The invention discloses a battery health state real-time evaluation method based on data flow clustering, which comprises the following steps: collecting operation
record data characteristics of a battery, grouping, dynamically segmenting according to a working
voltage fluctuation range of the battery, and constructing a
data set representing an initialized health state of the battery. The real-time operation
record data of the battery is compared with the
record of the existing health state
data set, and the health state is quantified in combination with the pseudo tag, so that the
evaluation result of the health state of the battery is comprehensively obtained, and the evaluation accuracy is improved. Meanwhile, in consideration of different operation states of the battery in different environments, a dynamically updated
data set maintenance method and a health state quantification method based on a neural network are adopted to evaluate and quantify the operation record data of the battery in real time, whether the operation record data is similar to an existing health state data set or not is observed, and the operation record data can be added; if the health state changes, a blank health state data set is newly established, and the characteristics of the health state are initialized; and meanwhile, the
quantitative model is trained by utilizing historical data in real time, so that the detection accuracy is further improved. According to the method, a large amount of data is summarized by adopting dynamic segmentation and the health state, and the calculated amount of the evaluation process is reduced, so that the real-time evaluation of the health state of the battery is realized.