Adaptive Optimization Control Strategy for Electric Vehicle Battery Thermal Management System Based on Pontryagin's Minimal Principle

Excessive temperature affects battery aging and stability, causing a significant impact on the economy and safety of electric vehicles (EVs). The battery thermal management (BTM) system is the key to keeping the battery temperature suitable, but it also consumes considerable energy. To minimize energy consumption for maintaining the battery temperature in the optimal range, a novel adaptive Pontryagin's minimum principle (PMP) optimization strategy based on velocity prediction is proposed in this article, which is achieved by online updating of the costate in the Hamiltonian function. A multimode velocity prediction model based on driving pattern recognition (DPR) is proposed to enhance the accuracy of the prediction. Moreover, the built self-learning Markov pattern recognizer distinguishes real-time driving segments into one of three predefined driving patterns, and the corresponding pattern velocity predictor is selected. The accuracy and effectiveness of the velocity predictor and driving pattern recognizer are verified. A comparison with the results is obtained by adopting typical controllers to indicate the feasibility and effectiveness of the proposed strategy. In addition, the BTM system energy consumption is evaluated in multiple test cycles and the results show that the energy consumption based on the proposed methods is reduced by 18.9%-24.9% which leads to considerable energy saving.