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. In order to benchmark and improve over the current scenario, data is collected from various vehicles operating in the field and the sigma rating is calculated. The Design of Experiments (DoE) is performed considering the factors such as Engine type, Gearbox type and final drive ratio. A full factorial method of DOE is developed and simulations are executed using the AVL Cruise® software for the selected combinations. 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, and tire. The traffic conditions and the transmission efficiency of the driveline are considered as noise factors and the variation in the kmpl due to these noise factors are studied. This has led to robust design of the prediction model. All the statistical evaluations have been carried out using Minitab v15 ®.
SIX SIGMA is an efficient and methodological procedure to evaluate and rationalize results to select the desired combination. The final combination selected through statistical and physical significance, was tested and improvements as expected in the theoretical calculations were observed. A design methodology for selecting the right kind of driveline for a heavy-duty truck has been established. Furthermore, on evaluation of the test results of the proposed driveline, a statistical measure of fuel mileage considering the chance causes is established in terms of mean and standard deviations.