Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

Journal of Power Sources 389 (2018) 230–239 Contents lists available at ScienceDirect Journal of Power Sources journal homepage: www.elsevier.com/lo...

2MB Sizes 0 Downloads 20 Views

Journal of Power Sources 389 (2018) 230–239

Contents lists available at ScienceDirect

Journal of Power Sources journal homepage: www.elsevier.com/locate/jpowsour

Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

T

Ke Songa,b,∗, Feiqiang Lic, Xiao Hua,b, Lin Hed, Wenxu Niua,b, Sihao Lua,b, Tong Zhanga,b a

School of Automotive Studies, Tongji University, Shanghai 201804, China National Fuel Cell Vehicle & Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China c Zhengzhou Yutong Bus Co.,Ltd, Yutong Industrial Park, Yutong Road, Zhengzhou 450061, China d Automotive Research Institute, HeFei University of Technology, Hefei 230009, China b

H I GH L IG H T S

multi-mode energy management strategy for fuel cell vehicle is developed. • ADriving patterns recognition is proposed using LVQ neural network. • The simulation models are validated through dynamometer tests. • The proposed strategy with other three strategies in six driving cycles. • Results verify dynamic isandcompared economy performances under complex driving conditions. •

A R T I C LE I N FO

A B S T R A C T

Keywords: Fuel cell electric vehicle Multi-mode energy management strategy Driving patterns identification LVQ neural network

The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

1. Introduction Due to the shortage of fossil fuel and the increasingly severe environmental pollution caused by its use, the automotive industry has responded with a transformation in energy structure which has reduced carbon emissions and will gradually replace the internal combustion engine (ICE) with power trains, which use multiple energy sources. During this process, fuel cell electric vehicles (FCEVs) will play an important role, as they have the advantages of rapid fueling, high energy density and efficiency, low operation temperature, and zero onboard emissions.



The performance of this new type of hybrid vehicle depends largely upon its energy management strategy, which distributes the power demand between the proton electrolyte membrane fuel cell (PEMFC) system and a battery system. The addition of a battery makes quick start-up and energy recovery from the braking process possible. To ensure the steady and efficient operation of the system, research on energy management has become a matter of great interest recently. The research done so far can mainly be categorized into rule-based and optimization-based algorithms. In the first class of strategies, the distribution of power demand is managed by several prearranged rules which are based on existing

Corresponding author. School of Automotive Studies, Tongji University, Shanghai 201804, China. E-mail address: [email protected] (K. Song).

https://doi.org/10.1016/j.jpowsour.2018.04.024 Received 15 November 2017; Received in revised form 16 March 2018; Accepted 5 April 2018 0378-7753/ © 2018 The Authors. 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/).

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

switches are used to control the energy transfer between the fuel cell, battery, and motor, enabling the switching of three modes. And this strategy was modified in Ref. [34], where a switch was added to supply energy produced by fuel cells when batteries are fully charged. In Ref. [35], an adaptive droop control method with the multi-mode strategy based on the ECMS was proposed to improve efficiency and prolong lifetime of power sources by avoiding quick changes of power demand. And three operation modes are defined to distribute the required power reasonably into a powertrain system by adjusting the unidirectional and bidirectional DC/DC converters, which contains two PEMFC systems, two batteries and two SCs [36]. In Ref. [37], a clustering analysis is used to classify the driving conditions into five typical types, and Euclidean proximity is used to realize pattern recognition. The energy management strategy parameters are optimized under each driving pattern using simulated annealing particle swarm optimization. The driving conditions of a vehicle can be predicted or identified, and the results indicate current or future driving conditions of the vehicle. At present, the study of driving pattern recognition has two main methods. Some researchers [38–42] use vehicle navigation systems geographic information systems (GPSs) and connected vehicles to obtain future traffic speed, slope, and other data. In other words, the current energy management strategy is determined by a forecast of future driving conditions. The characteristic parameters of current driving patterns can be obtained through calculation of current and historical route information. These parameters are then compared with typical driving cycles to recognize current driving patterns. Although this method is not as accurate as the former one, the realization is relatively simple. Some researchers [35,43–45] use fuzzy algorithm to establish the driving condition recognizer. Cluster analysis [37,46] is used in the classification of driving conditions. In Ref. [46], a new method of hierarchical clustering is used to classify the working cycle data into four groups, which are extracted from a sample of the historical driving condition cycle. Then the support vector machine method is used to predict the current driving cycle based on the classification results. Finally, according to the current operating cycle and slope information, a switchable driving controller is established. Some papers study the relationship between the accuracy and complexity of a calculation. In Ref. [47], the k-nearest neighbor algorithm is used to study the velocity extracted from facility-based driving cycles. The kfold cross validation technique has been used to evaluate the influence of window length on classification accuracy. The final selection is 10 driving patterns and 60 s window length. A neural network can correctly map the pattern from the feature space to the class space, and has a strong learning and adaptive ability, which is widely used in the field of pattern recognition [48]. Jeon et al. [49] called the vehicle energy management strategy based on driving conditions recognition “a multi-mode switching energy management strategy based on driving pattern recognition,” which consists of two parts: (1) a recognizer to identify the driving pattern according to the characteristic parameters, and (2) a control strategy optimized under selected driving cycles. The recognizer can be adjusted automatically to the optimal control strategy according to the results of driving pattern identification. In Ref. [50], the driving condition recognition is composed of two parts: (1) the driving condition information extractor and (2) the driving environment recognizer. In this research, the driving condition information extractor extracts 16 types of characteristic parameters and the driving environment recognizer includes a road type identifier, driving style identifier, driving trend recognizer, and driving mode recognizer. Among these parameters, 11 kinds of typical driving conditions were selected and constructed by the learning vector quantization (LVQ) neural network algorithm in the road type identifier. The latter three kinds of recognizers (environment, driving trend, and driving mode) were realized by the fuzzy controller. This article explains in detail the method of identifying the driving conditions but does not explain how to develop a vehicle's energy management

