Aviation lead-acid battery state-of-health assessment using PSO-SVM technique

It is an important issue to assess the state-of-health (SOH) of battery, including aircraft. There are several methods on the assessment of SOH of battery having been reported in recent years. Herein, we proposed a hybrid PSO+SVM model, which employed particle swarm optimization (PSO) algorithm to search the best parameters of support vector machine (SVM) to estimate SOH of aviation lead-acid battery. Results showed that the proposed PSO-SVM model achieved 98.75% classification accuracy using a real dataset provided by Civil Aviation Flight University of China. Hence, the hybrid PSO+SVM model is very promising compared to the previous reports.