Remaining Useful Life Indirect Prediction of Lithium-ion Batteries Based on Gaussian Mixture Regression

Meng-Wei, Min-Ye, Qiao-Wang, Gaoqi-Lian, Jiabo-Li
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引用次数: 1

Abstract

Remaining useful life (RUL) prediction of lithium-ion batteries is one of the key technologies on prognostics and health management. Highly accurate RUL prediction of lithium-ion batteries is a prerequisite to ensure the safety and reliability for electric vehicles. To describe the accurate RUL prediction, the RUL indirect prediction framework based on Gaussian mixture regression (GMR) is proposed. Firstly, the discharging voltage and current indirect health indicators are extracted, and grey relation analysis (GRA) is used to analyze the relation with capacity. Then, to improve the RUL prediction performance, GMR method is proposed for reducing the impact of external disturbances. Finally, the proposed method is compared with existing methods. The results show that the proposed method is superior to traditional methods.
基于高斯混合回归的锂离子电池剩余使用寿命间接预测
锂离子电池剩余使用寿命(RUL)预测是锂离子电池预测和健康管理的关键技术之一。锂离子电池RUL的高精度预测是保证电动汽车安全可靠运行的前提。为了描述准确的规则流预测,提出了基于高斯混合回归(GMR)的规则流间接预测框架。首先,提取放电电压和电流间接健康指标,利用灰色关联分析(GRA)分析其与容量的关系;然后,为了提高RUL的预测性能,提出了减少外部干扰影响的GMR方法。最后,将本文提出的方法与现有方法进行了比较。结果表明,该方法优于传统方法。
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