Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR model

Abstract

State of health (SOH) estimation of lithium-ion batteries is significant for safe and lifetime-optimized operation. In this study, support vector regression (SVR) is employed in battery SOH prognostics, and particle swarm optimization (PSO) is employed in obtaining the SVR kernel parameter. Through a new validation method, the proposed PSO–SVR model in this paper can well grasp the global degradation trend of SOH and is little affected by local regeneration and fluctuations. The case study shows that compared with the eight published methods, the proposed model can obtain more accurate SOH prediction results. Even SOH prediction starts from the cycle near capacity regeneration, the proposed model still can grasp the global degradation trend. Furthermore, the improved PSO–SVR model has great robustness when the training data contain noise and measurement outliers, which makes it possible to get satisfactory prediction performance without pre-processing the data manually.