Abstract: In recent years,electric vehicle battery swap services have rapidly gained popularity due to their fast and convenient energy replenishment capabilities.Accurate prediction of user battery swap demand is crucial for optimizing the operational efficiency of battery swap platforms.However,in cities with newly deployed battery swap stations,conventional prediction models often suffer from insufficient training due to the lack of historical data,resulting in degraded prediction accuracy.To address this challenge,this paper proposes a denoising diffusion model-enhanced algorithm for generating battery swap demand data.By synthesizing samples that preserve the statistical distribution of real-world battery swap demand data,the proposed approach effectively augments the training dataset,thereby significantly improving the model’s prediction accuracy.Specifically,it firstly employs analog bit encoding to represent mixed-type battery swap demand data in continuous space,enabling it to be processed by the diffusion model.Furthermore,it designs a conditional denoising network incorporating the cross-attention mechanism,utilizing station information to guide the generation of high-quality battery swap demand data.Finally,the proposed algorithm is evaluated on a real-world battery swap dataset collected from 40 battery swap stations in Chengdu over a one-month period.Experimental results demonstrate that combining the generated data from the proposed algorithm with the original training data reduces the MAE,RMSE,and MAPE of battery swap demand prediction by 9.29%,8.56%,and 8.23%,respectively,compared to using the original training data alone.

Key words: Electric vehicle battery swap service, Battery swap demand prediction, Battery swap demand data generation, Denoi-sing diffusion model, Cross-attention mechanism