Predictive Modelling of Battery Behavior for Enhanced Energy Management in Electric Vehicles

As electric vehicles (EVs) and smart energy systems continue to evolve, optimizing energy usage and ensuring system reliability has become critical. However, battery pack modelling remains challenging due to its highly dynamic and nonlinear behavior due to the temperature variations, aging, and variable load conditions. This paper presents a 1D digital twin framework designed for predictive monitoring of the battery parameters, including State of Charge (SoC), terminal voltage, temperature, and an indicative assessment of State of Health (SoH). The battery is modelled using a Thevenin equivalent circuit that covers both steady-state and dynamic electrical characteristics. Key parameters such as open-circuit voltage (OCV), internal resistance, and dynamic characteristics were derived through pulse testing conducted in our laboratory. A thermal modelling is done to capture the heat generation and cell temperature. SoC estimation is carried out using the Coulomb counting method and validated against experimental results under different load conditions. The system is evaluated under realistic scenarios by applying load profiles applicable for an electric bus. To further enhance the framework’s predictive capability, AI-based models are integrated to forecast power consumption trends under dynamic operating conditions. Under real-time conditions, by taking minimal sensor data like current and ambient temperature as inputs, the model can estimate SoC, voltage, pack temperature, and SOH within a 5% margin of error and forecast the power consumption. This hybrid digital twin combining physics-based simulation with AI-driven forecasting offers a scalable and accurate solution for real-time battery monitoring in EV and energy storage applications.