Hierarchical optimal intelligent battery thermal management strategy for an electric vehicle based on ant colony sliding mode control

Excessive temperature of battery pack causes the performance degradation, affecting the normal operation and safety of the electric vehicles (EVs). This paper proposes an ant colony optimization-fuzzy sliding mode control (ACO-FSMC) hierarchical strategy for the battery thermal management system (BTMS) of EVs. Ignoring the dynamic process of the pump and compressor leads to the deviation of the cooling efficiency of EVs. Therefore, a control-oriented BTMS model is established to describe the temperature dynamics of battery pack and coolant, considering driving current effect on the speed of pump and compressor. Then, a hierarchical optimization strategy is designed to realize the control of the battery cooling rate as well as the speed of the pump and compressor. The upper layer of controller use an ant colony optimization (ACO) to solve the reference speed of the pump and compressor while satisfying constraints of physical characteristics of actuators, aiming at reducing battery temperature deviation and saving energy of BTMS. Considering the nonlinearity of lower layer of BTMS caused by the disturbance of time-varying load torque, fuzzy sliding mode control (FSMC) is applied to control the speed of pump and compressor, in which an integral sliding surface is designed to reduce the influence of disturbance. In order to weaken the chattering of sliding mode control and reduce energy consumption, fuzzy controller is used to adjust the reaching law parameters of sliding mode surface. The simulation results show that compared with PID, the maximum temperature deviation of ACO-FSMC is reduced by 43%, and the energy consumption is reduced by 23%.