NDLI: An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation

Content Provider	IEEE Xplore Digital Library
Author	Datong Liu Wei Xie Haitao Liao Yu Peng
Copyright Year	1963
Abstract	Estimating lithium-ion battery remaining useful life (RUL) is a key issue in an intelligent battery management system. This paper presents an integrated prognostic approach that unifies two types of health indices (HIs), battery capacity and time interval of equal discharging voltage difference series, to perform direct and indirect RUL estimation for lithium-ion battery. To satisfy different practical requirements, a data-driven monotonic echo state networks (MONESNs) algorithm is adopted to track the nonlinear patterns of battery degradation. The main contributions of this paper are: 1) to enhance the predictive capability of each HI and identify its failure threshold by implementing an HI correlation model and cycle life threshold transformation and 2) to increase the computational stability of the proposed approach through the ensemble of MONESN submodels that can also describe the prognostic uncertainty. Essentially, this approach constitutes a probabilistic integration and data-driven prognostic framework with uncertainty management capability. Two sets of industrial lithium-ion battery data are used to show the capability of the proposed approach. It is expected that this approach can be broadly applied to other application areas, where data-driven prognostic approaches are needed.
Sponsorship	IEEE Instrumentation and Measurement Society Bureau international des poids et mesures IEEE International Bureau of Weights and Measures (BIPM) URSI NBS Agency Ind. Sci. & Technol., Min. Int. Trade & Ind. Sci. Council Japan International Union of Pure and Applied Physics (IUPAP) NIST NRCC National Conference of Standards Laboratories (NCSL) Allen Osborne Associates Andeen-Hagerling, Inc. Ballantine Lab. Inc. Clarke-Hess Communications Research Corp. Fluke Corporation Guildline Instruments Hewlett-Packard Co. Julie Res. Lab. Inc. Keithley Instruments, Inc. Measurements International Ltd. Canada QuadTech Inc. Quantum Design Inc. Rotek Instrum. Corp. Tegam Inc. Tektronix Inc
Starting Page	660
Ending Page	670
Page Count	11
File Size	2654918
File Format	PDF
ISSN	00189456
Volume Number	64
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Batteries Estimation Uncertainty Probabilistic logic Prediction algorithms Correlation Degradation remaining useful life (RUL) Ensemble learning (EL) lithium-ion battery maximum likelihood estimation (MLE) prognostic uncertainty
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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