Real-time adaptive neural network-based state of charge prediction of battery pack in a digital twin framework

The accurate and real-time prediction of state of charge (SoC) in battery pack is crucial for the safe and efficient operation of electric vehicles. Traditional estimation methods often suffer from reduced accuracy under sensor errors, battery aging, and dynamic load conditions. This study presents a real-time adaptive neural network (ANN)-based SoC prediction model integrated within a digital twin (DT) framework, designed, and validated using MATLAB/Simulink. The proposed algorithm continuously updates its parameters using real-time current, SoC, and voltage data of battery pack, enabling adaptive learning under varying load and ambient conditions. Compared with traditional methods such as Extended Kalman Filter and particle-filter based estimators, the proposed algorithm reduces the prediction error by 18–22 % and it shortens the response time by 30 %. The simulation results confirm that a strong correlation between the predicted and actual SoC values (R = 0.9999) with a maximum deviation of ±1.5 %. The proposed algorithm demonstrates robust convergence, improved generalization through Bayesian regularization, and high stability during real-time adaptation. This adaptive DT-integrated ANN framework enhances the BMS reliability, supports predictive maintenance, and provides a scalable, and intelligent solution for next-generation electric mobility applications.