针对退役动力电池存在初始特性参数不一致,梯次利用过程中存在安全稳定性降低等问题,提出一种基于风险防御的退役动力电池递进式分选方法。首先,提取电动汽车退役动力电池“灰箱”数据,选取剩余容量、内阻及端电压作为分选参数,采用改进K-均值算法对退役动力电池参数进行初始聚类数据提取。其次,估计梯次利用过程中退役动力电池荷电状态（SOC）、健康状态（SOH）等特性,通过功能状态（SOF）表征参数间的关联特性,并基于SOF动态一致性进行分选。最后,考虑风险防御预留系统的SOF动态安全裕度,降低退役动力电池初始特性缺陷对梯次利用重组的影响。通过仿真分析验证所提分选方法提高了退役动力电池的一致性,并有效降低了重组系统的寿命损耗。
Abstract
Aiming at the problems of inconsistent initial characteristic parameters of retired power batteries and reduced safety and stability in the process of cascade utilization, a progressive sorting method of retired power batteries based on risk defense is proposed. Firstly, the retired power batteries′"gray box" data of electric vehicles is extracted, and the remaining capacity, internal resistance and terminal voltage are selected as sorting parameters, and the improved K-means algorithm is used to extract the initial clustering data of retired power battery parameters. Secondly, the characteristics of the state of charge （SOC） and state of health （SOH） of the retired power battery during the cascade utilization process are estimated, and the relationship between the three parameters is characterized by the state of function （SOF）. Finally, the SOF dynamic safety margin of the risk defense reservation system is considered to reduce the impact of the initial characteristic defects of retired power batteries on echelon utilization and reorganization. Simulation analysis shows that the proposed sorting method improves the consistency of retired power batteries and effectively reduces the life loss of the reorganization system.
关键词
退役动力电池 /
风险防御 /
递进式分选 /
SOF /
安全裕度
Key words
retired power battery /
risk defense /
progressive sorting /
SOF /
safety margin
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基金
内蒙古自治区科技重大专项（2020ZD0018）
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