停工期
计算机科学
集成学习
极限学习机
均方误差
机器学习
健康状况
人工智能
数据挖掘
功率(物理)
电池(电)
统计
人工神经网络
数学
量子力学
操作系统
物理
作者
Bin Gou,Yan Xu,Feng Xue
出处
期刊:IEEE Transactions on Transportation Electrification
日期:2021-06-01
卷期号:7 (2): 422-436
被引量:68
标识
DOI:10.1109/tte.2020.3029295
摘要
The state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is of great importance to the safety of systems. In this article, a novel ensemble learning method is proposed to accurately estimate the SOH of LIBs. A feature defined as the duration of the same charging voltage range (DSCVR) is extracted as the key health indicator for the LIB. The Pearson correlation analysis is performed to select four optimal indicators that are used as inputs of the prediction model. A random learning algorithm named extreme learning machine (ELM) is applied to extract the mapping knowledge relationship between the health indicators and the SOH due to its fast learning speed and efficient tuning mechanism. Moreover, an ensemble learning structure is proposed to reduce the prediction error of the single ELM models. A reliable decision-making rule is then designed to evaluate the credibility of the output of each single ELM model and remove the unreliable outputs, thereby significantly improving the accuracy and reliability of the estimation results. The testing results on two public data sets show that the proposed method can accurately estimate the SOH in 1 ms and is robust to the operating temperature and load profile. The average root-mean-square error (RMSE) is as low as 0.78%. The proposed method does not require any additional hardware or downtime of the system, which makes the method suitable for online practical applications.
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