Electric Vehicle Charging Profile Forecasting Using Hybrid Models
This work provides a practical solution for battery-aware scheduling in EV fast charging stations, enabling more granular forecast updates and accurate departure time estimation.
The paper proposes a hybrid and lightweight method for forecasting individual EV charging profiles, addressing the underexplored problem of single-charger-level forecasting. The method is evaluated on a public dataset, showing improved accuracy over existing approaches.
Electric Vehicle (EV) fast charging stations require forecasting techniques both at the single charger level and aggregated level. While for the latter several models exist, forecasting individual EV charging profiles is still underexplored in literature. However, such methods may be potentially used by battery-aware scheduling, leading to a more granular update of the charging station aggregated forecast and provide a more accurate estimation of EVs departure times. Nonetheless, the variable extent of available information in time and in different settings could jeopardize these benefits. For this reason, we propose a hybrid and lightweight method to estimate the EV charging profile before and during the charging process. Besides evaluating this method on multiple EVs from a public dataset, we also assess the impact of different level of information in the time transposition of the charging profile.