概化理论
计算机科学
学习迁移
电池(电)
特征(语言学)
样品(材料)
机器学习
样本量测定
限制
均方误差
预测建模
人工智能
电池容量
数据挖掘
功率(物理)
统计
工程类
物理
语言学
哲学
化学
色谱法
量子力学
机械工程
数学
作者
Xiaoming Lu,Xianbin Yang,Xinhong Wang,Yu Shi,Jing Wang,Yiwen Yao,Xuefeng Gao,Haicheng Xie,Siyan Chen
出处
期刊:Batteries
[Multidisciplinary Digital Publishing Institute]
日期:2025-02-07
卷期号:11 (2): 62-62
标识
DOI:10.3390/batteries11020062
摘要
The accurate prediction of lithium-ion battery capacity is crucial for the safe and efficient operation of battery systems. Although data-driven approaches have demonstrated effectiveness in lifetime prediction, the acquisition of lifecycle data for long-life lithium batteries remains a significant challenge, limiting prediction accuracy. Additionally, the varying degradation trends under different operating conditions further hinder the generalizability of existing methods. To address these challenges, we propose a Multi-feature Transfer Learning Framework (MF-TLF) for predicting battery capacity in small-sample scenarios across diverse operating conditions (different temperatures and C-rates). First, we introduce a multi-feature analysis method to extract comprehensive features that characterize battery aging. Second, we develop a transfer learning-based data-driven framework, which leverages pre-trained models trained on large datasets to achieve a strong prediction performance in data-scarce scenarios. Finally, the proposed method is validated using both experimental and open-access datasets. When trained on a small sample dataset, the predicted RMSE error consistently stays within 0.05 Ah. The experimental results highlight the effectiveness of MF-TLF in achieving high prediction accuracy, even with limited data.
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