Battery temperature prediction using an adaptive neuro-fuzzy inference system

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dc.contributor.authorZhang, Hanwen
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorAuger, Daniel J.
dc.contributor.authorLowe, Matt
dc.date.accessioned2024-03-18T16:53:49Z
dc.date.available2024-03-18T16:53:49Z
dc.date.issued2024-03-01
dc.description.abstractMaintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.en_UK
dc.description.sponsorshipThis work was funded by the UKRI Faraday Battery Challenge project called Next Generation LFP Cathode Material (NEXLFP). In addition, Abbas Fotouhi acknowledges funding from the Faraday Institution (Industrial Fellowships FIIF-003 and FIIF-014).en_UK
dc.identifier.citationZhang H, Fotouhi A, Auger DJ, Lowe M. (2024) Battery temperature prediction using an adaptive neuro-fuzzy inference system. Batteries, Volume 10, Issue 3, March 2024, Article number 85en_UK
dc.identifier.issn2313-0105
dc.identifier.urihttps://doi.org/10.3390/batteries10030085
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21032
dc.language.isoen_UKen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectLi-ion batteryen_UK
dc.subjecttemperature predictionen_UK
dc.subjectbattery thermal managementen_UK
dc.subjectANFISen_UK
dc.subjectneural networken_UK
dc.subjectsystem identificationen_UK
dc.subjectelectric vehicleen_UK
dc.titleBattery temperature prediction using an adaptive neuro-fuzzy inference systemen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-02-26

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