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Design of experiments applied to lithium-ion batteries: A literature review

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  • Román-Ramírez, L.A.
  • Marco, J.

Abstract

The statistical design of experiments methodology (DoE) has been a valuable tool since its conception for the understanding of the relationship between factors and responses. Although it has been employed successfully in different research fields and industries for years, its application to the evaluation of lithium-ion batteries (LIBs) is just getting recognition. LIBs are one of the most promising technologies for a complete transition to sustainable energies, are the main technology behind electric vehicles and are fundamental for the continual development of portable electronic devices. This paper presents a critical literature review of the available DoE works applied to the manufacturing and characterisation of LIBs. An overview of DoE and the most important available designs are first presented, followed by a general introduction of the statistical analysis required for the interpretation of the results including regression models. Several aspects of the LIBs such as ageing, capacity, electrode formulation, active material synthesis, thermal design, charging and parameterisation are discussed based on the main objective of the respective DoE studies found in the literature. A case study is presented to visualise the practical application of DoE to the LIBs field. Perspectives and future outlook are given to highlight opportunities and potential areas of research in the application of traditional and modern designs to the LIB’s field. This critical review contributes to a better understanding of the DoE methodology with a focus on LIBs or LIBs related aspects which will lead to faster developments in the field.

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  • Román-Ramírez, L.A. & Marco, J., 2022. "Design of experiments applied to lithium-ion batteries: A literature review," Applied Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:appene:v:320:y:2022:i:c:s0306261922006596
    DOI: 10.1016/j.apenergy.2022.119305
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    3. Wang, Bing-Chuan & He, Yan-Bo & Liu, Jiao & Luo, Biao, 2024. "Fast parameter identification of lithium-ion batteries via classification model-assisted Bayesian optimization," Energy, Elsevier, vol. 288(C).
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    5. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    6. Huang, Zhiliang & Wang, Huaixing & Gan, Zhouwang & Yang, Tongguang & Yuan, Cong & Lei, Bing & Chen, Jie & Wu, Shengben, 2024. "An mechanical/thermal analytical model for prismatic lithium-ion cells with silicon‑carbon electrodes in charge/discharge cycles," Applied Energy, Elsevier, vol. 365(C).
    7. Ma, Qianli & Wei, Wei & Mei, Shengwei, 2024. "Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets," Applied Energy, Elsevier, vol. 356(C).
    8. Luo, Guiling & Li, Xiaowei & Chen, Linlin & Gu, Jun & Huang, Yuhong & Sun, Jing & Liu, Haiyan & Chao, Yanhong & Zhu, Wenshuai & Liu, Zhichang, 2023. "Electrochemical recovery lithium from brine via taming surface wettability of regeneration spent batteries cathode materials," Applied Energy, Elsevier, vol. 337(C).
    9. Hugo Silva & André S. Santos & Leonilde R. Varela, 2024. "Reducing Energy Consumption Using DOE and SPC on Cork Agglomeration Line," Clean Technol., MDPI, vol. 6(4), pages 1-24, October.
    10. Aghabalazadeh, Mohammad & Neshat, Elaheh, 2024. "Proposal and optimization of a novel biomass-based tri-generation system using energy, exergy and exergoeconomic analyses and design of experiments method," Energy, Elsevier, vol. 288(C).
    11. Rocio Camarena-Martinez & Roberto Baeza-Serrato & Rocio A. Lizarraga-Morales, 2023. "Optimization of Welding Process of Geomembranes in Biodigesters Using Design of Factorial Experiments," Energies, MDPI, vol. 16(18), pages 1-28, September.
    12. Mona Faraji Niri & Koorosh Aslansefat & Sajedeh Haghi & Mojgan Hashemian & Rüdiger Daub & James Marco, 2023. "A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation," Energies, MDPI, vol. 16(17), pages 1-38, September.

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