experiment results or research experiences, and consists of thermostat strategy [1,2], power follower strategy [3], or hybrid strategy, which combines both [4,5]. Because prior information about a predefined drive cycle is not needed, the application of such strategies for a realtime controller is expected [3,6,7]. Although vehicle performance can be improved partly by utilizing advanced algorithms, such as wavelet transform [8,9] and fuzzy logic control theory [9–11], the optimality of power distribution cannot be guaranteed due to a lack of road information. This inherent flaw results in the generation of strategies based on optimization. Optimization-based strategies [12–14] are conversely established to transform the aim of energy management into an optimal solution for a globally optimized problem, which primarily delivers a cost function using linear programming [15], dynamic programming [16,17], and genetic algorithm [18]. In such strategies, the system is optimized offline to minimize fuel consumption according to a determined driving cycle, which requires a huge amount of computation and previous information, and which, therefore, limits the use of such kinds of strategies in real-time controllers and in the environment [6,18]. Consequently, the globally optimal solution is generally used as a benchmark for other strategies. For the purpose of simplifying the complexity of calculations for real-time applications, several algorithm applications, such as stochastic dynamic programming [19], Pontryagin's minimum principle (PMP) [13,18], convex programming [12,14], or equivalent consumption minimization strategy (ECMS) [3,20,21] are suggested as solutions and can usually approximate the globally optimal solution obtained using dynamic programming. Genetic algorithms [5,22–27] are also often used for parameter optimization in energy management strategy to enhance economic performance, especially in rule-based strategies. In Ref. [5], a thermostat/ power following control strategy was proposed, where the main control parameters, such as power extremum, SOC upper and lower limits, and engine speed were optimized by a genetic algorithm [27]. proposed three different types of improved thermostat strategies for controlling the power output point of the internal combustion engine. Therefore, this paper suggests the use of an adaptive genetic algorithm to optimize the thermostat energy management strategy, so as to meet practical application needs and achieve parameter optimization. It has been found, however, that these optimized parameters are often obtained under specific conditions, so they are not appropriate for the complex road conditions under which most vehicles travel. Therefore, under real driving conditions without predefined driving cycles, an adaptive multi-mode energy management strategy should be proposed to optimize vehicle performance. In Ref. [28], driving patterns are divided into two operating modes, low-speed and high-speed; thus, a two-mode power-split hybrid electric vehicle (HEV) is proposed with off-line optimization by conjugating gradient-based back propagation through time (BPTT)-like optimal control algorithm, where the control system design is divided into two layers. In Ref. [29], the online supervised driving cycle recognition was achieved by calculating the feature vectors and classifying these feature vectors into one of the driving patterns in the reference database. Moulik and Söffker [30] presented adaptive, rule-based power management using driving pattern recognition. The driving patterns are classified in a look-up table offline according to power which, combined with the multi-objective optimization properties of genetic algorithms, decides the selected rule/ mode in the mode selection block of the supervisory controller [30]. In Ref. [31], Ryu et al. proposed a fuzzy controller, which was optimized using a genetic algorithm and combined with an adaptive membership function based on a stochastic approach to guarantee optimum performance under different driving cycles [32]. established energy management strategies for fuel cell electric vehicles based on a combination of power supply durability and fuel economy. The three modes of the multi-mode control strategy correspond to three typical processes: startup, normal operation, and downtime. But the strategy does not consider the actual traffic conditions. Driving condition is divided into three different mode: traction, braking and stopping mode in Ref. [33]. Three 231

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

when working conditions change, and they cannot satisfy the dynamic requirements under a low battery SOC state. The multi-mode energy management strategy proposed in this paper identifies driving conditions through the extraction of characteristic operating parameters. And the optimized energy management strategy is selected under corresponding operating conditions. Fig. 1(a) shows the multi-mode energy management strategy based on driving patterns recognition, and is mainly composed of a driving pattern recognizer and a multi-mode energy management controller. The driving pattern recognizer can collect real-time driving information and calculate the characteristic parameters of the current driving route, such as average speed and average acceleration values. The calculated characteristic information is then compared with the characteristic information of typical driving patterns by means of the LVQ neural network algorithm to determine the pattern of the current vehicle's driving cycles. Then, the multi-mode energy management controller selects the corresponding single-mode control strategy based on the recognition results selected herein for the current energy management strategy of the vehicle.

strategy [51]. proposes a driving pattern identifier based on a LVQ neural network, where six representative standard driving cycles are selected. Micro-trip extraction and principal component analysis methods are applied to ensure the magnitude and diversity of the training samples, so a window length of 120 s and four principal components are selected. The remainder of this paper is organized as follows. In Section 2, the research background is detailed and the fuel cell electric vehicle (FCEV) structure diagram is formulated to clearly express the research content and process. Section 3 proposes a method of driving pattern identification using the LVQ neural network algorithm and verifies this identification algorithm. A multi-mode energy management strategy for FCEV is presented in Section 4. In addition, simulation results are compared with other strategies and experimental results are discussed in Section 5. Section 6 provides concluding remarks and proposes future work. 2. Fuel cell electric vehicle system This article focuses on a low-speed fuel cell electric vehicle (FCEV) in large venues as the primary research subject, with the vehicle's powertrain system structure shown in Fig. 1(b). The power sources consist of a fuel cell range extender and a battery system for the sake of quick dynamic response and durability. The fuel cell system can drive the motor and simultaneously charge the power battery. Because of the complex actual driving conditions of the FCEV, the optimal control parameters of the energy management strategy obtained under certain working conditions cannot ensure fuel economy

3. Driving pattern recognition using LVQ neural network algorithm 3.1. Selection and modification of typical driving cycles The fuel cell electric vehicle in this article is a low-speed electric vehicle, with a maximum speed of less than 50 km/h. Therefore, this article uses scaled speed data in accordance with a maximum speed of

Fig. 1. Powertrain system architecture of FCEV. (a)multi-mode energy management strategy and (b) power train system topology. 232

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

Table 1 The main characteristic parameters of three typical driving patterns. Driving pattern

UDDS_40

EUDC_40

HWFET_40

Type of Representation Time(s) Distance(km) Average Speed(km/h) Maximum Acceleration(m/s2) Average Acceleration(m/s2) Maximum Deceleration(m/s2) Average Deceleration(m/s2) Idling Time(s) Parking Number

Urban 1369 5.26 13.81 0.65 0.22 −0.65 −0.25 259 17

Suburban 400 2.32 20.81 0.28 0.13 −0.46 −0.31 42 1

High-speed 765 6.85 32.19 0.59 0.08 −0.61 −0.09 6 1

Fig. 2. Typical mode feature collection using micro-trip extraction.

