IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p5992-d1531858.html
   My bibliography  Save this article

A Compact Overview on Li-Ion Batteries Characteristics and Battery Management Systems Integration for Automotive Applications

Author

Listed:
  • Andrea Ria

    (Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56100 Pisa, Italy)

  • Pierpaolo Dini

    (Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56100 Pisa, Italy)

Abstract

The transition to sustainable mobility is progressing rapidly, with electric vehicles (EVs) playing a pivotal role in lowering greenhouse gas emissions and reducing the reliance on fossil fuels. At the core of this transformation are lithium-ion batteries (Li-ion), valued for their high energy density and long cycle life. However, the increasing demand for EVs necessitates continuous improvements in battery technology and the integration of advanced systems to ensure safe, efficient, and reliable performance. This review offers a clear and comprehensive summary of the latest innovations in Li-ion battery chemistry, battery pack design, and Battery Management System (BMS) functionalities. Unlike other reviews, this work emphasizes practical considerations, such as voltage, power, size, and weight for commercial vehicles. It also addresses integrated safety solutions, including disconnection systems and pre-charge circuits, which are vital for enhancing battery safety and lifespan. Additionally, it explores key BMS functions, like cell monitoring, balancing, and thermal management, all crucial for maximizing battery performance and ensuring safe operation. By consolidating current research and industry practices, this article provides essential information in a concise yet accessible format. It enables researchers to quickly gain a solid understanding of the field, distinguishing itself from reviews that focus on narrower aspects of battery technology. Its holistic approach delivers valuable insights for improving EV charging systems’ safety and performance, making it a highly useful resource for researchers and industry professionals alike.

