IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p185-d1013609.html
   My bibliography  Save this article

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

Author

Listed:
  • Maryam Ghalkhani

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Saeid Habibi

    (Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

Abstract

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV).

Suggested Citation

  • Maryam Ghalkhani & Saeid Habibi, 2022. "Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application," Energies, MDPI, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:185-:d:1013609
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/185/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/185/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    2. Nian, Victor & Hari, M.P. & Yuan, Jun, 2019. "A new business model for encouraging the adoption of electric vehicles in the absence of policy support," Applied Energy, Elsevier, vol. 235(C), pages 1106-1117.
    3. Jonas Degrave & Federico Felici & Jonas Buchli & Michael Neunert & Brendan Tracey & Francesco Carpanese & Timo Ewalds & Roland Hafner & Abbas Abdolmaleki & Diego de las Casas & Craig Donner & Leslie F, 2022. "Magnetic control of tokamak plasmas through deep reinforcement learning," Nature, Nature, vol. 602(7897), pages 414-419, February.
    4. Jiang, Xiang & Luo, Ruilian & Peng, Feifei & Fang, Yutang & Akiyama, Tomohiro & Wang, Shuangfeng, 2015. "Synthesis, characterization and thermal properties of paraffin microcapsules modified with nano-Al2O3," Applied Energy, Elsevier, vol. 137(C), pages 731-737.
    5. Zhang, Xinghui & Li, Zhao & Luo, Lingai & Fan, Yilin & Du, Zhengyu, 2022. "A review on thermal management of lithium-ion batteries for electric vehicles," Energy, Elsevier, vol. 238(PA).
    6. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    7. Alex Davies & Petar Veličković & Lars Buesing & Sam Blackwell & Daniel Zheng & Nenad Tomašev & Richard Tanburn & Peter Battaglia & Charles Blundell & András Juhász & Marc Lackenby & Geordie Williamson, 2021. "Advancing mathematics by guiding human intuition with AI," Nature, Nature, vol. 600(7887), pages 70-74, December.
    8. Wang, Weilong & Yang, Xiaoxi & Fang, Yutang & Ding, Jing & Yan, Jinyue, 2009. "Enhanced thermal conductivity and thermal performance of form-stable composite phase change materials by using [beta]-Aluminum nitride," Applied Energy, Elsevier, vol. 86(7-8), pages 1196-1200, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    2. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    3. Xin Li & Qunxi Zhu & Chengli Zhao & Xiaojun Duan & Bolin Zhao & Xue Zhang & Huanfei Ma & Jie Sun & Wei Lin, 2024. "Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Stefano Bianchini & Moritz Muller & Pierre Pelletier, 2023. "Drivers and Barriers of AI Adoption and Use in Scientific Research," Papers 2312.09843, arXiv.org, revised Feb 2024.
    5. Lipeng Xu & Chongwang Tian & Chunjiang Bao & Jinsheng Zhao & Xuning Leng, 2023. "Improving the Electrochemical Performance of Core–Shell LiNi 0.8 Co 0.1 Mn 0.1 O 2 Cathode Materials Using Environmentally Friendly Phase Structure Control Process," Energies, MDPI, vol. 16(10), pages 1-17, May.
    6. Deyun Qiu & Jinxin V. Pei & James E. O. Rosling & Vandana Thathy & Dongdi Li & Yi Xue & John D. Tanner & Jocelyn Sietsma Penington & Yi Tong Vincent Aw & Jessica Yi Han Aw & Guoyue Xu & Abhai K. Tripa, 2022. "A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    7. Shuo-Shuo Liu & Tian-Xia Jiang & Fan Bu & Ji-Lan Zhao & Guang-Fei Wang & Guo-Heng Yang & Jie-Yan Kong & Yun-Fan Qie & Pei Wen & Li-Bin Fan & Ning-Ning Li & Ning Gao & Xiao-Bo Qiu, 2024. "Molecular mechanisms underlying the BIRC6-mediated regulation of apoptosis and autophagy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    8. Xiaoke Yang & Mingqi Zhu & Xue Lu & Yuxin Wang & Junyu Xiao, 2024. "Architecture and activation of human muscle phosphorylase kinase," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    9. Evangelos Katsamakas & Oleg V. Pavlov & Ryan Saklad, 2024. "Artificial intelligence and the transformation of higher education institutions," Papers 2402.08143, arXiv.org.
    10. Kristy Rochon & Brianna L. Bauer & Nathaniel A. Roethler & Yuli Buckley & Chih-Chia Su & Wei Huang & Rajesh Ramachandran & Maria S. K. Stoll & Edward W. Yu & Derek J. Taylor & Jason A. Mears, 2024. "Structural basis for regulated assembly of the mitochondrial fission GTPase Drp1," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. Fan Lu & Liang Zhu & Thomas Bromberger & Jun Yang & Qiannan Yang & Jianmin Liu & Edward F. Plow & Markus Moser & Jun Qin, 2022. "Mechanism of integrin activation by talin and its cooperation with kindlin," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    12. Wang, Weilong & Li, Hailong & Guo, Shaopeng & He, Shiquan & Ding, Jing & Yan, Jinyue & Yang, Jianping, 2015. "Numerical simulation study on discharging process of the direct-contact phase change energy storage system," Applied Energy, Elsevier, vol. 150(C), pages 61-68.
    13. Martin F. Peter & Christian Gebhardt & Rebecca Mächtel & Gabriel G. Moya Muñoz & Janin Glaenzer & Alessandra Narducci & Gavin H. Thomas & Thorben Cordes & Gregor Hagelueken, 2022. "Cross-validation of distance measurements in proteins by PELDOR/DEER and single-molecule FRET," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    14. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    15. Alva, Guruprasad & Lin, Yaxue & Fang, Guiyin, 2018. "An overview of thermal energy storage systems," Energy, Elsevier, vol. 144(C), pages 341-378.
    16. Jutta Diessl & Jens Berndtsson & Filomena Broeskamp & Lukas Habernig & Verena Kohler & Carmela Vazquez-Calvo & Arpita Nandy & Carlotta Peselj & Sofia Drobysheva & Ludovic Pelosi & F.-Nora Vögtle & Fab, 2022. "Manganese-driven CoQ deficiency," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    17. Alexander Kroll & Sahasra Ranjan & Martin K. M. Engqvist & Martin J. Lercher, 2023. "A general model to predict small molecule substrates of enzymes based on machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    18. Lisa-Marie Appel & Vedran Franke & Johannes Benedum & Irina Grishkovskaya & Xué Strobl & Anton Polyansky & Gregor Ammann & Sebastian Platzer & Andrea Neudolt & Anna Wunder & Lena Walch & Stefanie Kais, 2023. "The SPOC domain is a phosphoserine binding module that bridges transcription machinery with co- and post-transcriptional regulators," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    19. Maciej K. Kocylowski & Hande Aypek & Wolfgang Bildl & Martin Helmstädter & Philipp Trachte & Bernhard Dumoulin & Sina Wittösch & Lukas Kühne & Ute Aukschun & Carolin Teetzen & Oliver Kretz & Botond Ga, 2022. "A slit-diaphragm-associated protein network for dynamic control of renal filtration," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    20. Michael A. Longo & Sunetra Roy & Yue Chen & Karl-Heinz Tomaszowski & Andrew S. Arvai & Jordan T. Pepper & Rebecca A. Boisvert & Selvi Kunnimalaiyaan & Caezanne Keshvani & David Schild & Albino Bacolla, 2023. "RAD51C-XRCC3 structure and cancer patient mutations define DNA replication roles," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:185-:d:1013609. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.