基于WOA优化概率分布参考点的锂电池故障诊断

锂离子电池凭借其优越的储能性能被广泛应用在许多领域，而随着使用时间增加，锂离子电池的老化加剧容易导致不同程度的故障，因此对锂离子电池进行在线故障诊断至关重要。为了进一步提高故障诊断的准确率和透明性，提出使用连续概率分布证据推理(ER)规则的故障诊断模型，并使用优化方法优化相关参数。首先，从充放电过程中提取能反映电池健康状态(SOH)的特征指标，采用Spearman相关系数分析特征指标与SOH之间的关联来提取健康因子；第二，考虑到电池的故障信息具有不确定性，提出一种基于ER规则的连续概率分布参考点的故障诊断方法，采用高斯分布描述参考点，实现在线故障诊断；第三，设计了一种带约束的鲸鱼优化算法(WOA)优化证据参数，构建GER-W故障诊断模型，使模型故障诊断准确率达到最优；最后，通过分析SOH对故障进行模糊划分，以NASA电池数据集为例验证GER-W模型的有效性，此外还将模型拓展到电池SOH估计中。验证结果表明，GER-W模型对比其他故障诊断方法具有更高准确率且诊断过程更加透明，在SOH估计中也有一定效果。

Lithium batteries fault fiagnosis based on WOA optimized probability distribution reference points

Lithium-ion batteries are widely used in various fields due to their superior energy storage performance. However, with the increase of using time, the aging of lithium-ion batteries is prone to lead to different failure degrees, so online fault diagnosis for lithium-ion batteries is crucial. To improve the accuracy and transparency of fault diagnosis, this paper proposed a fault diagnosis model based on continuous probability distribution evidential reasoning(ER) rule, and optimized the related parameters by optimization method. Firstly, this paper extracted characteristic indicators that could reflect batteries'' state of health(SOH) from charging and discharging process, and used Spearman correlation coefficient to analyse the correlation between characteristic indicators and the SOH to extract health indexes. Secondly, considering the uncertainty of battery fault information, this paper proposed a fault diagnosis method based on continuous probability distribution reference points of evidential reasoning(ER) rule, it used Gaussian distribution to describe the reference points, so as to achieve online fault diagnosis. Thirdly, it designed a whale optimization algorithm(WOA) with constraints to optimize evidence parameters to construct the GER-W fault diagnosis model, so that the accuracy of model fault diagnosis reached the best. Finally, it made fuzzy division of faults by analysing SOH, and verified the effectiveness of the GER-W model by taking the NASA battery data set as an example. In addition, the model was extended to batteries'' SOH estimation. The verification results show that GER-W model has higher accuracy and more transparent process than other fault diagnosis methods, and it also has a certain effect in SOH estimation.