Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and ...Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.展开更多
Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehi...Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method.展开更多
基金Electric Automobile and Intelligent Connected Automobile Industry Innovation Project of Anhui Province of China(Grant No.JAC2019022505)Key Research and Development Projects in Shandong Province of China(Grant No.2019TSLH701).
文摘Proper braking force distribution strategies can improve both stability and economy performance of hybrid electric vehicles,which is prominently proved by many studies.To achieve better dynamic stable performance and higher energy recovery efficiency,an effective braking control strategy for hybrid electric buses(HEB)based on vehicle mass and road slope estimation is proposed in this paper.Firstly,the road slope and the vehicle mass are estimated by a hybrid algorithm of extended Kalman filter(EKF)and recursive least square(RLS).Secondly,the total braking torque of HEB is calculated by the sliding mode controller(SMC),which uses the information of brake intensity,whole vehicle mass,and road slope.Finally,comprehensively considering driver’s braking intention and regulations of the Economic Commission for Europe(ECE),the optimal proportional relationship between regenerative braking and pneumatic braking is obtained.Furthermore,related simulations and experiments are carried out on the hardware-in-the-loop test bench.Results show that the proposed strategy can effectively improve the braking performance and increase the recovered energy through precise control of the braking torque.
基金This paper was supported by the National Natural Science Foundation of China under Grant 52002284the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100 and the Fundamental Research Funds for the Central Universities.
文摘Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method.