Fig. 1. Characteristic terms for the Battery Technologies topic area. 8
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Table 8 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Battery Technologies topic. Economical
China: low weight, energy loss, optimal energy management, energy savings, performance evaluation, lifetime of battery, improve performance; older: motor eﬃciency, improve fuel economy, capacity estimation Europe: performance parameters, operating eﬃciency, optimal performance; older: capacity estimation Japan: fuel savings USA: weight cost, performance of electric vehicles, electric vehicle regenerative braking, control performance China: fault tolerant, energy capacity, cobalt manganese, cobalt oxide, energy management of fuel cell, electrical conductivity, solid electrolyte, nickel manganese cobalt, manganese cobalt, electrode materials, ultracapacitor, operating conditions, electrode active material, hybrid energy storage, battery power management; older: lead acid battery, fuel cell battery, lithium titanate oxide, charge discharge cycles, manganese oxide, pem fuel cell, battery aging, lithium polymer Europe: cobalt oxide, solid electrolyte, batteries & supercapacitors, electric vehicle hybrid energy storage; older: hydrogen consumption, electrochemical performance, lithium titanate oxide, anode separator Japan: nickel metal hydride, state of charge estimation, supercapacitor battery, fuel cell; older: nonaqueous electrolyte USA: electrical conductivity, state of charge estimation, modeling & simulation, simulation software, driving cycles; older: positive electrode active material, battery soc estimation, battery lifetime, negative electrode active material, hydrogen storage China: electric vehicles design, urban driving, city bus; older: vehicle design, road conditions Europe: older: typical driving China: exhaust emissions
3.7. Battery module
Table 10 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Battery Module topic.
Battery module topic area is a small area that includes publications that relate to the physical properties of the battery box. Service life of battery, battery mounting and protecting battery (232, 121 and 52 documents) are characteristic for the older publications in this area whereas replacement battery, anti-collision and improved safety (59, 32 and 27 documents) characterize newer high impact publications. Anti-theft, battery assembly and battery housing (132, 116 and 86 documents) represent characteristic terms for older lower impact publications. Newer lower impact publications are characterized with terms like prolong service life, battery replacing, reliability of and stability of battery (74, 62, 32 and 26 documents, respectively). China dominates patenting in all these areas (Table 10).
Europe: older: service lifetime China: stability of battery, anti collision, replacement battery, battery balancing, battery protection; older: anti theft, anti explosion Europe: battery balancing Japan: housing battery Japan: quick change NA
charging (48, 41, 38 and 33 documents, respectively). When looking at high impact articles and patents, charging plug/ socket, parking lots/spaces and charging safety (233, 48 and 25 documents) are characteristic for the publications that have appeared before the year 2015 whereas charging pile, intelligent charging and charging amount (269, 87 and 62 documents) characterize newer high impact publications. Similarly, some characteristic terms for lower impact publications that have appeared before the year 2015 are charging power, charging time, wireless charging and charging voltage (353, 343, 185 and 152 documents). Newer lower impact publications are characterized in terms like charging eﬃciency, battery charging
3.8. Charging EV In this area, we observe that Europe is once again the dominant region in terms of scientiﬁc publishing, whereas China is dominant in patenting. Especially notable here is that China dominates patenting in sub-areas that have high quality or impact in terms of patent citations and/or publishing journal status. Examples of such sub-area are: parking lots/spaces, charger control, touch screen and maximum
Table 9 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in EV Infrastructure topic. Economical
China: cost reduction, data analysis, battery cost, total cost of ownership, low manufacturing cost, maintenance costs, running cost; older: battery degradation, operating costs, driving range Europe: vehicle cost; older: electricity price Japan: cost eﬀectiveness USA: electric vehicle energy consumption China: charging loads, wireless sensor networks, power harvesting, wind energy, photovoltaic cells; older: solar power generation, energy harvesters, solar charging, inductive power transfer, magnetic coupling, piezoelectric materials, photovoltaic power generation, solar cell panel, vibration energy Europe: stability & reliability, electromagnetic energy harvester, energy harvesting systems, charging loads, wind power generator; older: piezoelectric energy, piezoelectric energy harvester Japan: charging requirements, battery loading, inductive charging, maximum charging, sensor node, load management; older: wireless sensor nodes, magnetic coupling USA: smart grid & electric vehicle, wireless power transfer, plug hybrid electric vehicles, photovoltaic cells; older: vibration energy harvesting China: stability of vehicle, peak hours, wearable devices, peak demand; older: daily driving, electric vehicle market, global positioning system, distributed energy resources, navigation system, electric vehicle charging stations, driving patterns Europe: electric vehicle ﬂeet; older: charging infrastructure Japan: transportation systems; older: electric vehicle fast charging USA: urban areas China: green energy, life cycle assessment, air quality, climate change, greenhouse gas emissions, reduce greenhouse gas, energy supply; older: renewable energy sources, low noise, reduce energy consumption, energy security, zero emissions Europe: environment pollution, impact electric vehicle, environmental conditions, environmentally friendly; older: carbon emissions, pollution free, low emission USA: environmental beneﬁts, renewable energy generation, battery & solar cell
