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EnOptiMine: Energy Optimization Framework for Electric Vehicles Through Object-Centric Process Mining.

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Title: EnOptiMine: Energy Optimization Framework for Electric Vehicles Through Object-Centric Process Mining.
Authors: Tripathi, Anukriti1 (AUTHOR), Vyas, Ranjana1,2 (AUTHOR), Holderbaum, William1,2 (AUTHOR) w.holderbaum@reading.ac.uk, Vyas, Om Prakash1,2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2944. 31p.
Subject Terms: *Electric vehicles, *Process mining, *Energy consumption, *Machine learning, *Energy demand management, *Infrastructure (Economics)
Abstract: Electric Vehicle (EV) charging infrastructure plays a critical role in modern energy systems, affecting energy load distribution, demand-response programs, and grid stability. As EV adoption accelerates globally, the varied charging habits and concurrent interactions among users, stations, and shared infrastructure create operational inefficiencies that existing machine learning and optimization approaches cannot fully diagnose, because these methods rely on aggregated or single-entity representations that discard cross-object process dependencies. To address this gap, we propose EnOptiMine (Energy Optimization Framework for Electric Vehicles through Object-Centric Process Mining), a novel four-phase analytical framework that applies Object-Centric Process Mining (OCPM) to EV charging infrastructure. EnOptiMine operates by transforming raw EV charging data into an Object-Centric Event Log (OCEL 2.0), discovering the complete charging lifecycle as a structured multi-object process through Object-Centric Directly-Follows Graphs (OC-DFGs), performing conformance analysis to detect and quantify process deviations across object-type lifecycles, and proposing process improvement interventions. Applied to the EV charging dataset, EnOptiMine identifies sessions that exhibit post-charge station idle-blocking, departure mismatch, and carry lifecycle ordering violations. In the present work, the real-world simulation confirms that a graduated idle fee policy recovers 22.9% of wasted station-hours, and a departure reconfirmation protocol reduces mismatch sessions by 54.0%. These results demonstrate that OCPM provides process-transparent diagnostic capabilities for EV charging infrastructure that are inaccessible to existing prediction- and optimization-based methods. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194909393
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PubTypeId: academicJournal
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  Data: EnOptiMine: Energy Optimization Framework for Electric Vehicles Through Object-Centric Process Mining.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2944. 31p.
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  Data: *<searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink><br />*<searchLink fieldCode="DE" term="%22Process+mining%22">Process mining</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+demand+management%22">Energy demand management</searchLink><br />*<searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Electric Vehicle (EV) charging infrastructure plays a critical role in modern energy systems, affecting energy load distribution, demand-response programs, and grid stability. As EV adoption accelerates globally, the varied charging habits and concurrent interactions among users, stations, and shared infrastructure create operational inefficiencies that existing machine learning and optimization approaches cannot fully diagnose, because these methods rely on aggregated or single-entity representations that discard cross-object process dependencies. To address this gap, we propose EnOptiMine (Energy Optimization Framework for Electric Vehicles through Object-Centric Process Mining), a novel four-phase analytical framework that applies Object-Centric Process Mining (OCPM) to EV charging infrastructure. EnOptiMine operates by transforming raw EV charging data into an Object-Centric Event Log (OCEL 2.0), discovering the complete charging lifecycle as a structured multi-object process through Object-Centric Directly-Follows Graphs (OC-DFGs), performing conformance analysis to detect and quantify process deviations across object-type lifecycles, and proposing process improvement interventions. Applied to the EV charging dataset, EnOptiMine identifies sessions that exhibit post-charge station idle-blocking, departure mismatch, and carry lifecycle ordering violations. In the present work, the real-world simulation confirms that a graduated idle fee policy recovers 22.9% of wasted station-hours, and a departure reconfirmation protocol reduces mismatch sessions by 54.0%. These results demonstrate that OCPM provides process-transparent diagnostic capabilities for EV charging infrastructure that are inaccessible to existing prediction- and optimization-based methods. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19122944
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      – Code: eng
        Text: English
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        PageCount: 31
        StartPage: 2944
    Subjects:
      – SubjectFull: Electric vehicles
        Type: general
      – SubjectFull: Process mining
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Energy demand management
        Type: general
      – SubjectFull: Infrastructure (Economics)
        Type: general
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      – TitleFull: EnOptiMine: Energy Optimization Framework for Electric Vehicles Through Object-Centric Process Mining.
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            NameFull: Vyas, Ranjana
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            NameFull: Holderbaum, William
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            NameFull: Vyas, Om Prakash
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            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19
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              Value: 12
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            – TitleFull: Energies (19961073)
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