Bio-Inspired Calibration of the Self-Correcting Battery Model as the Core of Digital Twin Framework

Accurate modeling of batteries is paramount in formulating the reliable digital twin (DT) in the areas of energy storage and smart mobility. The Enhanced Self-Correcting (ESC) model, an interesting improvement of standard equivalent circuit models (ECMs), is the main component of the physical twin as it encapsulates dynamic voltage behavior along with the hysteresis phenomena. This manuscript suggests the use of two new state-of-the-art metaheuristic algorithms in the calibration scheme of ESC model: the African Vultures Optimization Algorithm (AVOA) and the Walrus Optimization Algorithm (WaOA). Parameter identification is performed under a wide range of temperatures of -25degC to +45degC to achieve robustness under different operating conditions. Comparative analysis demonstrates that the bio-inspired methods increase the accuracy and convergence and reduce the sensitivity to the thermal fluctuations. Although this investigation would be restricted to offline calibration, the validated ESC (electromagnetic steering control) model provides a foundation for DT deployment and in turn allows for continuous and reliable estimation of state-of-charge (SoC) and state-of-health (SoH).