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首页> 外文期刊>Industrial and organizational psychology >SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators
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SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators

机译：SOH基于高斯工艺回归与间接健康指标的高斯进程回归的SOH和RUL预测

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摘要

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.
机译：锂离子电池的健康状况（SOH）和剩余的使用寿命（RUL）是通常使用电池容量预测的两个重要因素。但是，难以直接测量在线应用的锂离子电池的容量。在本文中，从充电和放电过程中的电压，电流和温度的曲线中提取间接健康指标（IHI），这响应了电池容量劣化过程。选择几个合理的指示器作为灰色关系分析方法的SOH预测的输入。通过将高斯过程回归（GPR）方法与概率预测组合来执行短期SOH预测。然后，考虑到SOH和RUL之间存在一定的映射关系，利用三个IHIS和本SOH值来通过GPR模型预测锂离子电池的ruL。结果表明，该方法具有高预测精度。

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