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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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Citations
Cites
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
(2012) · Cited by 9,062
Ramsay, J. O. (2005) · Wiley eBooks · doi:10.1002/0471667196.ess3138 · Cited by 2,908
Degradation diagnostics for lithium ion cells
Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.011 · Cited by 1,945 · cited 3×
“Li-ion cells undergo three primary modes of degradation: loss of lithium inventory, loss of active positive electrode material, and loss of active negative electrode material [17].”
BackgroundMethodSelf-citationExtended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.032 · Cited by 1,927
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
Main aging mechanisms in Li ion batteries
Broussely, M., Biensan, P., Bonhomme, F., Blanchard, P., Herreyre, S., Nechev, K., & Staniewicz, R. J. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.03.172 · Cited by 1,162
Jung, S., Gwon, H., Hong, J., Park, K., Seo, D., Kim, H., Hyun, J., Yang, W.-Y., & Kang, K. (2013) · Advanced Energy Materials · doi:10.1002/aenm.201300787 · Cited by 1,143
Synthesize battery degradation modes via a diagnostic and prognostic model
Dubarry, M., Truchot, C., & Liaw, B. Y. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.07.016 · Cited by 1,065
Gaussian Processes for Machine Learning (GPML) Toolbox
Rasmussen, C. E., & Nickisch, H. (2010) · Max Planck Institute for Plasma Physics · doi:10.5555/1756006.1953029 · Cited by 914
Prognostics methods for battery health monitoring using a bayesian framework
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.2005965 · Cited by 831
A review on prognostics and health monitoring of Li-ion battery
Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792
Gaussian process regression for forecasting battery state of health
Richardson, R. R., Osborne, M. A., & Howey, D. A. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.004 · Cited by 732
Self-citationAlgorithms for Advanced Battery-Management Systems
Chaturvedi, N. A., Klein, R., Christensen, J., Ahmed, J., & Kojić, A. (2010) · IEEE Control Systems · doi:10.1109/mcs.2010.936293 · Cited by 595
Weng, C., Cui, Y., Sun, J., & Peng, H. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.02.012 · Cited by 561
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.004 · Cited by 547
Farmann, A., Waag, W., Marongiu, A., & Sauer, D. U. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.01.129 · Cited by 472
Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443
Shen, J., Düşmez, S., & Khaligh, A. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2334233 · Cited by 441
Weng, C., Feng, X., Sun, J., & Peng, H. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.07.126 · Cited by 324
Sbordone, D., Bertini, I., Di Pietra, B., Falvo, M. C., Genovese, A., & Martirano, L. (2015) · Electric Power Systems Research · doi:10.1016/j.epsr.2014.07.033 · Cited by 278
Diagnosis of Electric Vehicle Batteries Using Recurrent Neural Networks
You, G.-W., Park, S., & Oh, D. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2674593 · Cited by 261
Battery state of health estimation: a structured review of models, methods and commercial devices
Ungurean, L., Carstoiu, G. N., Micea, M. V., & Groza, V. F. (2016) · International Journal of Energy Research · doi:10.1002/er.3598 · Cited by 260
Bole, B. M., Kulkarni, C. S., & Daigle, M. J. (2014) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2014.v6i1.2490 · Cited by 251
Model based identification of aging parameters in lithium ion batteries
Prasad, G. K., & Rahn, C. D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.01.041 · Cited by 234
Waag, W., & Sauer, D. U. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.05.001 · Cited by 216
Berecibar, M., Garmendia, M., Gandiaga, I., Crego, J., & Villarreal, I. (2016) · Energy · doi:10.1016/j.energy.2016.02.163 · Cited by 213
Online state of health estimation on NMC cells based on predictive analytics
Berecibar, M., Devriendt, F., Dubarry, M., Villarreal, I., Omar, N., Verbeke, W., & Van Mierlo, J. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.109 · Cited by 202
Oxford Battery Degradation Dataset 1
Birkl, C. (2017) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/bodleian:ko2kdmygg · Cited by 195
Self-citationSensorless Battery Internal Temperature Estimation Using a Kalman Filter With Impedance Measurement
Richardson, R. R., & Howey, D. A. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2015.2420375 · Cited by 193
Self-citationLewerenz, M., Marongiu, A., Warnecke, A., & Sauer, D. U. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.09.059 · Cited by 186
Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model
Bizeray, A. M., Kim, J.-H., Duncan, S., & Howey, D. A. (2018) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2018.2838097 · Cited by 168
Self-citationOptimal Battery Sizing in Microgrids Using Probabilistic Unit Commitment
Khorramdel, H., Aghaei, J., Khorramdel, B., & Siano, P. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2509424 · Cited by 158
Merla, Y., Wu, B., Yufit, V., Brandon, N. P., Martinez-Botas, R. F., & Offer, G. J. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.12.122 · Cited by 156
Differential thermal voltammetry for tracking of degradation in lithium-ion batteries
Wu, B., Yufit, V., Merla, Y., Martinez-Botas, R. F., Brandon, N. P., & Offer, G. J. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.09.127 · Cited by 151
MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts
Swanson, D. A., Tayman, J., & Bryan, T. M. (2011) · Journal of Population Research · doi:10.1007/s12546-011-9054-5 · Cited by 135
Hu, X., Xiong, R., & Egardt, B. S. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2284713 · Cited by 128
Learning Scalable Deep Kernels with Recurrent Structure
Al-Shedivat, M., Wilson, A. G., Saatchi, Y., Hu, Z., & Xing, E. P. (2016) · PubMed · doi:10.48550/arxiv.1610.08936 · Cited by 79
Tracking degradation in lithium iron phosphate batteries using differential thermal voltammetry
Shibagaki, T., Merla, Y., & Offer, G. J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.011 · Cited by 74
Learning Scalable Deep Kernels with Recurrent\nStructure
Al-Shedivat, M., Wilson, A. G., Saatchi, Y., Hu, Z., & Xing, E. P. (2017) · Europe PMC (PubMed Central) · Cited by 64
The Artemis European Tools for Estimating the Transport Pollutant Emissions
André, M., Keller, M., Sjödin, Å., Gadrat, M., Ian, M. C., & a., D. P. (2009) · Joint Research Centre (European Commission) · Cited by 28
Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves
Berecibar, M., Dubarry, M., Villarreal, I., Omar, N., & Mierlo, J. V. (2016) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc.2016.7791648 · Cited by 17
Research works printed but not linked: 3
- grewal, 2011, Kalman Filtering
- bole, 2014, Randomized battery usage data set, NASA Ames Prognostics Data Repository
- moura, 0, PDE estimation techniques for advanced battery management systems, Proc Amer Control Conf, 566
Cited by
Sorted by citations
Data-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
Li, Y., Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109254 · Cited by 1,193
Zhang, Y., Tang, Q., Zhang, Y., Wang, J., Stimming, U., & Lee, A. A. (2020) · Nature Communications · doi:10.1038/s41467-020-15235-7 · Cited by 889
“We use a zero mean function and a linear (LIN) covariance function Although GPR has been used in the literature in the context of Li-ion batteries 17,33,34 , we depart from those pioneering works by employing impedance spectra as input, as well employing ARD to shed light on salient frequencies.”
MethodState estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
Liu, K., Shang, Y., Ouyang, Q., & Widanage, W. D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2973876 · Cited by 838
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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