IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5580-d877610.html
   My bibliography  Save this article

Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries

Author

Listed:
  • Bo Pang

    (Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada)

  • Li Chen

    (Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada)

  • Zuomin Dong

    (Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada)

Abstract

Electrified vehicles (EV) and marine vessels represent promising clean transportation solutions to reduce or eliminate petroleum fuel use, greenhouse gas emissions and air pollutants. The presently commonly used electric energy storage system (ESS) is based on lithium-ion batteries. These batteries are the electrified or hybridized powertrain’s most expensive component and show noticeable performance degradations under different use patterns. Therefore, battery life prediction models play a key role in realizing globally optimized EV design and energy control strategies. This research studies the data-driven modelling and prediction methods for Li-ion batteries’ performance degradation behaviour and the state of health (SOH) estimation. The research takes advantage of the increasingly available battery test and data to reduce prediction errors of the widely used semi-empirical modelling methods. Several data-driven modelling techniques have been applied, improved, and compared to identify their advantages and limitations. The data-driven approach and Kalman Filter (KF) algorithm are used to estimate and predict the degradation of the battery during operation. The combined algorithm of Gaussian Process Regression (GPR) and Extended Kalman Filter (EKF) showed higher accuracy than other algorithms.

Suggested Citation

  • Bo Pang & Li Chen & Zuomin Dong, 2022. "Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries," Energies, MDPI, vol. 15(15), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5580-:d:877610
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5580/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/15/5580/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    2. Li Chen & Yuqi Tong & Zuomin Dong, 2020. "Li-Ion Battery Performance Degradation Modeling for the Optimal Design and Energy Management of Electrified Propulsion Systems," Energies, MDPI, vol. 13(7), pages 1-19, April.
    3. Shaheen, Susan A & Lipman, Timothy E, 2007. "Reducing Greenhouse Emissions and Fuel Consumption: Sustainable Approaches for Surface Transportation," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5c66j062, Institute of Transportation Studies, UC Berkeley.
    4. Shaheen, Susan & Lipman, Timothy, 2007. "Reducing Greenhouse Emissions and Fuel Consumption," Institute of Transportation Studies, Working Paper Series qt69c427zk, Institute of Transportation Studies, UC Davis.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Piotr Szewczyk & Andrzej Łebkowski, 2022. "Comparative Studies on Batteries for the Electrochemical Energy Storage in the Delivery Vehicle," Energies, MDPI, vol. 15(24), pages 1-28, December.
    2. Chunling Wu & Juncheng Fu & Xinrong Huang & Xianfeng Xu & Jinhao Meng, 2023. "Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR," Energies, MDPI, vol. 16(10), pages 1-16, May.

    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. Şahan, Duygu & Tuna, Okan, 2018. "Environmental innovation of transportation sector in OECD countries," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), The Road to a Digitalized Supply Chain Management: Smart and Digital Solutions for Supply Chain Management. Proceedings of the Hamburg International C, volume 25, pages 157-170, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    3. Nielsen, Jesper Riber & Hovmøller, Harald & Blyth, Pascale-L. & Sovacool, Benjamin K., 2015. "Of “white crows” and “cash savers:” A qualitative study of travel behavior and perceptions of ridesharing in Denmark," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 113-123.
    4. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    5. Yoon-Young Chun & Mitsutaka Matsumoto & Kiyotaka Tahara & Kenichiro Chinen & Hideki Endo, 2019. "Exploring Factors Affecting Car Sharing Use Intention in the Southeast-Asia Region: A Case Study in Java, Indonesia," Sustainability, MDPI, vol. 11(18), pages 1-26, September.
    6. Fabio Kon & Éderson Cássio Ferreira & Higor Amario Souza & Fábio Duarte & Paolo Santi & Carlo Ratti, 2022. "Abstracting mobility flows from bike-sharing systems," Public Transport, Springer, vol. 14(3), pages 545-581, October.
    7. Lisa Dang & Widar von Arx & Jonas Frölicher, 2021. "The Impact of On-Demand Collective Transport Services on Sustainability: A Comparison of Various Service Options in a Rural and an Urban Area of Switzerland," Sustainability, MDPI, vol. 13(6), pages 1-27, March.
    8. Martínez-Jaramillo, Juan Esteban & Arango-Aramburo, Santiago & Álvarez-Uribe, Karla C. & Jaramillo-Álvarez, Patricia, 2017. "Assessing the impacts of transport policies through energy system simulation: The case of the Medellin Metropolitan Area, Colombia," Energy Policy, Elsevier, vol. 101(C), pages 101-108.
    9. Shaheen, Susan A & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present and Future," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6qg8q6ft, Institute of Transportation Studies, UC Berkeley.
    10. Abid, Hussein Rasool & Iglauer, Stefan & Al-Yaseri, Ahmed & Keshavarz, Alireza, 2021. "Drastic enhancement of CO2 adsorption capacity by negatively charged sub-bituminous coal," Energy, Elsevier, vol. 233(C).
    11. Shaheen, Susan A. & Bejamin-Chung, Jade & Allen, Denise & Howe-Steiger, Linda, 2009. "Achieving California’s Land Use and Transportation Greenhouse Gas Emission Targets Under AB 32: An Exploration of Potential Policy Processes and Mechanisms," Institute of Transportation Studies, Working Paper Series qt8bm4t7w5, Institute of Transportation Studies, UC Davis.
    12. Nur Sabahiah Abdul Sukor & Surachai Airak & Sitti Asmah Hassan, 2021. "“More Than a Free Bus Ride”—Exploring Young Adults’ Perceptions of Free Bus Services Using a Qualitative Approach: A Case Study of Penang, Malaysia," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    13. Cohen, Adam P. & Shaheen, Susan & McKenzie, Ryan, 2008. "Carsharing: A Guide for Local Planners," Institute of Transportation Studies, Working Paper Series qt4kf3x31h, Institute of Transportation Studies, UC Davis.
    14. Ye Ma & Biying Yu & Meimei Xue, 2018. "Spatial Heterogeneous Characteristics of Ridesharing in Beijing–Tianjin–Hebei Region of China," Energies, MDPI, vol. 11(11), pages 1-21, November.
    15. Kim, Kyeongsu, 2015. "Can carsharing meet the mobility needs for the low-income neighborhoods? Lessons from carsharing usage patterns in New York City," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 249-260.
    16. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    17. Aditjandra, Paulus Teguh & Mulley, Corinne & Nelson, John D., 2013. "The influence of neighbourhood design on travel behaviour: Empirical evidence from North East England," Transport Policy, Elsevier, vol. 26(C), pages 54-65.
    18. Jessica Kersey & Natalie D. Popovich & Amol A. Phadke, 2022. "Rapid battery cost declines accelerate the prospects of all-electric interregional container shipping," Nature Energy, Nature, vol. 7(7), pages 664-674, July.
    19. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    20. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(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:15:y:2022:i:15:p:5580-:d:877610. 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.