Impact of Model Depth on State of Charge Estimation in Second-Life Batteries

This paper investigates the impact of different levels of modelling on the accuracy of state of charge estimation in battery storage systems. The aim is to compare simple and complex models to identify the optimal balance between accuracy and computational complexity. The results show that while deeper models improve estimation, the additional complexity provides only marginal benefits. A kinetic approach improves accuracy at high loads without significantly increasing the effort. Separating the inverter and battery models increases flexibility by allowing system changes without retooling. The results provide a basis for integrating temperature effects and optimising storage operation for economic efficiency.