State of Health Estimation of an Electric Vehicle Battery Using Fusion Technology

The development of electric cars with significant energy-saving and emission-reduction capabilities is stimulated by sustainable development. The health condition of the battery system in an electric vehicle is essential for the environment, charging station, operational and human safety. The current methods for determining the health measure, which is used in carefully regulated laboratory settings, are unable to notice unexpected driver actions and challenging road and weather circumstances when driving in real-world situations. Nevertheless, not all these techniques satisfy the needs of genuine vehicle industries. Further consideration is given to the diversity of user behavior as well as the equivalent circuit model. To accurately stand for the aging behavior linked to the progression of cell processes, an open circuit-voltage model is constructed and along with that RC equivalent model is also implemented. Estimation of the state of health of a battery has been done using a unique fusion method based on parametric modeling of open-circuit voltage. This fusion technology takes into consideration the relationship among current, temperature, state of charge, and variations in open-circuit voltage. This paper proposed a fusion model method for SOH estimation based on the Kalman filter in a specific unscented Kalman filter approach. This study describes a Simulink technique for estimating battery SOH that is based on the Kalman filter, State of health as a main parameter. The results have been verified and show that for a battery that is not undergoing any dynamics i.e., for the
$R_{int}$
model, over time, the health state of a battery decreases whereas the internal resistance rises gradually because of some irreversible chemical processes and deterioration of the battery structure. And for the battery which is undergoing the dynamics i.e., RC equivalent model, the error between real and estimated SOC was depicted through the graphs.