On-line Monitoring and State of Health Estimation Technology of Lead-Acid Battery

Valve regulated lead-acid (VRLA) battery is in the floating charge state for a long time, and the online accurate assessment of its state of health (SOH) is of great significance. In this paper, the online monitoring platform is built, and the discharge characteristics of battery are tested. Based on the phenomenon of terminal voltage “steep drop and rise again” during discharge, nine characteristics were extracted, including trough voltage, plateau voltage, voltage difference, trough current, plateau current, current difference, trough time, plateau time and time difference. The health factors were obtained by dimension reduction through principal component analysis (PCA) and Pearson correlation coefficient. The BP neural network is built to estimate SOH of the battery and is optimized using genetic algorithm (GA). The accuracy of the battery SOH assessment model is verified by comparing with the capacity check discharge experiment data, and the feasibility of the proposed battery SOH assessment method is also proved.