基于K最近邻遗传算法的电池均衡策略

Battery equalization strategy based on K nearest neighbor genetic algorithm

为缩短电池组均衡系统的均衡时间,降低均衡能量损耗,以电感为储能元件,基于升压斩波和降压斩波原理搭建均衡电路。采用K最近邻遗传算法实现电池组的均衡控制,与均值均衡策略进行对比。在MATLAB/Simulink中完成自均衡、充电和放电3种均衡工况的仿真实验。研究结果表明:采用本均衡策略后,3种工况下的均衡速度分别提高26.70%、22.70%、24.80%;减少开关动作次数降低了电池组的能量损耗,能量转化率提高了5.06%。因此,本均衡策略具有良好的均衡效果,能有效地改善电池组间的不一致性,缩短电池组的均衡时间,降低均衡能量耗损。

The inductor was used as the energy storage component and the equalization circuit was built based on the principle of step-up and step-down chopping to reduce the current equilibrium time and the balance energy loss.The K-nearest neighbor genetic algorithm was employed to realize the equalization control of the battery pack and it was compared with the mean equalization strategy.Simulations were performed in MATLAB/Simulink of self-equalization,charging and discharging in 3 balanced conditions.The results show that the equilibrium speed under the 3 conditions is improved by 26.70%,22.70%and 24.80%,respectively.The reduction of switching operations can reduce the energy loss of the battery pack,and the energy conversion rate is increased by 5.06%.Therefore,the proposed equilibrium strategy has a good balance effect,which can effectively improve the inconsistency among battery packs and reduce the equilibrium time and the balance energg loss.