Performance Study of the MPC based on BPNN Prediction Model in Thermal Management System of Battery Electric Vehicles

In this paper,a model predictive control(MPC)based on back propagation neural network(BPNN)prediction model was proposed for compressor speed control of air conditioning system(ACS)and battery thermal management system(BTMS)coupling system of battery electric vehicle(BEV).In order to solve the problem of high cooling energy consumption and inferior thermal comfort in the cabin of the battery electric vehicle thermal management system(BEVTMS)during summer time,this paper combines the respective superiorities of artificial neural network(ANN)predictive modeling and MPC,and creatively combines the two methods and uses them in the control of BEVTMS.Firstly,based on ANN and heat transfer theory,BPNN prediction model,ACS and BTMS coupling system were established and verified.Secondly,a mathematical method of MPC was established to control the speed of the compressor.Then,the state parameters of the coupled system were predicted using a BPNN prediction model,and the predicted values were passed to the MPC,thus achieving accurate control of the compressor speed using the MPC.Finally,the effects of PID control and MPC based on BPNN prediction model on thermal comfort of cabin and compressor energy consumption at different ambient temperatures were compared in simulation under New European Driving Cycle(NEDC)conditions.The results showed for the constructed BPNN prediction model predicted and tested values of the selected parameters the mean squared error(MSE)ranged from 2.498%to 8.969%,mean absolute percentage error(MAPE)ranged from 4.197%to 8.986%,and mean absolute error(MAE)ranged from 3.202%to 8.476%.At ambient temperatures of 25℃,35℃ and 45℃,the MPC based on the BPNN prediction model reduced the cumulative discomfort time in the cabin by 100 s,39 s and 19 s,respectively,compared with the PID control.Under three NEDC conditions,the energy consumption is reduced by 1.82%,2.35%and 3.48%,respectively.When the ambient temperature was 35℃,the MPC based on BPNN prediction model can make the ACS and BTMS coupling system have better thermal comfort,and the energy saving effect of the compressor was more obvious with the temperature.