Advanced Battery Thermal Management Via an Optimized NMPC Framework With Grey Wolf Optimization

Abstract Battery thermal management system is critically vital for ensuring operational safety and preventing thermal runaway incidents. A primary challenge in system operation management is achieving precise temperature control while abating energy consumption. A battery thermal management system with a strategy rooted in nonlinear model predictive control was put forward. The grey wolf optimization algorithm was innovatively introduced as an optimization solver for temperature–energy-performance collaborative control. First, a control-oriented dynamic model was built via the lumped-parameter method. Prediction accuracy was maintained while computational complexity was curtailed to satisfy real-time control requirements. Second, a nonlinear model predictive controller was designed using battery temperature and coolant temperature as state variables, with compressor speed and pump speed as control variables. A collaborative optimization framework for temperature control, energy minimization, and operational reliability was established through constraint boundaries. Comparative analysis between nonlinear model predictive control and traditional proportional-integral-derivative control was conducted using a amesim–simulink co-simulation platform. Temperature control accuracy, response speed, and energy efficiency were evaluated. Results demonstrated that nonlinear model predictive control exhibited pronounced advantages in temperature control response, energy consumption control, and system operational assurance. Thermal runaway risk is effectively lessened through intelligent coordinated control of compressor and pump operations. System safety and reliability were thereby enhanced. This research provided a novel solution for performance optimization design of battery thermal management systems with notable theoretical and practical values.