Parameter Optimization for Li-ion Battery Module Digital Twin Models

Technological advancements in batteries have paved new opportunities for energy storage technology, particularly in the transportation sector, including electric vehicles. In electric vehicles, optimizing battery performance in the context of safety and efficiency is crucial for users and manufacturers. However, accurately predicting battery degradation is challenging due to complex physical properties. Many parameters contribute to battery degradation, such as temperature variations, charging and discharging rates, number of cycles, internal resistance, depth of discharge, and electrode materials. It is crucial to investigate these variables in-depth to develop techniques that minimize battery degradation and maximize battery life. This paper proposes a digital twin technology as an adaptive modeling approach to follow the actual model in dynamic operating conditions and, eventually, minimize capacity degradation. This paper considers experimental data as an actual model, and a mathematical model using an electrical equivalent circuit is the basis of the battery model. The curve fitting has been applied using an error minimization technique to validate the adaptive model performance with the actual data. Further, the adaptive model performance has been calculated through sensitivity analysis. Simulation results demonstrate the effectiveness of the adaptive model in calculating degradation, enabling an achievable strategy for increasing battery life.