Numerical modeling of hybrid supercapacitor battery energy storage system for electric vehicles

Numerical modeling of hybrid supercapacitor battery energy storage system for electric vehicles

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Energy Procedia 158 Energy Procedia 00(2019) (2017)2750–2755 000–000 www.elsevier.com/locate/procedia

10thth International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10 International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China China

Numerical modeling of hybrid supercapacitor battery energy storage Numerical modeling of hybrid supercapacitor battery energy storage The 15th International Symposium on District Heating and Cooling system for electric vehicles system for electric vehicles Assessing the feasibility of using the heat demand-outdoor Lip Huat Sawa,a,*, Hiew Mun Poona a, Wen Tong Chongbb, Chin-Tsan Wangc c, Ming Chian Lip Huat Saw *, Hiew Mun , Chin-Tsan Wang , Ming Chian afor a, Wen temperature function district heat forecast YewaPoon , Minglong-term KunTong Yewa aChong , Tan Ching Nga a demand Yew , Ming Kun Yew , Tan Ching Ng I.Department Andrićof Mechanical *,Kong A. Chian Pina , P. ofFaculty Ferrão , J.andFournier ., ofB. Lacarrière , O. Le Malaysia. Correc Engineering, of Engineering, University Malaya, 50603, Kuala Lumpur, Lee Faculty Engineering Science, UTAR, Kajang, 43000, Malaysia and Science, UTAR, Kajang, 43000, Malaysia a,b,cLee Kong Chian aFaculty of Engineering a b c a

b

a

c Department of Mechanical and Electro-Mechanical Engineering, National I-Lan University, I-Lan, 26047, Malaysia. Taiwan. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, a IN+ Center for Innovation, Technology and Policy Research -Engineering, Instituto Superior Técnico, Av. Rovisco Pais 1,26047, 1049-001 Lisbon, Portugal c Department of Mechanical and Electro-Mechanical National I-Lan University, I-Lan, Taiwan. b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France b

Abstract Abstract Electric Abstractvehicle (EV) has been steadily gaining attention and as a viable alternative to mitigate pressing global energy crisis and Electric vehicle (EV) been by steadily gaining attention and as a viable alternative mitigate pressing global energy crisisofand environmental issueshas caused conventional internal combustion engine vehicles.toNonetheless, the dynamic operation EV environmental issues caused by conventional internal combustion engine vehicles. Nonetheless, the dynamic operation of EV encompassing high charging and discharging currents generated from regenerative braking and acceleration, respectively, may District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the encompassing high and discharging currents generated from regenerative braking andofacceleration, respectively, may adversely thecharging cycle life of the the conventional energy system. incorporation supercapacitors into thethe energy greenhouseaffect gas emissions from building sector. Thesestorage systems requireHence, high investments which are returned through heat adversely affect the cycle life of theinconventional energy storage system. Hence, incorporation of supercapacitors into the energy storage system is recommended view of its superior cycle efficiency and high power density, which aids in relieving the sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, storage system isand recommended initsview oflife. its In superior cyclea hybrid efficiency andstorage high power density, which aids inLi-ion relieving the battery’s stress thus extends cycle this study, energy system (HESS) comprising batteries prolonging the investment return period. battery’s stress and thus extends its cycle life. In study,and a hybrid energy storage system (HESS) Li-ionThe batteries and arepaper modeled evaluate its this electrical undertemperature differentcomprising driving cycles. results The supercapacitors main scope of this is to to assess the feasibility of usingthermal the heatperformances demand – outdoor function for heat demand and supercapacitors are modeled to stress, evaluate its power electrical and thermal performances underofdifferent driving cycles. The results obtained reveal that the dynamic peak demand and thermal performance the battery have been significantly forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 obtained reveal that the dynamic stress, peakinto power battery demandpack andinthermal performance of with the battery have been battery significantly improved by incorporating In comparison the conventional energy buildings that vary in both supercapacitors construction period the and typology. ThreeHESS. weather scenarios (low, medium, high) and three district improved by incorporating supercapacitors into the battery pack inforHESS. Inand comparison withhave the been conventional battery energy storage system, the peak current demands of the battery in HESS UDDS US06 cycles reduced by 63%, renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values72.9% were storage system, the peak current demands has of the battery in effective HESS forinUDDS and US06battery’s cycles have beenand reduced 72.9% and 71.7%, respectively. This approach shown to be is ablebyto63%, improve the compared with results from a dynamic heat demand model, previouslyextending developedthe and validatedlifespan by the authors. and 71.7%, respectively. This approach has shown to bestorage effective in extending the battery’s lifespan and is able to improve the safety and reliability of the conventional battery energy system. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications safety and reliability of the conventional battery energy storage system. (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Copyright © 2018 Ltd. All rights scenarios, the errorElsevier value increased up to reserved. 59.5% (depending on the weather and renovation scenarios combination considered). ©Selection 2019 The Authors. Published by Ltd. of th International Conference on Applied Copyright ©and 2018 Elsevier Ltd. AllElsevier rights reserved. peer-review under responsibility thewithin scientific range committee of the 108% Theisvalue of slope increased on average of 3.8% up to per decade, that corresponds to the This an open accesscoefficient article under the CC BY-NC-ND license the (http://creativecommons.org/licenses/by-nc-nd/4.0/) th International Conference on Applied Selection and peer-review under responsibility of the scientific committee of the 10 Energy decrease(ICAE2018). inunder the number of heating hours of 22-139h during the heating–season (depending on the combination of weather and Peer-review responsibility of the scientific committee of ICAE2018 The 10th International Conference on Applied Energy. Energy (ICAE2018). renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the Keywords: hybrid energy storage system; electric vehicle; Lithium-ion battery; supercapacitor; numerical modeling coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and Keywords: hybrid energy storage system; electric vehicle; Lithium-ion battery; supercapacitor; numerical modeling improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +603-9086 0288; fax: +603-9019 3886.

