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Despite the fact that urea-based selective catalytic reduction (SCR) of NOx is a key technology for achieving on- and off-highway diesel emission standards, significant control challenges remain. Transient operation, combined with dramatic changes in catalyst dynamics over the operating range, cause highly nonlinear system behavior. Moreover, these effects depend on catalyst formulation and new catalysts continue to be developed. With many controllers, any difference in catalyst formulation, converter size, and engine emissions calibration require control system re-tuning. To minimize control development effort, this paper presents a novel “generic” controller for SCR systems. Control action is grounded in a physics-based, nonlinear, embedded model. Through the model, controller parameters are adjusted a priori for catalyst formulation and converter size. The few remaining tuning levers are quite intuitive, and require no special knowledge of controls theory.

This paper describes a ‘toolbox’ for modeling liquid cooling system networks within vehicle thermal management systems. Components which can be represented include pumps, coolant lines, control valves, heat sources and heat sinks, liquid-to-air and liquid-to-refrigerant heat exchangers, and expansion tanks. Network definition is accomplished through a graphical user interface, allowing system architecture to be easily modified. The elements of the toolbox are physically based, so that the models can be applied before hardware is procured. The component library was coded directly into MATLAB / SIMULINK and is intended for control system development, hardware-in-the-loop (HIL) simulation, and as a system emulator for on-board diagnostics and controls purposes. For HIL simulation and on-board diagnostics and controls, it is imperative that the model run in real-time.

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.