40 km/h to obtain new test driving cycles. This best represents the speed patterns in the original test cycles; thus,

vt FCEV =

40 ·vt cycle vcycle max

(1)

where vt FCEV is the corresponding speed of the new test cycle at time t, km/h, vcycle max is the maximum speed of the original typical driving cycle, km/h, andvt cycle is the speed of the original driving cycle at time t, km/h. In this article, scaled UDDS_40, EUDC_40 and HWFET_40 are selected as typical driving patterns to recognize the main characteristic parameters as shown in Table 1. These three driving cycles represent three common types of driving patterns, namely, urban, suburban, and high-speed conditions. Fig. 3. Topology of an LVQ neural network.

3.2. Extraction of the driving patterns characteristic parameters is a hybrid network composed of an input layer, competition layer, and output layer. The structure of the LVQ neural network is shown in Fig. 3, where I, H and O represent the neurons of the input layer, the competition layer, and the output layer, respectively. The input layer contains 12 neurons, which represent the 12 characteristic parameters, respectively, of the driving cycles mentioned in Section 3.2 and further explained in Section 3.3.2. The neurons in the competition layer classify the input vector through learning. The linear output layer contains three neurons, which relate to the three kinds of driving patterns, respectively, namely UDDS_40, EUDC_40 and HWFET_40.

In the process of driving, the sensor constantly collects the vehicle's speed, road slope, and other information about driving conditions. This section selects the 12 most commonly used parameters as the characteristic parameters, which are described in Table 2. The time interval for characteristic parameters calculation is 120 s before the current time, and the data sampling time is set to 5 s. The duration of typical driving cycles and the changes in different time intervals always vary. For this reason, the method shown in Fig. 2 is used, which divides the typical driving cycles into six sections. Each of its characteristic parameters is extracted to reflect the features of typical driving patterns.

3.3.1. Preprocessing of input parameters To strengthen the neural network training ability, each input vector in the input layer needs to be transformed into a 1 × 3 matrix series composed of 1 and −1, and its classification is shown in Table 3, where p represents the data of the input vector, namely a characteristic parameter of the driving cycle, pavg and pSD representing the mean and standard deviation of a characteristic parameter in all driving cycles. Then, α is the factor that characterizes network performance, and the value of α is 0.55. By this means, a 36 × 18 matrix network training sample can be achieved.

3.3. Development of the LVQ neural network An LVQ (learning vector quantization) neural network used in this article is a forward neural network [52]. The advantage is that we can simply obtain the global optimum by direct calculation of the distance between the input vector and the competitive layer without normalization and orthogonalization of input vectors. The LVQ neural network Table 2 The characteristic parameters for driving pattern recognition. Number

Characteristic parameters

Unit

1 2 3 4 5 6 7 8 9 10 11 12

Average Speed Speed Standard deviation Average Acceleration Acceleration Standard deviation Average Deceleration Deceleration Standard deviation Average Driving Speed Percentage of Acceleration Time Percentage of Deceleration Time Percentage of High-speed Time Percentage of Middle-speed Time Percentage of Low-speed Time

km/h km/h m/s2 m/s2 m/s2 m/s2 km/h – – – – –

3.3.2. Training and testing of LVQ neural network The LVQ neural network selects the winning neuron according to the minimum Euclidean distance of the input vector and the reference vector, so that the output of the neuron is 1 and the output of the other Table 3 The characteristic parameters for driving pattern recognition. Category

233

Range

Array

L1

p > pavg + α × pSD

[1 1 1]

L2

pavg < p ≤ pavg + α × pSD

[1 1 −1]

L3

pavg − α × pSD < p ≤ pavg

[1 −1 −1]

L4

p ≤ pavg − α × pSD

[−1 −1 −1]

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

Table 4 LVQ neural network accuracy detection. Driving condition sample

1

2

3

4

5

6

UDDS_40

Expected value Actual Result

1 1

1 2

1 1

1 1

1 1

1 1

EUDC_40

Expected value Actual Result

2 2

2 1

2 2

2 2

2 1

2 2

HWFET_40

Expected value Actual Result

3 3

3 2

3 3

3 3

3 3

3 3

neuron is zero. The weight of the reference vector is updated using LVQ1 algorithm, as shown in Equation (2).

Fig. 5. One-to-one correspondence between typical operating conditions and single-mode control strategies.

+ η (x i − wi, j ), for correct classifications Δwi, j = ⎧ ⎨ ⎩− η (x i − wi, j ), for incorrect classifications

4. Multi-mode energy management strategies for FCEVs

(2)

After the training of LVQ neural network is finished, the characteristic parameters in each driving pattern are input to the neural network to test the accuracy, and the expected output is compared with the actual output, as shown in Table 4. The number of driving condition sample represents the different section extracted in Fig. 2, and numbers 1, 2, 3 of expected value and actual result represent three driving cycles, UDDS_40, EUDC_40 and HWFET_40 respectively. Differences between expected and actual values are emphasized in bold and italic font. The table shows the LVQ neural network output as being basically the same as the expected result.

Based on the LVQ neural network, the multi-mode energy management strategy outputs the corresponding typical driving patterns through the calculation of traffic condition information. The multimode energy management controller is mainly composed of a set of single-mode energy management strategies, and the switching algorithm, as shown in Fig. 1(a). The corresponding relationship between single-mode control strategies and typical patterns is shown in Fig. 5. More specifically, the single-mode energy management strategies are a set of optimal control strategies under certain conditions, and the function of the switching algorithm is to switch the current energy management strategy of the FCEV to the corresponding optimal control strategy, based on the recognition results obtained by the condition recognizer. In this paper, three typical driving patterns correspond directly to three single-mode control strategies, which are superior using genetic algorithm. Due to a lack of information in the initial start-up phase, conditions cannot be identified; thus, another single-mode control strategy, instantaneous optimization strategy based on a neural network is applied to ensure stability and continuity.

3.4. Results of driving pattern recognition: a comparative analysis The three typical conditions, as shown in Table 4, UDDS_40, EUDC_40, and HWFET_40, can produce three kinds of output results through the driving pattern recognizer, which are expressed respectively with numbers 1, 2, and 3. Considering the lack of information in the start-up period for driving pattern identification, the recognizer output is the number 4 at that moment. And an output holding module was added to avoid the frequent variations of the output in a certain period of time, where the module holding timekt is 5s. The typical driving condition UDDS_40 and the typical driving condition WVUCITY_40 are selected. The simulation is carried out using the multimode energy management strategy based on the LVQ network (MM_LVQ). Results are shown in Fig. 4. Fig. 4 (a) and (b) show that the recognizer can basically identify the UDDS_40 driving cycle, with error occurring at higher speeds. The recognition results of WVUCITY_40 indicates that the recognizer can find the unique characteristic parameters of the typical working conditions. Moreover, the accuracy is high, which verifies that this driving pattern recognizer is valid, since the results of identification conform to the actual situation.

