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<p>Battery electric vehicles (BEVs) have increasingly positioned themselves as a critical technology in the power system, impacting the world’s energy consumption. Understanding the BEV energy dynamics can contribute to vehicle, infrastructure, and grid optimization. Currently, BEV manufacturers provide limited access to the vehicle’s high energy consuming components, such as the battery and the charger. Therefore, existing public datasets consist mostly of aggregated data collected from charging points outside the vehicle, resulting in lower data resolution and not capturing the actual energy dynamics. This paper fills this dataset gap by developing a data generation method to collect datasets, including the actual energy values for the charger, the battery, and the auxiliary devices, using measurement with a second resolution. The collected dataset illustrates energy dynamics under different modes (charging, driving, parking) and environmental conditions. This dataset provides detailed technical insights that can be used to optimize smart charging, reduce operational costs, understand usage, improve the operation of high energy consuming components, build AI models, and analyze grid impact.</p>

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Unveiling Energy Dynamics of Battery Electric Vehicle Using High-Resolution Data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Mohamed Yasko,
  • Adamou Moussa Issaka,
  • Fanghao Tian,
  • Hussain Kazmi,
  • Johan Driesen,
  • Wilmar Martinez

摘要

Battery electric vehicles (BEVs) have increasingly positioned themselves as a critical technology in the power system, impacting the world’s energy consumption. Understanding the BEV energy dynamics can contribute to vehicle, infrastructure, and grid optimization. Currently, BEV manufacturers provide limited access to the vehicle’s high energy consuming components, such as the battery and the charger. Therefore, existing public datasets consist mostly of aggregated data collected from charging points outside the vehicle, resulting in lower data resolution and not capturing the actual energy dynamics. This paper fills this dataset gap by developing a data generation method to collect datasets, including the actual energy values for the charger, the battery, and the auxiliary devices, using measurement with a second resolution. The collected dataset illustrates energy dynamics under different modes (charging, driving, parking) and environmental conditions. This dataset provides detailed technical insights that can be used to optimize smart charging, reduce operational costs, understand usage, improve the operation of high energy consuming components, build AI models, and analyze grid impact.