The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve
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In the citing paper
“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 [34]
In the citing paper
“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 [34]
In the citing paper
“Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”
From Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019) · cites this paper as [80]
Citations
Cites
Gaussian Processes for Regression
Williams, C. K. I., & Rasmussen, C. E. (1995) · Aston Publications Explorer (Aston University) · Cited by 1,121
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
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
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.11.146 · Cited by 664
Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561
Weng, C., Cui, Y., Sun, J., & Peng, H. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.02.012 · Cited by 561
Wu, J., Zhang, C., & Chen, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.04.057 · Cited by 531
Self-citationTosun, N. (2005) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-004-2386-y · Cited by 471
Eddahech, A., Briat, O., Bertrand, N., Delétage, J.-Y., & Vinassa, J.-M. (2012) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2012.04.050 · Cited by 462
Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.03.010 · Cited by 443
Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries
Andre, D., Appel, C., Soczka-Guth, T., & Sauer, D. U. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.001 · Cited by 428
Gaussian Processes for Ordinal Regression
Chu, W., & Ghahramani, Z. (2005) · Cambridge University Engineering Department Publications Database · Cited by 370
A naive Bayes model for robust remaining useful life prediction of lithium-ion battery
Ng, S. S. Y., Xing, Y., & Tsui, K. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.12.020 · Cited by 340
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Zhou, Y., Huang, M., Chen, Y., & Tao, Y. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.119 · Cited by 289
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
A novel state of health estimation method of Li-ion battery using group method of data handling
Wu, J., Wang, Y., Zhang, X., & Chen, Z. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.07.065 · Cited by 235
Self-citationOlivares, B. E., Cerda, M. A., Orchard, M. E., & Silva, J. F. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2215142 · Cited by 217
Wei, Z., Tseng, K. J., Wai, N., Lim, T. M., & Skyllas‐Kazacos, M. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.09.123 · Cited by 214
Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques
Saha, B., & Goebel, K. (2008) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2008.4526631 · Cited by 201
A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries
Wang, Y., Zhang, C., & Chen, Z. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.081 · Cited by 200
Self-citationGaussian Processes for Ordinal Regression
ChuWei, & GhahramaniZoubin (2005) · Journal of Machine Learning Research · doi:10.5555/1046920.1088707 · Cited by 190
Gaussian process regression for multivariate spectroscopic calibration
Chen, T., Morris, J., & Martin, E. (2007) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2006.09.004 · Cited by 175
A Review of SOH Estimation Methods in Lithium-ion Batteries for Electric Vehicle Applications
Cheng, L., Tang, A., & Wang, W. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.199 · Cited by 174
Physically-based reduced-order capacity loss model for graphite anodes in Li-ion battery cells
Jin, X., Vora, A. P., Hoshing, V., Saha, T., Shaver, G. M., Garcı́a, R. E., Wasynczuk, O., & Varigonda, S. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.12.099 · Cited by 171
Method for estimating capacity and predicting remaining useful life of lithium-ion battery
Hu, C. K., Jain, G., Tamirisa, P. A., & Gorka, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.03.086 · Cited by 162
Xian, W., Long, B., Li, M., & Wang, H. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2276473 · Cited by 144
Zhang, X., Wang, Y., Yang, D., & Chen, Z. (2016) · Energy · doi:10.1016/j.energy.2016.08.109 · Cited by 143
Self-citationDu, J., Liu, Z., Wang, Y., & Wen, C. (2016) · Control Engineering Practice · doi:10.1016/j.conengprac.2016.05.014 · Cited by 132
Self-citationProbability based remaining capacity estimation using data-driven and neural network model
Wang, Y., Yang, D., Zhang, X., & Chen, Z. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.03.054 · Cited by 120
Self-citationTorai, S., Nakagomi, M., Yoshitake, S., Yamaguchi, S., & Oyama, N. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.11.070 · Cited by 104
Cheng, Y., Lü, C., Li, T., & Tao, L. (2015) · Energy · doi:10.1016/j.energy.2015.07.022 · Cited by 75
Sun, P., & Zhenpo, W. (2016) · Energy Procedia · doi:10.1016/j.egypro.2016.06.086 · Cited by 17
Flagged first, then by citations
Research works printed but not linked: 1
- Cheng, 2015, Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach, Energy, 90, 1983, 10.1016/j.energy.2015.07.022 · DOI
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Sorted by citations
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
“Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”
BackgroundZhang, 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.”
MethodLiu, K., Shang, Y., Ouyang, Q., & Widanage, W. D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2973876 · Cited by 838
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
Zhu, J., Wang, Y., Huang, Y., Gopaluni, R. B., Cao, Y., Heere, M., Mühlbauer, M. J., Mereacre, L., Dai, H., Liu, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29837-w · Cited by 835
Self-citationRandom forest regression for online capacity estimation of lithium-ion batteries
Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C.-W., van den Bossche, P., Van Mierlo, J., & Omar, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.09.182 · Cited by 727
Metadata
- Authors
- Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Chen, Zonghai
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 384
- Pages
- 387-395
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W2793702125
- Cited by
- 765
- References
- 33
- Citations by year
- 2026: 65 · 2025: 101 · 2024: 124 · 2023: 144 · 2022: 116 · 2021: 98 · 2020: 71 · 2019: 35 · 2018: 11
- FWCI
- 28.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Power Sources (publishedVersion)
- Funders
- National Natural Science Fund of China (61375079)
- CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellow (2017LH007)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- state of health estimation, lithium-ion batteries, Gaussian process regression, charging curves, battery degradation, grey relational analysis
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