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Jinhui, 2015) have pointed out that recycling of, for example, cobalt from batteries is more diﬃcult as compared to its recovery from alloys or magnetic materials. As revealed by, China holds reserves and/or mine production for all key raw materials needed in heavy duty electric vehicles. The dominance of China is superior in the area of magnet raw materials, above 95% of global mine production. USA has also some raw material reserves and/or production among all three raw material categories (battery, magnet and electric cabling raw materials), but their global contribution is signiﬁcantly lower than in the case of China. Europe has scarce battery raw material resources and scattered raw material reserves for electric cabling, occurring most abundantly in Poland. Among the studied areas, Japan does not have noteworthy reserves or mine production among any material categories. It is acknowledged that also other sectors than electric transportation and heavy duty vehicles use the raw materials such as cobalt, nickel and copper, and there are countries which have reserves and produce some of these materials, like Democratic Republic of Congo that possesses the majority of World’s cobalt resources, although they do not have a signiﬁcant role in the battery production or heavy-duty EV development. At present, almost half of the globally produced cobalt ends up in battery applications, with superalloys and wear-resistant materials being the other major cobalt end-uses (European Commission, 2017c), whereas copper is to a great extent used for building construction applications, followed in end use importance by infrastructure (Schipper et al., 2018). Electriﬁcation of traﬃc, not only heavy duty vehicles but also passenger transportation, is expected to signiﬁcantly increase the demand for these materials, putting pressures on both primary raw material production and recycling. This is primarily due to the market pressures, e.g., price volatility of the raw materials, yet legislative issues may also have role. Up to date, for example in Europe, Batteries Directive (European Union, 2006) has been in force since 2006. It has been evaluated to promote the recycling and better functioning of the internal market for batteries and recycled materials. The lists of CRMs for Europe have also awoken various stakeholders to the raw material criticality and scarcity. Some material design choices also respond to the present and future raw material demand situations: it has been a common practice to use cobalt-free LiFePO4 batteries for EV applications in China e.g. Nevertheless, value chain is a key term here, meaning that heavy duty vehicle manufacturers do not (necessarily) develop all the key technologies and solutions in their products themselves but obtain them in collaboration with their value chain. In this respect, the development strategies reﬂect the national development strategies through the focus on the respective core competences and links to other value chain steps. For example, battery value chain may contain several steps: raw materials, cell components, battery system and vehicle, with the car manufacturer the concentrating the own technological development on, e.g., vehicle parts manufacturing. The distribution of raw materials required for heavy duty vehicles shows interesting correlations with the national development strategies. China, in addition to having an extensive raw materials base, has protected the intellectual properties in many areas related to heavy duty electric vehicles thus protecting the raw materials also by trying to restrict the access to the technologies. This is partly consistent with literature. For example, concerning the battery value chain and the related patenting activities, Golembiewski et al. (Golembiewski et al., 2015) have concluded that Chinese often participate in patenting along the battery value chain but are accounting for very low shares of the overall number of patent families. Among the two geographic areas that have some important raw material resources with respect to electric vehicles, USA and Europe, both have been active in some speciﬁc areas of scientiﬁc research yet Europe holds the leading position. Japan, with very narrow natural resource base in general, has very limited patenting or scientiﬁc publishing in any of the studied sub-areas of heavy duty electric vehicle development. This in contrast what has been found in (light) electronic vehicle realm, where Japanese ﬁrms, mainly
Table 11 Main aspects included in the economical, technological, social and environmental dimensions of national battery development strategies Dimensions of national development strategies in Charging EV topic. Economical Technological
NA China: inductive charging, charging condition, maximum charging, charging eﬃciency, energy transmission, charging pile, charger control; older: mobile charging, charging power, non contact Europe: battery condition; older: rﬁd tag China: touch screen, charging schedule, parking lots/spaces Europe: vehicle network, mobile device; older: mobile communication USA: older: departure time NA
