Data-Driven Simulation Framework for Electric Vehicle Charging and Energy Management Using Real Driving and Grid Data
Keywords:
electric vehicles, smart charging, data-driven modeling, simulation framework, energy management, real driving data, grid integrationAbstract
A data-driven simulation framework is proposed to model electric vehicle (EV) charging and grid interactions using real mobility and grid data. The study integrates actual driving patterns and temporal grid load profiles to evaluate charging demand and energy management strategies. We develop a modular environment where battery charging behavior, network infrastructure, and user schedules are modeled from empirical datasets. The simulator ingests real trip data and grid load information to predict charging sessions and to test smart charging algorithms. Experiments use open mobility and grid datasets to analyze scenarios of uncontrolled and managed charging, including renewable integration and time-of-use pricing. Results demonstrate that data-driven models capture realistic demand patterns and can quantify benefits of controlled charging, such as peak shaving and cost savings. The framework supports incorporation of vehicle-to-grid (V2G) and battery storage, enabling exploration of advanced energy management. This work illustrates how using empirical driving and power data leads to more accurate assessments of EV impacts and management solutions. In particular, our results highlight the potential of coordinated charging and storage strategies to balance grid load while meeting driver needs. The paper presents a comprehensive simulation architecture, experimental case study, and discussion of implications for EV infrastructure planning.
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