IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v197y2020ics0360544220303443.html
   My bibliography  Save this article

Adaptive real-time optimal energy management strategy for extender range electric vehicle

Author

Listed:
  • Yang, Ye
  • Zhang, Youtong
  • Tian, Jingyi
  • Li, Tao

Abstract

The extender range electric vehicle (EREV) is an effective way to solve the “mileage anxiety” of pure electric vehicles, and the fuel economy of EREV is the key point of energy optimization. This paper designed an adaptive real-time optimal energy management strategy for EREV. Firstly, an improved shooting algorithm is proposed, which can determine the range of the equivalent factor (EF) according to the power configuration parameters of the vehicle, and then the secant method is used to quickly calculate the initial value of the EF. Secondly, from the perspective of energy flow, the intrinsic operation mechanism of equivalent consumption minimization strategy (ECMS) control strategy is revealed, and the working relationship between the five working modes of EREV is clarified. Thirdly, based on the car navigation and geographic location information system, the EF is periodically updated to achieve effective maintenance of the battery state of charge (SOC), so as to obtain the optimal power allocation. Finally, The fuel economy and real-time performance of the proposed energy management strategy are simulated and compared. To verify fuel economy, the rule-based control strategy and the power following control strategy were used as comparison. The results show that the proposed control strategy has better fuel economy and adaptability. To verify real-time performance, the proportional integral derivative ECMS (PID-ECMS) and shooting method ECMS (S-ECMS) were used as comparison. The results show that the proposed strategy is better in both fuel economy and real-time performance.

Suggested Citation

  • Yang, Ye & Zhang, Youtong & Tian, Jingyi & Li, Tao, 2020. "Adaptive real-time optimal energy management strategy for extender range electric vehicle," Energy, Elsevier, vol. 197(C).
  • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220303443
    DOI: 10.1016/j.energy.2020.117237
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220303443
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.117237?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. Hans P. Geering & Florian Herzog & Gabriel Dondi, 2010. "Stochastic Optimal Control with Applications in Financial Engineering," Springer Optimization and Its Applications, in: Altannar Chinchuluun & Panos M. Pardalos & Rentsen Enkhbat & Ider Tseveendorj (ed.), Optimization and Optimal Control, pages 375-408, Springer.
    2. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    3. 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.
    4. Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    5. Shaobo Xie & Huiling Li & Zongke Xin & Tong Liu & Lang Wei, 2017. "A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route," Energies, MDPI, vol. 10(9), pages 1-22, September.
    6. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
    7. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    8. Chen, Zheng & Xia, Bing & You, Chenwen & Mi, Chunting Chris, 2015. "A novel energy management method for series plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 172-179.
    9. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    10. Álvarez Fernández, Roberto & Corbera Caraballo, Sergio & Beltrán Cilleruelo, Fernando & Lozano, J. Antonio, 2018. "Fuel optimization strategy for hydrogen fuel cell range extender vehicles applying genetic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 655-668.
    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. Ren, Guizhou & Wang, Jinzhong & Chen, Changlei & Wang, Haoran, 2021. "A variable-voltage ultra-capacitor/battery hybrid power source for extended range electric vehicle," Energy, Elsevier, vol. 231(C).
    2. Liu, Yonggang & Wu, Yitao & Wang, Xiangyu & Li, Liang & Zhang, Yuanjian & Chen, Zheng, 2023. "Energy management for hybrid electric vehicles based on imitation reinforcement learning," Energy, Elsevier, vol. 263(PC).
    3. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    4. Pei Zhang & Xianpan Wu & Changqing Du & Hongming Xu & Huawu Wang, 2020. "Adaptive Equivalent Consumption Minimization Strategy for Hybrid Heavy-Duty Truck Based on Driving Condition Recognition and Parameter Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    5. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    6. Zhou, Wei & Chen, Yaoqi & Zhai, Haoran & Zhang, Weigang, 2021. "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning," Energy, Elsevier, vol. 220(C).
    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).
    8. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    9. Lan, Song & Stobart, Richard & Wang, Xiaonan, 2022. "Matching and optimization for a thermoelectric generator applied in an extended-range electric vehicle for waste heat recovery," Applied Energy, Elsevier, vol. 313(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. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    2. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. 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.
    4. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    5. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    6. Peng, Fei & Zhao, Yuanzhe & Chen, Ting & Zhang, Xuexia & Chen, Weirong & Zhou, Donghua & Li, Qi, 2018. "Development of robust suboptimal real-time power sharing strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation," Applied Energy, Elsevier, vol. 226(C), pages 503-521.
    7. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    8. Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    9. 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).
    10. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    11. 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.
    12. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.
    13. Yuping Zeng & Yang Cai & Changbao Chu & Guiyue Kou & Wei Gao, 2018. "Integrated Energy and Catalyst Thermal Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 11(7), pages 1-29, July.
    14. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    15. Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    16. 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.
    17. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    18. Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    19. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    20. 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.

    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:energy:v:197:y:2020:i:c:s0360544220303443. 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.journals.elsevier.com/energy .

    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.