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In the conceptualization phase of vehicle development, for achieving reasonable dynamics performance, proper selection of steering system meeting all the requirements is necessary. This requires accurate prediction of major steering performance attributes like steering effort, steering torque, Turning Circle Diameter (TCD), %Ackerman and steering returnability. However, currently available models majorly depend on Computer Aided Engineering (CAE)-analysis or physical trials which requires system detailing and the same cannot be used for early prediction of the steering performances in the concept phase. This paper aims to address this deficiency with the help of a new steering performance calculator. In the calculator, performance attributes namely steering effort, steering torque, TCD and %-Ackerman has been modelled with engineering calculations and other attributes namely steering returnability&precision has been modelled through machine learning techniques.

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.