Battery-Supercapacitor State-of-Health Estimation for Hybrid Energy Storage System Using a Fuzzy Brain Emotional Learning Neural Network

Abstract

This study proposes an efficient estimator and uses it to estimate the health of a lithium-ion battery and a supercapacitor in the hybrid energy storage system (HESS). A new type of online health estimator that uses a fuzzy brain emotional learning neural network (FBELNN) is proposed. This neural network is different to a conventional brain emotional learning neural network where the fuzzy inference system and a new reward signal are used. The effect capacity fading on the output of energy storage components is also determined. The proposed method uses a discrete wavelet transform (DWT) and principal component analysis (PCA) to extract features from the response signal for the impulse load. The DWT-PCA can reduce the workload for feature extraction. The parameter adaptation laws and convergence analysis for the FBELNN are derived and the internal parameters for the FBELNN are optimized using a genetic algorithm (GA). A neural network estimates the capacity of a supercapacitor and lithium-ion battery in real-time to better ensure the safety of HESS. The sample set is collected from the voltage response signal in the HESS simulation platform and practical experimental platform. Simulation and experimental results show that the proposed method has a faster learning speed and is more accurate than other methods.