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A predictive automatic gear shift system is currently under development. The system optimizes the gear shift process, taking the conditions of the road ahead into account, such that the fuel consumption is minimized. An essential part of the system is a module that predicts the vehicle speed dynamics: This calculates a speed trajectory, i.e. the most probable vehicle speed the driver will desire for the upcoming section of the route. In the paper the theoretical background for predicting the vehicle speed, and simulation results of the predictive shift algorithm are presented.

Historically, vehicle fluid condition has been monitored by measuring miles driven or hours operated. Many current vehicles have more sophisticated monitoring methods that use additional variables such as fuel consumption, engine temperature and engine revolutions to predict fluid condition. None of these monitoring means, however, actually measures a fluid property to determine condition, and that is about to change. New sensors and diagnostic systems are being developed that allow real time measurement of some lubricant physical and/or chemical properties and interpret the results in order to recommend oil change intervals and maximize performance. Many of these new sensors use electrochemical or acoustic wave technologies. This paper examines the use of these two technologies to determine engine oil condition and focuses on the effects of lubricant chemistry on interpreting the results.

The engine-out emissions and fuel consumption rates for a modern, heavy-duty diesel engine were compared when fueling with a conventional diesel fuel and three water-blend-fuel emulsions. Four different fuels were studied: (1) a conventional diesel fuel, (2) PuriNOx,™ a water-fuel emulsion using the same conventional diesel fuel, but having 20% water by mass, and (3,4) two other formulations of the PuriNOx™ fuel that contained proprietary chemical additives intended to improve combustion efficiency and emissions characteristics. The emissions data were acquired with three different injection-timing strategies using the AVL 8-Mode steady-state test method in a Caterpillar 3176 engine, which had a calibration that met the 1998 nitrogen oxides (NOX) emissions standard.

Current engine development processes typically involve extensive steady-state and simple transient testing in order to characterize the engine's fuel consumption, emissions, and performance based on several controllable inputs such as throttle, spark advance, and EGR. Steady-state and simple transient testing using idealistic load conditions alone, however, is no longer sufficient to meet powertrain development schedule requirements. Mapping and calibration of an engine under transient operation has become critically important. And, independent engine development utilizing accelerated techniques is becoming more attractive. In order to thoroughly calibrate new engines in accelerated fashion and under realistic transient conditions, more advanced testing is necessary.

Cam-phasing is increasingly considered as a feasible Variable Valve Timing (VVT) technology for production engines. Additional independent control variables in a dual-independent VVT engine increase the complexity of the system, and achieving its full benefit depends critically on devising an optimum control strategy. A traditional approach relying on hardware experiments to generate set-point maps for all independent control variables leads to an exponential increase in the number of required tests and prohibitive cost. Instead, this work formulates the task of defining actuator set-points as an optimization problem. In our previous study, an optimization framework was developed and demonstrated with the objective of maximizing torque at full load. This study extends the technique and uses the optimization framework to minimize fuel consumption of a VVT engine at part load.

Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine.

The popularity of light trucks and sport utility vehicles (SUVs), coupled with growing consumer demand for vehicles with more size, weight and horsepower, has challenged the original equipment manufacturers' (OEM) ability to meet the Corporate Average Fuel Economy (CAFE) specifications due to the increased contribution of these vehicle classes on fleet averages. The need for improved fuel economy is also a global issue due to the relationship of reduced fuel consumption to reduced CO2 emissions. Vehicle manufacturers are challenged to match the proper fluid with the application to provide the required durability protection while maximizing fuel efficiency. Recent new viscosity classifications outlined under SAE J306 aid in more tightly defining options for lubricant choice for a given application. Changes to the SAE J306 viscosity classification define new intermediate viscosity grades, SAE 110 and SAE 190.

This paper reports on DaimlerChrysler's participation in the Ad Hoc Diesel Fuels Test Program. This program was initiated by the U.S. Department of Energy and included major U.S. auto makers, major U.S. oil companies, and the Department of Energy. The purpose of this program was to identify diesel fuels and fuel properties that could facilitate the successful use of compression ignition engines in passenger cars and light-duty trucks in the United States at Tier 2 and LEV II tailpipe emissions standards. This portion of the program focused on minimizing engine-out particulates and NOx by using selected fuels, (not a matrix of fuel properties,) in steady state dynamometer tests on a modern, direct injection, common rail diesel engine.

The growing need for improved fuel economy is a global challenge due to continuously tightening environmental regulations targeting lower CO2 emission levels via reduced fuel consumption in vehicles. In order to reach these fuel efficiency targets, it necessitates improvements in vehicle transmission hardware components by applying advanced technologies in design, materials and surface treatments etc., as well as matching lubricant formulations with appropriate additive chemistry. Axle lubricants have a considerable impact on fuel economy. More importantly, they can be tailored to deliver maximum operational efficiency over specific or wide ranges of operating conditions. The proper lubricant technology with well-balanced chemistries can simultaneously realize both fuel economy and hardware protection, which are perceived to have a trade-off relationship.