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

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.