NDLI: A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

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A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Zhengyu Liu Jingjie Zhao Hao Wang Chao Yang
Abstract	An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.
e-ISSN	19961073
DOI	10.3390/en13040830
Journal	Energies
Issue Number	4
Volume Number	13
Language	English
Publisher	MDPI AG
Publisher Date	2020-02-01
Publisher Place	Switzerland
Access Restriction	Open
Subject Keyword	Technology Lithium-ion Battery State of Health Estimation Improved Support Vector Regression Differential Evolution
Content Type	Text
Resource Type	Article

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