LAPSE:2023.31675v1
Published Article

LAPSE:2023.31675v1
Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle
April 19, 2023
Abstract
This paper proposes a double layered multi parameters braking energy recovery control strategy for Hybrid Electric Vehicle, which can combine the mechanical brake system with the motor brake system in the braking process to achieve higher energy utilization efficiency and at the same time ensure that the vehicle has sufficient braking performance and safety performance. The first layer of the control strategy proposed in this paper aims to improve the braking force distribution coefficient of the front axle. On the basis of following the principle of braking force distribution, the braking force of the front axle and the rear axle is reasonably distributed according to the braking strength. The second layer is to obtain the proportional coefficient of regenerative braking, considering the influence of vehicle speed, braking strength, and power battery state of charge (SOC) on the front axle mechanical braking force and motor braking force distribution, and a three-input single-output fuzzy controller is designed to realize the coordinated control of mechanical braking force and motor braking force of the front axle. Finally, the AMESim and Matlab/Simulink co-simulation model was built; the braking energy recovery control strategy proposed in this paper was simulated and analyzed based on standard cycle conditions (the NEDC and WLTC), and the simulation results were compared with regenerative braking control strategies A and B. The research results show that the braking energy recovery rate of the proposed control strategy is respectively 2.42%, 18.08% and 2.56%, 16.91% higher than that of the control strategies A and B, which significantly improves the energy recovery efficiency of the vehicle.
This paper proposes a double layered multi parameters braking energy recovery control strategy for Hybrid Electric Vehicle, which can combine the mechanical brake system with the motor brake system in the braking process to achieve higher energy utilization efficiency and at the same time ensure that the vehicle has sufficient braking performance and safety performance. The first layer of the control strategy proposed in this paper aims to improve the braking force distribution coefficient of the front axle. On the basis of following the principle of braking force distribution, the braking force of the front axle and the rear axle is reasonably distributed according to the braking strength. The second layer is to obtain the proportional coefficient of regenerative braking, considering the influence of vehicle speed, braking strength, and power battery state of charge (SOC) on the front axle mechanical braking force and motor braking force distribution, and a three-input single-output fuzzy controller is designed to realize the coordinated control of mechanical braking force and motor braking force of the front axle. Finally, the AMESim and Matlab/Simulink co-simulation model was built; the braking energy recovery control strategy proposed in this paper was simulated and analyzed based on standard cycle conditions (the NEDC and WLTC), and the simulation results were compared with regenerative braking control strategies A and B. The research results show that the braking energy recovery rate of the proposed control strategy is respectively 2.42%, 18.08% and 2.56%, 16.91% higher than that of the control strategies A and B, which significantly improves the energy recovery efficiency of the vehicle.
Record ID
Keywords
braking strength, control strategy, energy recovery, hybrid electric vehicle
Subject
Suggested Citation
Geng C, Ning D, Guo L, Xue Q, Mei S. Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle. (2023). LAPSE:2023.31675v1
Author Affiliations
Geng C: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China
Ning D: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Guo L: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China
Xue Q: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Mei S: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China
Ning D: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Guo L: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China
Xue Q: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Mei S: Beijing Key Laboratory of Powertrain Technology for New Energy Vehicles, Beijing Jiaotong University, Beijing 100044, China
Journal Name
Energies
Volume
14
Issue
8
First Page
2202
Year
2021
Publication Date
2021-04-15
ISSN
1996-1073
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PII: en14082202, Publication Type: Journal Article
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LAPSE:2023.31675v1
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https://doi.org/10.3390/en14082202
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Apr 19, 2023
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