E-mail address: [email protected] * Corresponding author. Tel.: +603-9086 0288; fax: +603-9019 3886. Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected] 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of thereserved. scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 Copyright © 2018 Elsevier Ltd. All rights Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.02.033

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1. Introduction Global demand for energy consumption is expected to increase tremendously in the coming decades owing to the growing populations and improved standard of living driven by the advances in technology. It is noted that continued reliance on fossil fuels as the primary energy source is anticipated in the foreseeable future, particularly in lower-income developing countries [1]. Nonetheless, recognizing the long-term environmental impacts associated with fossil fuel burning, a gradual declining trend in non-renewable energy consumption is observed in many higherincome countries, with an apparent transition towards renewable energy sources. Additionally, the global fossil fuel reserves have been severely depleted in the past decades due to massive exploitation. This imminent threat of energy scarcity may potentially pose serious stumbling blocks to the future socio-economic development when the increased energy needs are unable to be fulfilled. Therefore, a sustainable alternative solution is necessary to meet the growing energy demands in the future. In line with this, electric vehicles (EVs) have received substantial attentions lately as one of the sustainable options for ground transportation purpose in an effort to alleviate environmental issues and the likelihood of increasing fuel prices (EVs) [1,2]. EV has a high-capacity battery energy storage system, which governs the vehicle’s performance and driving range. The system stores a large amount of energy during charging and regenerative braking. Successively, this energy is withdrawn from the energy storage system during cruising. A hybrid energy storage system (HESS) has the combination of high-energy and high power storage elements to increase overall specific power and/or specific energy. High power storage uses electrical double–layer capacitors (EDLC) to supply power during acceleration or to absorb the power during deceleration. On the other hand, high energy storage uses battery to provide long-term energy supply [3,4]. Both elements complement each other well in terms of energy and power requirements. Passive hybrid energy storage topology (P-HEST), active hybrid energy storage topology (A-HEST) and discrete hybrid energy storage topology (D-HEST) are the three main types of HESS topology. The performance of HESS could be enhanced by incorporating a power converter in A-HEST and D-HEST to improve the energy utilization of the EDLC and battery [3,4]. In addition, rule-based approaches and optimization-based approaches are the two common energy management strategies used in HESS [4-7]. Moreover, it is found experimentally that HESS is more cost-effective as compared to its individual component, with longer lifecycle and enhanced performance in terms of response time as well as peak power reduction. Furthermore, in comparison with a conventional battery pack, the vehicle’s acceleration, driving efficiency, driving range and battery life cycle are greatly improved with the use of HESS [5,8-10]. It is found that most of the reported work in the literature primarily focus on the electrical behaviour of HESS, while studies related to its thermal behaviour are still scare to date. Set against this background, an empirical equation coupled with a lumped thermal model is developed in this study to predict both the electrical and thermal behaviour of Li-ion battery and supercapacitor during constant-current discharging and square-wave charging, respectively. The validated single battery and supercapacitor model is then combined to establish a HESS model to investigate its the electrical and thermal responses under various types of driving cycle, such as Urban Dynamometer Driving Schedule (UDDS) and US06 Supplemental Federal Test Procedure (SFTP). Subsequently, the results of the total heat generation, driving range, energy recovered and temperature development of the battery and supercapacitor in the HESS are discussed. 2. Numerical model for HESS 2.1. Lithium-ion battery model Battery model is utilized to define the charging and discharging characteristics under different operating conditions. Equations used to describe the discharging and charging of the Lithium Iron Phosphate (LiFePO4) battery are represented by Eq. (1) and Eq. (2), respectively [11]. Internal resistance of the battery is assumed constant during the charging and discharging process and does not vary according to the magnitude of the current and temperature. The battery model parameters for charging are assumed to be identical to the discharging process and the hysteresis effect of the LiFePO4 battery is not modelled.

