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Leveraging look-ahead information for optimal battery thermal management

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Leveraging look-ahead information for optimal battery thermal management

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dc.contributor.author Broatch, A. es_ES
dc.contributor.author Pla Moreno, Benjamín es_ES
dc.contributor.author Bares-Moreno, Pau es_ES
dc.contributor.author Trintinaglia-Perin, Augusto es_ES
dc.date.accessioned 2024-04-11T07:56:21Z
dc.date.available 2024-04-11T07:56:21Z
dc.date.issued 2023-02-05 es_ES
dc.identifier.issn 1359-4311 es_ES
dc.identifier.uri http://hdl.handle.net/10251/203330
dc.description.abstract [EN] The efficiency and range of electric vehicles (EVs) is an actual object of concern among manufacturers. The fast market share growth together with issues such as range anxiety demand evermore robust battery thermal management system (BTMS) controllers to maximize its electrical output capability. Current rule-based controllers often cannot cope with the high variability of energy demand from EVs, leading to oscillations where derating occurs and increasing the EV overall energy consumption. This study proposes a prediction horizon estimation of the future energy demand based on driven cycles. Together with a look-ahead algorithm, it is possible to keep track of an optimal battery temperature which avoids battery derating during the warm-up phase of the vehicle. A battery temperature estimation using a probability matrix based on a Markov chain is proposed in which the controller improves its estimations by repeating the same route over several trips. Results show that the method can minimize the use of the electric battery heater by predicting the necessary battery temperature over a prediction horizon. Therefore, up to 4% of overall energy consumption is saved when the EV performs a daily commute driving cycle, when compared to the original controller. Also, a learning method is implemented, improving the future estimations by storing route data as more cycles are performed. es_ES
dc.description.sponsorship This research has been partially funded by the Agencia Valenciana de la innovación, Spain through the project INNEST/2021/120, entitled 'Demostrador Tecnológico de un paquete de baterias para vehiculo eléctrico'. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Applied Thermal Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Battery es_ES
dc.subject Derating es_ES
dc.subject Prediction es_ES
dc.subject.classification INGENIERIA AEROESPACIAL es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title Leveraging look-ahead information for optimal battery thermal management es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.applthermaleng.2022.119685 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AVI//INNEST%2F2021%2F120/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería del Diseño - Escola Tècnica Superior d'Enginyeria del Disseny es_ES
dc.description.bibliographicCitation Broatch, A.; Pla Moreno, B.; Bares-Moreno, P.; Trintinaglia-Perin, A. (2023). Leveraging look-ahead information for optimal battery thermal management. Applied Thermal Engineering. 220. https://doi.org/10.1016/j.applthermaleng.2022.119685 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.applthermaleng.2022.119685 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 220 es_ES
dc.relation.pasarela S\500305 es_ES
dc.contributor.funder Agència Valenciana de la Innovació es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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