Battery temperature Prediction in electric vehicles using bayesian regularization

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dc.contributor.authorAkpinar, R. Alp
dc.contributor.authorAchanta, Sreekar
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorZhang, Hanwen
dc.contributor.authorAuger, Daniel J.
dc.date.accessioned2024-11-22T10:11:31Z
dc.date.available2024-11-22T10:11:31Z
dc.date.freetoread2024-11-22
dc.date.issued2024-07-02
dc.date.pubOnline2024-11-11
dc.description.abstractThis study is focused on developing a new temperature prediction model to be used in battery thermal management systems. Electric vehicle (EV) application is considered as a case study however, the proposed model is applicable in other applications too. The final goal is to improve batteries’ performance, durability, and safety. By specifically examining two types of batteries, which are Lithium Iron Phosphate (LFP) and Nickel Cobalt Aluminum (NCA), the proposed model utilizes Bayesian Regularization to precisely predict variations in the battery’s surface temperature in an EV application. The present study experimentally evaluates the accuracy of the proposed model for prediction of the batteries’ surface temperature under various conditions. According to the results, average errors of less than 0.1°C and 0.3°C are achieved when predicting the batteries’ surface temperature in 30 and 90 seconds ahead. This study is expected to have an impact on the advancement of EVs’ battery technologies by improving the battery’s performance and safety.
dc.description.conferencename2024 20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)
dc.description.sponsorshipInnovate UK
dc.description.sponsorshipThis work was co-funded by the UKRI Faraday Battery Challenge project called Next Generation LFP Cathode Material (NEXLFP) and the High-performance LFP Cathode Active Material (HiCAM) project funded by the Advanced Propulsion Centre (APC) and the Innovate UK.
dc.description.sponsorshipIn addition, Abbas Fotouhi acknowledges funding from the Faraday Institution (Industrial Fellowships FIIF 003 and FIIF-014).
dc.identifier.citationAkpinar RA, Achanta S, Fotouhi A, et al., (2024) Battery temperature Prediction in electric vehicles using bayesian regularization. In: 2024 20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2-5 July 2024, Volos, Greece
dc.identifier.elementsID558942
dc.identifier.urihttps://doi.org/10.1109/smacd61181.2024.10745411
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23210
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10745411
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4016 Materials Engineering
dc.subject34 Chemical Sciences
dc.subject3406 Physical Chemistry
dc.subject7 Affordable and Clean Energy
dc.titleBattery temperature Prediction in electric vehicles using bayesian regularization
dc.typeConference paper
dcterms.coverageVolos, Greece
dcterms.dateAccepted2024-04-18
dcterms.temporal.endDate5 Jul 2024
dcterms.temporal.startDate2 Jul 2024

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