Automotive Science and Engineering
Volume:10 Issue: 3, Summer 2020

A comparative full life cycle assessment of a gasoline vehicle and a fuel cell vehicle (FCV) with five different fuel cycles including steam methane reforming (SMR), coal gasification, photovoltaic (PV), solar thermal, and grid-based electrolysis is presented in this paper. The results show that the total greenhouse gas emissions (GHG) are mainly found in the materials production and the component manufacturing stages of the FCV. Among various hydrogen production methods, the FCV with PV electrolysis has the lowest GHG emissions of 0.13 kg CO2 eq./km. The total GHG emissions of the gasoline vehicle are estimated as 0.30 kg CO2 eq./km mainly from the operation stage. An uncertainty analysis is carried out to assess the effects of variations of different input parameters on the total emissions. With a 95% level of confidence, the total emissions of the FCV with PV electrolysis is 0.18±0.05 kg CO2 eq./km. The component manufacturing and assembly stage drives the total GHG emissions uncertainty the most.

A multi-objective optimization and thermal analysis is performed by both experimental and numerical approaches on a Stirling engine cooler and heater. The power generated is measured experimentally by an electrical engine coupled with the crank case, and the friction is estimated by the difference between the necessary power used for rotating the engine at a specific pressure and speed, versus the actual power measured experimentally. In the experimental approach, different conditions were considered; for example, the charge pressure varied from 5-9 bars, and the engine speed varied from 286-1146 rpm. The maximum power generated was 461.3 W and was reported at 9 bars of charge pressure and 1146 rpm engine speed. Numerical approach was carried to simulate thermal balance for investigations on the effect of friction, engine speed and efficiency on generated engine power. Average values of Nusselt number and coefficient of friction were suggested from simulation results.
The multi-objective optimization was held using DOE method for maximizing engine efficiency and power, and also minimizing pressure drop. The top and bottom boundary values for our optimization were 5-9 bars of pressure and 286-1146 rpm of engine speed; for both helium and carbon dioxide. To do so, all three significance factors (engine speed, efficiency and friction) were given different weights, thus different combinations of weight value was investigated
Amongst different interesting findings, results showed that if the efficiency weight factor changed from 1 to 3, for helium in a specific condition, the optimum engine speed would increase by approximately 30.6 %

In internal combustion engines, the turbocharger and alternative fuels are two important factors affecting engine performance and exhaust emission. In this investigation, a one-dimensional computational fluid dynamics with GT-Power software was used to simulate a six-cylinder turbocharged diesel engine and the naturally aspirated diesel engine to study the performance and exhaust emissions with alternative fuels. The base fuel (diesel), methanol, ethanol, the blend of diesel and ethanol, biodiesel and decane was used. The results showed that decane fuel in the turbocharged engine has more brake power and torque (about 3.86%) compared to the base fuel. Also, the results showed that the turbocharger reduces carbon monoxide and hydrocarbon emissions, and biodiesel fuel has the least amount of carbon monoxide and hydrocarbon among other fuels. At the same time, the lowest NOX emission was obtained by decane fuel. As a final result can be demonstrated that the decane fuel in the turbocharged engine and the biodiesel fuel in the naturally aspirated engine could be a good alternative ratio to diesel fuel in diesel engines.

Lane detection is a crucial step for any autonomous driving system to decrease car accidents and increase safety. In this paper, based on inverse perspective mapping and Probabilistic Hough Transform, we propose a lane detection system which works on city street images. First, by using inverse perspective mapping the top view of the street is obtained. Second, the lanes are rectified using a specifically designed filter which enhances the lanes and suppresses other elements. Then, by using Probabilistic Hough transform the location of the lanes is detected in the images. For the final refinement, lane candidates are mapped to the road image using perspective mapping and the lane intensity is analyzed to reduce false acceptance. We evaluate the performance of the proposed method on Caltech-lane dataset and the obtained results show that the proposed method is able to detect straight lanes.

Reducing the fuel consumption and energy use in transportation systems are the active research areas in recent years. This paper considers the repetitive mission of the intercity passenger buses as a case for fuel reduction. A look-ahead energy management system is proposed which uses the information about the geometry and speed limits of the road ahead. This data can be extracted using road slope and speed limits database in combination with a GPS unit. A fuzzy gain scheduling algorithm is proposed to improve the performance of the look-ahead control. The road slope and speed limit specifications called road pattern can define some two dimensional regions. The main parameters of the proposed fuzzy look-ahead controller are optimized in each region using the genetic algorithm.  The final output of the proposed controller is the desired speed that regularly is fed to the conventional cruise controller with new set points. The simulation results of the proposed energy management system show that the fuel consumption is significantly reduced.

Traffic control is a major and common problem in large-scale urban decision-making, particularly in metropolises. Several models of intelligent highways have been proposed to tackle the issue, and the longitudinal speed control of vehicles remains a key issue in the field of intelligent highways. Many researchers have been investigating the longitudinal speed control of vehicles. However, their proposed models disregard important and influential presumptions. In the present study, the longitudinal dynamics control of vehicles in the presence of nonlinear factors, such as air resistance, rolling resistance, a not ideal gearbox, an internal combustion engine and a torque converter, is investigated. Moreover, considering the presented model and using model reference adaptive control, a proper controller is designed to control the longitudinal speed of intelligent vehicles. The results of the proposed model, which is validated by commercial software, are in good agreement with real-world situations. Hence, a positive step is taken for controlling longitudinal speed of intelligent vehicles on an intelligent highway platform.

Rollover of sport utility vehicles is a critical challenge for dynamic stability of the vehicle. Due to the high rate of fatalities resulted from the rollover, in order to reduces the injuries, the design of active vehicle controllers has received significant attention among the researchers and car companies. In this article, a multi-criteria optimum method is discussed in order to design a dynamics stabilizing controller via differential braking with an optimum braking torque distribution. To this end, the nonlinear control method on the basis of the sliding mode techniques has been implemented that provides ride comfort, improve safety performance, and maintain maneuverability. To address the trade-off between the conflicting requirements of vehicle dynamic control in terms of maneuverability and rollover prevention capability, we formulate an artificial intelligence-based multi-criteria genetic algorithms. The simulation verification analysis indicates that the utilized optimum distribution braking torques result in the desired enhancement in roll stability of the vehicle.