IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v217y2018icp153-165.html
   My bibliography  Save this article

Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle

Author

Listed:
  • Xiong, Rui
  • Duan, Yanzhou
  • Cao, Jiayi
  • Yu, Quanqing

Abstract

In order to meet the requirements of high specific energy and high specific power together and extend the service life of the energy storage system in temperature abusive conditions, a multi-power configuration with high specific energy lithium-ion battery and high specific power ultracapacitor is the best choice for the all-climate electric vehicle (ACEV). Aiming at real-time power management of a hybrid energy storage system (HESS), three power management strategies, which are respectively based on rules, dynamic programming algorithm, and real-time reinforcement learning algorithm, have been systematically compared in this study. To verify the performance of the control strategies, the hardware-in-loop (HIL) simulation test platform based on xPC Target has been built. The results show that the real-time power management strategy based on reinforcement learning algorithm is superior to the others. This strategy can reduce the charge and discharge ratio of the battery pack, which extends the life of battery pack and improves the efficiency of the system.

Suggested Citation

  • Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
  • Handle: RePEc:eee:appene:v:217:y:2018:i:c:p:153-165
    DOI: 10.1016/j.apenergy.2018.02.128
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918302599
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.02.128?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Chun & He, Hongwen & Zhang, Yongzhi & Mu, Hao, 2017. "A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures," Applied Energy, Elsevier, vol. 196(C), pages 268-278.
    2. Castaings, Ali & Lhomme, Walter & Trigui, Rochdi & Bouscayrol, Alain, 2016. "Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints," Applied Energy, Elsevier, vol. 163(C), pages 190-200.
    3. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    4. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    5. Yanzi Wang & Weida Wang & Yulong Zhao & Lei Yang & Wenjun Chen, 2016. "A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems," Energies, MDPI, vol. 9(1), pages 1-20, January.
    6. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    7. He, Hongwen & Xiong, Rui & Zhao, Kai & Liu, Zhentong, 2013. "Energy management strategy research on a hybrid power system by hardware-in-loop experiments," Applied Energy, Elsevier, vol. 112(C), pages 1311-1317.
    8. Li, Jianwei & Yang, Qingqing & Robinson, Francis. & Liang, Fei & Zhang, Min & Yuan, Weijia, 2017. "Design and test of a new droop control algorithm for a SMES/battery hybrid energy storage system," Energy, Elsevier, vol. 118(C), pages 1110-1122.
    9. Zhang, Shuo & Xiong, Rui & Cao, Jiayi, 2016. "Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system," Applied Energy, Elsevier, vol. 179(C), pages 316-328.
    10. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    11. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    12. Li, Jianwei & Wang, Xudong & Zhang, Zhenyu & Le Blond, Simon & Yang, Qingqing & Zhang, Min & Yuan, Weijia, 2017. "Analysis of a new design of the hybrid energy storage system used in the residential m-CHP systems," Applied Energy, Elsevier, vol. 187(C), pages 169-179.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yao He & Changchang Miao & Ji Wu & Xinxin Zheng & Xintian Liu & Xingtao Liu & Feng Han, 2021. "Research on the Power Distribution Method for Hybrid Power System in the Fuel Cell Vehicle," Energies, MDPI, vol. 14(3), pages 1-15, January.
    2. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    3. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    4. Imen Jarraya & Fatma Abdelhedi & Nassim Rizoug, 2023. "An Innovative Power Management Strategy for Hybrid Battery–Supercapacitor Systems in Electric Vehicle," Mathematics, MDPI, vol. 12(1), pages 1-23, December.
    5. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    6. Dimitrios Rimpas & Stavrοs D. Kaminaris & Dimitrios D. Piromalis & George Vokas, 2023. "Real-Time Management for an EV Hybrid Storage System Based on Fuzzy Control," Mathematics, MDPI, vol. 11(21), pages 1-18, October.
    7. Xu, Bin & Shi, Junzhe & Li, Sixu & Li, Huayi & Wang, Zhe, 2021. "Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle," Energy, Elsevier, vol. 229(C).
    8. Tengda Hu & Yunwu Li & Zhi Zhang & Ying Zhao & Dexiong Liu, 2021. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information," Energies, MDPI, vol. 14(9), pages 1-18, April.
