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 mot...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%and0.96%,respectively,and effectively limited motor temperature.Additionally,under global cycle conditions,this strategy demonstrated better MTP control performance compared to other benchmark strategies.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52275047,51975048)。
文摘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%and0.96%,respectively,and effectively limited motor temperature.Additionally,under global cycle conditions,this strategy demonstrated better MTP control performance compared to other benchmark strategies.
