IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v106y2016icp662-672.html
   My bibliography  Save this article

Prediction of lithium-ion battery capacity with metabolic grey model

Author

Listed:
  • Chen, Lin
  • Lin, Weilong
  • Li, Junzi
  • Tian, Binbin
  • Pan, Haihong

Abstract

Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures.

Suggested Citation

  • Chen, Lin & Lin, Weilong & Li, Junzi & Tian, Binbin & Pan, Haihong, 2016. "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, Elsevier, vol. 106(C), pages 662-672.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:662-672
    DOI: 10.1016/j.energy.2016.03.096
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216303401
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.03.096?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. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    3. Li, Junfu & Wang, Lixin & Lyu, Chao & Zhang, Liqiang & Wang, Han, 2015. "Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions," Energy, Elsevier, vol. 86(C), pages 638-648.
    4. Shifei Yuan & Hongjie Wu & Chengliang Yin, 2013. "State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model," Energies, MDPI, vol. 6(1), pages 1-27, January.
    5. Eddahech, Akram & Briat, Olivier & Vinassa, Jean-Michel, 2015. "Performance comparison of four lithium–ion battery technologies under calendar aging," Energy, Elsevier, vol. 84(C), pages 542-550.
    6. 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.
    7. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    8. Cheng, Yujie & Lu, Chen & Li, Tieying & Tao, Laifa, 2015. "Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach," Energy, Elsevier, vol. 90(P2), pages 1983-1993.
    9. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    10. Fernández, I.J. & Calvillo, C.F. & Sánchez-Miralles, A. & Boal, J., 2013. "Capacity fade and aging models for electric batteries and optimal charging strategy for electric vehicles," Energy, Elsevier, vol. 60(C), pages 35-43.
    11. Xia, Bizhong & Chen, Chaoren & Tian, Yong & Wang, Mingwang & Sun, Wei & Xu, Zhihui, 2015. "State of charge estimation of lithium-ion batteries based on an improved parameter identification method," Energy, Elsevier, vol. 90(P2), pages 1426-1434.
    12. 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. Yujie Cheng & Laifa Tao & Chao Yang, 2017. "Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition," Complexity, Hindawi, vol. 2017, pages 1-13, December.
    2. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    3. Minglu Ma & Min Su & Shuyu Li & Feng Jiang & Rongrong Li, 2018. "Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    4. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
    5. Pei, Pucheng & Zhou, Qibin & Wu, Lei & Wu, Ziyao & Hua, Jianfeng & Fan, Huimin, 2020. "Capacity estimation for lithium-ion battery using experimental feature interval approach," Energy, Elsevier, vol. 203(C).
    6. Pei Wang & Xue Dan & Yong Yang, 2019. "A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    7. Peng, Cheng & Chen, Heng & Lin, Chaoran & Guo, Shuang & Yang, Zhi & Chen, Ke, 2021. "A framework for evaluating energy security in China: Empirical analysis of forecasting and assessment based on energy consumption," Energy, Elsevier, vol. 234(C).
    8. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    9. Kanbur, Baris Burak & Kumtepeli, Volkan & Duan, Fei, 2020. "Thermal performance prediction of the battery surface via dynamic mode decomposition," Energy, Elsevier, vol. 201(C).
    10. Decai Tang & Tingyu Ma & Zhijiang Li & Jiexin Tang & Brandon J. Bethel, 2016. "Trend Prediction and Decomposed Driving Factors of Carbon Emissions in Jiangsu Province during 2015–2020," Sustainability, MDPI, vol. 8(10), pages 1-15, October.
    11. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    12. Pan, Haihong & Lü, Zhiqiang & Lin, Weilong & Li, Junzi & Chen, Lin, 2017. "State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model," Energy, Elsevier, vol. 138(C), pages 764-775.
    13. Chen, Lin & Ding, Yunhui & Liu, Bohao & Wu, Shuxiao & Wang, Yaodong & Pan, Haihong, 2022. "Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network," Energy, Elsevier, vol. 244(PA).
    14. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(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. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    2. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    3. 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).
    4. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    5. 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.
    6. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    7. Zhibing Zeng & Jindong Tian & Dong Li & Yong Tian, 2018. "An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-16, January.
    8. Farmann, Alexander & Waag, Wladislaw & Sauer, Dirk Uwe, 2016. "Application-specific electrical characterization of high power batteries with lithium titanate anodes for electric vehicles," Energy, Elsevier, vol. 112(C), pages 294-306.
    9. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    10. 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.
    11. Wang, Yujie & Liu, Chang & Pan, Rui & Chen, Zonghai, 2017. "Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator," Energy, Elsevier, vol. 121(C), pages 739-750.
    12. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    13. 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).
    14. Yujie Cheng & Laifa Tao & Chao Yang, 2017. "Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition," Complexity, Hindawi, vol. 2017, pages 1-13, December.
    15. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    16. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    17. Hong Zhang & Li Zhao & Yong Chen, 2015. "A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-18, December.
    18. Shokrzadeh, Shahab & Bibeau, Eric, 2016. "Sustainable integration of intermittent renewable energy and electrified light-duty transportation through repurposing batteries of plug-in electric vehicles," Energy, Elsevier, vol. 106(C), pages 701-711.
    19. Xiaopeng Tang & Boyang Liu & Furong Gao & Zhou Lv, 2016. "State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer," Energies, MDPI, vol. 9(9), pages 1-12, August.
    20. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, 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:eee:energy:v:106:y:2016:i:c:p:662-672. 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: http://www.journals.elsevier.com/energy .

    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.