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dc.contributor.author
Trimboli, Maximiliano Daniel
dc.contributor.author
Avila, Luis Omar
dc.date.available
2024-03-20T15:03:59Z
dc.date.issued
2024-03
dc.identifier.citation
Trimboli, Maximiliano Daniel; Avila, Luis Omar; Optimal battery charge with safe exploration; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 237; 3-2024; 1-12
dc.identifier.issn
0957-4174
dc.identifier.uri
http://hdl.handle.net/11336/231075
dc.description.abstract
Li-ion batteries have become the standard power source for electric vehicles (EVs) as the alternative of choice to reduce CO2 emissions. But before becoming a reliable technology, Li-ion batteries must deal with two significant challenges: undesirable electrochemical reactions caused by excessive charging rates and considerable time for an EV to get charged. It is necessary to employ balanced current profiles that prevent both serious battery degradation effects and the inconvenience to end users. In this work, the authors propose a safe exploration deep reinforcement learning (SDRL) approach in order to determine optimal charging profiles under variable operating conditions. One of the main advantages of RL techniques is that they can learn from interaction with the real or simulated system while incorporating the nonlinearity and uncertainty derived from fluctuating environmental conditions. However, since RL techniques must explore undesirable states before obtaining an optimal policy, no safety guarantees are provided. The proposed approach aims at maintaining zero-constraint violations throughout the learning process through the integration of a safety layer that corrects the action if a constraint is likely to be violated. The proposed method is tested in the equivalent circuit of a Li-ion battery under varying conditions. Results reveal that with the integration of this safety layer, SDRL is able to find safe optimized charging policies while considering a trade-off between the charging speed and the battery lifespan, including a 30% reduction in charging time while still maintaining temperatures within permissible limits and up to 38% of battery life conservation, compared with benchmark methods. Moreover, our approach does not experience episodes demonstrating restriction violations throughout the pre-training, training, and evaluation phases.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Pergamon-Elsevier Science Ltd
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
SAFE-RL
dc.subject
SOC
dc.subject
BATTERY AGING
dc.subject
VARIABILITY
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Optimal battery charge with safe exploration
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-03-19T14:17:36Z
dc.journal.volume
237
dc.journal.pagination
1-12
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Trimboli, Maximiliano Daniel. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
dc.description.fil
Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
dc.journal.title
Expert Systems with Applications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/journal/expert-systems-with-applications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.eswa.2023.121697
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