discharging and battery charging station (120, 102 and 48 documents). (Table 11) 4. Discussion 4.1. Comparison of strategies between areas Looking globally across all topic areas, it is easily noted that China is dominating most of the intellectual property (patents) in the vast majority of sub-areas. Europe is generally the most dominant region when it comes to scientiﬁc publishing when taking all areas pertaining to heavy duty electric battery vehicles into account. Thus, while Europe is strong in basic research knowledge that is required to develop electric mobility, Chinese actors appear to have been much more strategic in setting up competitive barriers and developing know-how on the more applied development and innovation side of the spectrum. As battery technology is at the core of electric mobility development, it is also worth stressing that in nearly all sub-areas of battery technology in which Europe dominates scientiﬁc publishing, China dominates patenting. Thus, apart from experiencing a strategic dependency on the import of commodities required for battery production, Europe appears to be also experiencing a strategically weakening dependency on Chinese patent owners for the application of battery technologies themselves. 4.2. Raw material reserves and the strategies The depth in the analysis is provided through examining the areal reserves of raw materials needed for heavy duty electric vehicles. Before that, a glance is cast upon recycling status and scenarios. It is foreseen that in 2020, the majority of market share of EV batteries is covered by NMC type Li ion batteries, while the corresponding value in 2030 is predicted to be 90%. The end-of-life criterion for the batteries is 80% of the nominal capacity. Nevertheless, due to the gradual increase in EV ﬂeet, the recycling ﬂows of Li ion batteries are expected to inﬂuence the raw material market only from the year 2025 onwards (Bobba et al., 2019). In practice this means that up to date and for some time in the near future, primary raw materials constitute almost all of the battery material ﬂows. Another scenario by Richa et al. (Richa et al., 2014) postulates that until year 2034, the majority (80%) of new batteries entering use would be paired with new EVs sold in the market, while the remaining 20% would be replacement batteries for existing in-use EVs. Additionally, according to scenarios by Richa et al. (Richa et al., 2014), 42% of materials in EV batteries would be recycled in 2040. However, there are diﬀerences in the recycling rate of battery materials; at present, 68% of end-of-life cobalt is recycled, with the corresponding ﬁgure for lithium being 1% (Helbig et al., 2018). However, the recycled materials originate from diﬀerent sources and applications, with recycled batteries still representing only a fraction of raw materials entering recycling. Additionally, Zeng et al. (Zeng and 10
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Table 12 Raw material reserves in the countries presented as % of either global reserves or of global mine production, depending on the availability of data. Source in most cases (European Commission, 2017c). Area
Battery raw materials (Li, Co, Mn, Ni, graphite)
Magnet raw materials (Nd, Dy)
Electric cabling raw materials (Cu)
Lithium* Cobalt Nickel*
Lithium* (Portugal) Cobalt (Finland)
Lithium* Cobalt Manganese* Nickel* Natural graphite
Lithium*: 0.2% of global reserves. Cobalt: < 1% of estimated global reserves. Nickel*: 0.2% of global reserves, 1% of global mine production. Neodymium: 1.7% of global mine production Dysprosium: 1.7% of global mine production Copper*: 6% of global reserves and global mine production.X: 9% of global mine production. Lithium* (Portugal): 0.4% of global reserves, 1% of global mine production. Cobalt (Finland): 1% of global mine production Copper (Poland)X: 2% of global mine production Lithium*: 20% of global reserves, 7% of global mine production. Cobalt: 1% of estimated global reserves, 5% of global mine production. Manganese*: 7% of global reserves, 16% of global mine production. Nickel*: 4% of global reserves, 5% of global mine production. Graphite: 69% of global mine production. Neodymium: 95.1% of global mine production Dysprosium: 95.1% of global mine production. Copper*: 3% of global reserves; *, X: 9% of global production.
* (U.S. Geological Survey, 2018) Values collected in the table are from 2017. X: (Brown et al., 2018) Values collected in the table are from 2016.
which occur fewer than 10 times have been removed from the analysis. LDA is still a developing technique, but it allows for the content analysis of large amount of text wording, which would be extremely time consuming if processed manually. While humans can easily interpret the meaning of text and words, a software is limited in that it may not be able to perceive, for instance, the level of abstraction, the multiple/contextual meanings or sentiment of a word. That information is thus lost in the results, making the interpretation of some keywords diﬃcult. As a result, we limited our analysis to sequences of words so that there is at least some chance to attribute certain qualitative or value connotation to a given term. Another limitation is that the number of topics within each individual corpus does not emerge from the analysis, but needs to be set a priori. Our aim was to highlight enough topics within each concept to uncover the internal variability, without having an excessive number of topics that would create noise and hamper comparability among concepts. Choosing an appropriate number of topics to be highlighted thus represents a trade-oﬀ between information loss and information overload. The number of topics needed to represent most comprehensively the diversity of the dataset can be calculated with a tuning algorithm. According to the algorithm, the optimal number of topics for our dataset was over 200, which we did not consider easily interpretable. We thus decided to set the model to identify eight topics, which appeared to have a critical meaning and raison d'etre in all concepts.
