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Fuel consumption for heavy trucks depends on many factors like roads, weather, and driver behavior that are hard for a manufacturer to influence. However, one design possibility is the power-train configuration. Here a new simulation program for heavy trucks is created and the configuration of the power-train that gives the lowest fuel consumption for each transport task is selected based on the simulation results. In this work, the operational conditions have been considered i.e. load, pavement, transmission efficiency and the building characteristics of the engine map, transmission, frontal area, tire. In this paper, we present a simulation software that enables a vehicle manufacturer or a customer to choose the right driveline for the customized application, depending on the acceleration and the fuel economy needs.

Fuel consumption for heavy trucks depends on many factors like roads, weather, and driver behaviour that are hard for a manufacturer to influence. However, one design possibility is the power-train configuration. In this paper, driveline of a heavy-duty truck is optimised using the six-sigma methodology. The focus of the task is selection of a power train configuration that gives the lowest fuel consumption for each transportation task. To reduce fuel consumption, it is important to choose a powertrain combination (gearbox, rear axle, tire dimension) that allows efficient use of the engine. Such an optimization of powertrain configuration is a complex task, but current simulation techniques provide means to reduce costly testing by replacing it partly with analysis. The DMAIC (Define, Measure, Analyze, Improve & Control) steps are followed to generate alternate solutions of the descriptive problem.

The primary factors influencing vehicle's dynamic behavior are the vehicle hard point definition, driver behavior and road inputs. The more the latter two are random and incorrigible in nature, the former one is quantifiable and can be controlled from designer's standpoint. In this paper, we have made an attempt to set targets to the vehicle hard point definition and thereby to optimize the vehicle for better ride behavior. This approach hence helped to converge to vehicle specifications set fundamentally designed to respond to random operating conditions and driving behavior intelligently. The work also involves study of various methodologies to predict roll, pitch, bounce and dive behaviors on a typical commercial passenger vehicle and is concluded by a sensitivity analysis to understand significance of these hard points on vehicle's real time behavior.

Automotive industry is progressively embracing newer technology for buses, as they are increasingly becoming the backbone of urban transportation. Buses are generally classified based on floor heights, lengths, seating capacity and applications besides lot of other parameters. Generally low floor / low entry buses are used for city transportation, while high floor / high deck buses are used for inter urban and intercity transportation. Yet in a few developing and underdeveloped geographies across the globe, high deck or the semi low floor buses are still used for city/urban transportation. There could be a lot of reasons like infrastructure limitations, the cost of ownership or in some cases even the topology of these geographies could be unfriendly towards low floor buses and low ground clearances. Varying customer requirements, applications and environmental factors necessitates a broad range of offerings from any bus OEM.