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Over the last two decades, global emission regulations have become more stringent and have required the use of more advanced fuel injection systems. This includes the use of tighter tolerances, more rapid injections and internal components actuated by weaker injection forces. Unfortunately, these design features make the entire system more susceptible to fuel contaminants. Over the last six years, the composition of these contaminants has evolved from hard insoluble debris, such as dust and rocks, to soluble chemical contaminants. Recent research by the diesel engine manufacturers, fuel injection equipment suppliers and the fuel and fuel additive industry has discovered a major source of the soluble chemical contaminant that leads to injector deposits to be derived from cost effective and commonly used additives used to protect against pipeline corrosion.

Wideband Acoustical Holography (WBH), which is a monopole-based, equivalent source procedure (J. Hald, “Wideband Acoustical Holography,” INTER-NOISE 2014), has proven to offer accurate noise source visualization results in experiments with a simple noise source: e.g., a loudspeaker (T. Shi, Y. Liu, J.S. Bolton, “The Use of Wideband Holography for Noise Source Visualization”, NOISE-CON 2016). From a previous study, it was found that the advantage of this procedure is the ability to optimize the solution in the case of an under-determined system: i.e., when the number of measurements is much smaller than the number of parameters that must be estimated in the model. In the present work, a diesel engine noise source was measured by using one set of measurements from a thirty-five channel combo-array placed in front of the engine.

Understanding customer usage space and its impact on engine, after treatment, and vehicle duty cycles poses challenges in terms of data noise, data variability and complex interrelations. Moreover, humans are only able to concurrently visualize at most 2 to 3 dimensions, limiting the number of engine parameters that can be considered. Previous studies in this field have been limited to understanding trends in data based on single duty cycle, comparatively short application period and time domain segmented clustering analysis. These techniques have been used to determine representative cycles for specific applications. In this paper, K-Means Clustering is used to classify customer usage space based on tens of dimensions, for multiple duty cycles, and over years of operation. The clusters are evaluated based on system, sub-system, and component-based metrics on a day based unsegmented engine parameter values.

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.