早期预测离子电池退化的产生预训练变压器的早期预测
Jincheng Hu1,2, Pengyu Fu1,2, Zhongbao Wei3
1School of Automotive Studies, Tongji University, Shanghai, China.
Nature communications
|December 5, 2025
概括
预测离子电池的早期退化现已成为 BatteryGPT.的可能. 这种人工智能模型使用初始充电数据准确预测电池的健康状况,从而实现更好的维护和回收.
科学领域:
- 电池技术 电池技术
- 人工智能的人工智能
- 预测性维护是指预测性维护.
背景情况:
- 早期检测离子电池 (LIB) 退化对于预测性维护和回收至关重要.
- 由于微妙的初始性能变化和非线性长期模式,难以准确预测早期的LIB退化.
研究的目的:
- 开发一个两阶段的方法,BatteryGPT,用于准确的早期LIB降解预测.
- 使用生成预训练式变压器 (GPT) 预测整个生命周期的充电数据和健康状况 (SOH) 估计器以与衰老特征相关联.
主要方法:
- 使用生成预训练变压器 (GPT) 来自动回归预测LIB充电数据.
- 使用健康状况 (SOH) 估计器将预测的充电数据与电池老化特征相关联.
- 在观察到显著容量退化之前验证BatteryGPT方法对早期充电数据的验证.
主要成果:
- 使用早期充电数据,BatteryGPT准确地预测了未来的LIB退化.
- 在电池寿命的前30%内实现了0.213%的根平均平方误差 (RMSE) 来预测SOH变化.
- 在电池寿命的前30%内,膝盖点的平均绝对百分比误差 (MAPE) 为2.30%,寿命末期 (EOL) 预测为1.18%.
- 证明了强大的早期预测性能,即使只有前5%的终身充电数据.
结论:
- 电池GPT为早期LIB降解预测提供了一个高度准确的解决方案.
- 该方法可以通过预测电池健康状况,在容量衰减明显之前,实现有效的预测性维护和回收策略.
- 这种人工智能驱动的方法在早期预测电池退化方面显著优于现有的方法.
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