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Physics-Informed Machine Learning for Battery Degradation Diagnostics: A Comparison of State-of-the-Art Methods (2404.04429v1)

Published 5 Apr 2024 in cs.CE

Abstract: Monitoring the health of lithium-ion batteries' internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell's major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

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References (88)
  1. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241:680–689, 2013.
  2. Lithium-ion battery life model with electrode cracking and early-life break-in processes. Journal of The Electrochemical Society, 168(10):100530, 2021.
  3. Comprehensive modeling of temperature-dependent degradation mechanisms in lithium iron phosphate batteries. Journal of The Electrochemical Society, 165(2):A181, 2018.
  4. Machine-learning assisted identification of accurate battery lifetime models with uncertainty. Journal of The Electrochemical Society, 169(8):080518, 2022a.
  5. Gregory L Plett. Extended kalman filtering for battery management systems of lipb-based hev battery packs: Part 3. state and parameter estimation. Journal of Power sources, 134(2):277–292, 2004.
  6. A multiscale framework with extended kalman filter for lithium-ion battery soc and capacity estimation. Applied Energy, 92:694–704, 2012a.
  7. Prognostics methods for battery health monitoring using a bayesian framework. IEEE Trans. Instrum. Meas, 58(2):291–296,, 2009. doi: 10.1109/TIM.2008.2005965.
  8. Remaining useful life assessment of lithium-ion batteries in implantable medical devices. J. Power Sources, 2018. doi: 10.1010/j.jpowsour.2017.11.056.
  9. Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction. Journal of Power Sources, 281:192–203, 2015.
  10. Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. ii: Modelling. Journal of Power Sources, 196(12):5349–5356, 2011.
  11. A comparative study of equivalent circuit models for li-ion batteries. Journal of Power Sources, 198:359–367, 2012b.
  12. Adaptive partial differential equation observer for battery state-of-charge/state-of-health estimation via an electrochemical model. Journal of Dynamic Systems, Measurement, and Control, 136(1), 2014.
  13. An electrochemical model based degradation state identification method of lithium-ion battery for all-climate electric vehicles application. Applied energy, 219:264–275, 2018.
  14. Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use. In Annual Conference of the PHM Society, volume 6, 2014.
  15. Online state of health estimation on nmc cells based on predictive analytics. Journal of Power Sources, 320:239–250, 2016.
  16. Online estimation of lithium-ion battery capacity using sparse bayesian learning. J. Power Sources, 289:105–113,, 2015. doi: 10.1016/j.jpowsour.2015.04.166.
  17. A novel gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. Journal of Power Sources, 384:387–395, 2018.
  18. Gaussian process regression for in situ capacity estimation of lithium-ion batteries. IEEE Transactions on Industrial Informatics, 15(1):127–138, 2018.
  19. Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries. Applied Energy, 260:114296, 2020.
  20. A deep learning method for online capacity estimation of lithium-ion batteries. J. Energy Storage, 25:100817,, 2019.
  21. Machine learning pipeline for battery state-of-health estimation. Nat. Mach. Intell, 3(5):447–456,, 2021. doi: 10.1038/s42256-021-00312-3.
  22. A data-fusion framework for lithium battery health condition estimation based on differential thermal voltammetry. Energy, 239:122206, 2022.
  23. On the feature selection for battery state of health estimation based on charging–discharging profiles. Journal of Energy Storage, 33:102122, 2021a.
  24. Battery state-of-health estimation based on multiple charge and discharge features. Energy, 263:125637, 2023.
  25. Data-driven battery state of health estimation based on random partial charging data. IEEE Transactions on Power Electronics, 37(5):5021–5031, 2021.
  26. Jiachi Yao and Te Han. Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data. Energy, 271:127033, 2023.
  27. Random forest regression for online capacity estimation of lithium-ion batteries. Applied energy, 232:197–210, 2018a.
  28. Predicting battery capacity from impedance at varying temperature and state of charge using machine learning. Cell Reports Physical Science, 3(12):101184, 2022b.
  29. Aging mechanisms and thermal stability of aged commercial 18650 lithium ion battery induced by slight overcharging cycling. Journal of power sources, 445:227263, 2020.
  30. Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries. Energy Storage Materials, 50:668–695, 2022.
  31. Degradation diagnostics for lithium ion cells. Journal of Power Sources, 341:373–386, 2017.
  32. Physics-informed machine learning for degradation diagnostics of lithium-ion batteries. In International design engineering technical conferences and computers and information in engineering conference, volume 85383, page V03AT03A041. American Society of Mechanical Engineers, 2021.
  33. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification. Journal of Power Sources, 251:38–54, 2014.
  34. State of health battery estimator enabling degradation diagnosis: Model and algorithm description. Journal of Power Sources, 360:59–69, 2017.
  35. Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries. Energy Storage Materials, 37:283–295, 2021.
  36. Revisiting the t0. 5 dependence of sei growth. Journal of the Electrochemical Society, 167(9):090535, 2020.
  37. Characterization technique for advanced materials for lithium batteries in an sem. Microscopy and Microanalysis, 26(S2):2790–2792, 2020.
  38. A robust and sleek electrochemical battery model implementation: a matlab® framework. Journal of The Electrochemical Society, 168(9):090527, 2021.
  39. A single particle model with chemical/mechanical degradation physics for lithium ion battery state of health (soh) estimation. Applied energy, 212:1178–1190, 2018b.
  40. Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries. Journal of The Electrochemical Society, 166(14):A3189–A3200, 2019.
  41. Nondestructive diagnostics and quantification of battery aging under different degradation paths. Journal of Power Sources, 557:232555, 2023.
