IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i4p2889-2907d48190.html
   My bibliography  Save this article

Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Author

Listed:
  • Kuo-Hsin Tseng

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

  • Jin-Wei Liang

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

  • Wunching Chang

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

  • Shyh-Chin Huang

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

Abstract

Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (V dis ) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using V dis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients.

Suggested Citation

  • Kuo-Hsin Tseng & Jin-Wei Liang & Wunching Chang & Shyh-Chin Huang, 2015. "Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 8(4), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:4:p:2889-2907:d:48190
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/4/2889/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/4/2889/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shuai Wang & Lingling Zhao & Xiaohong Su & Peijun Ma, 2014. "Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression," Energies, MDPI, vol. 7(10), pages 1-17, October.
    2. Datong Liu & Hong Wang & Yu Peng & Wei Xie & Haitao Liao, 2013. "Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction," Energies, MDPI, vol. 6(8), pages 1-15, July.
    3. Noshin Omar & Mohamed Daowd & Omar Hegazy & Peter Van den Bossche & Thierry Coosemans & Joeri Van Mierlo, 2012. "Electrical Double-Layer Capacitors in Hybrid Topologies —Assessment and Evaluation of Their Performance," Energies, MDPI, vol. 5(11), pages 1-36, November.
    4. Burke, Andrew & Miller, Marshall, 2009. "Performance Characteristics of Lithium-ion Batteries of Various Chemistries for Plug-in Hybrid Vehicles," Institute of Transportation Studies, Working Paper Series qt3mc7g3vt, Institute of Transportation Studies, UC Davis.
    5. Yinjiao Xing & Eden W. M. Ma & Kwok L. Tsui & Michael Pecht, 2011. "Battery Management Systems in Electric and Hybrid Vehicles," Energies, MDPI, vol. 4(11), pages 1-18, October.
    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. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    2. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    3. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    4. Calum Strange & Shawn Li & Richard Gilchrist & Gonçalo dos Reis, 2021. "Elbows of Internal Resistance Rise Curves in Li-Ion Cells," Energies, MDPI, vol. 14(4), pages 1-15, February.
    5. Prakash Venugopal & Vigneswaran T., 2019. "State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition," Energies, MDPI, vol. 12(22), pages 1-29, November.
    6. Shi, Man & He, Hongwen & Li, Jianwei & Han, Mo & Jia, Chunchun, 2021. "Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach," Applied Energy, Elsevier, vol. 300(C).
    7. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    8. Józef Pszczółkowski, 2021. "Description of Acid Battery Operating Parameters," Energies, MDPI, vol. 14(21), pages 1-17, November.

    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. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    2. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
    3. Shuai Wang & Lingling Zhao & Xiaohong Su & Peijun Ma, 2014. "Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression," Energies, MDPI, vol. 7(10), pages 1-17, October.
    4. Susanne Rothgang & Matthias Rogge & Jan Becker & Dirk Uwe Sauer, 2015. "Battery Design for Successful Electrification in Public Transport," Energies, MDPI, vol. 8(7), pages 1-23, June.
    5. Omar, Noshin & Monem, Mohamed Abdel & Firouz, Yousef & Salminen, Justin & Smekens, Jelle & Hegazy, Omar & Gaulous, Hamid & Mulder, Grietus & Van den Bossche, Peter & Coosemans, Thierry & Van Mierlo, J, 2014. "Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model," Applied Energy, Elsevier, vol. 113(C), pages 1575-1585.
    6. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
    7. Yu Peng & Yandong Hou & Yuchen Song & Jingyue Pang & Datong Liu, 2018. "Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression," Energies, MDPI, vol. 11(6), pages 1-20, June.
    8. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    9. Taichun Qin & Shengkui Zeng & Jianbin Guo & Zakwan Skaf, 2016. "A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena," Energies, MDPI, vol. 9(11), pages 1-18, November.
    10. Okay, Kamil & Eray, Sermet & Eray, Aynur, 2022. "Development of prototype battery management system for PV system," Renewable Energy, Elsevier, vol. 181(C), pages 1294-1304.
    11. Mazhar Abbas & Eung-sang Kim & Seul-ki Kim & Yun-su Kim, 2016. "Comparative Analysis of Battery Behavior with Different Modes of Discharge for Optimal Capacity Sizing and BMS Operation," Energies, MDPI, vol. 9(10), pages 1-19, October.
    12. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.
    13. Pavković, D. & Hoić, M. & Deur, J. & Petrić, J., 2014. "Energy storage systems sizing study for a high-altitude wind energy application," Energy, Elsevier, vol. 76(C), pages 91-103.
    14. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    15. Burke, Andy & Zhao, Hengbing, 2010. "Simulations of Plug-in Hybrid Vehicles Using Advanced Lithium Batteries and Ultracapacitors on Various Driving Cycles," Institute of Transportation Studies, Working Paper Series qt4wb3g744, Institute of Transportation Studies, UC Davis.
    16. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    17. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    18. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    19. Tsung-Hsi Wu & Chin-Sien Moo & Chih-Hao Hou, 2017. "A Battery Power Bank with Series-Connected Buck–Boost-Type Battery Power Modules," Energies, MDPI, vol. 10(5), pages 1-12, May.
    20. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.

    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:8:y:2015:i:4:p:2889-2907:d:48190. 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.