Multi-Objective Optimization of CPCM–Liquid Cooling Hybrid Thermal Management Systems for Lithium-Ion Batteries via NSGA-II Optimized Artificial Neural Networks

Considering the synergistic optimization design of battery thermal safety and system economy in extreme environments, a hybrid lithium-ion battery thermal management system (BTMS) employing composite phase change material (CPCM) with liquid cooling is proposed by comparing four BTMSs of pure air cooling, pure CPCM, pure liquid cooling, and the hybrid cooling using CPCM and liquid cooling. The proposed hybrid cooling system demonstrates the capability to maintain the maximum battery temperature at 45.27 °C under extreme operating conditions, including elevated ambient temperatures of 40 °C combined with 5C discharge rate. Notably, this thermal regulation performance is achieved without requiring additional power input, highlighting the energy-efficient design of the system. Further, to address the critical challenge of thermal runaway prevention under summer extreme temperature up to 50 °C, an artificial neural network (ANN) model is established for the hybrid cooling, integrated with the non-dominated sorting genetic algorithm II (NSGA-II) algorithm, leading to the maximum temperature controlled at 48.68 °C and minimum system power consumption of 158 W, achieving a 12.1% reduction in thermal fluctuation amplitude and a 5.9% reduction in power consumption compared to initial design and optimal solutions, respectively. The proposed BTMS introduces the NSGA-II-ANN model for multi-objective collaborative optimization to solve the contradiction between thermal safety and energy consumption under extreme working conditions, enhancing the safety measures of power batteries and economic viability for electric vehicles.