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预测电动汽车电池的健康状况,使用长期短期降解特征提取和FEA TimeMixer

Weijie Tang¹, Jiayan Chen², Dongjiao Chen³

¹College of Energy Environment and Safety Engineering and College of Carbon Metrology, China Jiliang University, Hangzhou, 310018, China.

Scientific reports

|January 17, 2025

概括

这项研究引入了一种用于预测电动汽车电池健康状况 (SOH) 的新方法,使用先进的特征提取和新的FEA-TimeMixer模型提高了短期和长期预测的准确性.

科学领域:

电池技术电池技术
人工智能的人工智能
机器学习机器学习

背景情况:

准确的健康状况 (SOH) 预测对于电动汽车电池的可靠性和寿命至关重要.
现有的方法在不同的预测地平线上都难以保持一致的SOH预测准确性.
捕捉复杂降解特征的局限性阻碍了精确的长期SOH预测.

研究的目的:

开发一种先进的电池SOH预测方法,解决不同预测长度的准确性问题.
为了提高长期和短期电池退化特征的提取和融合.
改进TimeMixer模型在捕获频域特征以进行SOH预测方面的能力.

主要方法:

开发了一种自动SOH提取算法,用于标记离线充电数据.
一个自动编码器融合了经验模型和完整集体经验模式分解与自适应噪声 (CEEMDAN) 的特征,用于降解趋势.
一个频率增强注意力 (FEA) 机制被整合到TimeMixer模型中,将时间和频率域信息结合起来.

主要成果:

拟议的方法在短期SOH预测中实现了低于0.0219的平均绝对误差 (MAE).
该方法证明了长期SOH预测的MAE低于0.1007.
FEA-TimeMixer模型在多个长度的SOH预测准确性方面超过了现有的深度学习模型.

结论:

新的SOH预测方法有效地提高了短期和长期预测的准确性.
特征融合和FEA机制显著提高了模型的预测性能.
这种方法为管理电动汽车电池健康和延长使用寿命提供了可靠的解决方案.

关键词:

自动编码器自动编码器电池电池电池电池在CEEMDAN,你会发现.专注于自己的注意力健康状况身体健康状况时间混合器 (TimeMixer) 的时间.

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