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An unsupervised machine-learning technique, aimed at the identification of the optimal rule-based control strategy, has been developed for parallel hybrid electric vehicles that feature a torque-coupling (TC) device, a speed-coupling (SC) device or a dual-mode system, which is able to realize both actions. The approach is based on the preliminary identification of the optimal control strategy, which is carried out by means of a benchmark optimizer, based on the deterministic dynamic programming technique, for different driving scenarios. The optimization is carried out by selecting the optimal values of the control variables (i.e., transmission gear and power flow) in order to minimize fuel consumption, while taking into account several constraints in terms of NOx emissions, battery state of charge and battery life consumption.

The sustainable use of energy and the reduction of pollutant emissions are main concerns of the automotive industry. In this context, Hybrid Electric Vehicles (HEVs) offer significant improvements in the efficiency of the propulsion system and allow advanced strategies to reduce pollutant and noise emissions. The paper presents the results of a simulation study that addresses the minimization of fuel consumption, NOx emissions and combustion noise of a medium-size passenger car. Such a vehicle has a parallel-hybrid diesel powertrain with a high-voltage belt alternator starter. The simulation reproduces real-driver behavior through a dynamic modeling approach and actuates an automatic power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). Typical characteristics of parallel hybrid technologies, such as Stop&Start, regenerative braking and electric power assistance, are implemented via an operating strategy that is based on the reduction of total losses.

This paper describes the optimization of the layout and of the control strategy of through-the-road (TTR) parallel hybrid electric vehicles equipped with two compression-ignition engines that feature different values of maximum output power. First, a tool has been developed to define the optimal layout of each TTR vehicle. This is based on the minimization of the powertrain and fuel cost over a 10-year time span, taking into account the fuel consumption. Several performance requirements are guaranteed during the optimization, namely maximum vehicle velocity, 0-100 km/h acceleration time, gradeability and the all-electric range. A benchmark optimizer that is based on the dynamic programming theory has been developed to identify the optimal working mode and the gear number, which are the control variables of the problem. A mathematical technique, based on the pre-processing of a configuration matrix, has been developed in order to speed up the calculation time.

The frequency spectral structure of turbulence spatial components was investigated in the cylinder of an automotive diesel engine with a high-squish reentrant in-piston bowl of the conical type and a helical inlet port. A sophisticated HWA technique using single- and dual-sensor probes was applied for instantaneous air velocity measurements along the injector axis at practical engine speeds, up to 3000 rpm, under motored conditions. The investigation was carried out for both cycle-resolved and conventional turbulence components, as were determined by different wire orientations, throughout the induction, the compression and the early stage of the expansion stroke. The anisotropy of turbulence spectral structure and its temporal evolution during the engine cycle were examined by evaluating the autospectral density functions and the time scales of each turbulence component in consecutive correlation crank-angle intervals.