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Using mobile charging stations as probes to discover latent EV charging demand in stochastic environments: A deep reinforcement learning approach.

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Title: Using mobile charging stations as probes to discover latent EV charging demand in stochastic environments: A deep reinforcement learning approach.
Authors: Hemmati Golsefidi, Atefeh1, ahego@dtu.dk, Hüttel, Frederik Boe1, fbohy@dtu.dk, Samaranayake, Samitha2, samitha@cornell.edu, Pereira, Francisco Câmara1, camara@dtu.dk
Source: Expert Systems with Applications; May2026, Vol. 312, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 192820717
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=192820717
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.eswa.2026.131433
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      – Code: eng
        Text: English
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      – TitleFull: Using mobile charging stations as probes to discover latent EV charging demand in stochastic environments: A deep reinforcement learning approach.
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            NameFull: Hemmati Golsefidi, Atefeh
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            NameFull: Hüttel, Frederik Boe
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            NameFull: Samaranayake, Samitha
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 312
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