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Article

A Methodological Framework for Inferring Energy-Related Operating States from Limited OBD Data: A Single-Trip Case Study of a PHEV

by
Michal Loman
1,*,
Branislav Šarkan
1,
Arkadiusz Małek
2,*,
Jacek Caban
3,
Beata Martyna-Syroka
2 and
Katarzyna Piotrowska
3
1
Department of Road and Urban Transport, Faculty of Operation and Economics of Transport and Communications, University of Žilina, Univerzitná 8215/1, 01026 Žilina, Slovakia
2
Department of Transportation and Informatics, WSEI University, 20-209 Lublin, Poland
3
Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
*
Authors to whom correspondence should be addressed.
Vehicles 2025, 7(4), 165; https://doi.org/10.3390/vehicles7040165
Submission received: 12 November 2025 / Revised: 12 December 2025 / Accepted: 15 December 2025 / Published: 17 December 2025
(This article belongs to the Special Issue Energy Management Strategy of Hybrid Electric Vehicles)
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Abstract

This paper presents a methodological framework for inferring energy-related operating states of plug-in hybrid electric vehicles (PHEVs) under conditions of limited and incomplete on-board diagnostic (OBD) data. The proposed approach is illustrated using a single short real-world urban trip recorded for one PHEV operating in electric mode. Unsupervised clustering based on k-means is applied in progressively expanded state spaces (3D–5D) to decompose the driving process into physically interpretable operating states, despite the absence of direct measurements of key variables such as regenerative braking power. Cluster validity indices, per-cluster silhouette values, temporal segmentation, and robustness checks are employed to support the interpretability and internal consistency of the results. The study demonstrates that even a single, non-representative OBD time series contains sufficient internal structure to recover meaningful energy-related information when appropriate state-space decomposition is applied. While no statistical generalization is intended, the results highlight the potential of the proposed framework for analyzing real-world vehicle operation under constrained data availability.
Keywords: plug-in hybrid electric vehicle (PHEV); unsupervised clustering; driving signatures; electric mode; renewable charging; photovoltaic carport; intelligent mobility; energy efficiency; emission reduction; sustainable transport plug-in hybrid electric vehicle (PHEV); unsupervised clustering; driving signatures; electric mode; renewable charging; photovoltaic carport; intelligent mobility; energy efficiency; emission reduction; sustainable transport

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MDPI and ACS Style

Loman, M.; Šarkan, B.; Małek, A.; Caban, J.; Martyna-Syroka, B.; Piotrowska, K. A Methodological Framework for Inferring Energy-Related Operating States from Limited OBD Data: A Single-Trip Case Study of a PHEV. Vehicles 2025, 7, 165. https://doi.org/10.3390/vehicles7040165

AMA Style

Loman M, Šarkan B, Małek A, Caban J, Martyna-Syroka B, Piotrowska K. A Methodological Framework for Inferring Energy-Related Operating States from Limited OBD Data: A Single-Trip Case Study of a PHEV. Vehicles. 2025; 7(4):165. https://doi.org/10.3390/vehicles7040165

Chicago/Turabian Style

Loman, Michal, Branislav Šarkan, Arkadiusz Małek, Jacek Caban, Beata Martyna-Syroka, and Katarzyna Piotrowska. 2025. "A Methodological Framework for Inferring Energy-Related Operating States from Limited OBD Data: A Single-Trip Case Study of a PHEV" Vehicles 7, no. 4: 165. https://doi.org/10.3390/vehicles7040165

APA Style

Loman, M., Šarkan, B., Małek, A., Caban, J., Martyna-Syroka, B., & Piotrowska, K. (2025). A Methodological Framework for Inferring Energy-Related Operating States from Limited OBD Data: A Single-Trip Case Study of a PHEV. Vehicles, 7(4), 165. https://doi.org/10.3390/vehicles7040165

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