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Simulations used to estimate carbon dioxide (CO2) emissions and fuel consumption of medium- and heavy-duty vehicles over prescribed drive cycles often employ engine fuel maps consisting of engine measurements at numerous steady-state operating conditions. However, simulating the engine in this way has limitations as engine controls become more complex, particularly when attempting to use steady-state measurements to represent transient operation. This paper explores an alternative approach to vehicle simulation that uses a “cycle average” engine map rather than a steady state engine fuel map. The map contains engine CO2 values measured on an engine dynamometer on cycles derived from vehicle drive cycles for a range of generic vehicles. A similar cycle average mapping approach is developed for a powertrain (engine and transmission) in order to show the specific CO2 improvements due to powertrain optimization that would not be recognized in other approaches.

This paper describes how mind mapping software can help to visualize: System performance requirements Product attributes that satisfy performance requirements Mapping between performance requirements and product attributes An example is given using a partial model for vehicle performance developed by the International Truck and Engine Corporation. The mind map software used in this study is Mind Manager Pro version 6 by Mindjet. Anecdotal evidence is offered for the benefits and challenges of implementing a visual Mind Map scheme; however, the judgment of overall effectiveness is left to the reader.

Improving fatigue resistance is a key factor to design components for advanced vehicle transmissions. The selection of materials and heat treatment plays a crucial role in controlling fatigue performance of power transmission components such as gears and shafts. Traditional, low frequency fatigue testing, used for identifying fatigue limit or generating S-N curve for multiple sets of material parameters is highly time consuming and expensive. Hence, it is necessary to develop the capability to predict fatigue performance of materials at different loading conditions with limited amount of data for instance the hardness and inclusion size. In the present work, we have evaluated behavior of the carburized steel subjected to axial and torsional cyclic loading conditions at low frequencies.