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Load prediction based remaining discharge energy estimation using a combined online and offline prediction framework
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Hatherall, Ollie, Marco, James, Barari, Anup and Niri, Mona Faraji (2022) Load prediction based remaining discharge energy estimation using a combined online and offline prediction framework. In: 2022 IEEE Conference on Control Technology and Applications (CCTA), Trieste, Italy, 23-25 Aug 2022. Published in: 2022 IEEE Conference on Control Technology and Applications (CCTA) ISBN 9781665473385. doi:10.1109/ccta49430.2022.9966117
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WRAP-Load-prediction-remaining-discharge-energy-estimation-22.pdf - Accepted Version - Requires a PDF viewer. Download (961Kb) | Preview |
Official URL: https://doi.org/10.1109/ccta49430.2022.9966117
Abstract
Remaining discharge energy (RDE) indicates how much useful energy can be extracted from a battery before reaching the discharge limit. Future current loading on vehicle battery systems can be predicted to increase the accuracy of RDE estimations. This is done by using clustering techniques to group load measurements into states, and then using a probability-based framework, along with real-world data, to calculate the transitional probabilities between states. Here, an adapted K-means clustering method is used to cluster load profile data. Markov modelling is used to produce state transition probabilities. Two methods for load prediction are used, which are referred to as the offline-training method and the moving window method, where the offline-training method has not been implemented for this application before. Additional control logic is implemented to combine the proposed load prediction methods to produce a new hybrid load prediction method. This hybrid method shows improved RDE accuracy for a generalised load case. The robustness of the proposed technique is assessed in the presence of model errors, still showing good accuracy when compared to state-of-charge based calculations.
Item Type: | Conference Item (Paper) | ||||||||
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Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Electric power consumption -- Forecasting, Markov processes, Electric automobiles -- Batteries, Electric discharges | ||||||||
Journal or Publication Title: | 2022 IEEE Conference on Control Technology and Applications (CCTA) | ||||||||
Publisher: | IEEE | ||||||||
ISBN: | 9781665473385 | ||||||||
Official Date: | 8 December 2022 | ||||||||
Dates: |
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DOI: | 10.1109/ccta49430.2022.9966117 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2022 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: | 11 April 2023 | ||||||||
Date of first compliant Open Access: | 11 April 2023 | ||||||||
Conference Paper Type: | Paper | ||||||||
Title of Event: | 2022 IEEE Conference on Control Technology and Applications (CCTA) | ||||||||
Type of Event: | Conference | ||||||||
Location of Event: | Trieste, Italy | ||||||||
Date(s) of Event: | 23-25 Aug 2022 |
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