数据驱动的离子电池可用容量的估计,基于碎片化的充电容量
Zhen Zhang1, Xin Gu1, Yuhao Zhu1
1School of Control Science and Engineering, Shandong University, Jinan, China.
Communications engineering
|February 24, 2025
概括
这项研究引入了一种新的方法,用于使用碎片化的充电数据来估计离子电池的可用容量. 这种方法通过克服不完整的充电周期的局限性来提高电动汽车的安全性和效率.
科学领域:
- 电化学 电化学 电化学
- 机器学习 机器学习
- 电动汽车技术 电动汽车技术
背景情况:
- 准确的可用容量估计对于安全的电动汽车 (EV) 运行至关重要.
- 在现实世界使用电动汽车时,充电周期不完整会阻碍传统的容量估计方法.
- 开发针对碎片化充电能力数据的可靠方法至关重要.
研究的目的:
- 开发和验证一种机器学习方法,用不完整的充电数据来估计离子电池的可用容量.
- 探索特征组合,包括相关性,充电时间和初始充电状态,以提高估计准确度.
- 在各种数据集上评估基本和转移学习模型的性能.
主要方法:
- 利用碎片化的充电能力数据,操纵不完整的充电周期进行估计.
- 开发了一个基本的机器学习模型,在11,500个循环样本上进行训练.
- 实现并微调一个在多个数据集中验证的转移学习模型.
- 评估了36个功能组合,考虑了相关性,充电时间和初始充电状态.
主要成果:
- 最好的基本机器学习模型实现了0.012.0的根平均平方误差 (RMSE).
- 转移学习模型在数据集中表现出一致的稳定性,RMSE波动在0.5%以内.
- 在不同周期间隔 (5,10和20) 的特征组合中,性能保持稳定.
结论:
- 碎片化的充电容量数据可以有效地用于可用容量的估计.
- 拟议的机器学习方法,特别是转移学习,为现实世界电动汽车电池管理提供了一个有前途的解决方案.
- 这种方法提高了电池状态监测的实用性和可靠性.
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