A Battery Thermal Management Strategy Based on Model Predictive Control with Online Markov Speed Predictor

The energy efficiency of battery electric vehicles has reached a high level, but there remains significant potential for optimizing the efficiency of battery thermal management systems (BTMS). Existing control strategies for BTMS often fail to adequately consider preview information of the trip, leading to suboptimal energy efficiency. In this paper, an online Markov Chain (MC)-based model predictive control (MPC) strategy is used to reduce the energy consumption of the battery thermal management system. The MC-based speed predictor gathers speed data during driving to continuously update the transition probability matrix(TPM), thereby enhancing prediction accuracy. Firstly, the predicted vehicle speed sequence is used as a disturbance of the model predictive control. Then the battery degradation cost, electric power consumption cost, and deviation of battery temperature from target are selected as the objective functions, and finally the dynamic programming (DP) algorithm is employed to solve the local optimization problem within the prediction time domain. For simulation verification, the CLTC-P cycle was repeated 17 times as a test condition. Compared with the rule-based controller, the MPC algorithm reduced power consumption by 5.4% and battery degradation by 3% under test conditions. In addition, the BTMS with MPC tracks the battery reference temperature better.