  42. Electrochemical modeling of linear and nonlinear aging of lithium-ion cells. Journal of The Electrochemical Society, 167(11):110535, 2020.
  43. Physics-informed machine learning. Nature Reviews Physics, 3(6):422–440, 2021.
  44. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378:686–707, 2019.
  45. Perspective—combining physics and machine learning to predict battery lifetime. Journal of The Electrochemical Society, 168(3):030525, 2021.
  46. Minn: Learning the dynamics of differential-algebraic equations and application to battery modeling. arXiv preprint arXiv:2304.14422, 2023.
  47. Physics-informed neural networks for prognostics and health management of lithium-ion batteries. IEEE Transactions on Intelligent Vehicles, 2023.
  48. Prognostics of lithium-ion batteries based on the verhulst model, particle swarm optimization and particle filter. IEEE Transactions on Instrumentation and Measurement, 63(1):2–17, 2013.
  49. An enhanced single-particle model using a physics-informed neural network considering electrolyte dynamics for lithium-ion batteries. Batteries, 9(10):511, 2023.
  50. Physics-informed neural networks for state of health estimation in lithium-ion batteries. Journal of The Electrochemical Society, 170(9):090524, 2023.
  51. Physics-informed neural networks for degradation diagnostics of lithium-ion batteries. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, volume 87301, page V03AT03A049. American Society of Mechanical Engineers, 2023.
  52. Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries. Journal of Power Sources, 506:230034, 2021b.
  53. Integrating physics-based modeling with machine learning for lithium-ion batteries. Applied Energy, 329:120289, 2023.
  54. Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model. Journal of Power Sources, 455:227935, 2020.
  55. Y.H. Lui. Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction. J. Power Sources, 2020. doi: 10.1016/j.jpowsour.2020.229372.
  56. Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction. Journal of Power Sources, 485:229327, 2021.
  57. Synthesize battery degradation modes via a diagnostic and prognostic model. Journal of power sources, 219:204–216, 2012.
  58. Characterising lithium-ion battery degradation through the identification and tracking of electrochemical battery model parameters. Batteries, 2(2):13, 2016.
  59. Big data training data for artificial intelligence-based li-ion diagnosis and prognosis. Journal of Power Sources, 479:228806, 2020.
  60. A mechanism identification model based state-of-health diagnosis of lithium-ion batteries for energy storage applications. Journal of Cleaner Production, 193:379–390, 2018.
  61. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. Journal of Power Sources, 235:36–44, 2013.
  62. The art of data augmentation. Journal of Computational and Graphical Statistics, 10(1):1–50, 2001.
  63. Predicting the output from a complex computer code when fast approximations are available. Biometrika, 87(1):1–13, 2000.
  64. Multivariate gaussian process regression for multiscale data assimilation and uncertainty reduction. arXiv preprint arXiv:1804.06490, 2018.
  65. Physics-informed cokriging: A gaussian-process-regression-based multifidelity method for data-model convergence. Journal of Computational Physics, 395:410–431, 2019.
  66. Engineering design via surrogate modelling: a practical guide. John Wiley & Sons, 2008.
  67. A taxonomic survey of physics-informed machine learning. Applied Sciences, 13(12):6892, 2023.
  68. When physics meets machine learning: A survey of physics-informed machine learning. arXiv preprint arXiv:2203.16797, 2022.
  69. Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges. Reliability Engineering & System Safety, page 108900, 2022.
  70. Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems. Computer Methods in Applied Mechanics and Engineering, 365:113028, 2020.
  71. Amortized finite element analysis for fast pde-constrained optimization. In International Conference on Machine Learning, pages 10638–10647. PMLR, 2020.
  72. Physics-informed generative adversarial networks for stochastic differential equations. SIAM Journal on Scientific Computing, 42(1):A292–A317, 2020.
  73. fpinns: Fractional physics-informed neural networks. SIAM Journal on Scientific Computing, 41(4):A2603–A2626, 2019.
  74. Gait data augmentation using physics-based biomechanical simulation. arXiv preprint arXiv:2307.08092, 2023.
  75. Physics-informed machine learning: case studies for weather and climate modelling. Philosophical Transactions of the Royal Society A, 379(2194):20200093, 2021.
  76. Benchmarking physics-informed machine learning-based short term pv-power forecasting tools. Energy Reports, 8:6512–6520, 2022.
  77. Physics-constrained deep learning of multi-zone building thermal dynamics. Energy and Buildings, 243:110992, 2021.
  78. Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence, 1(5):206–215, 2019.
  79. Physics-constrained machine learning of evapotranspiration. Geophysical Research Letters, 46(24):14496–14507, 2019.
  80. Overview of gaussian process based multi-fidelity techniques with variable relationship between fidelities, application to aerospace systems. Aerospace Science and Technology, 107:106339, 2020.
  81. Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inf. Process. Syst, m(Nips):6403–6414,, 2017.
  82. Alex J Cannon. Quantile regression neural networks: Implementation in r and application to precipitation downscaling. Computers & geosciences, 37(9):1277–1284, 2011.
  83. Bayesian neural networks. arXiv preprint arXiv:1801.07710, 2018.
  84. Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial. Mechanical Systems and Signal Processing, 205:110796, 2023.
  85. Multi-output local gaussian process regression: Applications to uncertainty quantification. Journal of Computational Physics, 231(17):5718–5746, 2012.
  86. Bayesian data augmentation methods for the synthesis of qualitative and quantitative research findings. Quality & quantity, 45:653–669, 2011.
  87. Confidence interval estimation by bootstrap method for uncertainty quantification using random sampling method. Journal of Nuclear Science and Technology, 52(7-8):993–999, 2015.
  88. Understanding and mitigating gradient pathologies in physics-informed neural networks. arXiv preprint arXiv:2001.04536, 2020.
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