Search Results

The complexity of Urban driving conditions and the human behavior introduces undesired variabilities while establishing Fuel economy for a vehicle. These variabilities pose a great challenge while trying to determine that single figure for assessment of vehicle’s fuel efficiency on an urban driving cycle. This becomes even more challenging when two or more vehicles are simultaneously evaluated with respect to a reference vehicle. The attempt to fit a generalized linear model, between Fuel Economy as predicted variable and components of a driving cycle as predictor variables produced oxymoronic and counter-institutive results. This is primarily due to existence of multi-collinearity among the predictor variables. The context of the study is to consider the event of driving on a cycle as a random sampling experiment. The outcome of a driving cycle is summarized into a list of predictor variables or components.

The Ride Comfort has always been an important attribute of a vehicle that gets trade-off with handling characteristics of a vehicle. However, to cater the growing customer requirements for better ride comfort in a vehicle without compromising on other attributes, evaluating and achieving optimal ride comfort has become a significant process in the vehicle development. In the current engineering capability and virtual engineering simulations, creating an accurate and real time model to predict ride comfort of a vehicle is a challenging task. The qualitative evaluation of ride attributes has always been the proven conventional method to finalize the requirements of a vehicle. However, quantitative evaluation of vehicle ride characteristics benefits in terms of target setting during vehicle development process and in robust validation of the final intended product against its specifications.