基于多源转移学习的电动汽车离子动力电池健康状况评估
Junyang Chen1, Wenbin Zheng1, Ping Fu1
1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
iScience
|January 29, 2026
概括
精确的电动汽车电池健康估计是使用一个新的多源转移学习框架改进. 这种方法通过选择相关来源并有效地调整领域,提高了健康状况 (SOH) 预测的准确性,即使数据有限或没有标签,也可以.
科学领域:
- 电池技术 电池技术
- 机器学习 机器学习
- 电动车辆 电动车辆
背景情况:
- 电动汽车电池健康状况 (SOH) 估计面临挑战,因为有限的现实世界标记数据.
- 转移学习提供解决方案,但与域选择和负转移问题作斗争.
研究的目的:
- 开发一个多源转移学习框架,以进行可靠的电池SOH估计.
- 为了解决数据稀缺问题,并减轻SOH预测模型中的负转移.
主要方法:
- 实施了系统的域名选择过程,使用相似度指标来识别相关的源数据.
- 使用深度域调整网络来最大限度地减少跨多个数据源的差异.
- 使用未标记和有限的电池数据应用健康状况 (SOH) 估计框架.
主要成果:
- 实现了较低的SOH估计误差:在无监督条件下为1.64%,在有限的样本中为1.58%
- 通过系统的域选择和多源适应,在估计准确度上显著改善.
- 在实际电池健康监测场景中验证了框架的有效性.
结论:
- 整合系统域选择与多源域适应对于准确的电池SOH估计至关重要.
- 拟议的框架为电动汽车电池健康监测提供了一个强大而有效的解决方案.
- 这种方法克服了在数据稀缺环境中传统转移学习的局限性.
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