Effects of ethanol on vehicle energy efficiency and implications on ethanol life-cycle greenhouse gas analysis.

Bioethanol is the world's largest-produced alternative to petroleum-derived transportation fuels due to its compatibility within existing spark-ignition engines and its relatively mature production technology. Despite its success, questions remain over the greenhouse gas (GHG) implications of fuel ethanol use with many studies showing significant impacts of differences in land use, feedstock, and refinery operation. While most efforts to quantify life-cycle GHG impacts have focused on the production stage, a few recent studies have acknowledged the effect of ethanol on engine performance and incorporated these effects into the fuel life cycle. These studies have broadly asserted that vehicle efficiency increases with ethanol use to justify reducing the GHG impact of ethanol. These results seem to conflict with the general notion that ethanol decreases the fuel efficiency (or increases the fuel consumption) of vehicles due to the lower volumetric energy content of ethanol when compared to gasoline. Here we argue that due to the increased emphasis on alternative fuels with drastically differing energy densities, vehicle efficiency should be evaluated based on energy rather than volume. When done so, we show that efficiency of existing vehicles can be affected by ethanol content, but these impacts can serve to have both positive and negative effects and are highly uncertain (ranging from -15% to +24%). As a result, uncertainties in the net GHG effect of ethanol, particularly when used in a low-level blend with gasoline, are considerably larger than previously estimated (standard deviations increase by >10% and >200% when used in high and low blends, respectively). Technical options exist to improve vehicle efficiency through smarter use of ethanol though changes to the vehicle fleets and fuel infrastructure would be required. Future biofuel policies should promote synergies between the vehicle and fuel industries in order to maximize the society-wise benefits or minimize the risks of adverse impacts of ethanol.

Innovation, the diesel engine and vehicle markets: Evidence from OECD engine patents

This paper uses a patent data set to identify factors fostering innovation of diesel engines between 1974 and 2010 in the OECD region. The propensity of engine producers to innovate grew by 1.9 standard deviations after the expansion of the car market, by 0.7 standard deviations following a shift in the EU fuel economy standard, and by 0.23 standard deviations. The propensity to develop emissions control techniques was positively influenced by pollution control laws introduced in Japan, in the US, and in the EU, but not with the expansion of the car market. Furthermore, a decline in loan rates stimulated the propensity to develop emissions control techniques, which were simultaneously crowded out by increases in publicly-funded transport research and development. Innovation activities in engine efficiency are explained by market size, loan rates and by (Organisation for Economic Cooperation and Development) diesel prices, inclusive of taxes. Price effects on innovation, outweigh that of the US corporate average fuel economy standards. Innovation is also positively influenced by past transport research and development. © 2014 Elsevier Ltd.

Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.