Search

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy's real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications. [ABSTRACT FROM AUTHOR]

Design and control co-optimization studies for hybrid vehicles have been proposed in the past. However, such works suffer from difficulties arising due to (a) diverse real- and integer-valued variables, (b) complex nonlinear powertrain dynamics and design interconnections, (c) conflicting objective functions with path constraints, and (d) high computational resources requirements. To meet these challenges, this study presents an efficient co-optimization framework for hybrid electric vehicles (HEVs) which is built using existing algorithms and coordination schemes. Particular emphasis is given to the simultaneous scheme and the decomposition-based scheme. The decomposition-based scheme with the problem decomposition proposed in this work can efficiently handle multitime scale state variables and both integer- and real-valued design and control optimization variables. This is demonstrated by solving the mixed-integer optimal design and control problem of a series hybrid vehicle over a 1-h long drive cycle with time discretization of 1 s. The problem complexity is elevated by using an increasing number of state variables (including battery state of charge, battery energy, and after-treatment system temperature), control variables (such as the engine power and engine on/off), and design parameters (such as the number of battery cells and the type and size of the engine). In addition, a multi-objective cost function is used to find a tradeoff solution between fuel consumption and emissions minimization. The results show that in terms of optimality of the solution, the decomposition-based scheme is comparable with the simultaneous but can give a 14% improvement in computational performance. The effectiveness of the proposed framework is demonstrated by comparing the co-optimization results against a baseline case in which only the optimal control problem is solved. The co-optimized solution yields up to 3.7% average genset efficiency improvement and a fuel consumption reduction to 1.6 kg from 2.5 kg, which is further reduced to 1.5 kg by adding the engine on-off control. Finally, a decision matrix is developed to provide guidance on the selection of the optimization algorithm and coordination scheme for any problem at hand. [ABSTRACT FROM AUTHOR]

The Special Issue "Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies" has been proposed with the main objective of contributing to the sustainable mobility agenda through enhanced scientific and multi-disciplinary topics, aimed at addressing concerns and real possibilities in the achievement of a greener mobility. 10.3390/en15020638 2 Lombardi S., Villani M., Chiappini D., Tribioli L. Cooling System Energy Consumption Reduction through a Novel All-Electric Powertrain Traction Module and Control Optimization. The study has shown that increasing the powertrain efficiency leads up to a 54% reduction of energy consumption (but only if the cooling circuit is stressed enough and the fan is activated during the vehicle operation). [Extracted from the article]

In this work, the problem of reducing the energy consumption of the cooling circuit for the propulsion system of an all-electric vehicle is approached with two different concepts: improvement of the powertrain efficiency and optimization of the control strategy. Improvement of the powertrain efficiency is obtained through a modular design, which consists of replacing the electric powertrain with several smaller traction modules whose powers sum up to the total power of the original powertrain. In this paper, it is shown how modularity, among other benefits, also allows reducing the energy consumption of the cooling system up to 54%. The energy consumption of the cooling system is associated with two components: the pump and the fan. They produce a so-called auxiliary load on the battery, reducing the maximum range of the vehicle. In conventional cooling systems, the pump and the fan are controlled with a thermostat, without taking into account the energy consumption. Conversely, in this work a control strategy to reduce the auxiliary loads is developed and compared with the conventional approach, showing that the energy consumption of the cooling system can be reduced up to 27%. To test the control strategy, numerical simulations have been carried out with a 1-D model of the cooling system. On the other hand, all the thermal loads of the components have been calculated with a vehicle simulator, which is able to predict the vehicle's behavior under different driving cycles. [ABSTRACT FROM AUTHOR]

Direct optical access to the combustion chamber of a gasoline direct injection (GDI) engine provides extremely valuable information about the combustion process. Experimental measurements of the geometric characteristics of the turbulent flame—such as the flame radius, flame center, flame edges and flame brush thickness—are of fundamental interest in support of the development and validation of any combustion model. To determine the macroscopic properties of sprays and flames, visualization and digital image processing techniques are typically used in controlled experimental setups like single-cylinder optical engines or closed vessels, while optical measurements on mass-production engines are more uncommon. In this paper the optical experimental setup (consisting of a high-speed camera, a laser light source and a data acquisition system) used to characterize the planar turbulent flame propagation in the cylinder of a 3.5 L GDI V6 mass-production engine, is described. The image acquisition process and the image processing that is necessary to evaluate the geometric characteristics of the propagating flame front, which are usually omitted in the referenced literature, are reported in detail to provide a useful guideline to other researchers. The results show that the step-by-step algorithm and the calculation formulae proposed allow to retrieve clear visualizations of the propagating flame front and measurements of its geometrical properties. [ABSTRACT FROM AUTHOR]