AVOA-Based Calibration of an Enhanced Self-Correcting Battery Model for Digital Twin Integration

Digital twin (DT) technology plays a pivotal role in enabling real-time monitoring, predictive diagnostics, and control of energy storage systems within smart grids and electric mobility. The development of accurate battery twins critically depends on robust parameter identification of battery models under dynamic load profiles and varying thermal conditions. This paper investigates the application of the recent African Vultures Optimization Algorithm (AVOA) for calibrating the Enhanced Self-Correcting (ESC) battery model, which incorporates feedback and hysteresis effects. Validation is performed using experimental current-voltage data from an Urban Driving Cycle at four different ambient temperatures. Comparative analyses against Artificial PSO, GWO, and Plett's original method demonstrate that AVOA achieves higher accuracy (RMSE of 4.5mV at 25°C), 20% faster convergence, and 12% lower computational cost. These results highlight AVOA's suitability for real-time smart grid applications, enhancing battery DT fidelity and supporting the reliable integration of electric vehicles and distributed energy storage into next-generation smart grids.