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Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

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  • Tang, Xiaopeng
  • Zou, Changfu
  • Yao, Ke
  • Lu, Jingyi
  • Xia, Yongxiao
  • Gao, Furong

Abstract

This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normal-speed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

Suggested Citation

  • Tang, Xiaopeng & Zou, Changfu & Yao, Ke & Lu, Jingyi & Xia, Yongxiao & Gao, Furong, 2019. "Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919312656
    DOI: 10.1016/j.apenergy.2019.113591
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    as
    1. Kouritzin, Michael A., 2017. "Residual and stratified branching particle filters," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 145-165.
    2. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    3. 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.
    4. Marzband, Mousa & Azarinejadian, Fatemeh & Savaghebi, Mehdi & Pouresmaeil, Edris & Guerrero, Josep M. & Lightbody, Gordon, 2018. "Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations," Renewable Energy, Elsevier, vol. 126(C), pages 95-106.
    5. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst & van den Broek, Machteld, 2018. "Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2181-2195.
    6. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
    7. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
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