The invention relates to a multi-dimensional feature fusion-based battery fault diagnosis method, which comprises the following steps of: acquiring basic data of a battery, performing cleaning processing, extracting voltage data, judging whether an overcharge or overdischarge fault occurs according to the voltage data, if not, performing sliding time window processing on the voltage data to obtain corresponding feature data, comprising standard deviation, Shannon entropy and stack type auto-encoder data; determining a threshold value corresponding to each feature data by combining each feature data and adopting a quartile method for comparing and judging whether a battery fault occurs or not, if so, correcting each feature data by utilizing a correction coefficient, and determining a current fault type according to a change trend of the corrected feature data; and a local outlier factor algorithm is adopted, and the current fault level is determined by calculating outlier factors of the feature data. Compared with the prior art, the method has the advantages that the accuracy and the economical efficiency of the faulty battery can be ensured, and meanwhile, the fault type is determined and the fault level is divided.
本发明涉及一种基于多维度特征融合的电池故障诊断方法,包括:采集电池基础数据并进行清洗处理,提取出电压数据,据此判断是否发生过充电或过放电故障,若判断为否,则针对电压数据进行滑动时间窗口处理,得到相应特征数据,包括标准差、香农熵和栈式自编码器数据;结合各特征数据,采用四分位法确定各特征数据对应阈值,用于比较判断是否发生电池故障,若判断为是,则利用修正系数分别对各特征数据进行修正处理,根据修正特征数据的变化趋势来确定当前故障类型;并采用局部离群因子算法,通过计算各特征数据的离群因子,确定当前故障等级。与现有技术相比,本发明能够在确保故障电池准确性和经济性的同时,实现故障类型的确定与故障等级的划分。
Battery fault diagnosis method based on multi-dimensional feature fusion
一种基于多维度特征融合的电池故障诊断方法
2025-01-24
Patent
Electronic Resource
Chinese
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