To reduce operational costs, accurate prediction of battery electric vehicle arrival time for battery swapping and strategic charging scheduling based on time-of-use electricity pricing are essential. This paper proposes a CatBoost model optimized by a genetic algorithm （GA）, which can accurately predicts vehicle arrivals for battery swapping. Based on the prediction results, the model enables optimal planning of charging ports during peak periods. Furthermore, an integrated photovoltaic and energy storage system is introduced to reduce the average electricity purchase cost. The results demonstrate that through SHAP summary plots and SHAP dependence plots, state-of-charge （SOC）, time, and distance are identified as the most influential features affecting model performance. The GA-CatBoost model outperforms three other prediction models in terms of accuracy, precision, recall, and F1 score. Based on the prediction outcomes, a multi-objective charging scheduling strategy is implemented for peak periods, significantly reducing the average electricity purchase cost while ensuring sufficient battery swapping services. Finally, with the introduction of a photovoltaic-storage system and simulation of the time-of-use pricing operation mode for an integrated system, the visual analysis of power flow and electricity cost validates the effectiveness of the system in optimizing electricity pricing, further reducing the station's electricity procurement costs.
Key words
battery electric vehicles /
photovoltaics /
energy storage /
prediction /
electric load dispatching /
battery swapping stations
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