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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 combustion characteristics and exhaust emissions of a diesel engine fuelled with conventional diesel fuel and a biodiesel blend, in particular a 20% v/v concentration of rapeseed methyl ester (RME) mixed with diesel fuel. The investigation was carried out on a prototype three-cylinder engine with 1000 cc of displacement for quadricycle applications. The engine is equipped with a direct common-rail injection system that reaches a maximum pressure of 1400 bar. The engine was designed to comply with Euro 4 and BS IV exhaust emission regulations without a diesel particulate filter. Both in-cylinder pressure and rate of heat release traces were analyzed at different engine speeds and loads. Gaseous emissions were measured at the exhaust. A smoke meter was used to measure the particulate matter concentration. The sizing and the counting of the particles were performed by means of an engine exhaust particle sizer spectrometer.

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.