Electric vehicle battery model identification and state of charge estimation in real world driving cycles

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dc.contributor.authorFotouhi, Abbas
dc.contributor.authorPropp, Karsten
dc.contributor.authorAuger, Daniel J.
dc.date.accessioned2016-08-16T12:50:56Z
dc.date.available2016-08-16T12:50:56Z
dc.date.issued2015-09-24
dc.description.abstractThis paper describes a study demonstrating a new method of state-of-charge (SoC) estimation for batteries in real-world electric vehicle applications. This method combines realtime model identification with an adaptive neuro-fuzzy inference system (ANFIS). In the study, investigations were carried down on a small-scale battery pack. An equivalent circuit network model of the pack was developed and validated using pulse-discharge experiments. The pack was then subjected to demands representing realistic WLTP and UDDS driving cycles obtained from a model of a representative electric vehicle, scaled match the size of the battery pack. A fast system identification technique was then used to estimate battery parameter values. One of these, open circuit voltage, was selected as suitable for SoC estimation, and this was used as the input to an ANFIS system which estimated the SoC. The results were verified by comparison to a theoretical Coulomb-counting method, and the new method was judged to be effective. The case study used a small 7.2 V NiMH battery pack, but the method described is applicable to packs of any size or chemistry.en_UK
dc.identifier.citationAbbas Fotouhi, Karsten Propp and Daniel J. Auger. Electric vehicle battery model identification and state of charge estimation in real world driving cycles. Proceedings of the 7th computer science and electronic engineering conference (CEEC 2015), 24-25th September 2015, Colchester, UK.en_UK
dc.identifier.isbn9781467394826
dc.identifier.urihttp://dx.doi.org/10.1109/CEEC.2015.7332732
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10318
dc.language.isoenen_UK
dc.publisherInstitute of Electrical and Electronics Engineersen_UK
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
dc.titleElectric vehicle battery model identification and state of charge estimation in real world driving cyclesen_UK
dc.typeArticleen_UK

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