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Research Papers: Energy Storage/Systems

Estimation of Remaining Useful Lifetime of Lithium-Ion Battery Based on Acoustic Emission Measurements

[+] Author and Article Information
Nejra Beganovic

Chair of Dynamics and Control,
University of Duisburg-Essen,
Lotharstraße 1-21,
Duisburg 47057, Germany
email: nejra.beganovic@uni-due.de

Dirk Söffker

Chair of Dynamics and Control,
University of Duisburg-Essen,
Lotharstraße 1-21,
Duisburg 47057, Germany
e-mail: soeffker@uni-due.de

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 28, 2018; final manuscript received November 27, 2018; published online January 9, 2019. Assoc. Editor: Esmail M. A. Mokheimer.

J. Energy Resour. Technol 141(4), 041901 (Jan 09, 2019) (10 pages) Paper No: JERT-18-1388; doi: 10.1115/1.4042234 History: Received May 28, 2018; Revised November 27, 2018

Lithium-ion battery (LIB) utilization as energy storage device in electric and hybrid-electric vehicles, wind turbine systems, a number of portable electrical devices, and in many other application fields is encouraged due to LIB small size alongside high energy density. Monitoring of LIB health state parameters, calculation of additional LIB operating parameters, and the fulfillment of safety requirements are provided through battery management systems. Prediction of remaining useful lifetime (RUL) of LIB and state-of-health (SoH) estimation are identified as still challenging and not completely solved tasks. In this contribution, previous works on RUL/SoH estimation, mainly relied on modeling of underlying electrochemical processes inside LIB, are compared with newly developed approach. The proposed approach utilizes acoustic emission measurements for LIB aging indicators estimation. Developed model for RUL estimation is closely related to frequency spectrum analysis of captured acoustic emission (AE) signal. Features selected from AE measurements are considered as model inputs. The novelty of this approach is the opportunity to estimate RUL/SoH of LIB without necessity to capture some intermediate variables, only indirectly related to RUL/SoH (charging/discharging currents, temperature, and similar). The proposed approach provides the possibility to obtain reliable information about current RUL/SoH without the knowledge about underlying physical processes occurred in LIB. Experimental data sets gathered from LIB aging tests are used for model establishment, training, and validation. The experimental results demonstrate the applicability of the novel approach.

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Figures

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Fig. 1

Measurable and immeasurable variables and their relation to aging indicators

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Fig. 2

Calculation of battery capacity based on terminal voltage and discharging current measurements (here: B8)

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Fig. 3

Test bench facility for LIB examination, Chair of Dynamics and Control, SRS, University of Duisburg-Essen

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Fig. 4

Terminal voltage measurements and AE energy (here for test sample B8)

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Fig. 5

Continuous wavelet transformation of AE signal gathered from test samples B6, B7, B8, B9, B10, and B11

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Fig. 6

Feature selection based on calculated energy of AE signal

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Fig. 7

Consumed lifetime modeling of LIB concerning AE-based features

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Fig. 8

Absolute error between estimated and experimental data sets (B9 and B11)

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