The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.
Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries
Open accessAbstract
Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage versus time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian process regression for in situ capacity estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as incremental capacity (IC) or differential voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process that amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells, respectively. In each case, within certain voltage ranges, as little as 10 s of galvanostatic operation enables capacity estimates with approximately 2%-3% root-mean-squared error (RMSE).
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“Features derived from the charging and discharging curve are by far the most commonly used inputs because typical battery management systems collect current–voltage data13–18.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [17]
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“However, those models are all developed with the charging and discharging curve as input.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [17]
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“Although GPR has been used in the literature in the context of Li-ion batteries17,33,34, we depart from those pioneering works by employing impedance spectra as input, as well employing ARD to shed light on salient frequencies.”
From Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning (2020) · cites this paper as [17]
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Cites
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Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
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Main aging mechanisms in Li ion batteries
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Cited by
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Items 401–407 of 407
Memon, A., Gulzar, M. M., Habib, S., Maaruf, M., & Shafiullah, M. (2026) · Energy Strategy Reviews · doi:10.1016/j.esr.2026.102359
Çoban, H., Erhan, K., & Baysal, M. (2026) · International Journal of Automotive Science And Technology · doi:10.30939/ijastech..1995891
The Role of Open-Circuit Voltage Characterization in Model-Based Battery Capacity Estimation
Sunil, S., Sundaresan, S., Pattipati, K. R., & Balasingam, B. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3738116
Liu, B., Zhen, W., & Huang, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7610919
Teng, L., Li, W., & Li, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7611886
Teng, L., Li, W., & Li, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7641880
Sang, J., Hong, Z., Yi, Z., Xu, W., Lin, S., Wan, X., Xu, W., & Zhao, H. (2026) · World Electric Vehicle Journal · doi:10.3390/wevj17100548
Metadata
- Authors
- Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A.
- Affiliations
- University of Oxford (GB)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-01
- Volume
- 15
- Issue
- 1
- Pages
- 127-138
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2772728162
- Cited by
- 407
- References
- 45
- Citations by year
- 2026: 46 · 2025: 59 · 2024: 58 · 2023: 62 · 2022: 70 · 2021: 61 · 2020: 29 · 2019: 18
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/K002252/1)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- lithium-ion batteries, battery capacity estimation, Gaussian process regression, galvanostatic operation, battery diagnostics
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