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Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries
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Tang, Xiaopeng, Liu, Kailong, Wang, Xin, Liu, Boyang, Gao, Furong and Widanage, Widanalage Dhammika (2019) Real-time aging trajectory prediction using a base model-oriented gradient-correction particle filter for Lithium-ion batteries. Journal of Power Sources, 440 . 227118. doi:10.1016/j.jpowsour.2019.227118
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WRAP-Real-time-aging-trajectory-gradient-battery-management-Liu-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (939Kb) | Preview |
Official URL: https://doi.org/10..1016/j.jpowsour.2019.227118
Abstract
Predicting batteries' future degradation is essential for developing durable electric vehicles. The technical challenges arise from the absence of full battery degradation model and the inevitable local aging fluctuations in the uncontrolled environments. This paper proposes a base model-oriented gradient-correction particle filter (GC-PF) to predict aging trajectories of Lithium-ion batteries. Specifically, under the framework of typical particle filter, a gradient corrector is employed for each particle, resulting in the evolution of particle could follow the direction of gradient descent. This gradient corrector is also regulated by a base model. In this way, global information suggested by the base model is fully utilized, and the algorithm's sensitivity could be reduced accordingly. Further, according to the prediction deviations of base model, weighting factors between the local observations and base model can be updated adaptively. Four different battery datasets are used to extensively verify the proposed algorithm. Quantitatively, the RMSEs of GC-PF can be limited to 1.75%, which is 44% smaller than that of the conventional particle filter. In addition, the consistency of predictions when using different size of training data is also improved by 32%. Due to the pure data-driven nature, the proposed algorithm can also be extendable to other battery types.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | |||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Lithium ion batteries, Lithium ion batteries -- Design and construction, Lithium ion batteries -- Design and construction -- Simulation methods, Product life cycle, Energy storage , Energy conservation -- Management, Monte Carlo method | |||||||||||||||
| Journal or Publication Title: | Journal of Power Sources | |||||||||||||||
| Publisher: | Elsevier S.A. | |||||||||||||||
| ISSN: | 0378-7753 | |||||||||||||||
| Official Date: | 15 November 2019 | |||||||||||||||
| Dates: |
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| Date of first compliant deposit: | 1 October 2019 | |||||||||||||||
| Volume: | 440 | |||||||||||||||
| Article Number: | 227118 | |||||||||||||||
| DOI: | 10.1016/j.jpowsour.2019.227118 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| RIOXX Funder/Project Grant: |
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