Abstract

The battery charging process has nonlinear and hysteresis properties. PID (Proportion Integration Differentiation) control is a conventional control method used in the battery charging process. The control effect is determined by the PID control parameters ${K}_p$, ${K}_i$ and ${K}_d$. The traditional PID parameter setting method is difficult to give the appropriate parameters, which affects the battery charging efficiency. In this paper, the particle swarm optimization (PSO) is used to optimize the PID parameters. Aiming at the defects of basic PSO, such as slow convergence speed, low convergence precision and easy to be premature, a modified particle swarm optimization algorithm is proposed, and the optimized PID parameters are applied to the battery charging control system. Also, the experimental results show that the battery charging process possesses better dynamic performance and the charging efficiency of the battery has increased from 86.44% to 91.47%, and the charging temperature rise has dropped by 1°C.

中文翻译：

---

基于粒子群优化（PSO）的PID优化控制策略在电池充电系统中的应用

摘要

电池充电过程具有非线性和迟滞特性。PID（比例积分微分）控制是在电池充电过程中使用的常规控制方法。控制效果由PID控制参数$ {K} _p $ ， $ {K} _i $和$ {K} _d $。传统的PID参数设置方法难以提供合适的参数，从而影响电池的充电效率。在本文中，粒子群优化（PSO）用于优化PID参数。针对基本粒子群优化算法收敛速度慢，收敛精度低，容易过早生成等缺点，提出了一种改进的粒子群优化算法，并将优化后的PID参数应用于电池充电控制系统。实验结果还表明，电池充电过程具有更好的动态性能，电池的充电效率从86.44％提高到91.47％，充电温度上升降低了1°C。