4.1. Three single-mode control strategies for three typical driving patterns The rule-based energy management strategy is suitable for use in real-time control because of many advantages, such as a simple structure, few control parameters, and no need for road information in advance. Therefore, the thermostat energy management strategy (THEMS) was selected by the authors as the basis of the single-mode control strategy. The basic idea is that the fuel cell operates at a constant power point, and its opening and closing depends on the battery SOC. This control strategy includes three control parameters: the battery SOC lower limit SOClow, the battery SOC upper limit SOChigh, and the constant output power valuePreout after the accelerator is turned on. This strategy, however, also has the following shortcomings. For an extended-range electric vehicle, the use of thermostat strategy will make the battery work alone most of the time. In the long-term, the battery's longevity and reliability will be affected by the high-power charge and discharge as well as the large discharge depth. Furthermore, the charge and discharge will cause additional energy loss; hence, energy conversion efficiency declines. In addition, the parameter selection of the thermostat strategy is mainly based on its experience, so it is unable to get the global optimization solution. A genetic algorithm (GA) is used to simulate the process of biological evolution. Based on the idea of survival of the fittest, the global optimal solution in the solution space is available. However, the classical genetic algorithm is prone to precocious phenomena, meaning that it cannot achieve a global optimal [53,54]. Therefore, the adaptive genetic algorithm (AGA) with dynamically changed crossover and mutation probability is adopted to optimize the key control parameters of the management strategy under the three selected typical operating

Fig. 4. Recognition results in different driving cycles. 234

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

discharge efficiency, and ηchr andηdis are the average charge and discharge efficiency of the battery. Hence, the battery efficiency is calculated as follows:

conditions. With fuel economy as the optimization target, which also considers economic and dynamic performance, optimized energy management strategy under different conditions can achieve better economic performance. The optimization process can be expressed as solving the following constrained nonlinear programming problem:

⎧ min Q (X ) = [FC (X ), BD (X )] ⎨ s. t . gj (X ) ≥ 0, j = 1, ⋯, m ⎩

ηdis, chr = 0.5 + 0.5 ×

where X is a vector containing the vehicle and control parameters; Q(X) is equivalent fuel consumption; FC(X) and BD(X) are the hydrogen consumption of the fuel cell and the power consumption of the battery respectively, which can be mutually converted by the calorific value; gj (X ) ≥ 0, j = 1, …, m is a set of nonlinear inequality constraints, which represent the vehicle's dynamic performance requirements, such as acceleration time, battery SOC variation. The battery SOC initial value is 0.95, empirical parameters SOChigh = 0.85, SOClow = 0.4 and PRE_out = 4000 W are initial values of the optimization process. The specific optimization process was described in detail in Ref. [55], which is a research result of the author. In view of the actual vehicle performance requirements of FCEVs and the performance characteristics of the key components, the specific constraints are shown as follows. The optimized parameter values for the three single-mode control strategies are shown in Table 5.

The Simulink model of multi-mode energy management strategy is based on the LVQ neural network can be obtained by using a driving pattern recognizer and a multi-mode energy management strategy. In this section, the validity of the model is first verified, and the dynamic performance and driving range are analyzed. Then, through the simulation experiments of multiple energy management strategies under different typical and atypical conditions, an economic analysis is compared and analyzed.

0–30 km/h acceleration time is less than 10 s; the battery SOC operating lower limit is 0.3; 0.3 ≤ SOClow ≤ 0.5, 0.51 ≤ SOChigh ≤ 0.95; Δ trace ≤ 2 km/h (the difference between the expected vehicle speed and the actual vehicle speed at each time); (5) fuel cell system net output power is no more than 5000 W.

5.1. Modeling and validation of fuel cell electric power system 5.1.1. Modeling of fuel cell powertrain system Because the main application venues of the vehicle featured in this paper are in various places such as parks and communities with lowspeed urban conditions, the vehicle's dynamic performance requirements are relatively low. The economic performance and emission requirements are conversely higher. Through key components selection and matching parameters of the vehicle, the key parameters of the vehicle are shown in Table 6. The lithium iron phosphate battery pack used in this paper is composed of 40 single cells, in which the minimum voltage of a single cell is 2.6 V and the maximum voltage is 3.6 V. The rated capacity of the battery pack is 40 Ah, and the rated voltage is 128 V. Also, the Rint model is used for battery modeling [57]. As shown in Fig. 6(b), UOC and Rbat are the open circuit voltage and the internal resistance of the battery pack respectively. Fig. 6(a) shows battery open circuit voltage and internal resistance change with SOC in the charge or discharge mode. Battery current Ibat is the function of UOC , Rbat and output powerPbat , as shown in Equation (8). The bus voltage Ubat calculation solved in Equation (9) is derived from this model. And the ampere-hour integral is used for SOC evaluation in Equation (10), where Cbat is the capacity of the battery, and ηC is the battery Coulomb efficiency [58].

4.2. Single-mode control strategy for start-up process In this research, the instantaneous optimization energy management strategy based on a neural network is adopted when the driving pattern cannot be identified in the vehicle start-up phase. With the goal of minimizing the total power loss to the system, this strategy realizes a reasonable allocation of fuel cell and battery output power according to the system power demand and the size of the current battery SOC [56]. The power demand of the systemPreq (t ) is defined as: (4)

wherePfc (t ) andPb are the output power of the fuel cell stack and the battery; ηDC / DC is DC/DC converter efficiency; andPfc acc (t ) is the required parasite system power for the fuel cell range extender, which is not indicated in Fig. 1. The power loss of the system in the charge and discharge state is:

PLoss (t ) = Pfc (t )(ηfc max − ηfc ) + Pb (t )(1 − ηdis ) + Pb (t )(ηfc max − ηfc ) (5)

PLoss (t ) = Pfc (t )(ηfc max − ηfc ) + Pb (t )(1 − ηchr ) + Pb (t ) ηchr (1 − ηdis )

Table 6 Vehicle specification.

