Vehicles 2025, 7(1), 23; https://doi.org/10.3390/vehicles7010023 - 25 Feb 2025
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This paper introduces a novel strategy for an intelligent plug-in hybrid electric vehicle (PHEV) energy optimization strategy based on machine learning (ML) prediction of the upcoming journey, without recourse to navigation or other external data, which underpins many of the existing approaches. This
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This paper introduces a novel strategy for an intelligent plug-in hybrid electric vehicle (PHEV) energy optimization strategy based on machine learning (ML) prediction of the upcoming journey, without recourse to navigation or other external data, which underpins many of the existing approaches. This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. In particular, the RusBoosted ensemble classifier performed consistently well across the heterogeneous dataset (volume of data for training and variable imbalance in the datasets, reflecting the natural variability in the vehicle usage profiles), providing sufficiently accurate predictions for the proposed EMS strategy. Performance evaluation experiments were carried out using a model-in-the-loop (MIL) simulation set-up developed in this research. The results demonstrated that the proposed strategy has the potential to deliver significant reductions in engine running time (up to 76% on routine short journeys), with associated benefits in CO2 consumption and tailpipe emissions, as well as enhanced engine reliability. The broader importance of this study is that it demonstrates the great potential of using predictive insights from computation-efficient and robust ML to learn vehicle usage patterns to optimize the control strategies without reliance on uncertain external inputs.
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