Integrating Physics-Based Modeling and Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Physical model-based SOH estimation methods require precise battery parameters and complex experimental equipment, making them difficult to apply broadly.
“SOH estimation methods based on physical models typically require precise battery parameters and complex experimental measurement equipment, making them difficult to apply broadly in large-scale and diverse application scenarios.28,29”
From Battery state of health estimation under fast charging via deep transfer learning (2025) · cites this paper as [28]
Cites
Electrical energy storage for the grid: A battery of choices
Dunn, B., Kamath, H., & Tarascon, J.-M. (2011) · Science · doi:10.1126/science.1212741 · Cited by 15,072 · see citing works
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285 · see citing works
A review on the key issues for lithium-ion battery management in electric vehicles
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837 · see citing works
Ageing mechanisms in lithium-ion batteries
Vetter, J., Novák, P., Wagner, M. R., Veit, C., Möller, K.-C., Besenhard, J. O., Winter, M., Wohlfahrt-Mehrens, M., Vogler, C., & Hammouche, A. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2005.01.006 · Cited by 3,987 · cited 1× · see citing works
“The continuous expansion and contraction may induce cracks in the material and could, in some cases, cause local delamination [27], [50].”
BackgroundThe Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L., & Wang, J. (2017) · arXiv · doi:10.48550/arxiv.1712.04621 · Cited by 2,499 · see citing works
A comparative study of equivalent circuit models for Li-ion batteries
Hu, X., Li, S., & Peng, H. (2012) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.10.013 · Cited by 2,179 · see citing works
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× · see citing works
“Birkl et al. proposed a diagnostic algorithm for estimating the degradation modes (LLI, LAM PE , and LAM NE ) of a battery cell [27].”
BackgroundRapidly falling costs of battery packs for electric vehicles
Nykvist, B., & Nilsson, M. (2015) · Nature Climate Change · doi:10.1038/nclimate2564 · Cited by 1,819 · see citing works
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 · see citing works
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758 · see citing works
Commercialization of Lithium Battery Technologies for Electric Vehicles
Zeng, X., Li, M., El‐Hady, D. A., Alshitari, W. H., Al‐Bogami, A. S., Lu, J. B., & Amine, K. (2019) · Advanced Energy Materials · doi:10.1002/aenm.201900161 · Cited by 1,546 · see citing works
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 · see citing works
van Dyk, D. A., & Meng, X.-L. (2001) · Journal of Computational and Graphical Statistics · doi:10.1198/10618600152418584 · Cited by 1,052 · see citing works
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 · see citing works
Han, X., Ouyang, M., Lu, L., Li, J., Zheng, Y., & Li, Z. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.11.029 · Cited by 822 · cited 3× · see citing works
“Three degradation modes in a Li-ion battery cell are commonly reported in the literature: loss of lithium inventory (LLI) and loss of active materials in the positive and negative electrodes (LAM PE and LAM NE ), respectively [26] – [29].”
BackgroundMethodYang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.03.015 · Cited by 765 · see citing works
Machine learning pipeline for battery state-of-health estimation
Roman, D., Saxena, S., Robu, V., PECHT, M. G., & Flynn, D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00312-3 · Cited by 689 · see citing works
Differential voltage analyses of high-power, lithium-ion cells
Bloom, I., Jansen, A. N., Abraham, D. P., Knuth, J., Jones, S. A., Battaglia, V. S., & Henriksen, G. L. (2005) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.07.021 · Cited by 585 · see citing works
Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561 · see citing works
Self-citationAndre, D., Meiler, M., Steiner, K., Walz, H., Soczka‐Guth, T., & Sauer, D. U. (2010) · Journal of Power Sources · doi:10.1016/j.jpowsour.2010.07.071 · Cited by 546 · see citing works
Shen, S., Sadoughi, M., Li, M., Wang, Z., & Hu, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.114296 · Cited by 537 · see citing works
Li, Y., Abdel-Monem, M., Gopalakrishnan, R., Berecibar, M., Nanini-Maury, E., Omar, N., van den Bossche, P., & Van Mierlo, J. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.10.092 · Cited by 531 · see citing works
Richardson, R. R., Birkl, C. R., Osborne, M. A., & Howey, D. A. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2794997 · Cited by 407 · see citing works
A deep learning method for online capacity estimation of lithium-ion batteries
Shen, S., Sadoughi, M., Chen, X., Hong, M., & Hu, C. K. (2019) · Journal of Energy Storage · doi:10.1016/j.est.2019.100817 · Cited by 399 · see citing works
Self-citationLithium-ion battery data and where to find it
dos Reis, G., Strange, C., Yadav, M., & Li, S. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100081 · Cited by 377 · see citing works
Xiong, R., Li, L., Li, Z., Yu, Q., & Mu, H. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.03.053 · Cited by 326 · see citing works
Weng, C., Feng, X., Sun, J., & Peng, H. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.07.126 · Cited by 324 · see citing works
Remarks on multi-output Gaussian process regression
Liu, H., Cai, J., & Ong, Y.-S. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.12.034 · Cited by 295 · see citing works