conglomerates, clearly dominate in the number of patent family applications and thus shape the research landscape, which has been explained by the high amount of ﬁrms active in automobile as well as in electronics manufacturing with headquarters in Japan (Golembiewski et al., 2015). One reason may be diﬀerent actors that are involved in the heavy duty EV area than in the corresponding passenger car sector. Another aspect worth mentioning is the foreseen competitiveness of fuel cell technologies in heavy duty transportation (Romejko and Nakano, 2017). Traditionally, Japan has strong capacity in terms of fuel cell vehicle technologies, which might be the reason why they are not so strong in heavy-duty electric vehicles side. (Table 12) 4.3. Validity, reliability and limitations Our analysis shows the countries where the literature is produced (based on authors’ aﬃliations) but it does not provide information on the geographical coverage of the studies. Literature published in languages other than English was not considered, and thus the analysis might not represent a global sample. Furthermore, the analysis was only performed on scientiﬁc publications, thus excluding other publication types such as policy documents, project reports and other grey literature. Majority of our references are patent applications. Since China is leading the patenting activity by the number of ﬁled patent applications, it tops most of the detected technology areas. However, a patent application does not imply that there is anything patentable (new) in the application and hence, Chinese dominance may be overestimated in this regard. Patent application counts per se does not reﬂect variation in technological quality or the commercialisation of inventions. In our analysis, we tried to estimate the quality of each of the patent application in our database by using forward citation as a measure of its value. However, this approach relies on average measures at best whereas we know that the patent value distribution is extremely skewed, few patents being able to claim most of the value at the market. To ensure a successful analysis of the data, the text material was pre-processed through tokenization. Tokenization separates tokens, that is, meaningful elements of the text (e.g. words) from spaces, punctuation, acronyms, numbers, hyphens or other symbols. Terms with a length below a certain minimum are removed. In addition, terms
5. Conclusions The results presented in this paper enable a number of conclusions to be drawn. The distribution of raw materials required for heavy duty vehicles seem to correlate with the national development strategies. China dominates the intellectual property rights, i.e., patents, in the majority of identiﬁed subareas of heavy duty electric vehicle technologies. Additionally, China holds reserves and/or mine production for all key raw materials categories (battery, magnet and electric cabling) needed in the electriﬁcation of heavy duty vehicles. Since China is leading the patenting activity in the ﬁled patent applications and most of the references are published as patent applications, China tops most of the detected technology areas. However, there are economic, environmental and social themes that seem to be important for China also. 11
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
These include air quality, exhaust emissions, urban driving and low price, for example. Europe is the most dominant region when it comes to scientiﬁc publishing in the areas related to heavy duty electric vehicles and the key technologies. Europe has scarce battery raw material resources and scattered raw material reserves for electric cabling. User related issues such as user and driving experience and easy maintenance come forward in Europe. However, general infrastructure related issues seem to be of importance, which is grasped in terms like electric vehicle ﬂeet, charging infrastructure and electricity price. USA is relatively more active in scientiﬁc publishing than in patenting and it has some raw material reserves and/or production among all three raw material categories. The interplay of an electric vehicle and larger EV infrastructure is present in the USA also. This is highlighted in themes like smart grid & electric vehicle, plug hybrid electric vehicles and urban areas. Japan seem to be a more passive actor in the development of technologies for heavy duty electric vehicles. The raw material reserves in Japan related to the main technologies are negligible. There are not that many themes where Japan is the leading country, but eﬃciency related issues seem to be important. This can be seen in terms like cost reduced, fuel savings and cost eﬀectiveness among others. To understand the relationships between science and technology, we need topic linkages between academic articles and patents. However, the purpose, quality and use of language are quite diﬀerent in these two sources of textual data. This heterogeneity results in many terms and constituents to appear in only one resource, which leads in a negative impact on topic similarity calculation. One possible direction could be to apply the author-topic model, a probabilistic model to link authors to observed terms and constituents in the academic literature, patents and patent applications in a speciﬁc R&D ﬁeld. This approach could provide a general framework for search, discovery and question answering in the context of the relationships between authors and topics. We stopped pressing forward when we generated our eight technology clusters and simply identiﬁed their relationships. It would be helpful to develop intelligent techniques to semi-automatically retrieve multi-dimensional relationships for further analyses, e.g. a systematic quantitative approach to weight/rank the analytic results to support expert-based decision making in further steps. The more heavily the analysis and analytic results can build on and interact with expert knowledge the more valuable results and insights it is likely to produce. The subjective insights of the experts can be blended with analytic results to better understand the relationships between observed data and possible future development paths. Also, especially for a deeper understanding of national development strategy aspects of the economical, technological, social and environmental dimensions, it would be useful to extend the empirical study to address multiple science, technology and innovation (STI) data sources and to consider external environmental factors (e.g., science policies and market forces).
