首页|A novel predictive braking energy recovery strategy for electric vehicles considering motor thermal protection

A novel predictive braking energy recovery strategy for electric vehicles considering motor thermal protection

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Braking energy recovery(BER)aims to recover the vehicle's kinetic energy by coordinating the motor and mechanical braking torque to extend the driving range of the electric vehicle(EV).To achieve this goal,the motor/generator mode requires frequent switching and prolonged operation during driving.In this case,the motor temperature will unavoidably rise,potentially triggering motor thermal protection(MTP).Activating MTP increases the risk of motor component failure,and the EV typically disables the BER function.Thus,maximizing BER while reducing the risk of motor overheating is a challenging problem.To address this issue,this article proposes a predictive BER strategy with MTP using the non-smooth Pontryagin Minimum Principle(NSPMP)for EVs.Firstly,a Markov long short-term memory(MLSTM)model is designed to obtain future velocity information.Secondly,the BER problem with MTP in the studied EV is embedded in a model predictive control(MPC)framework.Then,under the MPC framework,the NSPMP strategy is proposed to resolve the problem of MTP.Finally,the performance of the proposed strategy is verified through simulation and a hardware-in-loop test.The results show that in two real-world driving cycles,compared to the rule-based strategy,the proposed strategy reduced power consumption by 1.24%and 0.96%,respectively,and effectively limited motor temperature.Additionally,under global cycle conditions,this strategy demonstrated better MTP control performance compared to other benchmark strategies.

electric vehiclebraking energy recovery strategymotor thermal protectionnon-smooth PMP

YANG Chao、SUN TongLin、YANG LiuQuan、ZHANG YuHang、WANG WeiDa

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School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100084,China

国家自然科学基金国家自然科学基金

5227504751975048

2024

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子:1.056
ISSN:1674-7321
年,卷(期):2024.67(4)
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