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Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing (2307.05521v1)

Published 7 Jul 2023 in cs.LG

Abstract: The optimization of the electrode manufacturing process is important for upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing energy demand. In particular, LIB manufacturing is very important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery application conditions by proposing a powerful data-driven approach supported by a deterministic ML-assisted pipeline for bi-objective optimization of the electrochemical performance. This ML pipeline allows the inverse design of the process parameters to adopt in order to manufacture electrodes for energy or power applications. The latter work is an analogy to our previous work that supported the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical pseudo-two-dimensional model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic ML model to implement fast bi-objective optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.

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References (69)
  1. Current and future lithium-ion battery manufacturing. IScience, 24(4):102332, 2021.
  2. Arumugam Manthiram. An outlook on lithium ion battery technology. ACS central science, 3(10):1063–1069, 2017.
  3. Philip Cooke. Gigafactory logistics in space and time: Tesla’s fourth gigafactory and its rivals. Sustainability, 12(5):2044, 2020.
  4. European backing for northvolt’s battery gigafactory in sweden. https://ec.europa.eu/commission/presscorner/detail/it/IP{_}2014{_}22. Accessed on April 2023.
  5. From materials to cell: state-of-the-art and prospective technologies for lithium-ion battery electrode processing. Chemical Reviews, 122(1):903–956, 2021.
  6. Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs. International Journal of Production Economics, 232:107982, 2021.
  7. Advances of polymer binders for silicon-based anodes in high energy density lithium-ion batteries. InfoMat, 3(5):460–501, 2021.
  8. Toward low-cost, high-energy density, and high-power density lithium-ion batteries. Jom, 69:1484–1496, 2017.
  9. BatteryDesign. https://www.batterydesign.net/power-versus-energy-cells/. Accessed on April 2023.
  10. Recent progress in rechargeable sodium-ion batteries: toward high-power applications. Small, 15(32):1805427, 2019.
  11. Optimizing areal capacities through understanding the limitations of lithium-ion electrodes. Journal of The Electrochemical Society, 163(2):A138, 2015.
  12. Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations. Energy Storage Materials, 56:50–61, 2023.
  13. Digitalization of battery manufacturing: Current status, challenges, and opportunities. Advanced Energy Materials, 12(17):2102696, 2022.
  14. Accelerated optimization methods for force-field parametrization in battery electrode manufacturing modeling. Batteries & Supercaps, 3(8):721–730, 2020.
  15. Investigating electrode calendering and its impact on electrochemical performance by means of a new discrete element method model: Towards a digital twin of li-ion battery manufacturing. Journal of Power Sources, 485:229320, 2021.
  16. Data-driven battery electrode production process modeling enabled by machine learning. Journal of Materials Processing Technology, page 117967, 2023.
  17. Data mining in lithium-ion battery cell production. Journal of Power Sources, 413:360–366, 2019.
  18. Integrating physics-based modeling with machine learning for lithium-ion batteries. Applied Energy, 329:120289, 2023.
  19. Machine learning 3d-resolved prediction of electrolyte infiltration in battery porous electrodes. Journal of Power Sources, 511:230384, 2021.
  20. Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning. Journal of Power Sources, 480:229103, 2020.
  21. Artistic project. https://www.erc-artistic.eu/. Accessed on April 2023.
  22. Carbon-binder migration: A three-dimensional drying model for lithium-ion battery electrodes. Energy Storage Materials, 43:337–347, 2021.
  23. An experimentally-validated 3d electrochemical model revealing electrode manufacturing parameters’ effects on battery performance. Energy Storage Materials, 54:156–163, 2023.
  24. The artistic online calculator: exploring the impact of lithium-ion battery electrode manufacturing parameters interactively through your browser. Batteries & Supercaps, 5(3):e202100324, 2022.
  25. Artificial intelligence investigation of nmc cathode manufacturing parameters interdependencies. Batteries & Supercaps, 3(1):60–67, 2020.
  26. A robust numerical treatment of solid-phase diffusion in pseudo two-dimensional lithium-ion battery models. Journal of Power Sources, 556:232413, 2023.
  27. Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics. npj Computational Materials, 8(1):161, 2022.
  28. Multi-criteria optimization in the production of lithium-ion batteries. Procedia Manufacturing, 43:720–727, 2020.
  29. Battery production design using multi-output machine learning models. Energy Storage Materials, 38:93–112, 2021.
