IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i22p4376-d287978.html
   My bibliography  Save this article

A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries

Author

Listed:
  • Yuan Chen

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Yigang He

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Zhong Li

    (Jianghuai Automobile Co. Ltd, Hefei 230092, China)

  • Liping Chen

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

Abstract

Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.

Suggested Citation

  • Yuan Chen & Yigang He & Zhong Li & Liping Chen, 2019. "A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries," Energies, MDPI, vol. 12(22), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4376-:d:287978
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/22/4376/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/22/4376/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ahmadian, Ali & Sedghi, Mahdi & Elkamel, Ali & Fowler, Michael & Aliakbar Golkar, Masoud, 2018. "Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2609-2624.
    3. Su, Laisuo & Zhang, Jianbo & Wang, Caijuan & Zhang, Yakun & Li, Zhe & Song, Yang & Jin, Ting & Ma, Zhao, 2016. "Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments," Applied Energy, Elsevier, vol. 163(C), pages 201-210.
    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. Dominik Dvorak & Daniele Basciotti & Imre Gellai, 2020. "Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-18, October.
    2. Cong, Yuan & Wang, Heqi & Bie, Yiming & Wu, Jiabin, 2023. "Double-battery configuration method for electric bus operation in cold regions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(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. Song, Kai & Shi, Jian & Yi, Xiaojian, 2020. "A time-discrete and zero-adjusted gamma process model with application to degradation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    2. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    4. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    5. Moon, Yongma & Baran, Mesut, 2018. "Economic analysis of a residential PV system from the timing perspective: A real option model," Renewable Energy, Elsevier, vol. 125(C), pages 783-795.
    6. Tianyu Liu & Zhengqiang Pan & Quan Sun & Jing Feng & Yanzhen Tang, 2017. "Residual useful life estimation for products with two performance characteristics based on a bivariate Wiener process," Journal of Risk and Reliability, , vol. 231(1), pages 69-80, February.
    7. Zhai, Qingqing & Chen, Piao & Hong, Lanqing & Shen, Lijuan, 2018. "A random-effects Wiener degradation model based on accelerated failure time," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 94-103.
    8. Calise, Francesco & Cappiello, Francesco Liberato & Cimmino, Luca & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2023. "Renewable smart energy network: A thermoeconomic comparison between conventional lithium-ion batteries and reversible solid oxide fuel cells," Renewable Energy, Elsevier, vol. 214(C), pages 74-95.
    9. Pearre, Nathaniel S. & Ribberink, Hajo, 2019. "Review of research on V2X technologies, strategies, and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 61-70.
    10. Tao, Laifa & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou & Noktehdan, Azadeh, 2017. "Lithium-ion battery capacity fading dynamics modelling for formulation optimization: A stochastic approach to accelerate the design process," Applied Energy, Elsevier, vol. 202(C), pages 138-152.
    11. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    12. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
    13. Jiang, Yinghua & Kang, Lixia & Liu, Yongzhong, 2019. "A unified model to optimize configuration of battery energy storage systems with multiple types of batteries," Energy, Elsevier, vol. 176(C), pages 552-560.
    14. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
    15. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    16. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    17. Singh, Deepak Kumar & Tirkey, Jeewan Vachan, 2022. "Performance optimization through response surface methodology of an integrated coal gasification and CI engine fuelled with diesel and low-grade coal-based producer gas," Energy, Elsevier, vol. 238(PC).
    18. Wang, Zhijie & Zhai, Qingqing & Chen, Piao, 2021. "Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    19. Lazaroiu, Gheorghe & Pop, Elena & Negreanu, Gabriel & Pisa, Ionel & Mihaescu, Lucian & Bondrea, Andreya & Berbece, Viorel, 2017. "Biomass combustion with hydrogen injection for energy applications," Energy, Elsevier, vol. 127(C), pages 351-357.
    20. Zhai, Qingqing & Ye, Zhi-Sheng & Yang, Jun & Zhao, Yu, 2016. "Measurement errors in degradation-based burn-in," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 126-135.

    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:12:y:2019:i:22:p:4376-:d:287978. 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.