Online estimation of battery model parameters and state of health in electric and hybrid aircraft application

Abstract

The accuracy of state of health (SOH) estimation function in battery management systems is an essential factor for ensuring the safety and reliability of battery systems in electric aircraft. Most common SOH estimation approaches are model-based and work with constant model parameters. However, the model parameters vary by the change in operating temperature and state of charge (SOC). The variation of model parameters has adverse impact on the accuracy of battery state's estimation if they are not updated. In this paper, an accurate online parameter estimation method is proposed for lithium-ion batteries (LIBs) to increase the accuracy of the battery model. A more accurate model for the battery leads to a more accurate SOC and SOH estimation. An adaptive sliding observer is developed to estimate the SOC and capacity based on the proposed parameter estimator. An adaptive SOH estimation scheme is proposed to mitigate the temperature variation effect on the accuracy of the SOH estimation. The experimental results verify the effectiveness of the proposed parameter estimator along with the adaptive sliding observer on achieving accurate estimation of SOC and capacity. The proposed adaptive SOH shows good agreement with experiments by less than 1.3% estimation error at different operating temperature conditions.