IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v292y2021ics0306261921004128.html
   My bibliography  Save this article

Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting

Author

Listed:
  • Zhu, Tao
  • Wills, Richard G.A.
  • Lot, Roberto
  • Ruan, Haijun
  • Jiang, Zhihao

Abstract

The hybrid energy storage system (HESS) composed of batteries and supercapacitors (SCs) is a dual energy storage technology that can compensate for the shortcomings of a single energy storage technology acting alone. The energy management of HESS splits the power and energy demands from the electric vehicle (EV) to the battery and SC and thus is vital to EV propulsion. This paper presents an online energy management strategy (EMS) that optimises the operating costs of battery-SC HESS and can be adaptive to real-time EV driving conditions. We analyse the optimal offline benchmarks to guide online EMS design and propose the adaptive online EMS with variable perception horizon based on both neural network and rule-based techniques. Compared with existing research, the proposed EMS features reduced complexity, flexible perception and intelligent rulemaking. Case study results show that the proposed variable perception horizon and neural network fitting can improve EMS optimality compared with the conventional methods in existing research. The proposed EMS can realise more than 97% cost optimisation efficacy of offline benchmarks. By the proposed EMS, this paper is expected to provide a practical and effective energy management approach for the battery-SC HESS to reduce costs in EV applications.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921004128
    DOI: 10.1016/j.apenergy.2021.116932
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921004128
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.116932?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. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Zhang, Xiaowu & Ouyang, Minggao, 2015. "The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis," Applied Energy, Elsevier, vol. 159(C), pages 576-588.
    2. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    3. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
    4. Bonges, Henry A. & Lusk, Anne C., 2016. "Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 83(C), pages 63-73.
    5. Ruan, Jiageng & Song, Qiang & Yang, Weiwei, 2019. "The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle," Energy, Elsevier, vol. 183(C), pages 315-330.
    6. Li, Xunming & Han, Lijin & Liu, Hui & Wang, Weida & Xiang, Changle, 2019. "Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm," Energy, Elsevier, vol. 172(C), pages 1161-1178.
    7. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    8. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    9. 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.
    10. 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.
    11. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Hou, Jun & Han, Xuebing & Ouyang, Minggao, 2014. "Energy management strategies comparison for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 134(C), pages 321-331.
    12. Zhu, Tao & Lot, Roberto & Wills, Richard G.A. & Yan, Xingda, 2020. "Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles," Energy, Elsevier, vol. 208(C).
    13. 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.
    14. Du, Jiuyu & Zhang, Xiaobin & Wang, Tianze & Song, Ziyou & Yang, Xueqing & Wang, Hewu & Ouyang, Minggao & Wu, Xiaogang, 2018. "Battery degradation minimization oriented energy management strategy for plug-in hybrid electric bus with multi-energy storage system," Energy, Elsevier, vol. 165(PA), pages 153-163.
    15. Song, Ziyou & Zhang, Xiaobin & Li, Jianqiu & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "Component sizing optimization of plug-in hybrid electric vehicles with the hybrid energy storage system," Energy, Elsevier, vol. 144(C), pages 393-403.
    16. 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.
    17. Zhang, Shuo & Xiong, Rui & Sun, Fengchun, 2017. "Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system," Applied Energy, Elsevier, vol. 185(P2), pages 1654-1662.
    18. Chen, Zeyu & Xiong, Rui & Wang, Chun & Cao, Jiayi, 2017. "An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle," Applied Energy, Elsevier, vol. 185(P2), pages 1663-1672.
    19. Xu, Bin & Rathod, Dhruvang & Zhang, Darui & Yebi, Adamu & Zhang, Xueyu & Li, Xiaoya & Filipi, Zoran, 2020. "Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    20. Gert Berckmans & Maarten Messagie & Jelle Smekens & Noshin Omar & Lieselot Vanhaverbeke & Joeri Van Mierlo, 2017. "Cost Projection of State of the Art Lithium-Ion Batteries for Electric Vehicles Up to 2030," Energies, MDPI, vol. 10(9), pages 1-20, September.
    21. Li, Fan & Sun, Bo & Zhang, Chenghui & Liu, Che, 2019. "A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage," Energy, Elsevier, vol. 188(C).
    22. 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.
    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. Md. Sazal Miah & Molla Shahadat Hossain Lipu & Sheikh Tanzim Meraj & Kamrul Hasan & Shaheer Ansari & Taskin Jamal & Hasan Masrur & Rajvikram Madurai Elavarasan & Aini Hussain, 2021. "Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends," Sustainability, MDPI, vol. 13(22), pages 1-38, November.
    2. Zhangyu Lu & Xizheng Zhang, 2022. "Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle," Energies, MDPI, vol. 15(4), pages 1-15, February.
    3. Elango Sangeetha & Vijayapriya Ramachandran, 2022. "Different Topologies of Electrical Machines, Storage Systems, and Power Electronic Converters and Their Control for Battery Electric Vehicles—A Technical Review," Energies, MDPI, vol. 15(23), pages 1-28, November.
    4. 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.
    5. Xiao Hu & Shikun Liu & Ke Song & Yuan Gao & Tong Zhang, 2021. "Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health," Energies, MDPI, vol. 14(20), pages 1-20, October.
    6. Abdeldjalil Djouahi & Belkhir Negrou & Boubakeur Rouabah & Abdelbasset Mahboub & Mohamed Mahmoud Samy, 2023. "Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application," Energies, MDPI, vol. 16(9), pages 1-18, May.
    7. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(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. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
    2. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    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. Zhu, Tao & Lot, Roberto & Wills, Richard G.A. & Yan, Xingda, 2020. "Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles," Energy, Elsevier, vol. 208(C).
    5. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    6. Niu, Junyan & Zhuang, Weichao & Ye, Jianwei & Song, Ziyou & Yin, Guodong & Zhang, Yuanjian, 2022. "Optimal sizing and learning-based energy management strategy of NCR/LTO hybrid battery system for electric taxis," Energy, Elsevier, vol. 257(C).
    7. Yang, Weiwei & Ruan, Jiageng & Yang, Jue & Zhang, Nong, 2020. "Investigation of integrated uninterrupted dual input transmission and hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    8. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    9. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    10. Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.
    11. Lin, Xinyou & Zeng, Songrong & Li, Xuefan, 2021. "Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network," Energy, Elsevier, vol. 223(C).
    12. Ashleigh Townsend & Rupert Gouws, 2023. "A Comparative Review of Capacity Measurement in Energy Storage Devices," Energies, MDPI, vol. 16(10), pages 1-26, May.
    13. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    14. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    15. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    16. Song, Ziyou & Li, Jianqiu & Hou, Jun & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study," Energy, Elsevier, vol. 154(C), pages 433-441.
    17. Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    18. Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).
    19. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    20. Md. Sazal Miah & Molla Shahadat Hossain Lipu & Sheikh Tanzim Meraj & Kamrul Hasan & Shaheer Ansari & Taskin Jamal & Hasan Masrur & Rajvikram Madurai Elavarasan & Aini Hussain, 2021. "Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends," Sustainability, MDPI, vol. 13(22), pages 1-38, November.

    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:292:y:2021:i:c:s0306261921004128. 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.