IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v135y2014icp212-224.html
   My bibliography  Save this article

Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles

Author

Listed:
  • Song, Ziyou
  • Li, Jianqiu
  • Han, Xuebing
  • Xu, Liangfei
  • Lu, Languang
  • Ouyang, Minggao
  • Hofmann, Heath

Abstract

This paper proposes a semi-active battery/supercapacitor (SC) hybrid energy storage system (HESS) for use in electric drive vehicles. A much smaller unidirectional dc/dc converter is adopted in the proposed HESS to integrate the SC and battery, thereby increasing the HESS efficiency and reducing the system cost. We have also included a quantitative battery capacity fade model, in addition to the theoretical HESS model proposed in this paper. For the proposed HESS, we have examined the sizing optimization of the HESS parameters for an electric city bus, including the parallel and series number of the battery cell and the SC module. Considering the constraint of requirement on minimal mileage, the optimization goal is to simultaneously minimize (i) the total cost of the HESS and (ii) the capacity loss of a LiFePO4 battery over a typical China Bus Driving Cycle. The simulation result shows that these two objectives are conflicting, and trades them off using a non-dominated sorting genetic algorithm II. Finally, the Pareto front including optimal HESS parameter groups has been obtained, which indicates that the battery capacity loss can be reduced rapidly when the SC cost increases within the range from 10 to 40 thousand RMB.

Suggested Citation

  • Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
  • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:212-224
    DOI: 10.1016/j.apenergy.2014.06.087
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914008964
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2014.06.087?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. Trovão, João P. & Pereirinha, Paulo G. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2013. "A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach," Applied Energy, Elsevier, vol. 105(C), pages 304-318.
    2. 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.
    3. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2012. "An integrated optimization approach for a hybrid energy system in electric vehicles," Applied Energy, Elsevier, vol. 98(C), pages 479-490.
    4. Xu, Liangfei & Ouyang, Minggao & Li, Jianqiu & Yang, Fuyuan & Lu, Languang & Hua, Jianfeng, 2013. "Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system cost," Applied Energy, Elsevier, vol. 103(C), pages 477-487.
    Full references (including those not matched with items on IDEAS)

    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. Feroldi, Diego & Carignano, Mauro, 2016. "Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 645-658.
    2. Hou, Cong & Ouyang, Minggao & Xu, Liangfei & Wang, Hewu, 2014. "Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 115(C), pages 174-189.
    3. Zhuang, Weichao & Zhang, Xiaowu & Ding, Yang & Wang, Liangmo & Hu, Xiaosong, 2016. "Comparison of multi-mode hybrid powertrains with multiple planetary gears," Applied Energy, Elsevier, vol. 178(C), pages 624-632.
    4. Sadam Hussain & Muhammad Umair Ali & Gwan-Soo Park & Sarvar Hussain Nengroo & Muhammad Adil Khan & Hee-Je Kim, 2019. "A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-24, December.
    5. 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.
    6. Javier Sanfélix & Cristina De la Rúa & Jannick Hoejrup Schmidt & Maarten Messagie & Joeri Van Mierlo, 2016. "Environmental and Economic Performance of an Li-Ion Battery Pack: A Multiregional Input-Output Approach," Energies, MDPI, vol. 9(8), pages 1-15, July.
    7. Kashkooli, Ali Ghorbani & Farhad, Siamak & Chabot, Victor & Yu, Aiping & Chen, Zhongwei, 2015. "Effects of structural design on the performance of electrical double layer capacitors," Applied Energy, Elsevier, vol. 138(C), pages 631-639.
    8. Sanfélix, Javier & Messagie, Maarten & Omar, Noshin & Van Mierlo, Joeri & Hennige, Volker, 2015. "Environmental performance of advanced hybrid energy storage systems for electric vehicle applications," Applied Energy, Elsevier, vol. 137(C), pages 925-930.
    9. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    10. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    11. Jiajun Liu & Huachao Dong & Tianxu Jin & Li Liu & Babak Manouchehrinia & Zuomin Dong, 2018. "Optimization of Hybrid Energy Storage Systems for Vehicles with Dynamic On-Off Power Loads Using a Nested Formulation," Energies, MDPI, vol. 11(10), pages 1-25, October.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Hu, Xiaosong & Johannesson, Lars & Murgovski, Nikolce & Egardt, Bo, 2015. "Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus," Applied Energy, Elsevier, vol. 137(C), pages 913-924.
    17. Jun Peng & Rui Wang & Hongtao Liao & Yanhui Zhou & Heng Li & Yue Wu & Zhiwu Huang, 2019. "A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs," Energies, MDPI, vol. 12(3), pages 1-17, January.
    18. Ashleigh Townsend & Rupert Gouws, 2023. "A Comparative Review of Capacity Measurement in Energy Storage Devices," Energies, MDPI, vol. 16(10), pages 1-26, May.
    19. Trovão, João P. & Silva, Mário A. & Antunes, Carlos Henggeler & Dubois, Maxime R., 2017. "Stability enhancement of the motor drive DC input voltage of an electric vehicle using on-board hybrid energy storage systems," Applied Energy, Elsevier, vol. 205(C), pages 244-259.
    20. 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.

    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:135:y:2014:i:c:p:212-224. 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.