State-of-Charge and State-of-Health online estimation of Li-ion battery for the More Electrical Aircraft based on semi-empirical ageing model and Sigma-Point Kalman Filtering

Abstract

This paper proposes an online method to estimate the State-of-Charge (SoC) and State-of-Health (SoH) of a Li-ion battery for the More Electrical Aircraft (MEA) application. Based on an extended characterization of Li-ion cells, precise electrical and ageing models are established and used in the state estimation method. The SoC algorithm is based on a Sigma-Point Kalman Filter (SPKF) that handles the non-linearity of the electrical model. The results show stable SoC and SoH estimation precisions, respectively less than 1% and 2% for most of the temperature and ageing conditions. The algorithm is built to meet the requirements of the MEA in terms of robustness, reliability, precision, hardware integration and low maintenance.