The article describes an elaboration of the X-in-the-loop (XiL) testing environment for a thermal management system (TMS) intended for the traction electric drive of an electric vehicle, which has each of its wheels driven by an in-wheel motor. The TMS features the individual thermal regulation of each electric drive using a hydraulic layout with parallel pipelines and electrohydraulic pumps embedded into them. The XiL system is intended as a tool for studying and developing the TMS design and controls. It consists of the virtual part and the physical part. The former simulates the vehicle operating in a driving cycle with the heat power dissipated by the electric drive components, which entails the change in their temperature regimes. The physical part includes the TMS itself consisting of a radiator, pipelines, and pumps. The physical part also features devices intended for simulation of the electric drive components in terms of their thermal and hydraulic behaviors, as well as devices that simulate airflow induced by the vehicle motion. Bilateral, real-time interactions are established between the two said parts combining them into a cohesive system, which models the studied electric vehicle and its components. The article gives a description of a laboratory setup, which implements the XiL environment including the mathematical models, hardware devices, as well as the control loops that establish the interaction of those components. An example of using this system in a driving cycle test shows the interaction between its parts and operation of the TMS in conditions simulated in both virtual and physical domains. The results constitute calculated and measured quantities including vehicle speed, operating parameters of the electric drives, coolant and air flow rates, and temperatures of the system components.
<p>This article overviews the history of development of driverless vehicles both in Russia and the World. Foreign experience of development of driverless vehicles, including electric traction, is analyzed. Main stages of creation of experimental NAMI driverless electric vehicle are revised. Main engineering solutions are described concerning development of advanced NAMI driverless electric vehicle, its major components and control systems. Projects aimed at environmental safety of passengers in NAMI driverless electric vehicle are exemplified. Results of bench scale and running tests of NAMI driverless electric vehicle are summarized. Major advantages of driverless energy efficient and environmentally clean transport are demonstrated.</p>
Using biodiesel fuel in diesel engines for heavy-duty transport is important to meet the stringent emission regulations. Biodiesel is an oxygenated fuel and its physical and chemical properties are close to diesel fuel, yet there is still a need to analyze and tune the fuel injection parameters to optimize the combustion process and emissions. A four-injections strategy was used: two pilots, one main and one post injection. A highly advanced SOI decreases the NOx and the compression work but makes the combustion process less efficient. The pilot injection fuel mass influences the combustion only at injection close to the top dead center during the compression stroke. The post injection has no influence on the compression work, only on the emissions and the indicated work. An optimal injection strategy was found to be: pilot SOI 19.2 CAD BTDC, pilot injection fuel mass 25.4%; main SOI 3.7 CAD BTDC, main injection fuel mass 67.3% mg; post SOI 2 CAD ATDC, post injection fuel mass 7.3% (the injection fuel mass is given as a percentage of the total fuel mass injected). This allows the indicated work near the base case level to be maintained, the pressure rise rate to decrease by 20% and NOx emissions to decrease by 10%, but leads to a 5% increase in PM emissions.
This article describes a study that aimed to estimate the fuel-saving potential possessed by the hybridization of conventional powertrains intended for heavy-duty vehicles based on diesel and natural gas fueled engines. The tools used for this analysis constitute mathematical models of vehicle dynamics and the powertrain, including its components, i.e., the engine, electric drive, transmission, and energy storage system (ESS). The model of the latter, accompanied by experimental data, allowed for an analysis of employing a supercapacitor regarding the selection of its energy content and the interface between the traction electric drive and the ESS (in light of the wide voltage operating range of supercapacitors). The results revealed the influence of these factors on both the supercapacitor efficiency (during its operation within a powertrain) and the vehicle fuel economy. After implementation of the optimized ESS design within the experimentally validated vehicle model, simulations were conducted in several driving cycles. The results allowed us to compare the fuel economy provided by the hybridization for diesel and gas powertrains in different driving conditions, with different vehicle masses, taking into account the onboard auxiliary power consumption.The complicating aspect of replacing diesel engines with gas engines is the necessity of having an onboard storage for the compressed or liquefied gas, which constitutes a complex system that has its own cost, service, safety, and other issues. Therefore, whether to choose a gas-based vehicle or stay conservative with a diesel-based option becomes a trade-off decision.During the past few years, the National Research Center "NAMI" has been conducting an R&D project in cooperation with one of the country's major producers of heavy-duty vehicles. The project was aimed toward developing a gas-fueled engine family derived from the diesel engine that was newly developed by the aforementioned HD vehicle manufacturer. One can find the details on the project and its results in References [7][8][9]. The main outcomes of the project were the gas-fueled engines (see example in Figure 1) operating with the Otto and Miller thermodynamic cycles. The engines were installed in vehicles intended for long-haul operations (also shown in Figure 1) and tested in road conditions.
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