对于充电段的现实电动公交车的离子电池组的强大的SOH估计
Heng Li1, Shilong Zhuo1, Yun Zhou2
1School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Scientific reports
|July 10, 2025
概括
对电动巴士电池组的健康状况 (SOH) 准确估计至关重要. 一种新方法使用充电数据和神经网络进行精确的SOH评估,改善电池的再利用和维护.
科学领域:
- 电气工程 电气工程
- 运输科学 运输科学
- 材料科学 材料科学 材料科学
背景情况:
- 准确的健康状况 (SOH) 估计对于电动巴士电池的维护和再利用至关重要.
- 现实世界SOH估计受到电池不一致性和可变的电荷-放电深度的挑战.
- 现有的方法往往需要手动的特征提取,并与不同的数据长度作斗争.
研究的目的:
- 为电动巴士电池组的SOH估计制定一个强大的框架.
- 通过运营数据提出一种新的SOH标签校准方法.
- 为了使精确的SOH估计无需手动功能工程.
主要方法:
- 在充电数据上通过修改的安培小时积分公式进行SOH标签,并通过30个周期的平均过进行增强.
- 通过重新采样到100个时间域采样点来标准化收费数据.
- 使用卷积神经网络进行自动特征提取和随后的神经网络进行SOH映射.
主要成果:
- 提出的方法实现了高精度,超过80%的循环病例显示绝对误差低于3%.
- 在三种类型的九辆公共汽车中,总的平均绝对误差为1.585%.
- 该框架有效地处理不同的充电会话持续时间,并消除了手动功能提取的需要.
结论:
- 新的SOH标签和估计框架为电动巴士电池组提供了强大而准确的解决方案.
- 该方法的高精度和适用性得到了广泛的运行数据的验证,这些数据来自各种电动巴士类型.
- 这种方法提高了电池维护和再利用在电动公共交通中的效率.
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