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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
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    References listed on IDEAS

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    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.
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    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.
    2. Igor Maciejewski & Sebastian Pecolt & Andrzej Błażejewski & Bartosz Jereczek & Tomasz Krzyzynski, 2024. "Experimental Study of the Energy Regenerated by a Horizontal Seat Suspension System under Random Vibration," Energies, MDPI, vol. 17(17), pages 1-18, August.

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