(6)

where ηfc max is the maximum working efficiency of the fuel cell stack, ηfc is the working efficiency of the fuel cell stack when the output power isPfc (t ) . Furthermore, ηfc is the average working efficiency of the fuel cell stack when the battery is charged; ηchr and ηdis are the battery charge and Table 5 The main characteristic parameters of three typical driving patterns. Category

UDDS_40

EUDC_40

HWFET_40

Fuel Cell Output Power(W) SOC Upper Limit SOC Lower Limit

2450 0.85 0.40

2603 0.85 0.40

2958 0.85 0.40

(7)

5. Simulation and analysis of model-based multi-mode energy management strategy

(1) (2) (3) (4)

/ ηDC / DC ηchr

4·R·4·SOC UE

where ηdis, chr represents the battery charge and discharge efficiency, R is the battery resistance with a different SOC, UE is the open circuit voltage. Using Equations (5)–(7), combined with the test data of the operating characteristics of the fuel cell stack and the battery, energy management control rules can be obtained with vehicle power demand and battery SOC as the input parameters and the fuel cell output power as the output parameter.

(3)

Preq (t ) = Pfc (t ) ηDC / DC + Pb (t ) − Pfc acc (t )

1−

235

Characteristic

Unit

Value

Gross Weight Frontal Area Drag Coefficient Coefficient of Rolling Friction Battery Nominal Capacity Fuel Cell Stack Rated/Peak Power Fuel Cell Stack Maximum Efficiency Hydrogen Bottle Capacity DC/DC Efficiency Maximal Speed Drive Type In-Wheel Motor Rated/Peak Power

kg m2 – – Ah kW % L – km/h – kW

1600 2.8 0.35 0.01 40 5/6.5 59.43 26 0.94 50 4 In-Wheel Motor 4 × 0.8/4 × 2.5

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

Fig. 6. Parameters and model.

Ibat =

UOC −

2.5 kW, the rated power is 0.8 kW, and the maximum speed is 500r/ min. The motor efficiency model is described in equation (11), where ηm is the motor efficiency; nm is the motor speed, r/min, and Tm is the motor torque, Nm. Fig. 6(c) shows the relationship between motor efficiency, speed and output torque.

2 UOC − 4 × Rbat × Pbat

(8)

2 × Rbat

(9)

Ubus = UOC − Ibat × Rbat t

SOC (t f ) = SOC (t0) − ηC

∫t0 Ibat dt Cbat

ηm = f (nm , Tm)

(10)

The efficiency of the proton exchange membrane fuel cell stack used in this paper is shown in Fig. 6(c). It can be seen from the figure that the fuel cell stack achieves the highest efficiency 59.43% when the stack power is about 1700 W. Taken the efficiency of the DC/DC converter as a constant (0.94), the output power from the fuel cell stack minus the power consumed by the fuel cell auxiliary system is the fuel cell system net output power to the bus, which is shown in Fig. 6(d). In this paper, a permanent magnetic synchronous wheel motor is used as the calculation model, in which the maximum output power is

(11)

In the simulation process, the initial value of the battery SOC is set to 0.95. The total amount of hydrogen fuel for the hydrogen bottle is 181.8 g (11 MPa), and the simulation conditions are the typical driving cycle, HWFET_40, and the atypical driving cycle, ECE_40. When the battery SOC is less than 0.3 or the hydrogen is exhausted with the battery SOC falling below 0.3, the simulation process stops. Vehicle fuel economy is evaluated by the driving range where the battery is fully charged and the hydrogen bottle is full. 236

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

Table 7 Vehicle model simulation results. Driving cycle

Driving range(km)

ηFC(%)

η(%)

Hydrogen consumption (g)

ECE_40 HWFET_40

31.4 54.8

53 53

17.6 33.2

181.8 181.8

Fig. 8. Simulation results under Multi_cycle.

experiment due to depletion of hydrogen, the hydrogen consumption of the fuel cell was the amount of hydrogen stored in the vehicle, 181.8 g ηfc in the table above means the average efficiency of the fuel cell system, where η is the global efficiency of the vehicle. Obviously, the efficiency of the fuel cell is related to the adopted energy management strategy. The efficiency of the vehicle and the driving range are correspondingly related to the driving conditions. It can be seen from Fig. 7 (b) and (c) that the test and simulation results are basically consistent, and the demand power of the vehicle changes with driving conditions. The car's actual speed can track the expected speed changes in the entire driving process of the three driving cycles. This means that the fuel cell electric vehicle model can effectively reflect the vehicle's actual driving state, and this provides a basis for subsequent analysis and research. The car drives in a pure electric mode until the battery SOC drops to a certain value. Then, the fuel cell turns on automatically, and the battery SOC basically remains unchanged, which means the fuel cell system output power can meet the basic needs of the driving conditions. But the fuel cell start-up time lags behind the simulation time. The battery's state-of-charge (SOC) decreases slowly before it reaches 0.4, and the actual speed cannot track the expected speed when the desired speed changes with a large acceleration/deceleration. The main reason for the differences is as follows: the simplified vehicle model parameters and the actual vehicle cannot be completely matched; the vehicle model failed to adjust according to the degradation of some parts during operation; drift phenomenon may occur during data acquisition of vehicle sensors, and deviation will occur in the speed of driver tracking.

Fig. 7. Comparison of test and simulation results.

5.1.2. Validation of fuel cell electric power system In this article, the performance of the vehicle was measured and analyzed using a dynamometer test bench, as shown in Fig. 7 (a). The initial conditions of the experiment and simulation are the same. When the battery SOC decreased to 0.3 and the hydrogen bottle gas pressure dropped to 2 Mpa, the test was at an end. The simulation results of the vehicle model are shown in Table 7. Fig. 8 illustrates the vehicle power and the comparison of the actual vehicle speed and the expected vehicle speed in a single driving cycle. Since the fuel cell had been shut down before the end of the

5.2. Simulation and analysis of multi-mode energy management strategy In this paper, based on the above energy management strategy, several commonly used energy management strategies were added as comparison items to study vehicle performance. These contrasting items are thermostat control strategy with empirical value (TH_EXP), the thermostat control strategy optimized by genetic algorithm 237

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

Table 8 Vehicle fuel economy simulation results.

Table 10 Vehicle fuel economy simulation results under Multi_cycle.

