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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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Official URL: https://doi.org/10..1016/j.jpowsour.2019.227118

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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
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
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:
DateEvent
15 November 2019Published
20 September 2019Available
4 September 2019Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
61433005[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
16207717Research Grants Council, University Grants Committeehttp://dx.doi.org/10.13039/501100002920
2017B010120002Government of Guangdong Provincehttp://dx.doi.org/10.13039/501100002912
685716Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661

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