Linking Traffic Dynamics to Battery Stress in Electric Vehicles: A SUMO-Based Energy Modelling Framework with BMS-Oriented Indicators

In the context of escalating implementation of electric vehicles (EVs), further research is required to investigate the impact of empirical driving conditions on energy demand and battery performance. In spite of the fact that microscopic traffic simulation and EV energy modelling are already used extensively, their use is still limited in studies focusing on batteries. Specifically, in most existing approaches, the effect of traffic-induced variability on battery stress is not explicitly accounted for. The study presented here examines a systematic framework that combines energy demand, traffic dynamics and battery behaviour. Using the SUMO simulator, vehicle trajectories are converted into electric vehicle (EV) energy profiles via a physics-based longitudinal model, thereby estimating battery power, energy consumption, regenerative effects and changes in state of charge (SOC). Next, a variety of indicators related to the battery management system (BMS) are introduced, including the Battery Stress Index (BSI), a traffic–energy severity (TES) indicator and event-based measures for transient conditions. The results show that traffic variability leads to significant fluctuations in battery load, which are not fully captured by conventional energy metrics. The proposed indicators provide additional information on cumulative and dynamic battery solicitation while remaining physically interpretable. Taken together, this framework links traffic conditions and battery solicitation in a coherent approach, thereby creating a scalable approach to traffic-aware energy analysis.