Battery health state estimation based on support vector machine and simulated annealing

With the increasing importance of batteries in mass applications, this paper proposes a State of Health (SOH) indication method for batteries based on Support Machine (SVM) and simulated delay algorithm. SOH is an important indicator of the degree of long-term battery aging and performance degradation. By analyzing the charging and discharging characteristics of the battery, extracting feature representations that can reflect its aging, constructing a SOH estimation model based on multi GPU parallel accelerated SVM, and optimizing the hyperparameters of SVM through simulated annealing algorithm. The research results show that the optimized SVM model performs well in predicting SOH, with a determination coefficient of 99.73% and a prediction score below 0.05%, achieving high prediction accuracy. This research achievement provides important support for the safety maintenance of charging stations and significantly improves the safety of the charging process.