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Many aspects of battery electric vehicles are very challenging from the engineering point of view in terms of safety, weight, range, and drivability. Commercial vehicle engines are often subjected to high loads even at low speeds and this can lead to an intense increment of the battery pack temperature and stress of the cooling system. For these reasons the optimal design of the battery pack and the relative cooling system is essential. The present study deals with the challenge of designing a battery pack that satisfies both the conditions of lowest weight and efficient temperature control. The trade-off between the battery pack size and the electrical stress on the cells is considered. The electric system has the aim to substitute a 3.0 liters compression ignition engine mainly for commercial vehicles.

The in-cylinder direct injection of fuels can be a further step towards cleaner and more efficient internal combustion engines. However, the injector design and its characterization, both experimental and from numerical simulation require accurate diagnostics and efficient models. This work aims to simulate the complex behavior of the gaseous and liquid jets through an outwardly opening injector characterized by optical diagnostics using a one-dimensional model without using three dimensional models. The behavior of the jet from an outwardly opening injector changes according to the type of fuel. In the case of the gas, the experimental investigations put in evidence three main jet regions: 1) near-field region where the jet shows a complex gas-dynamic structure; 2) transition region characterized by intense mixing; 3) far-field region characterized by a fully developed subsonic turbulent jet.

This paper deals with the experimental and numerical investigation of a 2.0 litre single cylinder Heavy Duty Diesel Engine fuelled by natural gas and diesel oil in Dual Fuel mode. Due to the gaseous nature of the main fuel and to the high compression ratio of the diesel engine, reduced emissions can be obtained. An experimental study has been carried out at three different load level (25%, 50% and 75% of full engine load). Basing on experimental data, the authors recreated a 45° mesh sector of the engine cylinder and performed CFD simulations for the cases at 50% and 75% load levels. Numerical simulations were carried out on the 3D code Ansys FORTE. The aim of this work is to study combustion phenomena and, in particular, the interaction between natural gas and diesel oil, respectively represented by methane and n-dodecane. A reduced kinetic scheme for methane auto-ignition was implemented while for n-dodecane two set of reactions were utilised.

The introduction of new light-duty vehicle emission limits to comply under real driving conditions (RDE) is pushing the diesel engine manufacturers to identify and improve the technologies and strategies for further emission reduction. The latest technology advancements on the after-treatment systems have permitted to achieve very low emission conformity factors over the RDE, and therefore, the biggest challenge of the diesel engine development is maintaining its competitiveness in the trade-off “CO2-system cost” in comparison to other propulsion systems. In this regard, diesel engines can continue to play an important role, in the short-medium term, to enable cost-effective compliance of CO2-fleet emission targets, either in conventional or hybrid propulsion systems configuration. This is especially true for large-size cars, SUVs and light commercial vehicles.

This study aims at building an efficient and robust radial basis function (RBF) artificial neural network (ANN), to reconstruct the in-cylinder pressure of a diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. The RBF network is trained using measurements from different engine operating conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The RBF network is then validated using data not included in training and the results show good correspondence between measured and reconstructed pressure signal. Various network parameters are used to optimize the network quality.