Energy management strategy

TH_EXP

TH_OPT

PF

MM_LVQ

Energy management strategy

TH_EXP

TH_OPT

PF

MM_LVQ

UDDS_40

32.3 0 54.8 0 60.1 0 52.7 0 31.4 0

34.6 7.12 57.4 4.74 63.3 5.32 55.7 5.69 33.5 6.69

33.2 2.79 57.2 4.38 63.3 5.32 53.5 1.52 32.7 4.14

34.6 7.12 57.4 4.74 63.1 4.99 55.7 5.69 33.4 6.37

Driving Range (km) Increase proportion (%) ηFC (%) η (%)

48.6 0 53 27.7

50.9 4.73 57 29

48.7 0.21 55 27.8

52.7 8.44 57 29.9

HWFET_40 CONSTANT_30 EUDC_40 ECE_40

Driving Range (km) Increase proportion (%) Driving Range (km) Increase proportion (%) Driving Range (km) Increase proportion (%) Driving Range (km) Increase proportion (%) Driving Range(km) Increase proportion (%)

the adaptability of this strategy under complex driving conditions showing how FCEV performance is simulated, tested, and verified. The test conditions are the same as previously mentioned and are a mixed condition Multi_cycle features high speed, suburban and urban conditions, and is composed of four single cycle conditions: UDDS_40, EUDC_40, CONSTANT_30 and HWFET_40. The single driving cycle lasts 3737 s (62 h and 17 s), and the travel distance is 24.34 km. The velocity and vehicle required power profile are shown in Fig. 8(a). Among them, CONSTANT_30 represents the condition of 30 km/h uniform speed. The simulation results are shown in Fig. 8(b) and (c). It can be seen from Fig. 8(b) that the energy management strategy can better identify complex driving conditions. In this section, the power following strategy and the thermostat control strategy with empirical value as well as AGA optimized value under UDDS_40 were used as comparison items to study the economics of the strategy. The simulation results are shown in Table 10, in which the vehicle's driving range is 52.7 km with this multi-mode energy management strategy. It proves that the economy performance of the vehicle has greatly improved with 8.44% increase. Furthermore, the fuel cell efficiency in MM_LVQ is 57%, which is higher than in TH_EXP and PF strategies. Because the MM_LVQ strategy can identify different driving conditions, it can switch the used optimal strategy according to the operating conditions. The TH_OPT strategy can only be optimized for one driving condition, which is unable to adapt to complex traffic conditions. For this reason, it can be seen from results that the economic performance of MM_LVQ strategy is better than the performance of TH_OPT strategy by 3.71%.

(TH_OPT), and the power following strategy (PF). In TH_EXP, the parameters are set based mainly on experience, where the fuel cell engine output power is 4000 W, and battery SOC upper and lower limits are 0.85 and 0.4. In TH_OPT, the parameters are optimized under different driving conditions respectively. Peak motor power is limited to 2 kW. 5.2.1. Vehicle dynamic performance assessment According to the dynamic indicators mentioned in Section 4, we can get simulation results of vehicle dynamic performance under different strategies (TH_EXP, TH_OPT, PF and MM_LVQ). It can be concluded that the simulation results of dynamic indicators under different strategies are consistent, where the 0–30 km/h acceleration time, the maximum speed and the maximum grade are 9.3s, 50.5 km/h and 11.1%, which means that all strategies can meet the vehicle's dynamic requirements. 5.2.2. Fuel economy performance assessment Tables 8 and 9 list the driving ranges and the efficiency of the above strategies in the five selected operating conditions and the increased proportion compared with the driving range under TH_EXP strategy. As can be seen from the table, the economy of the multi-mode strategy is greatly improved compared with the conventional strategy, such as TH_EXP and PF strategy. Especially noteworthy is the increase of 7.12% in the UDDS_40 driving cycles. Driving range is basically the same under specific driving conditions in the multi-mode strategy and the optimized thermostat strategy. As can be seen from Table 9, TH_OPT improves the fuel cell efficiency by optimizing the operating point of the fuel cell, so that the vehicle economy is improved. As Table 4 shows, most of the time, the MM_LVQ strategy can identify the correct driving conditions. The optimized TH_OPT will be used to distribute the energy under such circumstance, while the fuel cell will be less efficient if driving conditions are not recognized. Therefore, the vehicle economy performance under MM_LVQ strategy can reach or be slightly lower than the economic performance under TH_OPT strategy.

6. Conclusions For the reason that single energy management strategies have not been able to meet the complex road conditions of an actual vehicle, a multi-mode energy management strategy based on driving condition recognition technology was proposed in this article. This strategy was developed using a combined genetic optimized thermostat strategy under specific driving conditions and the condition recognition method, which can be automatically switched to the optimal energy management strategy under corresponding conditions according to differences in condition recognition results. The simulation experiment in this paper was based on the correctness verification of the model by dynamometer test. The results show that the multi-mode energy management strategy can satisfy the needs of vehicle dynamic performance. And the energy management strategy can transform into a more suitable one based on driving conditions and produce a more economic performance than the thermostat strategy under the same dynamic conditions. In addition, we designed a complex driving cycle to verify the actual effect of this strategy MM_LVQ under complex driving conditions. The results show that the economy performance of MM_LVQ is 8.44% higher than that of TH_EXP, and 3.71% higher than that of TH_OPT optimized under specific driving conditions. At the same time, the condition recognition effect helped the multi-mode strategy to adapt to real driving conditions. In the next stage, we will consider the study from the recognition period of the driving condition, the sampling time, the selection of characteristic parameters, and the collection of characteristic information to further improve the accuracy of condition recognition.

5.2.3. Experimental verification and analysis of multi-mode energy management strategy under complex driving conditions The multi-mode energy management strategy has been developed based on complex and practical conditions, and so this section reports Table 9 Vehicle efficiency simulation results. Energy management strategy

TH_EXP

TH_OPT

PF

MM_LVQ

ηFC(%)

η(%)

ηFC(%)

η(%)

ηFC(%)

η(%)

ηFC(%)

η(%)

UDDS_40 HWFET_40 CONSTANT_30 EUDC_40 ECE_40

53 53 53 53 53

17 33.2 34.6 29 17.6

57 56 56 57 57

18.3 34.7 36.3 30.7 18.8

54 54 56 55 52

16.9 33.3 36.3 29.5 20.4

57 56 56 57 57

18.3 34.7 36.2 30.7 18.7

238

Journal of Power Sources 389 (2018) 230–239

K. Song et al.

Acknowledgments [28]

This work is financially supported by the National Key Research and Development Program (2017YFB0103100), the National Key Technology R&D Program (2015BAG06B01) and the Education Reform project of Tongji University (4250144904/007).

