Battery thermal management strategy for electric vehicles based on nonlinear model predictive control

As the temperature has a great effect on the cycle life and capacity of power battery on electric vehicles (EVs), a practical battery thermal management (BTM) strategy is required to adjust the battery temperature within an appropriate range and reduce the temperature inconsistency in the battery module. To achieve the multiple objectives, a nonlinear model predictive control (NMPC) method is proposed to optimize the cooling process of battery module. Firstly, a lumped thermal model of single lithium-ion battery under air cooling is presented, which considers the change of internal resistance with temperature and the change of heat transfer coefficient with coolant velocity. Considering the temperature inconsistency in the battery module, a thermal model of the battery module is derived based on the law of conservation of energy and verified. Due to the nonlinearity, time-varying parameters and multiple constraints of the thermal management system, the NMPC method is designed. Particle swarm optimization is used to solve the nonlinear programming problem in NMPC method. The simulation results show that the NMPC method ensures that the battery works near the target temperature under different working conditions, the deviation is less than 0.5 K, and the temperature inconsistency in the battery module is less than 1.2 K. In addition, compared with the PID method, the air flow consumption is effectively reduced.

Automated summary

The paper introduces a thermal management strategy for power batteries on electric vehicles, proposes a nonlinear model predictive control (NMPC) method, and utilizes particle swarm optimization to solve the nonlinear programming problem. Simulation results demonstrate that the NMPC method ensures the battery works near the target temperature and reduces temperature inconsistency in the battery module.