Vehicles

The automotive industry is currently undergoing significant transformations driven by challenges such as fierce competition, supply chain disruptions, and stringent legislative regulations aimed at reducing pollutant emissions. The research employs a combination of theoretical analysis and numerical modeling to investigate the manufacturing processes of stamped automotive components. Data collection methods include experimental testing of materials, LS-DYNA simulations, and non-contact scanning for dimensional analysis. The study also utilizes a workflow diagram to illustrate the various phases involved in the design and validation of automotive assemblies. The findings detail the critical role of digital transformation in the automotive industry, particularly in enhancing the accuracy and reliability of manufacturing processes. Implementing digital twins improves product quality and reduces product development time. The experimental results were compared with simulation data, and a good correlation was identified, showing, for the numerical model with complete history (thickness and stress), a difference of $1.6\%$. Furthermore, to simplify the process of developing the numerical models for the initial iterations, a scale factor of $~1.1$ is proposed for the testing load. This factor is not limited to the current design, as the manufacturing stages are similar for this range of products.

Some European Heavy Duty (HD) vehicle manufacturers have adopted Open Crankcase Ventilation (OCV) systems to improve reliability and performance. The emission compliance of HD vehicles both during certification and In-Service Conformity (ISC) testing need to also account for the crankcase ventilation. Despite that, the contribution of crankcase emissions to the overall emissions profile of modern trucks remains underexplored. This study experimentally characterizes the crankcase emissions of a Euro VI Step E HD truck equipped with an OCV system under controlled conditions on a chassis dynamometer. Emissions were measured over the World Harmonized Vehicle Cycle (WHVC) and an ISC-compliant driving cycle at two test cell temperatures. The results indicate that crankcase emissions account for up to 4% and 8% of the current regulatory limits for nitrogen oxides (NO_x) and 23 nm solid particle number (SPN₂₃), respectively. The tightening of NO_x limits under Euro 7 regulations would increase these contributions to approximately 11%. SPN₁₀ crankcase emissions were found to be on the order of 10¹¹ (11% of the Euro 7 limit). Real-time SPN₁₀ and SPN₂₃ measurements revealed that the fraction of nanosized particles increases significantly during cold start, suggesting increased oil combustion within the cylinder. These findings highlight the need to refine crankcase emissions measurement procedures within regulatory frameworks. A systematic investigation of measurement setups and ageing effects, taking into account variations in OCV system designs and piston ring wear, is essential to determine whether characterization during certification is sufficient or if ISC testing throughout the vehicle’s useful life will be required.

This study presents a predictive maintenance framework for hybrid electric vehicles (HEVs) based on emissions behaviour under laboratory-simulated driving conditions. Vehicle speed, road gradient, and ambient temperature were selected as the principal input variables affecting emission levels. Using simulated datasets, three machine learning model, specifically Linear Regression, Multilayer Perceptron (MLP), as well as Random Forest, were trained and evaluated. Within that set, the Random Forest model demonstrated the best performance, achieving an R² score of 0.79, Mean Absolute Error (MAE) of 12.57 g/km, and root mean square error (RMSE) of 15.4 g/km, significantly outperforming both Linear Regression and MLP. A MATLAB-based graphical interface was developed to allow real-time classification of emission severity using defined thresholds (Normal ≤ 150 g/km, Warning ≤ 220 g/km, Critical > 220 g/km) and to provide automatic maintenance recommendations derived from the predicted emissions. Scenario-based validation confirmed the system’s ability to detect emission anomalies, which might function as early indicators of mechanical degradation when interpreted relative to operating conditions. The proposed framework, developed using laboratory-simulated datasets, provides a practical, interpretable, and accurate solution for emissions-based predictive maintenance. Although the results demonstrate feasibility, the framework should be further confirmed with real-world on-road data prior to large-scale use.