lies: The distinctive eﬀects of patent characteristics on examiner and applicant citations. J. the American Society For. Information Science Technology. https://doi.org/ 10.1002/asi.21574. Blei, David M., 2012. Probabilistic topic models. Commun. ACM 55 (4), 77–84. https:// doi.org/10.1145/2133806.2133826. Blei, D., Jordan, M., Ng, A.Y., 2003. Latent dirichlet allocation. J. Machine Learning Res. 3, 993–1022. https://doi.org/10.1162/jmlr.2003.3.4-5.993. Bobba, Silvia, Mathieux, Fabrice, Blengini, Gian Andrea, 2019. How will second-use of batteries aﬀect stocks and ﬂows in the EU? A model for traction Li-ion batteries. Esources 145 (June), 279–291. https://doi.org/10.1016/J.RESCONREC.2019.02. 022. Brown, T.J., Idoine, N.E., Raycraft, E.R., Shaw, R.A., Hobbs, S.F., Everett, P., Deady, E.A., Bide, T., 2018. World mineral production 2012-16. Br. Geological Surv. https://doi. org/10.1136/JCP.54.5.356. Çabukoglu, Emir, Georges, Gil, Küng, Lukas, Pareschi, Giacomo, Boulouchos, Konstantinos, 2018. Battery electric propulsion: an option for heavy-duty vehicles? Results from a swiss case-study. Transportation Res. Part. C: Emerg. Technologies 88 (March), 107–123. https://doi.org/10.1016/J.TRC.2018.01.013. Çabukoglu, Emir, Georges, Gil, Küng, Lukas, Pareschi, Giacomo, Boulouchos, Konstantinos, 2019. Fuel cell electric vehicles: an option to decarbonize heavy-duty transport? Results from a swiss case-study. Transp.ation Res. Part. D: Transp. Environ. 70 (May), 35–48. https://doi.org/10.1016/J.TRD.2019.03.004. Chen, Si-Hua, 2016. The inﬂuencing factors of Enterprise sustainable innovation: an empirical study. Sustainability 8 (5), 425. https://doi.org/10.3390/su8050425. Copper Development Association, 2017. Copper Drives Electric Vehicles. Daim, Tugrul U., Rueda, Guillermo, Martin, Hilary, Gerdsri, Pisek, 2006. Forecasting emerging technologies: use of bibliometrics and patent analysis. Technological Forecasting Soc. Change 73 (8), 981–1012. https://doi.org/10.1016/J.TECHFORE. 2006.04.004. Deerwester, Scott, Dumais, Susan T., Furnas, George W., Landauer, Thomas K., Harshman, Richard, 1990. Indexing by latent semantic analysis. J. Am. Soc. Inform. Sci. 41 (6), 391–407. Dijk, Marc, Wells, Peter, Kemp, René, 2016. Will the momentum of the electric car last? Testing an hypothesis on disruptive innovation. Technological Forecasting Soc. Change 105 (April), 77–88. https://doi.org/10.1016/J.TECHFORE.2016.01.013. Ernst, H., 1997. The use of patent data for technological forecasting: The diﬀusion of CNC-technology in the machine tool industry. Small Bus. Economics 9 (4), 361–381. https://doi.org/10.1023/A:1007921808138. European Comission, 2014. Questions and Answers on the Commission Strategy for Reducing Heavy-Duty Vehicles’ (HDVs) Fuel Consumption and CO 2 Emissions No. May. pp. 1–4. European Commission, 2017a. BATTERIES - A Major Opportunity for a Sustainable Society. https://doi.org/10.2777/864893. European Commission, 2017b. Communication on the 2017 List of Critical Raw Materials for the EU. no. COM(2017) 490 ﬁnal. . European Commission, 2017c. Study on the Review of the List of Critical Raw Materials. CrItical Raw Materials Factsheets. European Commission, 2018. Europe on the Move: Sustainable Mobility for Europe: Safe, Connected and Clean (Annex). COM (2018) 293 Final. . European Union, 2006. Directive 2006/66/EC on Batteries and Accumulators and Waste Batteries and Accumulators and Repealing Directive 91/157/EEC. Frenken, Koen, Hekkert, Marko, Godfroij, Per, 2004. R&D portfolios in environmentally Friendly automotive propulsion: variety, competition and policy implications. Technological Forecasting Soc. Change 71 (5), 485–507. https://doi.org/10.1016/ S0040-1625(03)00010-6. Garﬁeld, E., 2003. The meaning of the impact factor. International J. Clinical Health 3 (2), 363–369. Global Patent Sources, An Overview of International Patents. 2007. http://www. integrityip.com/Patent_Library/Community/Other/GlobalPatentSources.pdf. Golembiewski, Birte, Stein, Nicolevom, Sick, Nathalie, Wiemhöfer, Hans-Dieter, 2015. Identifying trends in Battery technologies with regard to electric mobility: evidence from patenting activities along and across the Battery value Chain. J. Clean. Prod. 87 (January), 800–810. https://doi.org/10.1016/J.JCLEPRO.2014.10.034. Goodenough, Kathryn M., Wall, Frances, Merriman, David, 2018. The rare earth elements: demand, global resources, and challenges for resourcing future generations. Nat. Resources Res. 27 (2), 201–216. https://doi.org/10.1007/s11053-017-9336-5. Griliches, Zvi., 1990. Patent statistics as economic indicators: A survey. J. Econ. Lit. 28 (4), 1661–1707. Han, Eun Jin, So, Young Sohn, 2015. Patent valuation based on text mining and survival analysis. The J. Technology Transf. 40 (5), 821–839. https://doi.org/10.1007/ s10961-014-9367-6. Harhoﬀ, Dietmar, Narin, Francis, Scherer, F.M., Vopel, Katrin, 1999. Citation frequency and the value of patented inventions. Rev. Econ. Stat. 81 (3), 511–515. https://doi. org/10.1162/003465399558265. Harhoﬀ, Dietmar, Scherer, Frederic M., Vopel, Katrin, 2003. Citations, family size, opposition and the value of patent rights. Res. Policy 32 (8), 1343–1363. https://doi. org/10.1016/S0048-7333(02)00124-5. Harrison, Gillian, Thiel, Christian, 2017. An exploratory policy analysis of electric vehicle sales competition and sensitivity to infrastructure in Europe.”. Technological Forecasting Soc. Change 114 (January), 165–178. https://doi.org/10.1016/J. TECHFORE.2016.08.007. Helbig, Christoph, Bradshaw, Alex M., Wietschel, Lars, Thorenz, Andrea, Tuma, Axel, 2018. Supply risks associated with lithium-ion Battery materials. J. Clean. Prod. 172 (January), 274–286. https://doi.org/10.1016/J.JCLEPRO.2017.10.122. Hoed, Robertvanden, 2005. Commitment to fuel cell technology?: How to interpret carmakers’ eﬀorts in this radical technology. J. Power Sources 141 (2), 265–271.
Acknowledgements The ﬁnancial support by the Academy of Finland through the Strategic Research Council project CloseLoop (funding decision #303453) is gratefully acknowledged. References Abernathy, W.J., Utterback, J.M., 1978. Patterns of Industrial innovation. Technol. Rev. 80, 40–47. Alonso, Omar, Gertz, Michael, Baeza-Yates, Ricardo, 2009. Clustering and exploring search results using timeline constructions. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management - CIKM’ 09, 97. New York, New York, USA: ACM Press. https://doi.org/10.1145/1645953.1645968. Azagra-Caro Joaquín, M., Mattsson, Pauline, Perruchas, François, 2011. Smoothing the
