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State of power prediction for lithium-ion batteries in electric vehicles via Wavelet-Markov load analysis

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Faraji Niri, Mona, Dinh, Quang Truong, Yu, Tung Fai, Marco, James and Bui, Truong Minh Ngoc (2021) State of power prediction for lithium-ion batteries in electric vehicles via Wavelet-Markov load analysis. IEEE Transactions on Intelligent Transportation Systems, 22 (9). pp. 5833-5848. doi:10.1109/TITS.2020.3028024 ISSN 1524-9050.

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Official URL: https://doi.org/10.1109/TITS.2020.3028024

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Abstract

Electric vehicle (EV) power demands come from its acceleration/braking as well as consumptions of the components. The power delivered to meet any demand is limited to the available power of the battery. This makes the battery state of available power (SoAP) a critical variable for battery management purposes. This paper presents a novel approach for long-term SoAP prediction by supervising the working conditions for prediction of future load. Firstly, a battery equivalent circuit model (ECM) coupled with a thermal model is established to accurately capture the battery dynamics. The battery model is then connected to an EV model in order to interpret the working conditions to battery power demand. By supervising the historical usage conditions, a long-term load prediction mechanism is designed based on wavelet analysis and Markov models. This facilitates the separation of low and high frequency load demands and addresses future uncertainties. Finally, the SoAP prediction is put forward along with a sensitivity analysis with respect to battery model and load prediction mechanism parameters. It is demonstrated that compared to the existing approaches for load and SoAP prediction, the developed method is more practical and accurate. Co-simulations via MATLAB and AMESim as well as experiments on a set of commercially available Lithium-ion (Li-ion) cylindrical cells under real-world drive cycles prove the given concept and validate the performance of the method.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Lithium ion batteries, Electric vehicles -- Batteries , Markov processes, Wavelets (Mathematics), Load factor design
Journal or Publication Title: IEEE Transactions on Intelligent Transportation Systems
Publisher: IEEE
ISSN: 1524-9050
Official Date: September 2021
Dates:
DateEvent
September 2021Published
20 October 2020Available
22 September 2020Accepted
Volume: 22
Number: 9
Page Range: pp. 5833-5848
DOI: 10.1109/TITS.2020.3028024
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 1 October 2020
Date of first compliant Open Access: 2 October 2020
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDInnovate UKhttp://dx.doi.org/10.13039/501100006041
UNSPECIFIEDJaguar Land Rover (Firm)http://viaf.org/viaf/305209406
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