A Control Stratagy For Thermal Management System In Electric Vehicle Based On Model Predictive Control

To deal with environmental pollution and energy crisis,new energy vehicles emerge,and quickly occupy a large share in China’s automobile industry.As the power source of electric vehicle,batteries’ performance is greatly affected by temperature,so thermal management is required.In addition,cabin temperature is also very important to improve the driving experience,which is often integrated with battery into on-board air conditioning for collaborative thermal management.To improve the efficiency of the thermal management systems of battery and cabin,the control strategies are built.Firstly,the battery,crew cabin models and their thermal management systems models are built.The battery model is an electro-thermal-aging coupling model.The first-order RC model is used for the electric model of the battery,Bernardi model is used for the thermal model,and the aging model is based on the semi-empirical mathematical model of the experimental data.In order to measure the battery parameters and verify the model,a series of battery experiments are carried out.To establish thermal management model,this paper introduces the establishment process of the main components models,and the accuracy of the model is verified by experiments.This paper establishes the vehicle transmission model and speed prediction model.The driveline model and the thermal management system model are combined to form a vehicle thermal management model,which provides support for the thermal management of the battery and passenger cabin with variable speed.The vehicle speed prediction model can predict the change of vehicle speed and improve the performance of the controller.Then,the control strategy of the battery thermal management system is established.The control strategy is based on model predictive control,which combines the vehicle speed prediction model and the adaptive optimal battery temperature reference model.The speed prediction model helps the model prediction controller to reduce the disturbance of the speed change.The battery temperature reference Pareto boundary which changes with the ambient temperature neutralizes the contradiction between reducing the cooling energy consumption and improving the battery state of health value.Through comparative analysis,it is found that the controller can control the battery temperature better than other controllers,and reduce the energy consumption of the cooling system,and help improve the battery state of health.Finally,the control strategy of the battery and the cabin cooperative thermal management system is established.The control strategy is based on model predictive control,which combines the vehicle speed prediction model and the optimal battery temperature reference value.The best battery temperature reference value takes into account the change of heat loss and health state of the battery when working at different temperatures,and is the best battery operating temperature independent of driving conditions.Compared with the prioritized on-off control and the conventional model predictive control,it is found that it can control the temperature of the battery and the cabin better,improve the temperature comfort property of cabin,slow down the aging of the battery,reduce the energy consumption of the thermal management system.