Influence of Gear Oil Formulation on Fuel Economy of Passenger Cars

2000 ◽  
Author(s):  
Wilfried J. Bartz ◽  
Dirk Wienecke
Keyword(s):  
Fuel Economy ◽  
Passenger Cars ◽  
Oil Formulation ◽  
Gear Oil

scholarly journals Electric Boosting and Energy Recovery Systems for Engine Downsizing

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4636 ◽  
Author(s):  
Mamdouh Alshammari ◽  
Fuhaid Alshammari ◽  
Apostolos Pesyridis
Keyword(s):  
Fuel Economy ◽  
Energy Recovery ◽  
Passenger Cars ◽  
Pros And Cons ◽  
Boosting Method ◽  
Increasing Demand ◽  
Selection Of

Due to the increasing demand for better fuel economy and increasingly stringent emissions regulations, engine manufacturers have paid attention towards engine downsizing as the most suitable technology to meet these requirements. This study sheds light on the technology currently available or under development that enables engine downsizing in passenger cars. Pros and cons, and any recently published literature of these systems, will be considered. The study clearly shows that no certain boosting method is superior. Selection of the best boosting method depends largely on the application and complexity of the system.

Fuel economy of new passenger cars in Mexico: Trends from 1988 to 2008 and prospects

Energy Policy ◽  
2011 ◽  
Vol 39 (12) ◽  
pp. 8153-8162 ◽  
Author(s):  
Claudia Sheinbaum-Pardo ◽  
Carlos Chávez-Baeza
Keyword(s):  
Fuel Economy ◽  
Passenger Cars

Effect of Fuel Economy on Automobile Safety

2005 ◽  
Vol 1941 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Sanjana Ahmad ◽  
David L. Greene
Keyword(s):  
Fuel Economy ◽  
Negative Relationship ◽  
Cointegration Analysis ◽  
Corporate Average Fuel Economy ◽  
Traffic Fatalities ◽  
Passenger Cars ◽  
Light Duty ◽  
Light Trucks ◽  
Short Period ◽  
Light Duty Vehicle

Since 1975, the fuel economy of passenger cars and light trucks has been regulated by the corporate average fuel economy (CAFE) standards, established during the energy crises of the 1970s. Calls to increase fuel economy are usually met by a fierce debate on the effectiveness of the CAFE standards and their impact on highway safety. A seminal study of the link between CAFE and traffic fatalities was published by R. W. Crandall and J. D. Graham in 1989. They linked higher fuel economy levels to decreases in vehicle weight and correlated the decline in new car weight with about a 20% increase in occupant fatalities. The time series available to them, 1947–1981, includes only the first 4 years of fuel economy regulation, but any statistical relationship estimated over such a short period is questionable. This paper reexamines the relationship between U.S. light-duty vehicle fuel economy and highway fatalities from 1966 to 2002. Cointegration analysis reveals that the stationary linear relationships between the average fuel economy of passenger cars and light trucks and highway fatalities are negative: higher miles per gallon is significantly correlated with fewer fatalities. Log–log models are not stable and tend to produce statistically insignificant (negative) relationships between fuel economy and traffic fatalities. These results do not definitively establish a negative relationship between light-duty vehicle fuel economy and highway fatalities; instead they demonstrate that national aggregate statistics cannot support the assertion that increased fuel economy has led to increased traffic fatalities.

Efficiency of a planetary multiplier gearbox: Influence of operating conditions and gear oil formulation

2015 ◽  
Vol 92 ◽  
pp. 272-280 ◽  
Author(s):  
Pedro M.T. Marques ◽  
Raquel Camacho ◽  
Ramiro C. Martins ◽  
Jorge H.O. Seabra
Keyword(s):  
Operating Conditions ◽  
Oil Formulation ◽  
Gear Oil

Meeting the Challenge: A Stochastic Assessment of the U.S. Light-Duty Vehicle Fuel Economy Standards

2012 ◽  
Author(s):  
Parisa Bastani ◽  
John B. Heywood ◽  
Chris Hope
Keyword(s):  
Fuel Economy ◽  
Ghg Emissions ◽  
Corporate Average Fuel Economy ◽  
Passenger Cars ◽  
Policy Model ◽  
Light Duty ◽  
Stochastic Transport ◽  
Light Trucks ◽  
Light Duty Vehicle ◽  
The U.S

The U.S. Department of Transport and EPA have recently proposed further regulation of the light-duty vehicle corporate average fuel economy and GHG emissions for model years 2017 to 2025. Policy makers are setting more stringent targets out to 2025 in a context of significant uncertainty. These uncertainties need to be quantified and taken into account systematically when evaluating policies. In this paper, a stochastic technology and market vehicle fleet analysis is carried out, using the STEP (Stochastic Transport Emissions Policy model), to assess the probability of meeting the proposed CAFE targets in 2016 and 2025, and identify factors that play key roles in the near and midterm. Our results indicate that meeting the proposed targets requires (a) aggressive technological progress rate and deployment, (b)aggressive market penetration of advanced engines and powertrains, (c) aggressive vehicle downsizing and weight reduction, and (d) a high emphasis on reducing fuel consumption. Three scenarios are examined to assess the likelihood of meeting the proposed targets. The targets examined here, 32.5 and 34.1 mpg in 2016 and 44 and 54.5 mpg in 2025, are reduced from the nominal CAFE values after allowing for the various credits in the proposed rulemaking. The results show that there is about a 42.5% likelihood of the passenger cars average fuel economy falling below 32.5 mpg and a 5.3% likelihood of it exceeding 34.1 mpg in 2016, and about a 4% chance of it exceeding 44 mpg in 2025, under the plausible-ambitious scenario. Under the EPA/DOT preferred alternative scenario, the likelihood of passenger cars average fuel economy meeting or exceeding 34.1 mpg in 2016 and 44 mpg in 2025 increases to about 74% and 34.5% respectively. The probability of meeting these combined CAFE targets drops to less than 1% in both near and mid terms, once light trucks are included in the mix. This analysis quantifies the probability of meeting the targets therefore to enable risk-based contingency planning, and identifies key drivers of uncertainty where further strategic research is needed.

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