Resources, Conservation & Recycling 151 (2019) 104413
M. Naumanen, et al.
Saidani, Michael, Yannou, Bernard, Leroy, Yann, Cluzel, François, 2018. Heavy vehicles on the Road towards the circular economy: analysis and comparison with the automotive industry. Esources 135 (August), 108–122. https://doi.org/10.1016/j. resconrec.2017.06.017. Schipper, Branco W., Lin, Hsiu-Chuan, Meloni, Marco A., Wansleeben, Kjell, Heijungs, Reinout, Voet, Estervander, 2018. Estimating global copper demand until 2100 with regression and stock dynamics. Resour. Conserv. Recycl. 132 (May), 28–36. https:// doi.org/10.1016/J.RESCONREC.2018.01.004. Sen, Burak, Ercan, Tolga, Tatari, Omer, 2017. Does a Battery-electric truck make a difference? – life cycle emissions, costs, and externality analysis of alternative fuelpowered class 8 heavy-duty trucks in the United States. J. Clean. Prod. 141 (January), 110–121. https://doi.org/10.1016/J.JCLEPRO.2016.09.046. Seol, Hyeonju, Lee, Sungjoo, Kim, Chulhyun, 2011. Identifying New business areas using patent information: A DEA and text mining approach. Expert Syst. Appl. 38 (4), 2933–2941. https://doi.org/10.1016/J.ESWA.2010.06.083. Sievert, Carson, Shirley, Kenneth, 2014. LDAvis: A method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70. Stroudsburg, PA, USA: Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-3110. Spangher, Alexander., 2015. Building the next New York times recommendation engine. The New. York Times 2015. Steyvers, Mark, Griﬃths, Tom, 2006. Probabilistic topic models. In: Landauer, T., McNamara, D., Dennis, S., Kintsch, W. (Eds.), Latent Semantic Analysis: A Road to Meaning, https://doi.org/10.1016/s0364-0213(01)00040-4. Talebian, Hoda, Herrera, Omar E., Tran, Martino, Mérida, Walter, 2018. Electriﬁcation of Road freight transport: policy Implications in British Columbia. Energy Policy 115 (April), 109–118. https://doi.org/10.1016/J.ENPOL.2018.01.004. U.S. Geological Survey, 2018. Mineral Commodity Summaries 2018. https://doi.org/10. 3133/70194932. UNEP, 2011. Recycling Rates of Metals. A Status Report. Nederlands Tijdschrift Voor Geneeskunde. Valero, Alicia, Valero, Antonio, Calvo, Guiomar, Ortego, Abel, 2018. Material bottlenecks in the future development of Green technologies. Renew. Sustain. Energy Rev. 93 (October), 178–200. https://doi.org/10.1016/J.RSER.2018.05.041. Verspagen, Bart., 2007. Mapping technological trajectories as patent citation networks: A study on the history of fuel cell research. Advances Complex. Systems 10 (01), 93–115. https://doi.org/10.1142/S0219525907000945. Watts, Robert J., Porter, Alan L., 1997. Innovation forecasting. Technological Forecasting Soc. Change 56 (1), 25–47. https://doi.org/10.1016/S0040-1625(97)00050-4. Wesseling, J.H., Faber, J., Hekkert, M.P., 2014. How competitive forces sustain Electric Vehicle Development. Technological Forecasting and Social Change 81 (January), 154–164. https://doi.org/10.1016/J.TECHFORE.2013.02.005. Wolinetz, Michael, Axsen, Jonn, 2017. How policy can build the plug-in electric vehicle market: insights from the respondent-based preference and constraints (repac) model. Technological Forecasting and Soc. Change 117 (April), 238–250. https://doi.org/10. 1016/J.TECHFORE.2016.11.022. Yan, Zhenlei, Zhou, Jie, 2015. Optimal answerer ranking for New questions in Community question answering. Information Processing & Management 51 (1), 163–178. https://doi.org/10.1016/J.IPM.2014.07.009. Yang, Li-feng, Xu, Jin-hua, Neuhäusler, Peter, 2013. Electric vehicle technology in China: an exploratory patent analysis. World Pat. Information 35 (4), 305–312. https://doi. org/10.1016/J.WPI.2013.06.002. Yau, Chyi-Kwei, Porter, Alan, Newman, Nils, Suominen, Arho, 2014. Clustering scientiﬁc documents with topic modeling. Scientometrics 100 (3), 767–786. https://doi.org/ 10.1007/s11192-014-1321-8. Yoon, Byungun, Park, Yongtae, 2004. A text-mining-based patent network: analytical tool for High-technology trend. The J. High. Technology Management Res. 15 (1), 37–50. https://doi.org/10.1016/J.HITECH.2003.09.003. Zeng, Xianlai, Jinhui, Li., 2015. On the sustainability of cobalt utilization in China. Esources 104 (November), 12–18. https://doi.org/10.1016/J.RESCONREC.2015.09. 014.
