Online estimation of state of health for the airborne Li-ion battery using adaptive DEKF-based fuzzy inference system

Abstract

The quick and accurate estimation of the state of health (SOH) of Li-ion battery is a technical difficulty in battery management system research. For the low accuracy of Li-ion battery SOH estimation under complex stress conditions, an estimation method of SOH for Li-ion battery using the adaptive dual extended Kalman filter-based fuzzy inference system (ADEKF-FIS) is proposed. First, Li-ion battery SOH is online estimated by dual extended Kalman filter. Then the Sage–Husa adaptive algorithm and the fuzzy controller are used to correct the state noise covariance and the observed noise covariance, respectively. The algorithm is flat on the state variance and the noise variance. The recursive estimation of the square root ensures the symmetry and nonnegative nature of the state and noise variance. In the end, this paper performing the dynamic stress test condition experiment for confirmation. Experimental results show that, compared with the EKF algorithm, ADEKF-FIS algorithm can obtain state of charge estimation with higher accuracy, which further improves the prediction accuracy of SOH and makes this algorithm have higher accuracy and better convergence.