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.

• Publication year

  2025

• Venue

  Vehicles

• Publication date

  2025-12-17

• Fields of study

  Not labeled

• Identifiers
  DOI 10.3390/vehicles7040165
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Optimizing Energy Consumption in Electric Vehicles: A Systematic and Bibliometric Review of Recent Advances

  2025cited by this paper
• A Review of OBD-II-Based Machine Learning Applications for Sustainable, Efficient, Secure, and Safe Vehicle Driving

  2025cited by this paper
• Regenerative Braking Systems in Electric Vehicles: A Comprehensive Review of Design, Control Strategies, and Efficiency Challenges

  2025cited by this paper
• Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs

  2025cited by this paper
• Pre-Determining the Optimal Number of Clusters for k-Means Clustering Using the Parameters Package in R and Distance Metrics

  2025cited by this paper
• A New Cluster Validity Index Based on Local Density of Data Points

  2025cited by this paper
• Unsupervised Contrastive Learning for Time Series Data Clustering

  2025cited by this paper
• Defining Signatures for Intelligent Vehicles with Different Types of Powertrains

  2025cited by this paper
• Energy Consumption of Electric Vehicles in Europe

  2024influential reference
• Long Downhill Braking and Energy Recovery of Pure Electric Commercial Vehicles

  2024cited by this paper
• Vehicle Acceleration and Speed as Factors Determining Energy Consumption in Electric Vehicles

  2024cited by this paper
• Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

  2023cited by this paper
• Energy Flow and Electric Drive Mode Efficiency Evaluation of Different Generations of Hybrid Vehicles under Diversified Urban Traffic Conditions

  2023cited by this paper
• Applications of Clustering Methods for Different Aspects of Electric Vehicles

  2023cited by this paper
• Estimation of the Regenerative Braking Process Efficiency in Electric Vehicles

  2023cited by this paper
• The Concept of Determining Route Signatures in Urban and Extra-Urban Driving Conditions Using Artificial Intelligence Methods

  2023cited by this paper
• A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

  2023cited by this paper
• Utility Factor Curves for Plug-in Hybrid Electric Vehicles: Beyond the Standard Assumptions

  2023cited by this paper
• Location of Electric Vehicle Charging Stations in Inter-Andean Corridors Considering Road Altitude and Nearby Infrastructure

  2023cited by this paper
• The Analysis of Energy Recovered during the Braking of an Electric Vehicle in Different Driving Conditions

  2022cited by this paper
• Framework for Building Low-Cost OBD-II Data-Logging Systems for Battery Electric Vehicles

  2022cited by this paper
• Hybrid Electric Vehicles: A Review of Existing Configurations and Thermodynamic Cycles

  2021cited by this paper
• A Survey on Through-the-Road Hybrid Electric Vehicles

  2020cited by this paper
• A Fast Method for Estimating the Number of Clusters Based on Score and the Minimum Distance of the Center Point

  2019cited by this paper
• Modelling and control of hybrid electric vehicles (a comprehensive review)

  2017cited by this paper
• An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis

  2017cited by this paper
• Comparison of real driving cycles and consumed braking power in suburban Slovakian driving

  2017influential reference
• Research on Electric Vehicle Regenerative Braking System and Energy Recovery

  2016cited by this paper

• No citing papers are available for this paper.