IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v216y2021ics0951832021004610.html
   My bibliography  Save this article

Battery asset management with cycle life prognosis

Author

Listed:
  • Liu, Xinyang
  • Zheng, Zhuoyuan
  • Büyüktahtakın, Ä°. Esra
  • Zhou, Zhi
  • Wang, Pingfeng

Abstract

Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge (DOD), charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.

Suggested Citation

  • Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, Ä°. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004610
    DOI: 10.1016/j.ress.2021.107948
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021004610
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107948?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Roe, S. & Mba, D., 2009. "The environment, international standards, asset health management and condition monitoring: An integrated strategy," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 474-478.
    2. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    3. Kilsby, Paul & Remenyte-Prescott, Rasa & Andrews, John, 2017. "A modelling approach for railway overhead line equipment asset management," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 326-337.
    4. 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.
    5. Yuri Yatsenko & Natali Hritonenko, 2016. "Asset replacement under improving operating and capital costs: a practical approach," International Journal of Production Research, Taylor & Francis Journals, vol. 54(10), pages 2922-2933, May.
    6. Natali Hritonenko & Yuri Yatsenko, 2012. "Fleet replacement under technological shocks," Annals of Operations Research, Springer, vol. 196(1), pages 311-331, July.
    7. İ. Esra Büyüktahtakın & Joseph C. Hartman, 2016. "A mixed-integer programming approach to the parallel replacement problem under technological change," International Journal of Production Research, Taylor & Francis Journals, vol. 54(3), pages 680-695, February.
    8. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    9. El-Akruti, Khaled & Dwight, Richard & Zhang, Tieling, 2013. "The strategic role of Engineering Asset Management," International Journal of Production Economics, Elsevier, vol. 146(1), pages 227-239.
    10. Nakousi, C. & Pascual, R. & Anani, A. & Kristjanpoller, F. & Lillo, P., 2018. "An asset-management oriented methodology for mine haul-fleet usage scheduling," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 336-344.
    11. Patil, Meru A. & Tagade, Piyush & Hariharan, Krishnan S. & Kolake, Subramanya M. & Song, Taewon & Yeo, Taejung & Doo, Seokgwang, 2015. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied Energy, Elsevier, vol. 159(C), pages 285-297.
    12. Mishra, Madhav & Martinsson, Jesper & Rantatalo, Matti & Goebel, Kai, 2018. "Bayesian hierarchical model-based prognostics for lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 25-35.
    13. Xu, Xin & Chen, Nan, 2017. "A state-space-based prognostics model for lithium-ion battery degradation," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 47-57.
    14. 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.
    15. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    16. Sun, Fengchun & Xiong, Rui & He, Hongwen & Li, Weiqing & Aussems, Johan Eric Emmanuel, 2012. "Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries," Applied Energy, Elsevier, vol. 96(C), pages 378-386.
    17. Catrinu, M.D. & Nordgård, D.E., 2011. "Integrating risk analysis and multi-criteria decision support under uncertainty in electricity distribution system asset management," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 663-670.
    18. Li, Sai & Fang, Huajing & Shi, Bing, 2021. "Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    19. Zhang, Sen-Ju & Kang, Rui & Lin, Yan-Hui, 2021. "Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    20. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.
    21. Xiaoyu Li & Xing Shu & Jiangwei Shen & Renxin Xiao & Wensheng Yan & Zheng Chen, 2017. "An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-15, May.
    22. Xi, Zhimin & Zhao, Xiangxue, 2019. "An enhanced copula-based method for data-driven prognostics considering insufficient training units," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 181-194.
    23. Polterovich, Victor & Popov, Vladimir, 2006. "Эволюционная Теория Экономической Политики: Часть I: Опыт Быстрого Развития [An Evolutionary Theory of Economic Policy: Part I: The Experience of Fast Development]," MPRA Paper 22168, University Library of Munich, Germany.
    24. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    25. Chiachío, Juan & Chiachío, Manuel & Prescott, Darren & Andrews, John, 2019. "A knowledge-based prognostics framework for railway track geometry degradation," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 127-141.
    26. Tian, Jiaqiang & Xu, Ruilong & Wang, Yujie & Chen, Zonghai, 2021. "Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries," Energy, Elsevier, vol. 221(C).
    27. İ. Esra Büyüktahtakın & J. Cole Smith & Joseph C. Hartman & Shangyuan Luo, 2014. "Parallel Asset Replacement Problem under Economies of Scale with Multiple Challengers," The Engineering Economist, Taylor & Francis Journals, vol. 59(4), pages 237-258, October.
    28. Emmanuel des-Bordes & İ. Esra Büyüktahtakın, 2017. "Optimizing capital investments under technological change and deterioration: A case study on MRI machine replacement," The Engineering Economist, Taylor & Francis Journals, vol. 62(2), pages 105-131, April.
    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. Zhang, Xiaoxi & Pan, Yongjun & Zhou, Junxiao & Li, Zhixiong & Liao, Tianjun & Li, Jie, 2024. "Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    3. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Mizutani, Daijiro & Nakazato, Yuto & Ikushima, Rie & Satsukawa, Koki & Kawasaki, Yosuke & Kuwahara, Masao, 2024. "Optimal intervention policy of emergency storage batteries for expressway transportation systems considering deterioration risk during lead time of replacement," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Tian, Yuan & Han, Minghao & Kulkarni, Chetan & Fink, Olga, 2022. "A prescriptive Dirichlet power allocation policy with deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    7. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Bian, Yifan & Xie, Lirong & Ye, Jiahao & Ma, Lan, 2024. "A new shared energy storage business model for data center clusters considering energy storage degradation," Renewable Energy, Elsevier, vol. 225(C).
    9. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(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. Xu, Yanwen & Kohtz, Sara & Boakye, Jessica & Gardoni, Paolo & Wang, Pingfeng, 2023. "Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Bai, Guangxing & Su, Yunsheng & Rahman, Maliha Maisha & Wang, Zequn, 2023. "Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    3. 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).
    4. 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.
    5. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    6. Prakash, Om & Samantaray, Arun Kumar, 2021. "Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    7. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Zhang, Le & Gu, Weihua & Fu, Liangliang & Mei, Yu & Hu, Yaohua, 2021. "A two-stage heuristic approach for fleet management optimization under time-varying demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    9. Zhou, Danhua & Wang, Bin & Zhu, Chao & Zhou, Fang & Wu, Hong, 2023. "A light-weight feature extractor for lithium-ion battery health prognosis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.
    11. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    13. 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.
    14. 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).
    15. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2024. "Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning," Applied Energy, Elsevier, vol. 358(C).
    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. Ansari, Amir Babak & Esfahanian, Vahid & Torabi, Farschad, 2016. "Discharge, rest and charge simulation of lead-acid batteries using an efficient reduced order model based on proper orthogonal decomposition," Applied Energy, Elsevier, vol. 173(C), pages 152-167.
    18. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    19. Saleh, Ali & Remenyte-Prescott, Rasa & Prescott, Darren & Chiachío, Manuel, 2024. "Intelligent and adaptive asset management model for railway sections using the iPN method," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    20. Zheng, Yuejiu & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Ma, Hongbin & Dollmeyer, Thomas A. & Freyermuth, Vincent, 2013. "Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model," Applied Energy, Elsevier, vol. 111(C), pages 571-580.

    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:eee:reensy:v:216:y:2021:i:c:s0951832021004610. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.