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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.

Since vehicles are comprised of thousands of components, it is essential to reduce the Life Cycle Inventory (LCI) modelling workload. This study aims to compare different LCI modeling workload-reducing scenarios to provide a trade-off between the workload efforts and result accuracy. To achieve the optimal balance between computational effort and data specification requirements, the driver seat is used as a case study, instead of the entire vehicle. When all the components of a conventional light-duty commercial vehicle are sorted by mass descending order, seats are among the first five. In addition, unlike the other components, seats are comprised of metals as well as a wide range of plastics and textiles, making them a representative test case for a general problem formulation. In this way, methodology and outcomes can be reasonably extended to the entire vehicle.

This study assesses the capabilities of dynamic wireless power transfer with respect to range extension and payload capacity of heavy-duty trucks. Currently, a strong push towards tailpipe CO2 emissions abatement in the heavy-duty transport sector by policymakers is driving the development of battery electric trucks. Yet, battery-electric heavy-duty trucks require large battery packs which may reduce the payload capacity and increase dwell time at charging stations, negatively affecting their acceptance among fleet operators. By investigating various levels of development of wireless charging technology and exploring various deployment scenarios for an electrified highway lane, the potential for a more efficient and environmentally friendly battery sizing was explored.

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.