A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

2015 ◽  
Vol 45 (6) ◽  
pp. 915-928 ◽  
Author(s):  
Datong Liu ◽  
Jianbao Zhou ◽  
Haitao Liao ◽  
Yu Peng ◽  
Xiyuan Peng
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Health Indicator ◽  
Optimization Framework ◽  
Degradation Modeling ◽  
Battery Degradation

scholarly journals An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD

2020 ◽  
Vol 38 (4) ◽  
pp. 814-821
Author(s):  
Zhuqing Wang ◽  
Yangming Guo ◽  
Cong Xu
Keyword(s):  
Lithium Ion Battery ◽  
Present Method ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Variational Mode Decomposition ◽  
Health Indicator ◽  
Mode Decomposition ◽  
Battery Degradation ◽  
Useful Life

The signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variational mode decomposition(SAE-VMD) based HI construction framework is proposed. Firstly, the stacked auto encoder(SAE) is used to reduce the noises of battery parameters and lower the data dimensionality and construct a syncretic HI that contains the battery degradation characters. Then the variational mode decomposition(VMD) is employed for effectively separating the syncretic HI into three modalities: the global attenuation, the local regeneration and the noises. The three modalities are selected as HIs to eliminate the HI noises and improve the RUL prediction precision. The RUL prediction results of lithium-ion battery indicate that the HI extracted by using the present method can obtain a better RUL prediction precision and verify the high quality of the extracted HI.

Matrix-matched standards for the quantification of elemental lithium ion battery degradation products deposited on carbonaceous negative electrodes using pulsed-glow discharge-sector field-mass spectrometry

2017 ◽  
Vol 32 (10) ◽  
pp. 1862-1867 ◽  
Author(s):  
Marco Evertz ◽  
Timo Schwieters ◽  
Markus Börner ◽  
Martin Winter ◽  
Sascha Nowak
Keyword(s):  
Mass Spectrometry ◽  
Glow Discharge ◽  
Lithium Ion Battery ◽  
Degradation Products ◽  
Lithium Ion ◽  
Field Mass ◽  
Pulsed Glow Discharge ◽  
Negative Electrodes ◽  
Battery Degradation ◽  
Sector Field Mass Spectrometry

A glow discharge-sector field-mass spectrometry (GD-SF-MS) method using matrix-matched self-prepared carbonaceous standards for elemental battery degradation products of (NCM111) electrodes was developed.

Fast Simulations of Lithium-Ion Battery Degradation

2021 ◽  
Vol MA2021-02 (1) ◽  
pp. 179-179
Author(s):  
Valentin Sulzer ◽  
Peyman Mohtat ◽  
Sravan Pannala ◽  
Jason Siegel ◽  
Anna Stefanopoulou
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Degradation

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

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 830 ◽  
Author(s):  
Zhengyu Liu ◽  
Jingjie Zhao ◽  
Hao Wang ◽  
Chao Yang
Keyword(s):  
Support Vector Regression ◽  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Health Indicators ◽  
Support Vector ◽  
Health Indicator ◽  
De Algorithm ◽  
Key Factor ◽  
Charging And Discharging Processes

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.

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