    9. Wegmann, Raphael & Döge, Volker & Sauer, Dirk Uwe, 2018. "Assessing the potential of a hybrid battery system to reduce battery aging in an electric vehicle by studying the cycle life of a graphite∣NCA high energy and a LTO∣metal oxide high power battery cell," Applied Energy, Elsevier, vol. 226(C), pages 197-212.
    10. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    11. Lv, Jie & Lin, Shili & Song, Wenji & Chen, Mingbiao & Feng, Ziping & Li, Yongliang & Ding, Yulong, 2019. "Performance of LiFePO4 batteries in parallel based on connection topology," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    12. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    13. Xiaogang Wu & Zhihao Cui & Xuefeng Li & Jiuyu Du & Ye Liu, 2019. "Control Strategy for Active Hierarchical Equalization Circuits of Series Battery Packs," Energies, MDPI, vol. 12(11), pages 1-18, May.
    14. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
    15. Liu, Xinhua & Ai, Weilong & Naylor Marlow, Max & Patel, Yatish & Wu, Billy, 2019. "The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs," Applied Energy, Elsevier, vol. 248(C), pages 489-499.
    16. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Development of energy management system based on a rule-based power distribution strategy for hybrid power sources," Energy, Elsevier, vol. 175(C), pages 1055-1066.
    17. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    18. Ma, Fangwu & Yang, Yu & Wang, Jiawei & Liu, Zhenze & Li, Jinhang & Nie, Jiahong & Shen, Yucheng & Wu, Liang, 2019. "Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication," Energy, Elsevier, vol. 189(C).
    19. Berrueta, Alberto & Heck, Michael & Jantsch, Martin & Ursúa, Alfredo & Sanchis, Pablo, 2018. "Combined dynamic programming and region-elimination technique algorithm for optimal sizing and management of lithium-ion batteries for photovoltaic plants," Applied Energy, Elsevier, vol. 228(C), pages 1-11.
    20. Sun, Qixing & Xing, Dong & Alafnan, Hamoud & Pei, Xiaoze & Zhang, Min & Yuan, Weijia, 2019. "Design and test of a new two-stage control scheme for SMES-battery hybrid energy storage systems for microgrid applications," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    21. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    22. Bo, Lin & Han, Lijin & Xiang, Changle & Liu, Hui & Ma, Tian, 2022. "A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles," Energy, Elsevier, vol. 252(C).
    23. Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
    24. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    25. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    2. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    3. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    4. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    5. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    6. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    8. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    9. Bizon, Nicu, 2017. "Energy optimization of fuel cell system by using global extremum seeking algorithm," Applied Energy, Elsevier, vol. 206(C), pages 458-474.
    10. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    11. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
    12. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    13. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    14. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    15. Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    16. Mingjie Zhao & Junhui Shi & Cheng Lin & Junzhi Zhang, 2018. "Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission," Energies, MDPI, vol. 11(2), pages 1-16, February.
    17. Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Li, Guangmin, 2020. "Comparison of semi-active hybrid battery system configurations for electric taxis application," Applied Energy, Elsevier, vol. 259(C).
    18. Li, Jianwei & Yang, Qingqing & Mu, Hao & Le Blond, Simon & He, Hongwen, 2018. "A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm," Applied Energy, Elsevier, vol. 220(C), pages 13-20.
    19. Wegmann, Raphael & Döge, Volker & Sauer, Dirk Uwe, 2018. "Assessing the potential of a hybrid battery system to reduce battery aging in an electric vehicle by studying the cycle life of a graphite∣NCA high energy and a LTO∣metal oxide high power battery cell," Applied Energy, Elsevier, vol. 226(C), pages 197-212.
    20. Sun, Qixing & Xing, Dong & Alafnan, Hamoud & Pei, Xiaoze & Zhang, Min & Yuan, Weijia, 2019. "Design and test of a new two-stage control scheme for SMES-battery hybrid energy storage systems for microgrid applications," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:217:y:2018:i:c:p:153-165. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.