IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1895-d1068380.html
   My bibliography  Save this article

Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning

Author

Listed:
  • Xiaobin Ning

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Jiazheng Wang

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Yuming Yin

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Jiarong Shangguan

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Nanxin Bao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Ning Li

    (School of Intelligent Manufacturing, Taizhou University, Taizhou 318000, China)

Abstract

The use of regenerative braking systems is an important approach for improving the travel mileage of electric vehicles, and the use of an auxiliary hydraulic braking energy recovery system can improve the efficiency of the braking energy recovery process. In this paper, we present an algorithm for optimizing the energy recovery efficiency of a hydraulic regenerative braking system (HRBS) based on fuzzy Q-Learning (FQL). First, we built a test bench, which was used to verify the accuracy of the hydraulic regenerative braking simulation model. Second, we combined the HRBS with the electric vehicle in ADVISOR. Third, we modified the regenerative braking control strategy by introducing the FQL algorithm and comparing it with a fuzzy-control-based energy recovery strategy. The simulation results showed that the power savings of the vehicle optimized by the FQL algorithm were improved by about 9.62% and 8.91% after 1015 cycles and under urban dynamometer driving schedule (UDDS) cycle conditions compared with a vehicle based on fuzzy control and the dynamic programming (DP) algorithm. The regenerative braking control strategy optimized by the fuzzy reinforcement learning method is more efficient in terms of energy recovery than the fuzzy control strategy.

Suggested Citation

  • Xiaobin Ning & Jiazheng Wang & Yuming Yin & Jiarong Shangguan & Nanxin Bao & Ning Li, 2023. "Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning," Energies, MDPI, vol. 16(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1895-:d:1068380
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1895/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1895/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tian, He & Li, Shengbo Eben & Wang, Xu & Huang, Yong & Tian, Guangyu, 2018. "Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus," Energy, Elsevier, vol. 142(C), pages 55-67.
    2. Ramakrishnan, R. & Hiremath, Somashekhar S. & Singaperumal, M., 2014. "Design strategy for improving the energy efficiency in series hydraulic/electric synergy system," Energy, Elsevier, vol. 67(C), pages 422-434.
    3. Zeyu Chen & Weiguo Liu & Ying Yang & Weiqiang Chen, 2015. "Online Energy Management of Plug-In Hybrid Electric Vehicles for Prolongation of All-Electric Range Based on Dynamic Programming," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, December.
    4. Xiaobin Ning & Jiarong Shangguan & Yong Xiao & Zhijun Fu & Gaolun Xu & Anqing He & Bin Li, 2019. "Optimization of Energy Recovery Efficiency for Parallel Hydraulic Hybrid Power Systems Based on Dynamic Programming," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, February.
    5. Liqiang Jin & Duanyang Tian & Qixiang Zhang & Jingjian Wang, 2020. "Optimal Torque Distribution Control of Multi-Axle Electric Vehicles with In-wheel Motors Based on DDPG Algorithm," Energies, MDPI, vol. 13(6), pages 1-19, March.
    6. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.
    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. Zongjun Yin & Xuegang Ma & Chunying Zhang & Rong Su & Qingqing Wang, 2023. "A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles," Sustainability, MDPI, vol. 15(24), pages 1-33, December.

    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. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    2. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    3. Wang, Feng & Wu, Jiaming & Lin, Zichang & Zhang, Haoxiang & Xu, Bing, 2023. "A power-sharing electro-hydraulic actuator system to downsize electric motors for electric mobile machines," Energy, Elsevier, vol. 284(C).
    4. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. 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).
    6. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    7. 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).
    8. 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.
    9. Sun, Wenjing & Zou, Yuan & Zhang, Xudong & Guo, Ningyuan & Zhang, Bin & Du, Guodong, 2022. "High robustness energy management strategy of hybrid electric vehicle based on improved soft actor-critic deep reinforcement learning," Energy, Elsevier, vol. 258(C).
    10. Wu, Changcheng & Ruan, Jiageng & Cui, Hanghang & Zhang, Bin & Li, Tongyang & Zhang, Kaixuan, 2023. "The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle, part I: Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid ," Energy, Elsevier, vol. 262(PB).
    11. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    12. Qu, Shaoyang & Fassbender, David & Vacca, Andrea & Busquets, Enrique, 2021. "A high-efficient solution for electro-hydraulic actuators with energy regeneration capability," Energy, Elsevier, vol. 216(C).
    13. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    14. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    15. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    16. Rudravaram Venkatasatish & Dhanamjayulu Chittathuru, 2023. "Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    17. Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
    18. Jau-Woei Perng & Yi-Horng Lai, 2016. "Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller," Energies, MDPI, vol. 9(4), pages 1-15, April.
    19. Ma, Yufei & Guan, Guoqing & Hao, Xiaogang & Cao, Ji & Abudula, Abuliti, 2017. "Molybdenum carbide as alternative catalyst for hydrogen production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1101-1129.
    20. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).

    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:16:y:2023:i:4:p:1895-:d:1068380. 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.