https://doi.org/10.1016/J.JPOWSOUR.2004.09.017. Hoed, Robertvanden, 2007. Sources of radical technological innovation: The emergence of fuel cell technology in the automotive industry. J. Clean. Prod. 15 (11–12), 1014–1021. https://doi.org/10.1016/J.JCLEPRO.2006.05.032. Hofmann, Thomas., 1999. Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR’ 99. New York, New York, USA: ACM Press. pp. 50–57. https://doi.org/10.1145/312624.312649. Huang, Allen H., Lehavy, Reuven, Zang, Amy Y., Zheng, Rong, 2018. Analyst information Discovery and interpretation roles: A topic modeling approach. Management Science 64 (6), 2833–2855. https://doi.org/10.1287/mnsc.2017.2751. International Energy Agency, 2017. Global EV Outlook 2017: Two Million and Counting. IEA Publicationshttps://doi.org/10.1787/9789264278882-en. Jeon, Jeonghwan, Lee, Changyong, Park, Yongtae, 2011. How to use patent information to search potential technology partners in Open innovation. J. Intellect. Property Rights 16 (5), 385–393. Kast, James, Vijayagopal, Ram, Gangloﬀ, John J., Marcinkoski, Jason, 2017. Clean commercial transportation: medium and heavy duty fuel cell electric trucks. Int. J. Hydrogen Energy 42 (7), 4508–4517. https://doi.org/10.1016/J.IJHYDENE.2016. 12.129. Lebedeva, Natalia, Di Persio, Franco, Boon-Brett, Lois, 2017. Lithium Ion Battery Value Chain and Related Opportunities for Europe. https://doi.org/10.2760/6060. Li, Rui, Chambers, Tamy, Ding, Ying, Zhang, Guo, Meng, Liansheng, 2014. Patent citation analysis: calculating science linkage based on citing motivation. J. the Assoc. For. Infor. Sci. Technol. https://doi.org/10.1002/asi.23054. Li, Xiang-Yang, Ge, Jian-Ping, Chen, Wei-Qiang, Wang, Peng, 2019. Scenarios of rare earth elements demand driven by automotive electriﬁcation in China: 2018–2030. Resour. Conserv. Recycl. 145 (June), 322–331. https://doi.org/10.1016/J. RESCONREC.2019.02.003. Liebesny, Felix, Hewitt, James W., Hunter, Paul S., Hannah, Maureen, 1974. The scientiﬁc and technical information contained in patent speciﬁcations-the extent and tie factors of its publication in other forms of literature. J. Am. Soc. Inform. Sci. 25 (5). https:// doi.org/10.1002/asi.4630250512. 339–339. Liu, Shang Jyh, Shyu, Joenson, 1997. Strategic planning for technology development with patent analysis. Int. J. Technol. Management 13 (5/6), 661–680. https://doi.org/10. 1504/IJTM.1997.001689. Ma, Jing, Porter, Alan L., 2015. Analyzing patent topical information to identify technology pathways and potential opportunities. Scientometrics 102 (1), 811–827. https://doi.org/10.1007/s11192-014-1392-6. Manzetti, Sergio, Mariasiu, Florin, 2015. Electric vehicle Battery technologies: from present State to future systems. Renew. Sustain. Energy Rev. 51 (November), 1004–1012. https://doi.org/10.1016/J.RSER.2015.07.010. Mayyas, Ahmad, Mann, Margaret, 2019. Emerging manufacturing technologies for fuel cells and electrolyzers. Procedia Manufacturing 33 (January), 508–515. https://doi. org/10.1016/J.PROMFG.2019.04.063. Narin, F., 1994. Patent bibliometrics. Scientometrics. https://doi.org/10.1007/ BF02017219. Narin, Francis, Noma, Elliot, Perry, Ross, 1987. Patents as indicators of Corporate technological strength. Res. Policy 16 (2–4), 143–155. https://doi.org/10.1016/00487333(87)90028-X. Olivetti, Elsa A., Ceder, Gerbrand, Gaustad, Gabrielle G., Xinkai, Fu., 2017. Lithium-ion Battery supply Chain considerations: analysis of potential bottlenecks in critical metals. Joule 1 (2), 229–243. https://doi.org/10.1016/J.JOULE.2017.08.019. Pilkington, Alan, Dyerson, Romano, Tissier, Omid, 2002. The electric vehicle:: patent data as indicators of technological development. World Pat. Information 24 (1), 5–12. https://doi.org/10.1016/S0172-2190(01)00065-5. Richa, Kirti, Babbitt, Callie W., Gaustad, Gabrielle, Wang, Xue, 2014. A future perspective on lithium-ion Battery waste ﬂows from electric vehicles. Esources 83 (February), 63–76. https://doi.org/10.1016/J.RESCONREC.2013.11.008. Romejko, Kamila, Nakano, Masaru, 2017. Portfolio analysis of alternative fuel vehicles considering technological advancement, energy security and policy. J. Clean. Prod. 142 (January), 39–49. https://doi.org/10.1016/J.JCLEPRO.2016.09.029.