A Lithium-Ion Battery Remaining Using Life Prediction Method Based on Multi-kernel Relevance Vector Machine Optimized Model

doi:10.3969/j.issn.0372-2112.2019.06.015

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A Lithium-Ion Battery Remaining Using Life Prediction Method Based on Multi-kernel Relevance Vector Machine Optimized Model

更新时间：2025-07-02

- A Lithium-Ion Battery Remaining Using Life Prediction Method Based on Multi-kernel Relevance Vector Machine Optimized Model
- Acta Electronica Sinica Vol. 47, Issue 6, Pages: 1285-1292(2019)
- 作者机构：
  
  1. 内蒙古科技大学信息工程学院,内蒙古,包头,014010
  2. 哈尔滨工业大学自动化测试与控制研究所,黑龙江,哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.06.015
  CLC： TP206+.3
- Published：2019
- 稿件说明：
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A Lithium-Ion Battery Remaining Using Life Prediction Method Based on Multi-kernel Relevance Vector Machine Optimized Model[J]. Acta Electronica Sinica, 2019, 47(6): 1285-1292.
DOI：

A Lithium-Ion Battery Remaining Using Life Prediction Method Based on Multi-kernel Relevance Vector Machine Optimized Model[J]. Acta Electronica Sinica, 2019, 47(6): 1285-1292. DOI： 10.3969/j.issn.0372-2112.2019.06.015.

摘要

基于相关向量机的剩余寿命预测方法，核函数是影响相关向量机模型预测性能的重要因素.目前的相关向量机预测模型以单核为主，且核函数的选择存在较大主观性，导致所构建的预测模型性能有限.本文提出一种融合多个核函数构建相关向量机预测模型的方法，通过果蝇算法优化多个核函数优化组合的线性方程系数，提高了模型的预测性能，并将该方法应用于预测锂离子电池的循环剩余寿命.分别采用美国NASA和马里兰大学的电池退化数据集，对本文的方法进行了实验验证.实验结果表明：多核相关向量机预测方法的平均绝对误差和均方根误差都小于最优的单核相关向量机预测方法.

Abstract

For the remaining useful life (RUL) prediction method based on relevance vector machine (RVM)

kernel function is the important item of RVM model for the final prediction result.The current RVM prediction models are dominated by single kernel

and the selection of RVM kernel is a little bit subjective.So

the prediction performance of the constructed RVM model is limited.To address this problem

a multi kernel RVM model is proposed for the RUL estimation

using the fruit fly optimization algorithm (FOA) to find the best corresponding coefficients of multi kernel in the linear combination of multi kernel functions

and to improve the prediction performance of RVM model applied in the RUL estimation of lithium-ion battery.The battery test data sets of the national aeronautics and space administration (NASA) and the center of advanced life cycle engineering (CACLE) in the university of Maryland are used respectively.Experiments have been carried out to test the performance of the proposed method.The results show that the mean absolute error (MAE) and root mean square error (RMSE) of multi kernel RVM method are both less than the single kernel RVM algorithm.

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