State of Power Prediction for Battery Packs Based on Particle Swarm Optimization SVM

Lithium-ion batteries are widely used in energy storage systems and consumer electronics; however, long-term usage leads to capacity degradation, which affects system efficiency and safety. Existing studies have largely focused on individual cells and their SOH and SOC metrics, with less attention paid to larger battery packs, while the SOP more effectively reflects the overall operational characteristics of a battery pack. Therefore, this study proposes a parameter-optimized prediction method based on PSO and SVM, using SOP as a key health indicator for life prediction. In this method, the global optimal solution is obtained by simulating the collaborative search behavior of the particle swarm, dynamically updating particle positions and velocities; this solution determines the SVM’s critical parameters, namely the kernel parameter g and the penalty coefficient c, which are then used to train the SVM model to enhance its generalization ability and prediction accuracy. The results indicate that the PSO-SVM model can effectively capture the degradation characteristics of battery packs, achieving high-precision SOP prediction, with most prediction errors below 5% and the maximum error reaching 10.91%.