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

Xiaobin Ning (), Jiazheng Wang, Yuming Yin, Jiarong Shangguan, Nanxin Bao and Ning Li
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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

Energies, 2023, vol. 16, issue 4, 1-18

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.

Keywords: fuzzy q-learning (FQL); hydraulic regenerative braking system (HRBS); bench test; energy recovery efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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