An Integrated Data-Driven and Physics-Based Approach for Dynamic Operation Simulation of Electric Vehicles

To address the challenges of complex operational simulation for Electric Vehicles (EVs) caused by spatial-temporal variations and driver behavior heterogeneity, this study introduces a dynamic operation simulation model that integrates both data-driven and physics-based principles, referred to as the Electric Vehicle-Dynamic Operation Simulation (EV-DOS) model. The physics-based component encompasses critical aspects such as the powertrain energy transfer module, heat transfer module, charge/discharge module, and battery state estimation module. The data-driven component derives key features and labels from second-by-second real-world vehicle driving status data and incorporates a Long Short-Term Memory (LSTM) network to develop a State-of-Health (SOH) prediction model for the EV power pack. This model framework combines the interpretability of physical modeling with the rapid simulation capabilities of data-driven techniques under dynamic operating conditions. Finally, this study validates the hybrid model using one year of real-world driving data, and the simulation results showed that, under various spatial-temporal conditions and different driver behaviors, the monthly average energy consumption estimation error remains consistently low, with the majority of cases falling below 1.0 kWh/100 km, while the SOH prediction error remains below 0.8%. These results demonstrate the model's reliability for energy consumption and battery health estimation, providing robust support for EV performance analysis and energy management.