模型预测控制在电池二次冷却系统中应用研究

The Research of MPC in Secondary Cooling System of Battery

本文针对动车电池二次冷却系统,提出一种基于模型预测控制(Model Predictive Control)算法的电池冷却系统控制策略。研究主要针对电池二次冷却系统,设计了MPC控制策略和比例积分(Proportion Integration)控制策略,并对比了两者在电池冷却速率方面的控制效果。在电池充放电速率分别为20 A和30 A的工况下,MPC控制策略的电池温度达到稳定时间分别为37 s和63 s,而PI控制策略的电池温度达到稳定时间分别为280 s和500 s。结果表明采用MPC控制策略相较于PI控制策略,MPC控制策略能够使电池冷却速度显著加快,控制系统具有更优的鲁棒性和适应性。

This paper proposes control strategy based on Model Predictive Control(MPC)algorithm for the secondary cooling system of train battery.The study focuses on designing MPC and Proportion Integration(PI)control strategies for the battery secondary cooling system,and compares their control effects on the battery cooling rate.Under the battery charging and discharging conditions of 20 A and 30 A,the MPC control strategy achieves stable battery temperature in 37 s and 63 s,respectively,while the PI control strategy achieves stable battery temperature in 280 s and 500 s,respectively.The results show that compared with the PI control strategy,the MPC control strategy can significantly accelerate the battery cooling rate,and the control system has better robustness and adaptability.