基于混合机器学习方法与时间序列增强的离子电池的早期剩余使用寿命预测
Jingwei Zhang1, Jian Huang2, Taihua Zhang1,3,4
1School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
新能源汽车电池的准确剩余使用寿命 (RUL) 预测通过一种新的混合框架得到了改进. 这种方法通过信号分解,数据增强和深度学习来增强早期预测,即使数据有限,噪音大.
科学领域:
- 电池健康监测 电池健康监测
- 预测和健康管理 (PHM)
- 新能源汽车 (NEV) 是一种新能源汽车.
背景情况:
- 准确的剩余使用寿命 (RUL) 预测对于NEV可靠性至关重要.
- 挑战包括噪音,容量再生,以及早期预测中的数据稀缺性.
- 现有的方法在有限的早期电池数据中扎.
研究的目的:
- 为早期和准确的电池RUL预测开发一个强大的混合框架.
- 在电池预测中解决数据稀缺和噪声污染问题.
- 提高新能源汽车电池管理系统的可靠性.
主要方法:
- 信号分解使用完整集体实证模式分解与自适应噪声 (CEEMDAN).
- 使用变压器增强生成对抗网络 (HyT-GAN) 的时间序列增强.
- 使用CNN-BiGRU预测器进行深度预测,该预测器由Dung Beetle Optimizer (DBO) 优化.
主要成果:
- 使用仅20%的历史数据实现了准确的早期RUL预测.
- 预测准确度高,R2值从0.9643到0.9972.2之间.
- 在基准数据集 (NASA,CALCE) 上使用优异的性能指标 (RMSE/MAE < 0.0296/0.0198).
结论:
- 拟议的混合框架在数据有限和噪音条件下提供可靠的RUL估计.
- 该方法显著改善了电池健康管理的早期预后.
- 这种方法提高了新能源汽车的运行安全性和效率.
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