融合KAN与LSTM，提升锂电池健康状态预测精度
SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring
- 用KAN捕捉电池退化非线性特征，LSTM建模时间依赖关系
- 相比传统方法，显著提高锂电池健康状态估计准确性
- 适合电动车、储能等对电池寿命敏感的场景
锂离子电池的健康状态（SOH）精确评估对电动汽车、无人机、消费电子及可再生能源存储系统的寿命、安全与性能至关重要。传统SOH估计方法难以有效表征电池退化的非线性与时间特性。本文提出一种新型SOH预测框架SOH-KLSTM，结合柯尔莫哥洛夫-阿诺德网络（KAN）与长短期记忆网络（LSTM），利用KAN的非线性逼近能力捕捉复杂退化行为，同时借助LSTM学习长期依赖关系，实现更精准的时间序列预测。
原文摘要 · Abstract (English)
Accurate and reliable State Of Health (SOH) estimation for Lithium (Li) batteries is critical to ensure the longevity, safety, and optimal performance of applications like electric vehicles, unmanned aerial vehicles, consumer electronics, and renewable energy storage systems. Conventional SOH estimation techniques fail to represent the non-linear and temporal aspects of battery degradation effectively. In this study, we propose a novel SOH prediction framework (SOH-KLSTM) using Kolmogorov-Arnold Network (KAN)-Integrated Candidate Cell State in LSTM for Li batteries Health Monitoring. This hybrid approach combines the ability of LSTM to learn long-term dependencies for accurate time series predictions with KAN's non-linear approximation capabilities to effectively capture complex degradation behaviors in Lithium batteries.
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