Remaining useful life prediction of Lithium-ion batteries by PSO-SVM based on multi-feature extraction

Abstract

With the development of renewable energy, energy storage system can solve the problem of high volatility of wind and solar power generation, and lithium battery is widely used in energy storage field because of its small size, lightweight and strong storage capacity. But safety and reliability issues need to be considered during its use. Predicting the remaining life of the battery is convenient for managing and maintaining the energy storage system, which is an important step to ensure the safe operation of the equipment. In this paper, we adopt a data-driven approach to predict the remaining life of lithium batteries, comprehensively considering the whole charging and discharging process and the influence of temperature on the aging of lithium batteries, from which eight suitable features are extracted, followed by a feature-enhanced Box-Cox transformation to improve the correlation between the features and the aging state of batteries. Based on this, SVM is used to construct the prediction model and PSO is used to adaptively optimize the parameters of the original SVM model, thus further improving the prediction accuracy. The feature-enhanced PSO-SVM prediction can be performed on a single group of batteries, and also has high prediction accuracy for untrained batteries. The proposed features and the optimized model possess a certain reliability, and the model outperforms general algorithms in terms of accuracy.