Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162872
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dc.contributor.authorZhang, Liangen_US
dc.contributor.authorGao, Tianen_US
dc.contributor.authorCai, Guoweien_US
dc.contributor.authorKoh, Leong Haien_US
dc.date.accessioned2022-11-11T05:34:28Z-
dc.date.available2022-11-11T05:34:28Z-
dc.date.issued2022-
dc.identifier.citationZhang, L., Gao, T., Cai, G. & Koh, L. H. (2022). Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm. Journal of Energy Storage, 49, 104092-. https://dx.doi.org/10.1016/j.est.2022.104092en_US
dc.identifier.issn2352-152Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/162872-
dc.description.abstractNew energy vehicles have become a global transportation development trend in order to achieve considerable fuel consumption and carbon emission reductions. However, as the number of new energy cars grows, new energy vehicle safety concerns are becoming more evident, posing a major threat to drivers' lives and property and limiting the industry's growth. This paper develops a charging safety early warning model for electric vehicles (EV) based on the Improved Grey Wolf Optimization (IGWO) algorithm in order to improve the timeliness and accuracy of charging safety early warning. The greatest voltage of a single battery was chosen as the study goal based on the polarization characteristics of lithium-ion batteries and the equalization features of a vehicle lithium-ion battery pack. The IGWO-BP algorithm is then used to fit the entire EV charging process and anticipate the vehicle's charging condition. At the same time, set the warning threshold and the warning error code. In real time, comparing the EV charging data with the fitted data, computing the residual, and building the EV charging safety warning model based on the residual change. Finally, case analysis is performed using daily charging data from both rapid and slow charging. The findings reveal that the proposed early warning model based on the IGWO-BP algorithm can reliably recognize the abnormal state of EV charging voltage and issue timely warnings.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Energy Storageen_US
dc.rights© 2022 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleResearch on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithmen_US
dc.typeJournal Articleen
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.identifier.doi10.1016/j.est.2022.104092-
dc.identifier.scopus2-s2.0-85123946545-
dc.identifier.volume49en_US
dc.identifier.spage104092en_US
dc.subject.keywordsElectric Vehicleen_US
dc.subject.keywordsCharging Safety Early Warningen_US
dc.description.acknowledgementThis research was supported in part by the International Science and Technology Cooperation Project of Jilin Province Science and Technology Department, grant number 20210402080GH, the author hereby expresses his gratitude to the above-mentioned institution for their support.en_US
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