Feng, T., Yang, L., Zhao, X., Zhang, H., & Qiang, J. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.01.154 · Cited by 285 · see citing works
Perspective—Combining Physics and Machine Learning to Predict Battery Lifetime
Aykol, M., Gopal, C. B., Anapolsky, A., Herring, P. K., van Vlijmen, B., Berliner, M. D., Bazant, M. Z., Braatz, R. D., Chueh, W. C., & Storey, B. D. (2021) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/abec55 · Cited by 267 · see citing works
Moura, S. J., Chaturvedi, N. A., & Krstić, M. (2013) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.4024801 · Cited by 262 · see citing works
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 · see citing works
Dahn, H. M., Smith, A. J., Burns, J. C., Stevens, D. A., & Dahn, J. R. (2012) · Journal of The Electrochemical Society · doi:10.1149/2.013209jes · Cited by 246 · see citing works
Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries
Tian, J., Xiong, R., Shen, W., & Sun, F. (2021) · Energy storage materials · doi:10.1016/j.ensm.2021.02.018 · Cited by 217 · see citing works
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 · see citing works
A capacity model based on charging process for state of health estimation of lithium ion batteries
Li, X., Jiang, J., Wang, L. Y., Chen, D., Zhang, Y., & Zhang, C. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.05.109 · Cited by 195 · see citing works
Online estimation of lithium-ion battery capacity using sparse Bayesian learning
Hu, C., Jain, G., Schmidt, C., Strief, C., & Sullivan, M. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.04.166 · Cited by 180 · see citing works
Self-citationState of health battery estimator enabling degradation diagnosis: Model and algorithm description
Dubarry, M., Berecibar, M., Devie, A., Anseán, D., Omar, N., & Villarreal, I. (2017) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.05.121 · Cited by 163 · see citing works
Lui, Y. H., Li, M., Downey, A. R. J., Shen, S., Nemani, V. P., Ye, H., VanElzen, C. M., Jain, G., Hu, S., Laflamme, S., et al. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.229327 · Cited by 143 · see citing works
Self-citationPhysics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds
Downey, A. R. J., Lui, Y. H., Hu, C. K., Laflamme, S., & Hu, S. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.09.018 · Cited by 141 · see citing works
Self-citationLong-Term Low-Rate Cycling of LiCoO2/Graphite Li-Ion Cells at 55°C
Smith, A. J., Dahn, H., Burns, J. C., & Dahn, J. R. (2012) · Journal of The Electrochemical Society · doi:10.1149/2.056206jes · Cited by 115 · see citing works
Remaining useful life assessment of lithium-ion batteries in implantable medical devices
Hu, C., Ye, H., Jain, G., & Schmidt, C. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2017.11.056 · Cited by 110 · see citing works
Self-citationRevisiting the t 0.5 Dependence of SEI Growth
Attia, P., Chueh, W., & Harris, S. J. (2020) · Journal of The Electrochemical Society · doi:10.1149/1945-7111/ab8ce4 · Cited by 100 · see citing works
State Analysis of Lithium-Ion Batteries Using Discharge Curves
Honkura, K., Honbo, H., Koishikawa, Y., & Horiba, T. (2008) · ECS Transactions · doi:10.1149/1.3018750 · Cited by 68 · see citing works
On-board capacity estimation of lithium iron phosphate batteries by means of half-cell curves
Marongiu, A., Nlandi, N., Rong, Y., & Sauer, D. U. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.05.041 · Cited by 66 · see citing works
Ultra High-Precision Studies of Degradation Mechanisms in Aged LiCoO2/Graphite Li-Ion Cells
Fathi, R., Burns, J. C., Stevens, D. A., Ye, H., Hu, C. K., Jain, G., Scott, E. R., Schmidt, C., & Dahn, J. R. (2014) · Journal of The Electrochemical Society · doi:10.1149/2.0321410jes · Cited by 65 · see citing works
Self-citationBraun, A., Hu, Y., Boudoire, F., et al. (2015) · Catalysis Today · doi:10.1016/j.cattod.2015.07.024 · Cited by 35 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
Wang, F., Zhai, Z., Zhao, Z., Di, Y., & Chen, X. (2024) · Nature Communications · doi:10.1038/s41467-024-48779-z · Cited by 897 · see citing works
Wang, Y., Guo, S., Cui, Y., Deng, L., Zhao, L., Li, J., & Wang, Z. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116125 · Cited by 107 · see citing works
Battery state of health estimation under fast charging via deep transfer learning
Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53 · see citing works
“28,29 In addition, physical models struggle to adapt to different types of batteries and operating conditions.”
Liu, X., Hu, Z., Mao, L., & Xie, M. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3500072 · Cited by 9 · see citing works
- Authors
- Thelen, A., Lui, Y. H., Shen, S., Laflamme, S., Hu, S., Ye, H., & Hu, C.
- Affiliations
- Iowa State University (US)
- Medtronic (United States) (US)
- Corresponding author
- Hu, Chao
- Venue
- Energy storage materials
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 50
- Pages
- 668-695
- ISSN
- 2405-8289 (ISSN-L)
- 2405-8297
- OpenAlex
- W4281695775
- Cited by
- 148
- References
- 47
- Citations by year
- 2026: 36 · 2025: 50 · 2024: 33 · 2023: 24 · 2022: 5
- FWCI
- 7.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 287 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Energy storage materials (acceptedVersion)
- Iowa State University Digital Repository (Iowa State University) (submittedVersion)
- Funders
- National Science Foundation (ECCS-1611333, ECCS-2015710)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- lithium-ion batteries, state of health estimation, capacity fade, physics-informed machine learning, second-life batteries, temperature effects on degradation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
