Performance Comparison of MATLAB and Neuro Solution Software on Estimation of Fuel Economy by Using Artificial Neural Network

In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.

Pricing Automobile Fuel Economy : A New Hedonic Approach

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Evaluating New Policy Instruments of the Corporate Average Fuel Economy Standards : Footprints, Credit Transferring and Credit Trading

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Evaluating New Policy Instruments of the Corporate Average Fuel Economy Standards : Footprints, Credit Transferring and Credit Trading

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Cooking in the ungra area: Fuel efficiency, energy losses, and opportunities for reducing firewood consumption

Cooking efficiency and related fuel economy issues have been studied in a particular rural area of India. Following a description of the cooking practices and conditions in this locale, cooking efficiency is examined. A cooking efficiency of only 6% was found. The use of aluminium rather than clay pots results in an increased efficiency. In addition, cooking efficiency correlates very well with specific fuel consumption. The latter parameter is much simpler to analyse than cooking efficiency. The energy losses during cooking are examined in the second part of this case study. The major energy losses are heating of excess air, heat carried away by the combustion products, heat transmitted to the stove body and floor, and the chemical energy in charcoal residue. The energy loss due to the evaporation of cooking water is also significant because it represents about one-third of the heat reaching the pots.

Simulating early adoption of alternative fuel vehicles for sustainability

We quantify the conditions that might trigger wide spread adoption of alternative fuel vehicles (AFVs) to support energy policy. Empirical review shows that early adopters are heterogeneous motivated by financial benefits, environmental appeal, new technology, and vehicle reliability. A probabilistic Monte Carlo simulation model is used to assess consumer heterogeneity for early and mass market adopters. For early adopters full battery electric vehicles (BEVs) are competitive but unable to surpass diesels or hybrids due to purchase price premium and lack of charging availability. For mass adoption, simulations indicate that if the purchase price premium of a BEV closes to within 20% of an in-class internal combustion engine (ICE) vehicle, combined with a 60% increase in refuelling availability relative to the incumbent system, BEVs become competitive. But this depends on a mass market that values the fuel economy and CO2 reduction benefits associated with BEVs. We also find that the largest influence on early adoption is financial benefit rather than pro-environmental behaviour suggesting that AFVs should be marketed by appealing to economic benefits combined with pro-environmental behaviour to motivate adoption. Monte Carlo simulations combined with scenarios can give insight into diffusion dynamics for other energy demand-side technologies. © 2012 Elsevier Inc.

New trucks save fuel. 1979.

Mode of access: Internet.

Consumer behavior towards [sic] fuel efficient vehicles. Volume I: executive summary. Final report.

National Highway Traffic Safety Administration, Technology Assessment Division, Washington, D.C.

Consumer behavior towards [sic] fuel efficient vehicles. Volume II. Final report.

National Highway Traffic Safety Administration, Technology Assessment Division, Washington, D.C.

Consumer orientations toward fuel efficient vehicles. Executive summary. Final report.

National Highway Traffic Safety Administration, Office of Passenger Vehicle Research, Washington, D.C.

Consumer orientations toward fuel efficient vehicles. Final report.

National Highway Traffic Safety Administration, Office of Passenger Vehicle Research, Washington, D.C.

Analysis of the future effects of the fuel shortage and increased small car usage upon traffic deaths and injuries. Final report. Executive summary.

Transportation Department, Office of Systems Engineering, Washington, D.C.

Index of the relative importance of fuel efficiency (IFE) in the motor vehicle market. Final report.

National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

Fuel consumption measurements - 1979-1980 model year vehicles.

Mode of access: Internet.