Nonlinear Model Predictive Control for Coordinated Temperature and Energy Optimization in Battery Thermal Management System

Battery thermal control serves as fundamental safeguards in electric vehicle. Energy consumption is inherently necessary during the operation of the thermal management system. Maintaining battery temperature within safe ranges while minimizing energy consumption represents a major challenge in current research. To address this challenge, an innovative battery thermal management strategy based on nonlinear model predictive control was proposed for coordinated temperature-energy consumption regulation. First, a model of the battery thermal management system was built using the lumped parameter method. Second, a controller based on nonlinear model predictive control was designed based on the established model, with a cooperative optimization framework integrating temperature regulation and energy minimization. Finally, comparative analyses were conducted between nonlinear model predictive control, threshold control, and PID control. The results demonstrate that the controller based on nonlinear model predictive control exhibits significant advantages over both PID and threshold control methods in temperature stabilization and energy efficiency. This study provides a novel solution framework for optimized battery thermal management system design.