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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 (2020) State of power prediction for lithium-ion batteries in electric vehicles via Wavelet-Markov load analysis. IEEE Transactions on Intelligent Transportation Systems . doi:10.1109/TITS.2020.3028024 (In Press)
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WRAP-state-power-prediction-lithium-ion-batteries-electric-vehicles-via-wavelet-Markov-load-analysis-Marco-2020.pdf - Accepted Version - Requires a PDF viewer. Download (3669Kb) | Preview |
Official URL: https://doi.org/10.1109/TITS.2020.3028024
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 | |||||||||
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| 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 |
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| Divisions: | Faculty of Science > 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: | 20 October 2020 | |||||||||
| Dates: |
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| Date of first compliant deposit: | 1 October 2020 | |||||||||
| DOI: | 10.1109/TITS.2020.3028024 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Publisher Statement: | © 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 | |||||||||
| RIOXX Funder/Project Grant: |
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