多物理
稳健性(进化)
健康状况
非线性系统
计算机科学
离群值
冗余(工程)
特征提取
人工智能
工程类
数据挖掘
机器学习
可靠性工程
电池(电)
有限元法
物理
生物化学
基因
结构工程
功率(物理)
化学
量子力学
作者
Seho Son,Siheon Jeong,Eunji Kwak,Jun Hyeong Kim,Ki‐Yong Oh
出处
期刊:Energy
[Elsevier]
日期:2022-01-01
卷期号:238: 121712-121712
被引量:43
标识
DOI:10.1016/j.energy.2021.121712
摘要
This study proposes a highly reliable, robust, and accurate integrated framework to estimate the state-of-health (SOH) of lithium-ion batteries (LIBs), focusing on feature extraction and manipulation. This framework comprises three phases: feature extraction, feature manipulation, and SOH estimation. First, multiphysics features are extracted from mechanical and electrochemical evolutionary responses as distinct health indicators (HIs) to account for the multiphysics degradation mechanisms. Second, these features are manipulated to eliminate outliers and noises. This phase is especially effective for impedance HIs, considering the high sensitivity of these HIs to minor environmental perturbations. Third, a multivariate Gaussian distribution theory estimates the SOH combined with a nonlinear quadratic kernel to account for nonlinear characteristics in degradation modes of LIBs. The estimated results under various environments verify that the multiphysics feature primarily increases accuracy, whereas the feature manipulation ensures reliability and robustness. However, both phases are complementary in securing the accuracy, reliability, and robustness of the framework. Although the lifespan of LIBs is estimated using the training set in the 5 % SOH range, the estimation errors of the proposed framework are less than 2.5 % in all test sets. Thus, the proposed method ensures its potential applicability in practical implementations of onboard battery management systems.
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