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Abstract China transport energy consumption grows greatly along with dramatically escalated carbon dioxide emissions. Saving energy in transportation is requested for both reducing energy pressure intensity and decreasing relevant emissions. Large scale energy conservation and emission reduction are Chinese automotive industrys' fundamental objectives. This paper addresses a nonlinear hybrid fuzzy based proportional integral derivative (PID) control methodology to reduce the automobile fuel over-consumption and gases over-emission. After introducing the mathematical model of the automobile Pierburg mechatronic actuator of nonlinear character with parameter uncertainties, its angular displacement is controlled via the proposed PID-fuzzy logic control (FLC) approach. The outputs of the PID controller are the entries of the series-connected FLC. The hybrid topology merges the simplicity and robustness advantages of both the classical PID and the FLC methods respectively. The feedback controller, based on FLC technique, is designed to desirably maintain the angular displacement towards their desired reference values with possible least steady state errors. The proper steady state error elimination will lead to considerable gas emission reduction together with improved energy efficiency. Through Matlab/Simulink tools, the numerical simulations significantly illustrate that the hybrid PID-FLC technique can contribute efficiently in ameliorating the system dynamic behavior in presence of parameter uncertainties. The overall system behavioral analysis enhancement and the proposed controller robustness are experimentally validated. [ABSTRACT FROM AUTHOR]

In response to concerns related to climate change, and an attempt to encourage more sustainable behavior, individuals are often provided with information on greenhouse gas emissions (GHGs) of consumer items, such as personal vehicles. Currently in the US, information on vehicle efficiency is provided as grams of carbon dioxide (CO 2 ) per mile. Previous research presenting CO 2 as a mass and testing willingness-to-pay through Discrete Choice Experiment has found that such information can influence vehicle choice. However, other research has questioned whether how this information is presented might affect choice. That research argues that CO 2 emission information generally lacks contextualization that allows for interpretation. As well, it argues that the type of contextualization may affect choices. That research though did not test willingness-to-pay and the strength of its influence is not clear. In addition, research exists that argues that using pro-social, as opposed to financial, contextualization might be more influential on people’s choices. Thus, the purpose of this paper is to build on these previous findings on how CO 2 emissions are presented to determine whether changing how that information impacts vehicle choice with a Discrete Choice Experiment of vehicle choice analyzed using latent class modeling. No previous study has so robustly studied the influence that different framings might have on vehicle purchase. Five different methods of presenting CO 2 information are tested in this experiment: CO 2 emissions as grams per mile (current method), CO 2 emissions as pounds per year (consistent imperial units), CO 2 emissions as tons per year (yearly contextualization), an annual tax on CO 2 (yearly financial contextualization), and CO 2 as a percentage of the 2025 US EPA reduction target of 26% from 2005 levels (social goal contextualization). Results demonstrate that the current method results in lowest willingness to pay for CO 2 emission reductions, while the social goal contextualization results in the highest. [ABSTRACT FROM AUTHOR]

Abstract: The automobile industry in China has rapidly developed in recent years which resulted in an increase in gasoline usage and greenhouse gas (GHG) emissions. Focus on climate change has also accelerated to grow pressure on reducing vehicle weight and improving fuel efficiency. Aluminum (Al) as a light metal has demonstrated a great potential for weight savings in applications such as engine blocks, cylinder heads, wheels, hoods, tailgates etc. However, primary Al production requires intensive energy and the cost of Al is more than traditional steel, which may affect the total benefits realized from using Al in automobiles. Therefore, it is very essential to conduct a study to quantify the life cycle GHG emissions and energy consumption if the plan is to achieve fleet-wide Al intensive vehicles. This paper describes a life cycle assessment (LCA) methodology and the general modeling assumptions used to evaluate the impact of Al intensive vehicle on GHG emissions and energy consumption. The results indicated that the reductions in life cycle GHG emissions and energy consumption were not significant when the maximum Al content in an automobile is 145 kg, which is the average level of Al usage in automobiles in North America. A neural network methodology was used to forecast the vehicle stock in China from 2010 to 2020 and a vehicle fleet model was established to track GHG emissions and energy consumption of the vehicle fleet. A material availability factor was also introduced into the LCA methodology to further assist decision makers in providing rational proposals for a widespread implementation of Al in automobiles. A sensitivity analysis was also conducted to study the impact of the Al content in a vehicle on the final outcomes. The GHG emissions and energy consumption could be further reduced when the Al content in an automobile increases. [ABSTRACT FROM AUTHOR]