The objective of this study is to understand how each variable impacts the optimal configuration of a marine diesel engine equipped with an electric hybrid air-charging system that allows energy assistance and recovery. The aim is to minimize CO₂ emissions by reducing fuel consumption. The hybrid system offers flexibility in adjusting parameters from both the engine and air-charging system. It is compared with the baseline engine, which uses a free-floating turbocharger. The results show a significant improvement at low engine loads, where the baseline engine struggles to provide sufficient air. While turbine speed has little influence, compressor power reduces fuel consumption at low loads. However, at mid loads, resizing the turbomachine is necessary for further improvements. At high loads, full optimization of all variables is required to reduce fuel consumption. The electric hybrid system is particularly effective in tugboat-like conditions, where low loads dominate, but less impactful for ro-pax ferries. Despite the potential of the hybrid system, a fully optimized turbocharger could provide greater benefits due to reduced losses. Future studies could explore combining the adaptability of the hybrid system with a highly efficient turbocharger to reduce emissions across all load conditions.

Predicting end-of-process variables in internal combustion engines, such as brake-specific fuel consumption or pollutant emissions, is crucial for engine design decisions. However, errors in common multi-layer-perceptron-based artificial neural network models often match the magnitude of the expected fuel consumption improvements, potentially leading to incorrect decisions. This study introduces a hybrid model where artificial neural networks replace engine block elements, while the 1D gas circuit and turbocharger models are retained. To enhance metamodel accuracy, two modifications are proposed: incorporating a pulsating mass flow rate in the exhaust line to capture pulsating effects missing in mean-value engine models and using a hierarchical arrangement of several multi-layer perceptrons instead of a parallel arrangement. The pulsating mass flow rate approach improves the accuracy of all tested configurations by replicating pulsating effects from a detailed 1D engine model. Meanwhile, the hierarchical arrangement refines predictions of end-of-process variables, such as fuel consumption, by increasing the total layers, with a minimal trade-off in the accuracy of other variables. These findings are validated using a metamodel derived from a calibrated 1D engine model in GT-Suite. The proposed methods are expected to enhance the accuracy of data-driven artificial neural network approaches, contributing to more reliable engine design optimization.

This study investigates the effect of different variables in the optimization of a hybrid air charging systems for marine diesel engines, exploring the potential for energy recovery and assistance using electric machines. The optimization parameters include turbine speed, compressor power, start of injection, turbomachine size, and valve timing. At low engine speeds, the larger improvements come from increasing compressor power, which increases air-to-fuel ratio compared with the baseline turbocharging. As free-floating turbocharged engines are known to have low air-to-fuel ratio at low engine speeds, the relative improvement of the hybrid architecture is large at low engine speeds, being near to 10% less BSFC. At high engine speeds, the largest influence is marked by the SOI, an early injection obtains a faster combustion, which improves gross efficiency. However, the relative improvement compared with baseline turbocharger is less than 1% at high engine speeds. Due to the distribution of relative improvement of BSFC, this architecture has a deeper impact on fuel consumption in vessel operating cycles that focus on low engine speeds and loads, such as those of tugboats.