Battery capacity fading estimation using a force-based incremental capacity analysis
Open accessAbstract
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 on measured force (ICF) instead of voltage (ICV). The force is measured 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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A linear relationship was found between battery capacity fade and increase of peak voltage in both ICF and IC curves.
“A linear relationship was found between the battery capacity fade and the increase of peak voltage in both ICF and IC curves as shown in Fig. 4.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [74]
In “Incremental capacity analysis for bulk capacity estimation” (page not supplied), the paper reports a linear relationship between capacity fade and increasing ICF peak voltage. It also says ICF and ICV peak voltages shift linearly with cycle count, but does not establish the claimed linear relationship between capacity fade and ICV peak voltage. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIdentified ICF peaks are at higher SOC level (around 70%) than peaks in IC curves (around 40%), indicating ICF-based SOH monitoring could be updated more frequently during regular use of EV/HEV where SOC does not usually fall below 50%.
“The identified ICF peaks are at a higher SOC level (around 70%) than the peaks in IC curves (around 40%) and indicate that an ICF-based SOH monitoring method could be updated more frequently during regular use of an EV or HEV where the SOC does not usually fall below 50%.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [74]
The Abstract and Conclusions report ICF peaks around 70% SOC versus ICV peaks around 40% for NMC cells, and explain why monitoring could be updated more often when SOC stays above 50%. The paper applies this use-case to EVs and plug-in hybrids (PHEVs), not hybrid electric vehicles (HEVs) generally. (LLM, conf=0.87)
Experimental — accuracy not yet measuredICF data processing is easier than IC analysis because ICF has better signal-to-noise ratio than DV curves as amplitude of force signal is much bigger than voltage signal.
“They claimed that the data processing of the ICF curve was easier than IC analysis, as ICF has a better signal to noise ratio than DV curves as the amplitude of the force signal is much bigger than that of the voltage signal.”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [74]
Samad et al. measured stress with force sensor on end plates of battery pack and developed method for battery capacity estimation using IC curves based on measured force (ICF, dQ/dF).
“Samad et al. [74] measured it with a force sensor on the end plates of a battery pack, and developed a method for battery capacity estimation by using IC curves based on measured force (ICF, dQ/dF).”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [74]
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- Authors
- Samad, N. A., Kim, Y., Siegel, J. B., & Stefanopoulou, A. G.
- Affiliations
- University of Michigan (US)
- Southwest Research Institute (US)
- Venue
- Journal of The Electrochemical Society
- Publisher
- The Electrochemical Society
- Published
- 2016
- Volume
- 163
- Issue
- 8
- Pages
- A1584-A1594
- ISSN
- 0013-4651 (ISSN-L)
- 1945-7111
- OpenAlex
- W2405206984
- Cited by
- 130
- References
- 37
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 18 · 2023: 19 · 2022: 6 · 2021: 17 · 2020: 15 · 2019: 11 · 2018: 5 · 2017: 5 · 2016: 2
- FWCI
- 6.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of The Electrochemical Society (publishedVersion, cc-by-nc-nd)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DEAR0000269)
- Ford Motor Company
- Advanced Research Projects Agency - Energy (DE-AR0000269)
- Advanced Research Projects Agency (DE-AR0000269)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Keywords
- incremental capacity analysis, mechanical cues, NMC cells, prismatic cells, state of charge, electric vehicles, plug-in hybrid electric vehicles
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