在动态负载条件下分析电池的增量容量
Urvashi Saini1,2, Sindhuja Renganathan1,2
1Central Electrochemical Research Institute, Karaikudi, Tamil Nadu, 630003, India.
MethodsX
|May 8, 2025
概括
准确评估电动汽车电池健康状况 (SOH) 是一个挑战,因为使用变化. 本研究介绍了一种基于分析的方法,使用增量容量分析 (ICA) 来确定SOH并提取健康特征以改善电池管理.
科学领域:
- 电化学 电化学 电化学
- 电池技术 电池技术
- 数据科学数据科学数据科学
背景情况:
- 电动汽车 (EV) 电池健康状况 (SOH) 的评估是困难的,因为充电/放电不一致和不同的操作参数.
- 传统的SOH估计方法需要控制的校准周期,这在现实世界EV应用中是不可行的.
- 准确的SOH监测对于EV性能,安全和寿命管理至关重要.
研究的目的:
- 开发一种基于分析的方法,在现实操作条件下从电动汽车电池中获得标记容量和SOH值.
- 使用增量容量分析 (ICA) 提取与电池年龄相关的电池健康特征.
- 通过机器学习或深度学习模型,实现对电动汽车电池容量和SOH的数据驱动预测.
主要方法:
- 将增量容量分析 (ICA) 方法应用于电动汽车 (EV) 电池运行数据.
- 使用ICA方法,取出电池健康指标作为电池年龄的函数.
- 电动汽车电池的健康状况 (SOH) 的计算,采用基于分析的方法.
主要成果:
- 拟议的方法成功地从在动态条件下运行的电动汽车电池中获得标记容量和SOH值.
- 使用ICA从电池数据中识别和提取关键健康特征,证明与电池老化存在相关性.
- 使用开发的基于分析的技术验证了汽车电池的SOH计算.
结论:
- 这种基于分析的方法为EV电池的SOH评估提供了一个实际的解决方案,而不需要控制的校准.
- 提取的健康特征为先进的机器学习模型提供了有价值的投入,以预测未来的电池容量和SOH.
- 这项研究有助于为电动汽车提供更可靠,更高效的电池管理系统.
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