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Prediction of lithium-ion battery capacity with metabolic grey model

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  • 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
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    Cited by:

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    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).

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