IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3306-d377397.html
   My bibliography  Save this article

Development of Enhancing Battery Management for Reusing Automotive Lithium-Ion Battery

Author

Listed:
  • Wen-Poo Yuan

    (Wistron Corporation, Hsichih 22181, Taiwan)

  • Se-Min Jeong

    (Department of Naval Architecture and Ocean Engineering, Chosun university, Gwangju 61452, Korea)

  • Wu-Yang Sean

    (Center for Environmental Risk Management, Chung Yuan Christian University, ChungLi 32023, Taiwan)

  • Yi-Hsien Chiang

    (Vigourpack Co., Ltd., Taichung 408, Taiwan)

Abstract

In this study, a battery management system (BMS) is developed for reused lithium-ion battery (RLIB). Additional enhancing functions of battery management are established, i.e., estimation of life-sensitized parameters and life extension. Life-sensitizing parameters mainly include open-circuit voltage (OCV) and internal resistances (IRs). They are sensitized parameters individually relative to state of charge (SOC) and state of health (SOH). For estimating these two parameters, an adaptive control scheme is implemented in BMS. This online adaptive control approach has been extensively applied to nonlinear systems with uncertainties. In two experiments, OCV and IRs of reused battery packs are accurately extracted from working voltage and discharge current. An offline numerical model using a schematic method is applied to verify the applicability and efficiency of this proposed online scheme. Furthermore, a solution of actively extending life by using an ultracapacitor to share peak power of RLIB through adjusting duty ratio is also proposed. It is shown that this enhancing battery management for RLIB can properly estimate OCV and IRs, and actively extend the life of the RLIB in two experiments.

Suggested Citation

  • Wen-Poo Yuan & Se-Min Jeong & Wu-Yang Sean & Yi-Hsien Chiang, 2020. "Development of Enhancing Battery Management for Reusing Automotive Lithium-Ion Battery," Energies, MDPI, vol. 13(13), pages 1-15, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3306-:d:377397
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3306/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/13/3306/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hu, Chao & Jain, Gaurav & Tamirisa, Prabhakar & Gorka, Tom, 2014. "Method for estimating capacity and predicting remaining useful life of lithium-ion battery," Applied Energy, Elsevier, vol. 126(C), pages 182-189.
    3. van der Hoek, Jan Peter & Mol, Stefan & Giorgi, Sara & Ahmad, Jawairia Imtiaz & Liu, Gang & Medema, Gertjan, 2018. "Energy recovery from the water cycle: Thermal energy from drinking water," Energy, Elsevier, vol. 162(C), pages 977-987.
    4. Xiaoxu Song & Shanying Hu & Dingjiang Chen & Bing Zhu, 2017. "Estimation of Waste Battery Generation and Analysis of the Waste Battery Recycling System in China," Journal of Industrial Ecology, Yale University, vol. 21(1), pages 57-69, February.
    5. Shareef, Hussain & Islam, Md. Mainul & Mohamed, Azah, 2016. "A review of the stage-of-the-art charging technologies, placement methodologies, and impacts of electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 403-420.
    6. Speirs, Jamie & Contestabile, Marcello & Houari, Yassine & Gross, Robert, 2014. "The future of lithium availability for electric vehicle batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 183-193.
    7. Noshin Omar & Mohamed Daowd & Omar Hegazy & Grietus Mulder & Jean-Marc Timmermans & Thierry Coosemans & Peter Van den Bossche & Joeri Van Mierlo, 2012. "Standardization Work for BEV and HEV Applications: Critical Appraisal of Recent Traction Battery Documents," Energies, MDPI, vol. 5(1), pages 1-19, January.
    8. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    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. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.
    2. Al-Wreikat, Yazan & Attfield, Emily Kate & Sodré, José Ricardo, 2022. "Model for payback time of using retired electric vehicle batteries in residential energy storage systems," Energy, Elsevier, vol. 259(C).

    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. 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.
    2. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    3. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    5. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    6. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    7. Alejandro Gismero & Erik Schaltz & Daniel-Ioan Stroe, 2020. "Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage," Energies, MDPI, vol. 13(7), pages 1-11, April.
    8. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    9. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    10. Dong, Guangzhong & Zhang, Xu & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state of energy estimation of lithium-ion batteries based on neural network model," Energy, Elsevier, vol. 90(P1), pages 879-888.
    11. Bizhong Xia & Zizhou Lao & Ruifeng Zhang & Yong Tian & Guanghao Chen & Zhen Sun & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang & Huawen Wang, 2017. "Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-23, December.
    12. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    13. 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.
    14. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    15. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    16. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
    17. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    18. Bhatti, Ghanishtha & Mohan, Harshit & Raja Singh, R., 2021. "Towards the future of smart electric vehicles: Digital twin technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    19. Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.
    20. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.

    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:13:y:2020:i:13:p:3306-:d:377397. 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.