IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v248y2022ics0360544222004467.html
   My bibliography  Save this article

Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery

Author

Listed:
  • He, Qiang
  • Yang, Yang
  • Luo, Chang
  • Zhai, Jun
  • Luo, Ronghua
  • Fu, Chunyun

Abstract

Braking energy recovery technology, which is widely used in new energy vehicles, can extend the endurance range and reduce the wear of hydraulic braking system. However, due to its direct impact on the economy and safety of the vehicle, maximizing the recovery efficiency and coordinating its work with the hydraulic system are essential to improve vehicle performance. In this study, a pure electric vehicle driven by dual-motor is considered, and an optimized energy recovery strategy based on braking safety and efficient recovery is proposed, which not only enhances the energy recovery rate, but also shortens the braking distance. For the motor braking part, a torque optimization strategy with the goal of minimizing the energy loss of the regenerative braking system is proposed to improve energy recovery. Simulation results show that after applying this strategy, compared with the average distribution strategy, the energy recovery rate is increased by 3.35% under WLTC cycles. For the electro-hydraulic compound braking part, a dynamic coordinated control strategy with variable reserved motor braking force is proposed for the first time to reduce the error between the actual braking torque and the target braking torque, and the effectiveness is verified by simulation results under typical conditions.

Suggested Citation

  • He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:energy:v:248:y:2022:i:c:s0360544222004467
    DOI: 10.1016/j.energy.2022.123543
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222004467
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123543?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. Yang, Yang & He, Qiang & Fu, Chunyun & Liao, Shuiping & Tan, Peng, 2020. "Efficiency improvement of permanent magnet synchronous motor for electric vehicles," Energy, Elsevier, vol. 213(C).
    2. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    3. 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).
    4. Huiyuan Xiong & Xionglai Zhu & Ronghui Zhang, 2018. "Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation," Complexity, Hindawi, vol. 2018, pages 1-14, August.
    5. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    6. 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.
    7. He, Hongwen & Wang, Chen & Jia, Hui & Cui, Xing, 2020. "An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    8. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    9. Wei, Zhongbao & Zhao, Difan & He, Hongwen & Cao, Wanke & Dong, Guangzhong, 2020. "A noise-tolerant model parameterization method for lithium-ion battery management system," Applied Energy, Elsevier, vol. 268(C).
    10. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    11. Yu, Wei & Wang, Ruochen, 2019. "Development and performance evaluation of a comprehensive automotive energy recovery system with a refined energy management strategy," Energy, Elsevier, vol. 189(C).
    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. 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.
    2. 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.
    3. Wang, Shuai & Wu, Xiuheng & Zhao, Xueyan & Wang, Shilong & Xie, Bin & Song, Zhenghe & Wang, Dongqing, 2023. "Co-optimization energy management strategy for a novel dual-motor drive system of electric tractor considering efficiency and stability," Energy, Elsevier, vol. 281(C).
    4. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    5. Ruan, Jiageng & Wu, Changcheng & Liang, Zhaowen & Liu, Kai & Li, Bin & Li, Weihan & Li, Tongyang, 2023. "The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode," Energy, Elsevier, vol. 269(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. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    2. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    4. Bian, Chong & He, Huoliang & Yang, Shunkun, 2020. "Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 191(C).
    5. Zhang, Junjiang & Yang, Yang & Hu, Minghui & Yang, Zhong & Fu, Chunyun, 2021. "Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort," Energy, Elsevier, vol. 236(C).
    6. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    7. Jie Xing & Peng Wu, 2021. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    8. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    9. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    10. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    11. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Zhang, Lei & Dorrell, David G. & Sun, Fengchun, 2022. "Big data-driven decoupling framework enabling quantitative assessments of electric vehicle performance degradation," Applied Energy, Elsevier, vol. 327(C).
    12. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
    13. 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.
    14. Xinyu Gu & KW See & Yunpeng Wang & Liang Zhao & Wenwen Pu, 2021. "The Sliding Window and SHAP Theory—An Improved System with a Long Short-Term Memory Network Model for State of Charge Prediction in Electric Vehicle Application," Energies, MDPI, vol. 14(12), pages 1-15, June.
    15. Mohammadmahdi Ghiji & Vasily Novozhilov & Khalid Moinuddin & Paul Joseph & Ian Burch & Brigitta Suendermann & Grant Gamble, 2020. "A Review of Lithium-Ion Battery Fire Suppression," Energies, MDPI, vol. 13(19), pages 1-30, October.
    16. Yezhen Wu & Yuliang Xu & Jianwei Zhou & Zhen Wang & Haopeng Wang, 2020. "Research on Starting Control Method of New-Energy Vehicle Based on State Machine," Energies, MDPI, vol. 13(23), pages 1-16, November.
    17. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    18. Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
    19. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    20. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.

    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:248:y:2022:i:c:s0360544222004467. 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.