research-article

Lithium-ion battery diagnostics and prognostics enhanced with Dempster-Shafer decision fusion

Authors:

John Weddington,

Bin ZhangAuthors Info & Claims

Volume 458, Issue C

Pages 440 - 453

https://doi.org/10.1016/j.neucom.2021.06.057

Published: 11 October 2021 Publication History

Abstract

Prognostics is the discipline of predicting the remaining useful life of a component or system in order to optimize the maintenance planning or the mission execution. Prognostics-enabled systems likely reduce the overall life-cycle cost and increase the reliability. In the Bayesian estimation framework, the widely used techniques are the traditional Kalman filter, along with its non-linear extensions, and the particle filter. Each technique has different advantages. This paper investigates the fusion of prognostic results from different techniques in order to achieve a more trustworthy remaining useful life (RUL) prediction, as measured by a reduction in uncertainty. Models for extended Kalman filter (EKF) and particle filter (PF) are developed from the feature data. The results from EKF and PF are then fused using an application of Dempster-Shafer theory (DST). Separate models are utilized for EKF and PF in order to introduce multi-model prognostics and to optimize the performance of each technique for both diagnosis and RUL prediction. Prognostics is triggered when degradation is detected by diagnosis. DST is then applied to the prognostic results from EKF and PF. The result of DST is a density function whose performance can be compared with that of EKF and PF. DST allows for the fusion of multiple sensors and state estimates. The proposed method is verified with the prognosis of a set of lithium-ion batteries.

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Cited By

Acar EJain NRamu PHwang CLee I(2024)A survey on design optimization of battery electric vehicle components, systems, and managementStructural and Multidisciplinary Optimization10.1007/s00158-024-03737-767:3Online publication date: 24-Feb-2024
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Chen MMa GLiu WZeng NLuo X(2023)An overview of data-driven battery health estimation technology for battery management systemNeurocomputing10.1016/j.neucom.2023.02.031532:C(152-169)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.02.031
Yuan YSun BLiu G(2022)Dual attention and dual fusionNeurocomputing10.1016/j.neucom.2022.05.013500:C(965-977)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.05.013
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Lithium-ion battery diagnostics and prognostics enhanced with Dempster-Shafer decision fusion
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Published In

cover image Neurocomputing

Neurocomputing Volume 458, Issue C

Oct 2021

728 pages

ISSN:0925-2312

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 11 October 2021

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Cited By

Acar EJain NRamu PHwang CLee I(2024)A survey on design optimization of battery electric vehicle components, systems, and managementStructural and Multidisciplinary Optimization10.1007/s00158-024-03737-767:3Online publication date: 24-Feb-2024
https://dl.acm.org/doi/10.1007/s00158-024-03737-7
Chen MMa GLiu WZeng NLuo X(2023)An overview of data-driven battery health estimation technology for battery management systemNeurocomputing10.1016/j.neucom.2023.02.031532:C(152-169)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.neucom.2023.02.031
Yuan YSun BLiu G(2022)Dual attention and dual fusionNeurocomputing10.1016/j.neucom.2022.05.013500:C(965-977)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.05.013
Wang CXin CXu ZQin MHe M(2022)Mix-VAEsNeurocomputing10.1016/j.neucom.2022.04.044492:C(234-244)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.04.044

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