储能科学与技术 ›› 2023, Vol. 12 ›› Issue (8): 2575-2584.doi: 10.19799/j.cnki.2095-4239.2023.0119
收稿日期:
2023-03-07
修回日期:
2023-04-21
出版日期:
2023-08-05
发布日期:
2023-08-23
通讯作者:
孙大帅
E-mail:dcc@sermatec-ess.com;sds@sermatec-ess.com
作者简介:
董臣臣(1992—),男,工程师,研究方向为电池故障预警,E-mail:dcc@sermatec-ess.com;
Chenchen DONG(), Dashuai SUN(), Jinglong WANG
Received:
2023-03-07
Revised:
2023-04-21
Online:
2023-08-05
Published:
2023-08-23
Contact:
Dashuai SUN
E-mail:dcc@sermatec-ess.com;sds@sermatec-ess.com
摘要:
锂电池作为一种电化学设备,在发生故障前的特征较为复杂,难以分析,并且生产环境中故障样本数量较少,正负样本比例严重不平衡。针对以上问题,本工作提出基于改进的在线迁移学习算法的电池高压故障预警。首先,引入下采样技术,解决样本不均衡问题,从而降低计算资源的使用。在电池高压故障预警场景下,设计分段下采样策略,使得算法模型在故障发生前能学习到更多细微的特征。其次,提出基于分批增量学习的在线迁移学习方法(homogeneous online transfer learning under incremental training,HomOTL-UIT),源域中训练的离线分类器需要在合适的时间进行更新,以此来适应目标域中不断变化的数据分布,解决数据分布偏移和在线迁移学习退化为在线学习的问题。分批处理降低多次训练带来的计算资源的开销,通过增量学习,不断从目标域中学习,从而不断提高离线分类器的准确度。然后,设计一种滑动窗口下的F1-score评分方法,解决模型权重缓慢失衡问题,从而提高模型的准确性。最后,通过储能集装箱的运行数据验证所提出方法的有效性和准确性,在正负样本严重不均衡时,F1-score达到0.88。
中图分类号:
董臣臣, 孙大帅, 王景龙. 基于改进的在线迁移学习算法的电池高压故障预警[J]. 储能科学与技术, 2023, 12(8): 2575-2584.
Chenchen DONG, Dashuai SUN, Jinglong WANG. Battery high-voltage fault early warning based on improved online migration learning algorithm[J]. Energy Storage Science and Technology, 2023, 12(8): 2575-2584.
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