  30. Machine learning pipeline for battery state-of-health estimation. Nature Machine Intelligence, 3(5):447–456, 2021.
  31. Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach. Energy Storage Materials, 36:435–458, 2021.
  32. Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries. Energy Storage Materials, 50:668–695, 2022.
  33. Modeling and optimization of electrode modified with poly (3, 4-ethylenedioxythiophene)/graphene oxide composite by response surface methodology/box-behnken design approach. Journal of Electroanalytical Chemistry, 787:1–10, 2017.
  34. Optimization of titanium and vanadium co-doping in lifepo 4/c using response surface methodology. Ionics, 21:2447–2455, 2015.
  35. Bayesian optimization and data science, volume 849. Springer, 2019.
  36. Propertydag: Multi-objective bayesian optimization of partially ordered, mixed-variable properties for biological sequence design. arXiv preprint arXiv:2210.04096, 2022.
  37. Extreme learning machine and bayesian optimization-driven intelligent framework for iomt cyber-attack detection. The Journal of Supercomputing, 78(13):14866–14891, 2022.
  38. Determining the optimal weights in multiple objective function optimization. In ICCV, pages 87–89, 1988.
  39. Hybridization of rechargeable batteries and electrochemical capacitors: Principles and limits. Electrochimica Acta, 72:1–17, 2012.
  40. The effects of electrode thickness on the electrochemical and thermal characteristics of lithium ion battery. Applied Energy, 139:220–229, 2015.
  41. F C De Rainville. Design d’experimentations interactif: Aide à la compréhension de systèmes complexes. http://hdl.handle.net/20.500.11794/22172, 2010.
  42. Conveying advanced li-ion battery materials into practice the impact of electrode slurry preparation skills. Advanced Energy Materials, 6(21):1600655, 2016.
  43. Impact of the calendering process on the interfacial structure and the related electrochemical performance of secondary lithium-ion batteries. ECS Transactions, 50(26):59, 2013.
  44. Design of experiments (doe) and process optimization. a review of recent publications. Organic Process Research & Development, 19(11):1605–1633, 2015.
  45. Taufactor: An open-source application for calculating tortuosity factors from tomographic data. SoftwareX, 5:203–210, 2016.
  46. Geodict startpage.
  47. Overpotential analysis of graphite-based li-ion batteries seen from a porous electrode modeling perspective. Journal of Power Sources, 509:230345, 2021.
  48. Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences. SIAM Journal on Numerical Analysis, 59(3):1811–1834, 2021.
  49. LA Román-Ramírez and J Marco. Design of experiments applied to lithium-ion batteries: A literature review. Applied Energy, 320:119305, 2022.
  50. A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85:1–16, 2018.
  51. A tutorial on bayesian nonparametric models. Journal of Mathematical Psychology, 56(1):1–12, 2012.
  52. A tutorial on kernel methods for categorization. Journal of Mathematical Psychology, 51(6):343–358, 2007.
  53. Martin D Buhmann. Radial basis functions. Acta numerica, 9:1–38, 2000.
  54. Scikit-Learn. https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.ExpSineSquared.html. Accessed on April 2023.
  55. Application of gaussian process regression for bearing degradation assessment. In 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012), pages 644–648. IEEE, 2012.
  56. Balancing exploration and exploitation in multiobjective evolutionary optimization. Information Sciences, 497:129–148, 2019.
  57. Michael Adam Gelbart. Constrained Bayesian optimization and applications. PhD thesis, 2015.
  58. Evolutionary algorithms for solving multi-objective problems, volume 5. Springer, 2007.
  59. Tinkle Chugh. Scalarizing functions in bayesian multiobjective optimization. In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–8. IEEE, 2020.
  60. An integrated approach towards modeling ranked weights. Computers & Industrial Engineering, 147:106629, 2020.
  61. Ewa Roszkowska. Rank ordering criteria weighting methods–a comparative overview. Optimum. Studia Ekonomiczne, (5 (65)):14–33, 2013.
  62. Attribute weighting methods and decision quality in the presence of response error: a simulation study. Journal of Behavioral Decision Making, 11(2):85–105, 1998.
  63. Vladimir Nasteski. An overview of the supervised machine learning methods. Horizons. b, 4:51–62, 2017.
  64. Estimating the confidence interval for prediction errors of support vector machine classifiers. The Journal of Machine Learning Research, 9:521–540, 2008.
  65. KF Yee. Confidence interval approach for evaluating bias in laboratory methods. Journal of Automatic Chemistry, 10(3):144–146, 1988.
  66. Portfolio allocation for bayesian optimization. In UAI, pages 327–336, 2011.
  67. scikit-optimize/scikit-optimize: v0. 8.1. Zenodo, 2020.
  68. Cycle life modeling of lithium-ion batteries. Journal of The Electrochemical Society, 151(10):A1584, 2004.
  69. Toward safe lithium metal anode in rechargeable batteries: a review. Chemical reviews, 117(15):10403–10473, 2017.
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