基于模糊逻辑的多特征参量综合锂电池SOH评估

  [目的]  针对锂离子电池SOH(State of Health)评估易受电池特性不一致影响，从而产生评估结果分散并最终导致难以满足电动汽车服役环境需求的问题。  [方法]  分析了典型储能元件NCM电池在寿命循环测试过程中的开路电压曲线、脉冲电压响应和增量容量曲线的相应变化。选取与电池容量衰减密切相关的6种特征参量，提出一种基于模糊逻辑的隶属函数，建立SOH评价集关联特征参量，并采用以相关系数为标度的层次分析法确定参量指标对评估结果影响权值的SOH综合评估方法，最后以完成寿命循环测试的4只NCM-21700电池对所提出方法的有效性进行了验证。  [结果]  结果表明:该方法能有效消减SOH评估结果的分散，评估平均误差不超过3%，最大误差不超过5%。  [结论]  所提SOH综合评估方法是正确并有效的，可为实际应用提供指导。

Multiple Characteristic Variables Comprehensive SOH Evaluation Based on Fuzzy Logic for Lithium-ion Battery

  [Introduction]  (The SOH(State of Health) evaluation of lithium-ion batteries is difficult to meet the on-board environment requirements of electric vehicles because of the dispersion of evaluation results was caused by the inconsistent characteristics of batteries.  [Method]  To solve the problem, the corresponding changes of open circuit voltage curve, pulse voltage response and incremental capacity curve of typical energy storage component NCM battery during life cycle test were analyzed. 6 characteristic variables which were closely related to the battery capacity loss were selected, and a comprehensive SOH evaluation method based on fuzzy logic was proposed. In this method, membership function was used to establish the relationship between SOH evaluation sets and variable indicators, and analytic hierarchy process based on correlation coefficient was used to determine the weights of variable indicators that have an impact on the evaluation results. Finally, the validity of the proposed method was verified by four NCM-21700 batteries that completed the life cycle test.  [Result]  The results show that the method can effectively reduce the dispersion of SOH evaluation, and the average error is not more than 3% as well as the maximum error is no more than 5%.  [Conclusion]  This work provides some guidance for further study on state of health evaluation of lithium-ion batteries.