Predictive battery thermal management for fast charging of electric vehicles using nonlinear model predictive control and dynamic programming

Abstract

This paper addresses the thermal management of batteries during fast charging of electric vehicles. Using comprehensive measurement data from a state-of-the-art battery electric vehicle (BEV), a control-oriented model of the battery and its thermal system is developed and parameterized. The existing thermal management strategy for fast harging is first nalyzed, after which a predictive strategy specifically for this use case is proposed. The approach consists of two steps: offline setpoint optimization via dynamic programming and optimal control allocation using nonlinear model predictive control (NMPC). The strategy’s performance is evaluated using a validated high-fidelity simulation model. Compared to the existing state-of-the-art strategy, the proposed predictive approach reduces energy consumption by up to 0.41 kWh at moderate ambient temperatures through efficient cooling, and shortens charging time by up to 4.5% at low ambient temperatures through aggressive heating.