[29] [30]

[31]

References

[32]

[1] N. Jalil, N.A. Kheir, M. Salman, A rule-based energy management strategy for a series hybrid vehicle, June, IEEE Proceedings of the American Control Conference, Albuquerque, New Mexico, USA, 1997, pp. 689–693 (1). [2] D.G. Li, D.W. Feng, Thermostatic control for series hydraulic hybrid vehicle (SHHV) energy management, Advanced Materials Research, vol.512, Trans Tech Publications, 2012, pp. 2676–2681. [3] H. Hemi, J. Ghouili, A. Cheriti, A real-time energy management for electrical vehicle using combination of rule-based and ECMS, Proceedings of the IEEE Electrical and Power Energy Conference (EPEC), 2013, pp. 1–6. [4] M. Kim, D. Jung, K. Min, Hybrid thermostat strategy for enhancing fuel economy of series hybrid intracity bus, IEEE Trans. Veh. Technol. 63 (8) (2014) 3569–3579. [5] D. Liu, Y. Wang, X. Zhou, Z. Lv, Extended range electric vehicle control strategy design and muti-objective optimization by genetic algorithm, IEEE Chinese Automation Congress (CAC), 2013, pp. 11–16. [6] A. Ravey, B. Blunier, A. Miraoui, Control strategies for fuel-cell-based hybrid electric vehicles: from offline to online and experimental results, IEEE Trans. Veh. Technol. 61 (6) (2012) 2452–2457. [7] C. Mansour, N. Salloum, S. Francis, W. Baroud, Adaptive energy management strategy for a hybrid vehicle using energetic macroscopic representation, IEEE Vehicle Power and Propulsion Conference (VPPC), 2016, pp. 1–7. [8] X. Zhang, C.C. Mi, A. Masrur, D. Daniszewski, Wavelet-transform-based power management of hybrid vehicles with multiple on-board energy sources including fuel cell, battery and ultracapacitor, J. Power Sources 185 (2) (2008) 1533–1543. [9] O. Erdinc, B. Vural, M. Uzunoglu, A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system, J. Power Sources 194 (1) (2009) 369–380. [10] A. Ravey, A. Mohammadi, D. Bouquain, Control strategy of fuel cell electric vehicle including degradation process, Industrial Electronics SOCiety, IECON 2015-41st Annual Conference of the IEEE, 2015, pp. 003508–003513. [11] A. Ravey, S. Faivre, C. Higel, F. Harel, A. Djerdir, Energy management of fuel cell electric vehicle with hydrid tanks, Industrial Electronics SOCiety, IECON 2014-40th Annual Conference of the IEEE, 2014, pp. 3962–3967. [12] X. Hu, N. Murgovski, L.M. Johannesson, B. Egardt, Optimal dimensioning and power management of a fuel cell/battery hybrid bus via convex programming, IEEE/ASME Trans Mechatron. 20 (1) (2015) 457–468. [13] F. Odeim, J. Roes, L. Wülbeck, A. Heinzel, Power management optimization of fuel cell/battery hybrid vehicles with experimental validation, J. Power Sources 252 (2014) 333–343. [14] J. Guanetti, S. Formentin, S.M. Savaresi, Energy management system for an electric vehicle with a rental range extender: a least costly approach, IEEE Trans Intell. Transport. Syst. 17 (11) (2016) 3022–3034. [15] D. Fares, R. Chedid, S. Karaki, R. Jabr, F. Panik, H. Gabele, Y. Huang, Optimal power allocation for a FCHV based on linear programming and PID controller, Int. J. Hydrogen Energy 39 (36) (2014) 21724–21738. [16] C. Lin, J.M. Kang, J.W. Grizzle, H. Peng, Energy management strategy for a parallel hybrid electric truck, IEEE Proceedings of the American Control Conference, 2001, pp. 2878–2883 (4). [17] V. Larsson, L. Johannesson, B. Egardt, Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management, IEEE Trans. Veh. Technol. 64 (4) (2015) 1458–1467. [18] F. Odeim, J. Roes, A. Heinzel, Power management optimization of a fuel cell/battery/supercapacitor hybrid system for transit bus applications, IEEE Trans. Veh. Technol. 65 (7) (2016) 5783–5788. [19] P. Elbert, M. Widmer, H.J. Gisler, C. Onder, Stochastic dynamic programming for the energy management of a serial hybrid electric bus, Int. J. Vehicle Design 69 (1–4) (2015) 8–112. [20] L. Xu, J. Li, J. Hua, X. Li, M. Ouyang, Optimal vehicle control strategy of a fuel cell/ battery hybrid city bus, Int. J. Hydrogen Energy 34 (17) (2009) 7323–7333. [21] W. Shabbir, S.A. Evangelou, Exclusive operation strategy for the supervisory control of series hybrid electric vehicles, IEEE Trans. Contr. Syst. Technol. 24 (6) (2016) 2190–2198. [22] M. Sorrentino, G. Rizzo, I. Arsie, Analysis of a rule-based control strategy for onboard energy management of series hybrid vehicles, Contr. Eng. Pract. 19 (12) (2011) 1433–1441. [23] B. Huang, X. Shi, Y. Xu, Parameter optimization of power control strategy for series hybrid electric vehicle, Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, Canada, 2006, pp. 1989–1994 July. [24] A. Meintz, M. Ferdowsi, Control strategy optimization for a parallel hybrid electric vehicle, IEEE Vehicle Power and Propulsion Conference (VPPC’08), Harbin, China, 2008, pp. 1–5 September. [25] S.M. Mehdi, M. Ansarey, M. Mohammadian, S.M. Taghi Bathaee, Power Flow Distribution for Hybrid Fuel Cell Vehicle via Genetic Algorithm Method, (2004) SAE Technical Paper 2004-01-3040. [26] A. Piccolo, L. Ippolito, V.Z. Galdi, A. Vaccaro, Optimisation of energy flow management in hybrid electric vehicles via genetic algorithms, Proceedings of the IEEE/ ASME International Conference on Advanced Intelligent Mechatronics, 2001, pp. 434–439 (1). [27] P. Hu, J. Seibel, H. Zhang, Strategy of range extending electric vehicle based on

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43] [44]

[45]

[46]

[47]

[48] [49]

[50]

[51] [52]

[53]

[54]

[55]

[56]

[57] [58]

