基于荷电状态差异的退役电池健康状态快速估计研究

Research on fast estimation of the state of health of retired batteries based on the state of charge differences

随着新能源产业的迅速发展,大量动力电池面临退役回收后如何处理的问题。退役电池的二次利用场景需要根据健康状态(SOH)确定,然而不同退役电池的荷电状态不同,这使得快速估计SOH十分困难。为此,提出了一种基于荷电状态差异的退役电池的SOH快速获取策略。在本策略中,不同SOH退役电池的荷电状态差异被用于产生多种健康特征。同时,为了选取随机森林算法合适的超参数,遗传优化随机森林回归算法被提出应用于SOH的估计。通过验证,本文策略大幅降低了退役电池SOH的估计时间。并且通过多种避免测量时接触电阻和导线电阻策略,使得10节退役电池的健康状态估计误差低于3%。

With the rapid development of the new energy industry,how to deal with a large number of retired batteries is problem.The secondary utilization scenarios of retired batteries need to be determined based on the state of health(SOH).However,the traditional method of obtaining SOH is time-consuming and energy-consuming.Therefore,the study of fast SOH estimation is very meaningful.The unavailability of historical working condition information and the unknown state of charge at the time of detection make fast SOH estimation very difficult.For this reason,this article proposes a fast SOH acquisition strategy for retired batteries based on the difference in state of charges.In this article,the state of charge′s differences of different SOH retired batteries are used to generate multiple health features.Meanwhile,to select suitable hyperparameters for the random forest algorithm,the genetic optimization random forest regression algorithm is proposed to be applied for SOH estimation.Through experiments,the proposed strategy substantially reduces the estimation time of SOH for retired batteries.Through multiple strategies to avoid contact resistance and wire resistance during measurement,the error of health state estimation of 10 retired batteries is lower than 3%.