Battery State of Health Estimation Using Adaptive Kalman Filter Integrated with Advanced Charging Techniques

Abstract

In order to replace internal combustion engine vehicles, EVs are used which have no carbon emissions. The EVs are driven using electrical power from a storage device which is preferable a high rating battery pack. The battery pack used in the EV technology is a Li-ion made which has high current discharge and fast charging capabilities. With the fast charging and high current discharge of the battery pack the health of the cells diminishes over a period of time. In this paper a battery SOH estimator is designed with Adaptive Kalman filter included. In order to improve the SOH of the battery, different charging techniques are adopted and imposed on the EV charger. The charging techniques are a) Continues charging b) Pulse charging and c) Burp charging. The SOH of the battery is determined by the Adaptive Kalman filter based estimator with these types of charging techniques. These charging techniques are applied to the same rating of the battery pack and also the charge/discharge current magnitude for the same time. The analysis is simulated in MATLAB Simulink software with block considered from ‘Simelectronics’ and ‘Powersystems’ subsets of the Simulink library browser. The graphs of the battery SOH are compared with different charging techniques for the same simulation time. The SOH of the battery estimation validates the optimal charging technique for the EV battery charging.