239

User's approval, Proceedings of the 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition, Shenzhen, China, 2010 November. M. Cipek, D. Pavković, J. Petrić, A control-oriented simulation model of a powersplit hybrid electric vehicle, Appl. Energy 101 (2013) 121–133. L. Feng, W. Liu, B. Chen, Driving pattern recognition for adaptive hybrid vehicle control, SAE Int. J. Alternative Powertrains (1) (2012) 169–179 (2012-01-0742). B. Moulik, D. Söffker, Online power management with embedded offline-optimized parameters for a three-source hybrid powertrain with an experimental emulation application, Energies 9 (6) (2016) 439. J. Ryu, Y. Park, M. Sunwoo, Electric powertrain modeling of a fuel cell hybrid electric vehicle and development of a power distribution algorithm based on driving mode recognition, J. Power Sources 195 (17) (2010) 5735–5748. L. Xu, J. Li, M. Ouyang, J. Hua, G. Yang, Multi-mode control strategy for fuel cell electric vehicles regarding fuel economy and durability, Int. J. Hydrogen Energy 39 (5) (2014) 2374–2389. N. Mebarki, T. Rekioua, Z. Mokrani, D. Rekioua, et al., PEM fuel cell/battery storage system supplying electric vehicle, Int. J. Hydrogen Energy 41 (45) (2016) 20993–21005. Z. Mokrani, D. Rekioua, N. Mebarki, T. Rekioua, et al., Proposed energy management strategy in electric vehicle for recovering power excess produced by fuel cells, Int. J. Hydrogen Energy 42 (30) (2017) 19556–19575. Q. Li, T. Wang, C. Dai, et al., Power management strategy based on adaptive droop control for a fuel cell-battery-supercapacitor hybrid tramway, IEEE Trans. Veh. Technol. (2017), http://dx.doi.org/10.1109/TVT.2017.2715178. Y. Hang, Q. Li, T. Wang, et al., Multi-source coordination energy management strategy based on SOC consensus for a PEMFC-battery-supercapacitor hybrid tramway, IEEE Trans. Veh. Technol. 67 (1) (2018) 296–305. Z. Lei, D. Cheng, Y. Liu, et al., A dynamic control strategy for hybrid electric vehicles based on parameter optimization for multiple driving cycles and driving pattern recognition, Energies 10 (1) (2017) 54. G.K. Fraidl, F. Beste, P.E. Kapus, M. Korman, B. Sifferlinger, V. Benda, Challenges and solutions for range extenders-from concept considerations to practical experiences, (2011) SAE Technical Paper 2011-37-0019. C. Zhang, A. Vahidi, P. Pisu, X. Li, K. Tennaut, Role of terrain preview in energy management of hybrid electric vehicles, IEEE Trans. Veh. Technol. 59 (3) (2010) 1139–1147. R. Wang, S.M. Lukic, Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles, IEEE Vehicle Power and Propulsion Conference, 2011, pp. 1–7. C. Hu, A.Q. Huang, Y. Gao, Comprehensive lost minimization strategy for parallel plug-in hybrid electric vehicles, IEEE Transportation Electrification Conference and Expo (ITEC), 2013, pp. 1–7. T. Donateo, D. Pacella, D. Laforgia, Development of an energy management strategy for plug-in series hybrid electric vehicle based on the prediction of the future driving cycles by ICT technologies and optimized maps, (2011) SAE Technical Paper 2011-01-0892. Z. Wei, Z. Xu, D. Halim, Study of HEV power management control strategy based on driving pattern recognition, Energy Procedia 88 (2016) 847–853. S. Zhang, R. Xiong, Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming, Appl. Energy 155 (2015) 68–78. M.K. Dayeni, M. Soleymani, Intelligent energy management of a fuel cell vehicle based on traffic condition recognition, Clean Technol. Environ. Policy 18 (6) (2016) 1945–1960. Z. Chen, L. Li, B. Yan, C. Yang, C.M. Martínez, D. Cao, Multimode energy management for plug-In hybrid electric buses based on driving cycles prediction, IEEE Trans Intell. Transport. Syst. 17 (10) (2016) 2811–2821. N. Denis, M.R. Dubois, R. Dubé, A. Desrochers, Blended power management strategy using pattern recognition for a plug-in hybrid electric vehicle, Int. J. Intell. Trans. Syst. Res. 14 (2) (2016) 101–114. Y. Xu, X. Liu, Several main methods and their comparison of neural network in pattern- recognition, Inf. Technol. Inf. 4 (2005) 120–123. S. Jeon, S. Jo, Y. Park, J. Lee, Multi-mode driving control of a parallel hybrid electric vehicle using driving pattern recognition, J. Dyn. Syst. Meas. Contr. 124 (1) (2002) 141–149. R. Langari, J.S. Won, Intelligent energy management agent for a parallel hybrid Vehicle-Part I: system architecture and design of the driving situation identification process, IEEE Trans. Veh. Technol. 54 (3) (2005) 925–934. H. He, C. Sun, X. Zhang, A method for identification of driving patterns in hybrid electric vehicles based on a LVQ neural network, Energies 5 (9) (2012) 3363–3380. D.T. Pham, S. Otri, A. Ghanbarzadeh, E. Koc, Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition, IEEE, 2006. B. Huang, Z. Wang, Y. Xu, Multi-objective genetic algorithm for hybrid electric vehicle parameter optimization, IEEE International Conference on Intelligent Robots and Systems, 2006, pp. 5177–5182. A. Hasanzadeh, C.S. Edrington, Y. Liu, et al., An LQR based optimal tuning method for IMP-based VSI controller for electric vehicle traction drives, IEEE Vehicle Power and Propulsion Conference(VPPC), 2011, pp. 1–7. Q. Xu, K. Song, X. Hong, T. Zhang, Optimization of energy management strategy for extended-range electric vehicle based on adaptive genetic algorithm, Automob. Technol. 10 (2012) 19–23. K. Song, T. Zhang, Instantaneous optimization energy management for extendedrange electric vehicle based on minimum loss power algorithm, (2013) SAE Technical Paper 2013-24-0073. V.H. Johnson, Battery performance models in ADVISOR, J. Power Sources 110 (2) (2002) 321–329. L. Lu, X. Han, J. Li, J. Hua, M. Ouyang, A review on the key issues for lithium-ion battery management in electric vehicles, J. Power Sources 226 (3) (2013) 272–288.