用稀疏识别算法快速估算锂电池健康状态，提升精度与可解释性。
Fast State-of-Health Estimation Method for Lithium-ion Battery using Sparse Identification of Nonlinear Dynamics
- 基于SINDy算法挖掘电池退化非线性动力学方程
- 相比多种机器学习方法，估计误差更低且计算更快
- 适合需要实时监测与模型可解释性的电池管理系统
锂离子电池（LIBs）因高能量密度和长寿命被广泛应用于多个领域。然而在反复充放电过程中，电池性能退化导致最大输出功率和工作时间下降，不仅影响性能，还威胁系统安全。因此，实时准确估计电池健康状态（SOH）至关重要。本文提出一种基于稀疏非线性动力学识别（SINDy）的快速SOH估计算法。SINDy可在少量数据下发现系统主导演化方程，假设仅有少数函数对系统行为起决定作用。通过相关性分析确定退化状态模型，结合SINDy与相关性分析构建数据驱动的SOH模型，增强系统可解释性。为验证方法可行性，将该方法在SOH估计精度和计算时间上与多种机器学习算法对比，结果表明其具有更高效率与良好估计性能。
原文摘要 · Abstract (English)
Lithium-ion batteries (LIBs) are utilized as a major energy source in various fields because of their high energy density and long lifespan. During repeated charging and discharging, the degradation of LIBs, which reduces their maximum power output and operating time, is a pivotal issue. This degradation can affect not only battery performance but also safety of the system. Therefore, it is essential to accurately estimate the state-of-health (SOH) of the battery in real time. To address this problem, we propose a fast SOH estimation method that utilizes the sparse model identification algorithm (SINDy) for nonlinear dynamics. SINDy can discover the governing equations of target systems with low data assuming that few functions have the dominant characteristic of the system. To decide the state of degradation model, correlation analysis is suggested. Using SINDy and correlation analysis, we can obtain the data-driven SOH model to improve the interpretability of the system. To validate the feasibility of the proposed method, the estimation performance of the SOH and the computation time are evaluated by comparing it with various machine learning algorithms.
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