Integrated Three-Step Optimization for Energy-Efficient Battery Thermal Management in Electric Vehicles

With the rapid growth of electric vehicles (EVs), efficient battery thermal management (BTM) is essential for ensuring battery performance, safety, and longevity. This paper proposes an integrated three-step optimization framework for energy-efficient BTM using nonlinear model predictive control (NMPC). The method combines a control-oriented electro-thermal battery model, static and dynamic modeling of the coolant and refrigerant circuits, and an extended Kalman filter (EKF) for state estimation. The three-step process involves: (1) real-time estimation of the maximum cooling capacity of the battery chiller, (2) NMPC-based optimization of coolant flow and temperature under physical constraints, and (3) optimization of compressor and fan power to achieve cooling demand. Simulation results demonstrate that the proposed approach reduces energy consumption by up to 7.9 % compared to rule-based control, while maintaining precise temperature regulation.