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Discharging (i* > 0) (1)

Charging (i* < 0)

(2) 2.2. The supercapacitor model Supercapacitor model is used to model the change of voltage, capacitance and state of charge using the input current. The Stern model describes in Eq. (3) is used to model the voltage change of the Maxwell BCAP3000 P270 supercapacitor [12]. In this study, internal resistance of the supercapacitor is assumed to be constant during the charging and discharging process, and it is not affected by temperature and magnitude of current. Besides, temperature effect on the electrolyte material and aging effect is not taken into consideration.

(3) 2.3. Li-ion Battery thermal model Ohmic heat, reversible heat and irreversible heat are types of heat generated in the battery [4]. Ohmic heat is generated by ohmic resistance of the solid active material and the electrolyte present in the battery. Irreversible heat is generated by movement of electrons due to the electrochemical reaction during the charging and discharging process. On the other hand, reversible heat is generated from the entropy changes of anode and cathode. Heat generation in the battery is modelled using Eq. 4 [11]. Entropy change in the anode and cathode is represented by the change of equilibrium potential with temperature (dU/dT) and varies with SOC. External electrical contact resistance (i2Rc) is added to the model to improve the prediction of the heat generation in the battery. (4) Parallel air flow cooling strategy is introduced to the battery pack that consists of numerous battery modules. Each module is subjected to an equal amount of air flow and the incoming air temperature to yield even cooling of the entire battery pack. Therefore, it is adequate to investigate the thermal performance of a single battery module. Thermostat feature is incorporated into the battery model to provide active air cooling if the surface temperature of the battery exceeds 35 oC. The cooling system will be switched on to deliver 25 CFM of cooling air per module. For the battery surface temperature below 35 oC, batteries are cooled by natural convection. 2.4. Supercapacitor thermal model Convection and radiation are heat dissipation mechanisms for the supercapacitor and battery. Heat generated in the supercapacitor is also comprised of ohmic heat, irreversible heat and reversible heat. The heat generated in the supercapacitor is defined by Eq. (6). External electrical contact resistance (i2Rc) is also added to improve the prediction of the heat generated by the supercapacitor. Since supercapacitor has a wide range of operating temperature from -40 oC to 65 oC, natural convection is sufficient to maintain the supercapacitor at its optimum operating temperature window. Incorporation of a thermal management will incur additional cost and reduce reliability and effective energy density of the HESS. (5)

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2.5. Experimental setup and parameter extraction A commercial 8 Ah 38120P Lithium Iron Phosphate battery (Headway) and supercapacitor 3000 F BCAP3000 P270 (Maxwell) are used in the HESS. The details of the battery and supercapacitor parameters are tabulated in Table 1. Battery cycler (Maccor Instrument 4000) is utilized to obtain the discharging data of the battery at different C-rate. Cut-off voltage of 2.3 V is set for constant current discharging of the battery. Internal resistance (R int) of the battery is analysed using an Impedance analyser (Solartron analytical 1400). Three T-type thermocouples are attached in the axial direction and three sides of the battery surface to measure the surface temperature of the battery. Temperature readings are recorded using the HP34970A data logger. Experimental measurements of the voltage and temperature changes in the supercapacitor are extracted from the Blaud study [13]. Constant current of 11 A is used to charge the supercapacitor until the voltage reaches 2.7 V. Next, the current source is removed and the self-discharging characteristic of the supercapacitor is recorded for 45 minutes. Finally, development of the supercapacitor surface temperature under 200 A of square wave current charging is recorded. Table 1. Details of Li-ion battery and supercapacitor.

