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连接到光伏的离子电池的数据驱动直接诊断

Matthieu Dubarry¹, Nahuel Costa², Dax Matthews³

¹Hawai'i Natural Energy Institute, University of Hawai'i at Mānoa, 1680 East West Road, POST 109, Honolulu, HI, 96822, USA. matthieu@hawaii.edu.

Nature communications

|May 30, 2023

概括

这项研究引入了一种新的机器学习诊断方法,用于用于太阳能光伏的离子电池. 它通过分析太阳能充电条件下的电池性能来确保安全和长时间的运行.

科学领域:

可再生能源系统可再生能源系统
电化学储能储能电化学储能
机器学习应用机器学习应用

背景情况:

光伏 (PV) 越来越多地集成到电网中,需要可靠的储能解决方案,如离子电池.
太阳能系统中的二次离子电池偶尔使用,阻碍了传统的诊断方法.
确保这些电池的长期安全和运行完整性对于电网稳定性至关重要.

研究的目的:

为离子电池开发一种新的诊断方法,专门为光伏充电场景量身定制.
为了能够准确评估电池的健康状况,尽管间歇性使用模式.
提高与光伏相结合的电池储能系统的安全性和寿命.

主要方法:

使用在光伏充电中获得的合成电压数据上训练的机器学习算法.
基于清天空模型辐射和各种电池退化状态生成训练数据.
从现实世界的光伏系统辐射数据中对合成电压响应的方法验证.

主要成果:

在诊断中达到2.75%的平均根平均平方误差.
成功分析了超过1万个不同的降解路径.
对于降解率高达25%的离子电池,已证明有效.

结论:

拟议的机器学习方法为光伏系统中的离子电池提供了有效和准确的诊断工具.
这种方法解决了零星使用的挑战,改善了电池监控和安全.
这些发现支持电池存储与可再生能源的可靠整合.

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