Battery life prediction method, device, cloud server and storage medium

Abstract

The invention discloses a battery life prediction method, device, cloud server and storage medium, wherein the method includes: determining an empirical model, and using the empirical model to learn the trajectory of battery historical data; estimating the state of battery capacity based on the historical data of the battery, and obtaining a first estimate As a result, and in the process of learning the battery historical data trajectory, the parameters of the empirical model are corrected according to the first estimation result; the mechanism model is determined, and the battery capacity state is estimated according to the mechanism model, and the second estimation result is obtained; the revised experience is used The model predicts the battery life, and during the prediction process, the revised empirical model parameters are corrected again according to the second estimation result, and finally the battery life prediction result is obtained. Therefore, based on the battery historical data and the determined mechanism model, the determined empirical model parameters are revised twice successively, so that a more accurate battery life prediction model can be constructed and the accuracy of battery life prediction can be improved.

Description

technical field [0001] The present invention relates to the technical field of batteries, and in particular, to a battery life prediction method, device, cloud server and storage medium. Background technique [0002] With the rapid development of electric vehicles, people put forward higher requirements for power batteries. The service life of power batteries will directly affect the performance of electric vehicles. Therefore, people pay more and more attention to the research on power battery life prediction (RUL). Life prediction can not only improve the user's driving experience, but also build a dynamic and intelligent health management system for the full life cycle of power batteries, which has huge social and economic benefits. [0003] In the related art, when predicting the life of the power battery, the prediction of the life of the power battery is usually driven by the data of historical trajectories, or the life prediction of the power battery is predicted base...

AI Technical Summary

Problems solved by technology

However, these commonly used power battery life prediction methods are difficult to establish a more accurate prediction model, and the accuracy of battery life prediction is poor.

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