IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i6p1526-d336251.html
   My bibliography  Save this article

An Optimal Slip Ratio-Based Revised Regenerative Braking Control Strategy of Range-Extended Electric Vehicle

Author

Listed:
  • Hanwu Liu

    (State Key Laboratory of Automotive Simulation and Control, School of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Yulong Lei

    (State Key Laboratory of Automotive Simulation and Control, School of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Yao Fu

    (State Key Laboratory of Automotive Simulation and Control, School of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Xingzhong Li

    (State Key Laboratory of Automotive Simulation and Control, School of Automotive Engineering, Jilin University, Changchun 130022, China)

Abstract

The energy recovered with regenerative braking system can greatly improve energy efficiency of range-extended electric vehicle (R-EEV). Nevertheless, maximizing braking energy recovery while maintaining braking performance remains a challenging issue, and it is also difficult to reduce the adverse effects of regenerative current on battery capacity loss rate (Q loss,% ) to extend its service life. To solve this problem, a revised regenerative braking control strategy (RRBCS) with the rate and shape of regenerative braking current considerations is proposed. Firstly, the initial regenerative braking control strategy (IRBCS) is researched in this paper. Then, the battery capacity loss model is established by using battery capacity test results. Eventually, RRBCS is obtained based on IRBCS to optimize and modify the allocation logic of braking work-point. The simulation results show that compared with IRBCS, the regenerative braking energy is slightly reduced by 16.6% and Q loss,% is reduced by 79.2%. It means that the RRBCS can reduce Q loss,% at the expense of small braking energy recovery loss. As expected, RRBCS has a positive effect on prolonging the battery service life while ensuring braking safety while maximizing recovery energy. This result can be used to develop regenerative braking control system to improve comprehensive performance levels.

Suggested Citation

  • Hanwu Liu & Yulong Lei & Yao Fu & Xingzhong Li, 2020. "An Optimal Slip Ratio-Based Revised Regenerative Braking Control Strategy of Range-Extended Electric Vehicle," Energies, MDPI, vol. 13(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1526-:d:336251
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/6/1526/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/6/1526/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdel-Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Van den Bossche, Peter & Van Mierlo, Joeri, 2017. "Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries," Energy, Elsevier, vol. 120(C), pages 179-191.
    2. Li, Junqiu & Jin, Xin & Xiong, Rui, 2017. "Multi-objective optimization study of energy management strategy and economic analysis for a range-extended electric bus," Applied Energy, Elsevier, vol. 194(C), pages 798-807.
    3. Qiu, Chengqun & Wang, Guolin & Meng, Mingyu & Shen, Yujie, 2018. "A novel control strategy of regenerative braking system for electric vehicles under safety critical driving situations," Energy, Elsevier, vol. 149(C), pages 329-340.
    4. Ma, Hongrui & Balthasar, Felix & Tait, Nigel & Riera-Palou, Xavier & Harrison, Andrew, 2012. "A new comparison between the life cycle greenhouse gas emissions of battery electric vehicles and internal combustion vehicles," Energy Policy, Elsevier, vol. 44(C), pages 160-173.
    5. 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.
    6. Jingang Guo & Xiaoping Jian & Guangyu Lin, 2014. "Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller," Energies, MDPI, vol. 7(10), pages 1-18, October.
    7. Wu, Jian & Wang, Xiangyu & Li, Liang & Qin, Cun'an & Du, Yongchang, 2018. "Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control," Energy, Elsevier, vol. 145(C), pages 301-312.
    8. Hongqiang Guo & Hongwen He & Fengchun Sun, 2013. "A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle," Energies, MDPI, vol. 6(12), pages 1-21, December.
    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. Yang, Chao & Sun, Tonglin & Wang, Weida & Li, Ying & Zhang, Yuhang & Zha, Mingjun, 2024. "Regenerative braking system development and perspectives for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    2. Peter Girovský & Jaroslava Žilková & Ján Kaňuch, 2020. "Optimization of Vehicle Braking Distance Using a Fuzzy Controller," Energies, MDPI, vol. 13(11), pages 1-15, June.
    3. Cong Geng & Dawen Ning & Linfu Guo & Qicheng Xue & Shujian Mei, 2021. "Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle," Energies, MDPI, vol. 14(8), pages 1-19, April.
    4. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(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. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    2. Ramesh Kumar Chidambaram & Dipankar Chatterjee & Barnali Barman & Partha Pratim Das & Dawid Taler & Jan Taler & Tomasz Sobota, 2023. "Effect of Regenerative Braking on Battery Life," Energies, MDPI, vol. 16(14), pages 1-24, July.
    3. Mokesioluwa Fanoro & Mladen Božanić & Saurabh Sinha, 2022. "A Review of the Impact of Battery Degradation on Energy Management Systems with a Special Emphasis on Electric Vehicles," Energies, MDPI, vol. 15(16), pages 1-29, August.
    4. Yang, Chao & Sun, Tonglin & Wang, Weida & Li, Ying & Zhang, Yuhang & Zha, Mingjun, 2024. "Regenerative braking system development and perspectives for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    5. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    6. 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).
    7. Yang Yang & Yundong He & Zhong Yang & Chunyun Fu & Zhipeng Cong, 2020. "Torque Coordination Control of an Electro-Hydraulic Composite Brake System During Mode Switching Based on Braking Intention," Energies, MDPI, vol. 13(8), pages 1-19, April.
    8. Changran He & Guoye Wang & Zhangpeng Gong & Zhichao Xing & Dongxin Xu, 2018. "A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle," Energies, MDPI, vol. 11(9), pages 1-18, September.
    9. Hong Zhang & Zhuang Xing & Jiajian Song & Qiangqiang Yang, 2018. "Development and Test Application of an Auxiliary Power-Integrated System," Energies, MDPI, vol. 11(1), pages 1-18, January.
    10. Xiaoping Li & Junming Zhou & Wei Guan & Feng Jiang & Guangming Xie & Chunfeng Wang & Weiguang Zheng & Zhijie Fang, 2023. "Optimization of Brake Feedback Efficiency for Small Pure Electric Vehicles Based on Multiple Constraints," Energies, MDPI, vol. 16(18), pages 1-20, September.
    11. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    12. Qi, Lingfei & Wu, Xiaoping & Zeng, Xiaohui & Feng, Yan & Pan, Hongye & Zhang, Zutao & Yuan, Yanping, 2020. "An electro-mechanical braking energy recovery system based on coil springs for energy saving applications in electric vehicles," Energy, Elsevier, vol. 200(C).
    13. Cong Geng & Dawen Ning & Linfu Guo & Qicheng Xue & Shujian Mei, 2021. "Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle," Energies, MDPI, vol. 14(8), pages 1-19, April.
    14. Zhao, Mingjie & Shi, Junhui & Lin, Cheng, 2019. "Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus," Applied Energy, Elsevier, vol. 243(C), pages 21-34.
    15. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    16. Chen, Xu & Li, Mince & Chen, Zonghai, 2023. "Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles," Energy, Elsevier, vol. 285(C).
    17. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    18. Nenming Wang & Guwen Tang, 2022. "A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis," Sustainability, MDPI, vol. 14(6), pages 1-35, March.
    19. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    20. Yu-Huei Cheng & Ching-Ming Lai, 2017. "Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm," Energies, MDPI, vol. 10(3), pages 1-21, March.

    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:gam:jeners:v:13:y:2020:i:6:p:1526-:d:336251. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.