Hybrid-Adaptive Modeling of Electric Vehicle Batteries Considering Dynamic Aging Parameters: A Comprehensive Approach

With rising demand for electric vehicles, development of battery management systems has become a key area of research. One of the essential tasks of battery management is to accurately determine the state of charge (SOC) of the electric vehicle battery under different operating conditions. In this contribution the effect of operating conditions is considered to update the parameters of an equivalent circuit battery model, leading to a more realistic SOC determination. A previously proposed hybrid-adaptive method is used for SOC estimation and three existing models from MATLAB/Simulink are compared: a generic model, a behavioral model (with and without parameter adaption), and a table-based model. The table-based model can be populated with experimental data. The simulation results indicate that the generic model is not capable of representing the aging behavior as accurately as the adaptive behavioral model. The SOC estimation with the behavioral model is also more accurate with the hybrid-adaptive method than the generic model which employs the open circuit voltage method.