Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features
Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features
复制标题
DOI：
10.3390/en12224366
复制
发表时间：
2019-11-02
期刊：
影响因子：
3.2
通讯作者：
Kim, Hee-Je
中科院分区：
文献类型：
--
作者：
Ali, Muhammad Umair;Zafar, Amad;Kim, Hee-Je
在线准确估计锂离子电池的剩余使用寿命（RUL）是任何智能电池管理系统（BMS）的必要功能。本文提出了一种基于局部放电数据的支持向量机（SVM）预测模型。该算法从PDD的电压和温度中提取关键特征来训练SVM模型。利用支持向量机的分类和回归属性对规则语言进行分类和预测。分析不同的PDD范围，以找到训练SVM模型的最佳范围。使用最优PDD特征训练的SVM模型将RUL分为六类进行粗略估计，并使用支持向量回归来估计最后一类的准确值。分类和使用全放电数据和PDD训练的SVM模型的预测性能进行了比较，公开可用的数据。结果表明，使用PDD特征训练的SVM分类回归模型可以准确预测BMS低储存压力下的RUL。基于PDD的SVM模型可用于电动汽车的在线RUL估计。
Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.