Li-ion battery (Headway 38120P) Parameters

Value

Nominal Voltage, V

3.2

Supercapacitor (Maxwell BCAP 3000)

Parameters

Value

Initial Voltage, V

2.7

Nominal Capacity, mAh

8000

Rated Capacitance, F

3000

Diameter, m

0.038

Diameter, m

0.060

Length, m

0.0146

Length, m

0.138

Weight, kg

0.355

Weight, kg

0.510

Specific heat, Jkg K

998

Specific heat, Jkg K

1079.6

E0, V

3.27

Equivalent DC series resistance, 

0.00029

Leakage current, A

0.0052

-1

-1

Internal resistance R, 

0.0034

-1

-1

2.6. Numerical procedures The Li-ion battery and supercapacitor models are developed using Matlab-Simulink 2015b. Numerical simulation results are then compared with the experimental measurements. The validated models are extended to establish the HESS model. Next, the HESS model is used to investigate the electrical and thermal behaviours of a converted EV under Urban Dynamometer Driving Schedule (UDDS) and US06-SFTP driving cycles [11]. In the HESS simulation, energy recovery from the regenerative braking is stored into the supercapacitor bank through bidirectional DC-DC converter. Power distribution ratio between the battery pack to supercapacitor bank is taken as 50:50. There are 49 batteries connected in series and 16 batteries connected in parallel to achieve nominal voltage of 147 V and 128 Ah. On the other hand, 16 supercapacitors are connected in series and 14 supercapacitors connected in parallel to achieve nominal voltage of 43.2 V and capacitance of 42,000 F. 3. Results and Discussions UDDS and US06 driving cycles are used to evaluate the performance of HESS on a converted EV. The HESS is cycled continuously until one of the energy storage elements reaches the cut-off voltage. The thermal responses of the HESS under UDDS and US06 cycle are shown in Fig. 1. When the temperature is greater than 35 oC, 25 CFM of cooling air per module will be introduced into the battery pack. As shown in Fig. 1, the HESS is able to complete 8.2 cycles of UDDS (11184 s) and 4.7 cycles of US06 (2811 s) before reaching the cut-off voltage of the battery pack or supercapacitor bank. The temperature of the battery pack and supercapacitor bank is increasing continuously throughout the cycles. It is also observed that the temperature of the supercapacitor is relatively higher than the battery pack for all driving cycles. As illustrated in Fig. 1, temperature of the supercapacitor and battery during the end of the UDDS cycle is about 30.6 oC and 32.3 oC, respectively. On the other hand, temperature of the supercapacitor and battery during the end of US06 cycle is about 33.4 oC and 36.1 oC, respectively. For both cases,

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temperature of the battery in the US06 driving cycle is higher than 35 oC and forced convection is activated to bring down the temperature of the battery.

Fig. 1 Thermal responses of the HESS on (a) UDDS driving cycle, and (b) US06 driving cycle. Electrical responses of the HESS on the UDDS driving cycles is shown in Fig. 2. Maximum discharge current for the HESS in the UDDS is about 642 A (2.7 C-rate). Supercapacitor in HESS plays an important role during the dynamic operation of the energy storage system, which includes both the hard acceleration and storing of the regenerative braking energy. In contrast, in an energy storage system which consists of battery alone, it is necessary to store the energy generated from regenerative braking inside the battery. In this case, the use of a supercapacitor has effectively reduced the stress on the battery pack and thus the battery pack is no longer subjected to high current flow. Maximum current discharge from the battery pack in the HESS is less than 5 C-rate only. This is more prevailing in the US06, which more energy is withdrawn from and stored into the energy storage system. Consequently, it is projected that more heat is generated in the US06 driving cycle.

Fig. 2 Electrical responses of the (a) Battery pack, and (b) Supercapacitor bank in HESS on UDDS driving cycle. Driving Cycle

Table 2. Summary of driving cycle results for HESS. UDDS

battery Duration, s

supercapacitor

US06 battery

11184

supercapacitor 2811

Maximum current, A

255

3014

573

7260

Charging energy, MJ

-

23.38

-

22.34

Discharging energy, MJ

74.94

25.13

71.23

23.64

Heat generation, MJ

0.475

0.248

2.648

0.760

Maximum temperature under natural convection, °C

32.3

30.6

37.9

33.4

-

-

36.1

-

Maximum temperature under 25 CFM of cooling air, °C

Table 2 summarizes the driving cycle’s results for the HESS. Among all driving cycles, most of the regenerative braking energy is recovered from the UDDS driving cycle. UDDS is an intensive start-and-stop driving cycle that represents the driving trend in the city with frequent use of the accelerator and brake. Therefore, most energy can be recovered in this driving cycle. In the HESS, heat generated by the battery for UDDS and US06 is about 0.475 MJ

