用弛豫时间分布分析电池阻抗，提升多种工况下健康状态估计精度。
Onboard Health Estimation using Distribution of Relaxation Times for Lithium-ion Batteries
- 通过弛豫时间分布技术将阻抗谱分解为多时间尺度特征。
- 在10个测试集上实现平均1.69%的根均方百分比误差。
- 适合需要高精度电池健康管理的车载系统应用。
实际电池常面临多种运行条件，因日历老化与循环老化共同作用而退化。现有车载健康状态（SOH）估计算法通常仅依赖循环老化数据，并最多考虑单一工况（如温度），限制了模型在复杂条件下的准确性。本文利用5组日历老化和17组循环老化电池的电化学阻抗谱（EIS）数据，在多种工况下进行SOH估计。通过弛豫时间分布（DRT）技术对EIS曲线进行解卷积，将其映射为表示电池内部不同电阻特性的函数g。该DRT函数作为输入，驱动基于长短期记忆（LSTM）的神经网络模型进行SOH估计。模型在10个不同测试集上验证，平均根均方百分比误差（RMSPE）为1.69%。
原文摘要 · Abstract (English)
Real-life batteries tend to experience a range of operating conditions, and undergo degradation due to a combination of both calendar and cycling aging. Onboard health estimation models typically use cycling aging data only, and account for at most one operating condition e.g., temperature, which can limit the accuracy of the models for state-of-health (SOH) estimation. In this paper, we utilize electrochemical impedance spectroscopy (EIS) data from 5 calendar-aged and 17 cycling-aged cells to perform SOH estimation under various operating conditions. The EIS curves are deconvoluted using the distribution of relaxation times (DRT) technique to map them onto a function $\textbf{g}$ which consists of distinct timescales representing different resistances inside the cell. These DRT curves, $\textbf{g}$, are then used as inputs to a long short-term memory (LSTM)-based neural network model for SOH estimation. We validate the model performance by testing it on ten different test sets, and achieve an average RMSPE of 1.69% across these sets.
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