Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

Samad, Nassim A.; Kim, Youngki; Siegel, Jason B.; Stefanopoulou, Anna G.

doi:10.1149/2.0511608jes

Title: Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

Journal Article · Fri May 27 00:00:00 EDT 2016 · Journal of the Electrochemical Society

DOI:https://doi.org/10.1149/2.0511608jes· OSTI ID:1437279

Samad, Nassim A. ^[1]; Kim, Youngki ^[2]; Siegel, Jason B. ^[1]; Stefanopoulou, Anna G. ^[1]

Univ. of Michigan, Ann Arbor, MI (United States). Department of Mechanical Engineering
Southwest Research Institute, Ann Arbor, MI (United States). Ann Arbor Technical Center

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based onmeasured force (ICF) instead of voltage (ICV). The force ismeasured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replace the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.

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Research Organization:: General Electric Co., Boston, MA (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0000269

OSTI ID:: 1437279

Journal Information:: Journal of the Electrochemical Society, Vol. 163, Issue 8; ISSN 0013-4651

Publisher:: The Electrochemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 55 works

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Battery State of Health Monitoring by Estimation of Side Reaction Current Density Via Retrospective-Cost Subsystem Identification Zhou, Xin; Bernstein, Dennis S.; Stein, Jeffrey L. Journal of Dynamic Systems, Measurement, and Control, Vol. 139, Issue 9 https://doi.org/10.1115/1.4036030	journal	June 2017
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Localized Swelling Inhomogeneity Detection in Lithium Ion Cells Using Multi-Dimensional Laser Scanning Zhao, Yan; Spingler, Franz B.; Patel, Yatish Journal of The Electrochemical Society, Vol. 166, Issue 2 https://doi.org/10.1149/2.0011902jes	journal	January 2019
Non-Destructive Detection of Local Aging in Lithium-Ion Pouch Cells by Multi-Directional Laser Scanning Sturm, Johannes; Spingler, F. B.; Rieger, B. Journal of The Electrochemical Society, Vol. 164, Issue 7 https://doi.org/10.1149/2.0161707jes	journal	January 2017

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
25 ENERGY STORAGE
Capacity estimation
Force
Incremental capacity analysis
State of health
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Title: Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

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