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<p>Reliable electric vehicle (EV) charging depends on both sufficient infrastructure and stable power quality. In real-world distribution networks, single power quality (PQ) disturbances, such as frequency deviation, harmonics, temporary undervoltage/overvoltage, transient events, voltage deviation, interruptions, sags, and swells can significantly influence charging efficiency, equipment safety, and battery longevity. However, existing public resources rarely provide standardized, high-resolution datasets linking specific PQ disturbances to EV charging performance under controlled and replicable conditions. We present a dataset that systematically evaluates the impact of ten representative single PQ disturbances on EV charging. Test cases were designed following IEEE standards, and experiments were conducted on a proprietary full-vehicle charging test platform to capture authentic charging responses. The dataset includes grid-side voltage and current waveforms, charger telemetry, and battery charging profiles at high temporal resolution, covering the most representative AC charging scenarios. Technical validation demonstrates the reliability of data collection, consistency across repeated tests, and alignment with PQ definitions. The dataset provides foundation for: (i) benchmarking diagnostic and classification algorithms for PQ events, (ii) quantifying the impact of specific disturbances on charging current and efficiency, and (iii) supporting the design of robust EV chargers and grid-integration strategies. While the present release focuses on single disturbances, it establishes a reference framework for future studies on more complex or composite PQ scenarios.</p>

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High-resolution Dataset of Electric Vehicle Charging Responses Under Varied Power Quality Disturbances

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

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  • Honghan Li,
  • Yuwei Zhang,
  • Shuo Yang,
  • Xiaobo Liu

摘要

Reliable electric vehicle (EV) charging depends on both sufficient infrastructure and stable power quality. In real-world distribution networks, single power quality (PQ) disturbances, such as frequency deviation, harmonics, temporary undervoltage/overvoltage, transient events, voltage deviation, interruptions, sags, and swells can significantly influence charging efficiency, equipment safety, and battery longevity. However, existing public resources rarely provide standardized, high-resolution datasets linking specific PQ disturbances to EV charging performance under controlled and replicable conditions. We present a dataset that systematically evaluates the impact of ten representative single PQ disturbances on EV charging. Test cases were designed following IEEE standards, and experiments were conducted on a proprietary full-vehicle charging test platform to capture authentic charging responses. The dataset includes grid-side voltage and current waveforms, charger telemetry, and battery charging profiles at high temporal resolution, covering the most representative AC charging scenarios. Technical validation demonstrates the reliability of data collection, consistency across repeated tests, and alignment with PQ definitions. The dataset provides foundation for: (i) benchmarking diagnostic and classification algorithms for PQ events, (ii) quantifying the impact of specific disturbances on charging current and efficiency, and (iii) supporting the design of robust EV chargers and grid-integration strategies. While the present release focuses on single disturbances, it establishes a reference framework for future studies on more complex or composite PQ scenarios.