crossrefEnergies2025-10-14Cited by 6

Explainable Machine Learning Prediction of Vehicle CO2 Emissions for Sustainable Energy and Transport

Dong Yuan, Long Tang, Xueyuan Yang, Fanqin Xu, Kailong Liu

Transport is a major contributor to anthropogenic greenhouse gases, making accurate assessment of vehicle emissions essential for climate change mitigation. This study develops a comparative machine learning framework to predict CO2 emissions from internal combustion engines (ICEs) and hybrid electric vehicles (HEVs), using data from the UK Vehicle Certification Agency. In addition to standard technical variables, the study considers noise level, a factor seldom integrated into emission modeling, reflecting potential interactions between acoustic conditions and vehicular emission patterns. Explainable machine learning techniques, including accumulated local effects, are employed to clarify how engine capacity, fuel consumption and pollutant indicators influence CO2 outputs under different driving conditions. Results show that medium- and high-speed driving dominate ICE emissions, whereas HEVs maintain lower emissions except under high power demand. By combining predictive modeling with interpretability, the study advances environmental informatics and provides actionable insights for low-carbon vehicle design, emission standards and sustainable transportation policies aligned with global climate goals.

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An Integrated CFD–Machine Learning Framework for Flow Assurance Risk Assessment During Hot Oil Commissioning of Subsea Jumpers

To mitigate flow assurance risks during the hot oil commissioning of deepwater jumpers, this study develops a transient displacement risk assessment framework integrating CFD–machine learning surrogate models. A 3D numerical model using VOF and conjugate heat transfer simulated h…

Alternative Thermal Technologies for Industrial Process Heat: Barriers and Opportunities

Energy scarcity and subsequent global fuel market shocks have become a significant concern for the United States. Process heating in industry accounts for over half of all industrial energy usage and is almost entirely (>95%) supplied by natural gas, coal, and byproduct fuels.…

Nonlinear Dynamics and Spatial Correlation Pattern of the Digital Economy on Energy Efficiency: Evidence from Ensemble Learning and Spatio-Temporal Graph Neural Network

Achieving synergy between the digital economy and energy efficiency is pivotal for realizing high-quality development under the “Dual Carbon” targets. However, traditional econometric methods struggle to capture the complex nonlinear and spatio-temporal dependencies inherent in t…

Prediction of Waterflooding Performance with a New Machine Learning Method by Combining Linear Dynamical Systems with Neural Networks

Machine learning methods have gained significant attention in forecasting waterflooding performance in recent years, but their accuracy often remains insufficient for practical field applications. This study proposes a hybrid framework that integrates a linear dynamical system (L…

Machine Learning-Based Lifetime Prediction of Lithium Batteries: A Comparative Assessment for Electric Vehicle Applications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aim…