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Vehicle electrification is being actively expanded into coming generations of passenger and commercial vehicles. This technology trend is helping vehicles to become more energy efficient. For electric vehicle (EV) city bus application, the system designers have been experimenting with a number of options including direct drive and multi-speed gearbox architectures. Direct drive scenario offers simplified drivetrain system, however requires a large and powerful electric motor. Multi-speed transmission system provides an opportunity to reduce motor size and optimize its operating points, but increases complexity from the architecture and controls point of view. This paper provides an overview of several common system layouts and examines their advantages and disadvantages. Vehicle simulation results are presented to compare direct drive vs. multi-speed technology from the gradeability, acceleration and energy consumption points of view.

Vapor management system is critical to manage fuel tank capacity, evaporative emissions and pressure control for hybrid applications. Due to stringent emission norms and other regulations there has been lot of advancements in design and application of vapor control valves that are used in automotive fuel tanks. Continuous exposure of these valves to fuel vapor or fuel in some instances led to swelling of assemblies and poses serious threat to product functionality and maintaining required tolerances. Swelling of plastics in fuel is ideally a case of multi physics, which involves modeling of complex mass transfer phenomena. In this study a simple thermal analogous approach has been used to model swelling behavior by characterizing the basic plastic-fuel soaking through coefficient of hygroscopic swelling. Extensive testing has been performed with multiple plastic-fuel combinations with different shapes at different temperatures.

Commercial vehicle operators and governments around the world are looking for ways to cut down on fuel consumption for economic and environmental reasons. Two main factors affecting the fuel consumption of a vehicle are the drive route and the driver behavior. The drive route can be specified by information such as speed limit, road grade, road curvature, traffic etc. The driver behavior, on the other hand, is difficult to classify and can be responsible for as much as 35% variation in fuel consumption. In this work, nearly 600,000 miles of drive data is utilized to identify driving behaviors that significantly affect fuel consumption. Based on this analysis, driving scenarios and related driver behaviors are identified that result in the most efficient vehicle operation. A driver assistance system is presented in this paper that assists the driver in driving more efficiently by issuing scenario specific advice.