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and 2.648 MJ, respectively. While heat generated in the supercapacitor for UDDS and US06 is about 0.248 MJ and 0.760 MJ, respectively. Incorporating supercapacitor into the battery pack to form a HESS is important to optimize the performance of the energy storage system in the EV, in which the quality of energy is recovered, the cycle life of the battery pack is prolonged, and the thermal and power stress on the battery pack is alleviated. Furthermore, less energy is consumed for the thermal management system of the energy storage system. However, the performance of the HESS is still dependent on the power distribution and energy management system between battery and supercapacitor. Optimal energy management strategy will bring out the full potential of the battery and supercapacitor, as well as improving the EV cost and performance. 4. Conclusions In this study, the Simulink model is used to predict the performance of the HESS under different driving cycles. Calibrated battery and supercapacitor models are integrated to produce HESS model to investigate the electrothermal performance of the converted EV under UDDS and US06 driving cycles. The power distribution ratio between the supercapacitor and battery is set to be 50:50 in order to reduce the battery peak current. Besides, energy recuperated from braking is stored into the supercapacitor. The simulation results show that the dynamic stress, peak current demand and thermal performance of the battery in the HESS have been improved significantly. Supercapacitor plays an important role in fulfilling the high-power demand and fully utilizes the limited energy to assist the operations of the EV. Natural convection is sufficient to ensure that the HESS operates at its optimum operating temperature window. Future research work can be conducted by setup an electric propulsion system test bench for validation of the simulation results and incorporation of an intelligent energy management system in the model is recommended. In summary, HESS is able to extend the battery lifespan, as well as to ensure safety and reliability of the conventional energy storage system. Acknowledgements This work is supported by Science fund Grant No. 03-02-11-SF2016 from Ministry of Science, Technology and innovation, Malaysia. References [1] Saw LH, Poon HM, Thiam HS, Cai Z, Chong WT, Pambudi NA, King YJ. Novel thermal management system using mist cooling for lithiumion battery packs. Applied Energy 2018; 223:146-158. [2] Saw LH, Ye, Y, Tay AAO. Feasibility study of boron nitride coating on Lithium-ion battery casing. Applied Thermal Engineering 2014;73;154-161. [3] Zimmermann T, Keil P, Hofmann M, Horsche MF, Pichlmaier S. Review of system topologies for hybrid electrical energy storage systems. Journal of Energy Storage 2016;8:78-90. [4] Ostadi A, Kazerani M, Chen SK. Hybrid energy storage system (HESS) in vehicular applications: A review on interfacing battery and ultracapacitor units. IEEE Transportation Electrification Conference and Expo 2013;13697534. [5] Veneri O, Capasso C, Patalano S. Experimental investigation into the effectiveness of a super-capacitor based hybrid energy storage system for urban commercial vehicles. Applied Energy 2017; in Press. [6] Zhang S, Xiong R, Cao J. Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system. Applied Energy 2016;179:316-328. [7] Castaings A, Lhomme W, Trigui R, Bouscayrol A. Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints. Applied Energy 2016;163:190-200. [8] Capasso C, Sepe V, Veneri O, Montanari M, Poletti L. Experimentation with a ZEBRA plus EDLC based hybrid storage system for urban means of transport. International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles 2015;15110758. [9] Jarushi AM, Schofield N. Battery and supercapacitor combination for a series hybrid electric vehicle. 5 th IET International Conference on Power Eletronics, Machines and Drives. 2010;1-6. [10] Capasso C, Veneri O. Laboratory bench to test ZEBRA battery plus super-capacitor based propulsion systems for urban electric transportation. Energy Procedia 2015;75:1956-1961. [11] Saw LH, Somasundaram K, Ye Y, Tay AAO. Electro-thermal analysis of lithium iron phosphate battery for electric vehicle. J. Power Sources 2014;249:231-238. [12] Oldham KB. A Gouy-Chapman-Stern model of the double layer at a (metal)/(ionic liquid) interface. J. Electroanal Chem 2008;613:131-138. [13] Blaud PC. Development of a simulation model supercapacitor and experimental validation. Mastet thesis. University of Quebec. 2012.