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A predictive model for estimating the fuel saving of “top tier” engine, axle and transmission lubricants (compared to “mainstream” lubricants), in a heavy duty truck, operating on a realistic driving cycle, is described. Simulations have been performed for different truck weights (10, 20 and 40 tonnes) and it was found that the model predicts percentage fuel economy benefits that are of a similar magnitude to those measured in well controlled field trials1. The model predicts the percentage fuel saving from the engine oil should decrease as the vehicle load increases (which is in agreement with field trial results). The percentage fuel saving from the axle and gearbox oils initially decreases with load and then stays more or less constant. This behaviour is due to the detailed way in which axle and gearbox efficiency varies with speed/load and lubricant type.

Due to the complex powertrain layout in hybrid vehicles, different configurations concerning internal combustion engine, electric motor and transmission can be combined - as is demonstrated by currently produced hybrid vehicles ([1], [2]). At the Institute for Combustion Engines (VKA) at RWTH Aachen University a combination of simulation, Design of Experiments (DoE) and numerical optimization methods was used to optimize the combustion engine, the powertrain configuration and the operation strategy in hybrid powertrains. A parametric description allows a variation of the main hybrid parameters. Parallel as well as power-split hybrid powertrain configurations were optimized with regard to minimum fuel consumption in the New European Driving Cycle (NEDC). Besides the definition of the optimum configuration for engine, powertrain and operation strategy this approach offers the possibility to predict the fuel consumption for any modifications of the hybrid powertrains.

Environmental concern demands that emissions and fuel consumption of vehicles have to improve considerably in the next 10 years. New technologies for gasoline engines, downsizing with high boosting, direct injection and fully variable valve train systems, are being developed. For Diesel engines, improved components including piezobased injectors and particle filters are expected. In the drive train new starter-generator systems as well as automated manual transmissions are being developed. In parallel alternative fuels are investigated and the use of hybrid drives and fuel cells are developed. This paper reports the progress made in the recent years and gives a comparative assessment on the different technologies with a prediction of the introduction dates and volumes into the market.

We review some of the key tribological issues of relevance to motorsport applications. Tribology is the science of friction and wear, and in a high performance engine, friction and wear are controlled by good component design (e.g. the engine and the transmission) and also by the use of high performance lubricants with the correct physical (and chemical) properties, matched to the machine they are used in. In other words, design of a specific lubricant for specific hardware can lead to optimised performance. (Tribology is also important in the tire-road contact but are not considered here.) The importance of key physical properties of a lubricant is demonstrated with an emphasis on how the choice of the correct lubricant can help to minimize engine friction (and thus increase available power output) whilst protecting against engine wear. Key lubricant parameters discussed in the paper are the viscosity variation of a lubricant with temperature, shear rate and pressure.

Current simulation tools for the investigation of the dynamic system response as well as for the component stresses on the basis of multi-body and finite-element techniques are integral part of today's powertrain development efforts. These tools are typical used for the analysis and optimization of shafts, clutches, chain/belt drives, bearings, levers, brackets, housings and many other components. An exception is made by gears which today are still frequently investigated by the help of semi-empirical methods based on DIN, ISO, AGMA and the specific knowledge base of well experienced developers. The main difficulty is that the gears are rolling off via large contact surfaces with complex nonlinear mechanical contact properties. Within the scope of research work FEV developed a new method for the analysis and optimization of gear drives based on comercial multi-body and finite-element software platforms.

A customer's perception of vehicle quality very closely parallels the noise vibration and harshness (NVH) characteristics of the vehicle. Consequently, automotive manufacturers are investing significant resources into optimizing the NVH performance of their vehicles. Automatic transmission shift quality is one of a number of attributes where NVH optimization is critical towards providing customers with a pleasant driving experience. This paper addresses various aspects of understanding, quantifying and optimizing a vehicle's shift quality characteristics. Following an introductory treatment of automatic transmission planetary gear systems, the interaction between the engine/transmission system during shifts is summarized. Various shift quality metrics used to quantify a vehicle's response and its sensitivity to transient inputs are provided. Approaches to manage the engine torque output during the shifts are discussed.

The attributes of a traction fluid are fundamental to the successful operation of a traction drive transmission. The fluid must lubricate and protect the components against wear and corrosion, whilst simultaneously providing high traction to transmit power efficiently. A selection of commercial and candidate fluids have been assessed with both a bench-test and a novel traction rig. The principal objective has been to achieve a balance between the conflicting requirements of low temperature viscometrics and high temperature traction. Fluid performance is found to vary according to the rig employed underlining the need to test under prevailing conditions. Data from the traction rig is validated against a variator module.