State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved Group Method of Data Handling

Abstract

Lithium-ion batteries are complex electrochemical systems, and the degradation of their state-of-health (SOH) is a nonlinear process. Accurate SOH estimation is critical to lithium-ion battery life and safety. We used a data-driven approach to study SOH estimation of lithium-ion batteries. First, we used the singular value decomposition method to extract features from the battery charging history data. Second, the particle swarm (PSO) algorithm was used to optimize the parameter configuration of the group method of data handling (GMDH). Finally, the SOH estimation wa completed using the optimized GMDH. The results show that the proposed PSO-GMDH estimation model maintains an error within 0.89% for estimating its subsequent SOH using historical data of a certain battery, and maintains an error within 0.5% for estimating the SOH of another battery of the same model using historical data of multiple batteries. At the same time, the results also show that the PSO-GMDH estimation model has higher estimation accuracy than the GMDH model without parameter optimization.