Suggested Citation

  • Andrea Ria & Pierpaolo Dini, 2024. "A Compact Overview on Li-Ion Batteries Characteristics and Battery Management Systems Integration for Automotive Applications," Energies, MDPI, vol. 17(23), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5992-:d:1531858
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/5992/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/23/5992/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manoj Mathew & Stefan Janhunen & Mahir Rashid & Frank Long & Michael Fowler, 2018. "Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems," Energies, MDPI, vol. 11(6), pages 1-15, June.
    2. T. N. V. Krishna & Seelam V. S. V. Prabhu Deva Kumar & Sunkara Srinivasa Rao & Liuchen Chang, 2024. "Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles," Energies, MDPI, vol. 17(14), pages 1-18, July.
    3. Szabolcs Kocsis Szürke & Mátyás Szabó & Szabolcs Szalai & Szabolcs Fischer, 2024. "Deformation Analysis of Different Lithium Battery Designs Using the DIC Technique," Energies, MDPI, vol. 17(2), pages 1-23, January.
    4. Lander, Laura & Tagnon, Chris & Nguyen-Tien, Viet & Kendrick, Emma & Elliott, Robert J.R. & Abbott, Andrew P. & Edge, Jacqueline S. & Offer, Gregory J., 2023. "Breaking it down: A techno-economic assessment of the impact of battery pack design on disassembly costs," Applied Energy, Elsevier, vol. 331(C).
    5. Davoudkhani, Iraj Faraji & Dejamkhooy, Abdolmajid & Nowdeh, Saber Arabi, 2023. "A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging," Applied Energy, Elsevier, vol. 344(C).
    6. Talluri, Teressa & Angani, Amarnathvarma & Shin, kyooJae & Hwang, Myeong-Hwan & Cha, Hyun-Rok, 2024. "A novel design of lithium-polymer pouch battery pack with passive thermal management for electric vehicles," Energy, Elsevier, vol. 304(C).
    7. Pannee Suanpang & Pitchaya Jamjuntr, 2024. "Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability," Sustainability, MDPI, vol. 16(16), pages 1-50, August.
    8. Buchicchio, Emanuele & De Angelis, Alessio & Santoni, Francesco & Carbone, Paolo & Bianconi, Francesco & Smeraldi, Fabrizio, 2023. "Battery SOC estimation from EIS data based on machine learning and equivalent circuit model," Energy, Elsevier, vol. 283(C).
    9. Jie Yang & Chunyu Du & Ting Wang & Yunzhi Gao & Xinqun Cheng & Pengjian Zuo & Yulin Ma & Jiajun Wang & Geping Yin & Jingying Xie & Bo Lei, 2018. "Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model," Energies, MDPI, vol. 11(12), pages 1-15, December.
    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. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Anjie Lu & Jianguo Zhou & Minglei Qin & Danchen Liu, 2024. "Considering Carbon–Hydrogen Coupled Integrated Energy Systems: A Pathway to Sustainable Energy Transition in China Under Uncertainty," Sustainability, MDPI, vol. 16(21), pages 1-32, October.
    3. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    4. Yingjie Chen & Geng Yang & Xu Liu & Zhichao He, 2019. "A Time-Efficient and Accurate Open Circuit Voltage Estimation Method for Lithium-Ion Batteries," Energies, MDPI, vol. 12(9), pages 1-20, May.
    5. Yongcun Fan & Haotian Shi & Shunli Wang & Carlos Fernandez & Wen Cao & Junhan Huang, 2021. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation," Energies, MDPI, vol. 14(8), pages 1-18, April.
    6. Zhou, Yifei & Wang, Shunli & Feng, Renjun & Xie, Yanxin & Fernandez, Carlos, 2024. "Multi-temperature capable enhanced bidirectional long short term memory-multilayer perceptron hybrid model for lithium-ion battery SOC estimation," Energy, Elsevier, vol. 312(C).
    7. Zhang, Yongji & Lan, Minghui & Zhao, Yapu & Su, Zhi & Hao, Yu & Du, Heran, 2024. "Regional carbon emission pressure and corporate green innovation," Applied Energy, Elsevier, vol. 360(C).
    8. Wang, Shaojin & Tang, Jinrui & Xiong, Binyu & Fan, Junqiu & Li, Yang & Chen, Qihong & Xie, Changjun & Wei, Zhongbao, 2024. "Comparison of techniques based on frequency response analysis for state of health estimation in lithium-ion batteries," Energy, Elsevier, vol. 304(C).
    9. Abraham Alem Kebede & Md Sazzad Hosen & Theodoros Kalogiannis & Henok Ayele Behabtu & Towfik Jemal & Joeri Van Mierlo & Thierry Coosemans & Maitane Berecibar, 2022. "Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile," Energies, MDPI, vol. 15(18), pages 1-15, September.
    10. Anna I. Pózna & Katalin M. Hangos & Attila Magyar, 2019. "Temperature Dependent Parameter Estimation of Electrical Vehicle Batteries," Energies, MDPI, vol. 12(19), pages 1-18, September.
    11. Joselyn Stephane Menye & Mamadou-Baïlo Camara & Brayima Dakyo, 2025. "Lithium Battery Degradation and Failure Mechanisms: A State-of-the-Art Review," Energies, MDPI, vol. 18(2), pages 1-43, January.
    12. Tao, Junjie & Wang, Shunli & Cao, Wen & Cui, Yixiu & Fernandez, Carlos & Guerrero, Josep M., 2024. "Innovative multiscale fusion – Antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 312(C).
    13. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Artal-Sevil, Jesús Sergio & García-Paricio, Eduardo, 2024. "Design of small-scale hybrid energy systems taking into account generation and demand uncertainties," Renewable Energy, Elsevier, vol. 227(C).
    14. Duan, Fude & Bu, Xiongzhu, 2024. "Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model," Renewable Energy, Elsevier, vol. 237(PC).
    15. Massimo Ceraolo & Giovanni Lutzemberger & Davide Poli & Claudio Scarpelli, 2021. "Experimental Evaluation of Aging Indicators for Lithium–Iron–Phosphate Cells," Energies, MDPI, vol. 14(16), pages 1-15, August.
    16. Jannesar Niri, Anahita & Poelzer, Gregory A. & Zhang, Steven E. & Rosenkranz, Jan & Pettersson, Maria & Ghorbani, Yousef, 2024. "Sustainability challenges throughout the electric vehicle battery value chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    17. Igor TARAN, 2024. "Investigation of the future of electric mobility in the EU: the dependency on the USA," Cognitive Sustainability, Cognitive Sustainability Ltd., vol. 3(4), pages 41-46, December.
    18. Xuning Feng & Caihao Weng & Xiangming He & Li Wang & Dongsheng Ren & Languang Lu & Xuebing Han & Minggao Ouyang, 2018. "Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study," Energies, MDPI, vol. 11(9), pages 1-21, September.
    19. Woeste, Richard & Drude, Emanuel-Sebastian & Vrucak, Dzeneta & Klöckner, Kai & Rombach, Elinor & Letmathe, Peter & Friedrich, Bernd, 2024. "A techno-economic assessment of two recycling processes for black mass from end-of-life lithium-ion batteries," Applied Energy, Elsevier, vol. 361(C).
    20. P, Aravind & D, Prince Winston & S, Sugumar & M, Pravin, 2024. "Optimal battery based electrical reconfiguration technique for partial shaded PV system," Applied Energy, Elsevier, vol. 361(C).

    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:17:y:2024:i:23:p:5992-:d:1531858. 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.