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A torque converter is a type of fluid coupling device used to transfer engine power to the gearbox and driveline. A bypass clutch equipped in a torque converter assembly is a friction element which when fully engaged, can directly connect the engine to the gearbox. The torque converter is an important launch device in an automatic transmission which decouples engine speed from gearbox input speed while providing torque multiplication to drive the vehicle. During partial pedal launch, it is desired to engage the bypass clutch early and reduce the converter slippage in order to reduce power loss and achieve better fuel economy. However, engaging the bypass clutch early and aggressively may disturb the wheel torque and cause unpleasant driving experiences. This paper describes a multi-input multi-output (MIMO) control method to coordinate both engine and converter bypass clutch to simultaneously deliver desired wheel torque and reduce converter slippage.

A transmission system model is developed at various complexities in order to capture the transient behaviors in drivability and fuel economy simulations. A large number of model parameters bring more degree of freedom to correlate with vehicular test data. However, in practice, it requires extensive time and effort to tune the parameters to satisfy the model performance requirements. Among the transmission model, a hydraulic clutch actuator plays a critical role in transient shift simulations. It is particularly difficult to tune the actuator model when it is over-parameterized. Therefore, it is of great importance to develop a hydraulic actuator model that is easy to adjust while retaining sufficient complexity for replicating realistic transient behaviors. This paper describes a systematic approach for reducing the hydraulic actuator model into a piecewise 1st order representation based on piston movement.

This paper proposes an optimal method for the air suspension linkages design, which takes multiple critical geometric dimensions of the linkage as design variables, takes the maximum stress of critical areas in certain parts and the total mass of the assembly as objective functions, normalizes those objectives, and combines them together to evaluate the design. A design flow is developed to integrate the proposed method with a 3-D modeling software and a finite element software, and to expedite the design process. The effect of design variables on the stress of each part and on the mass is analyzed. Based on the sensitivity analysis and the objectives' values, the best design is determined from several available candidate schemes. The stress at critical areas is decreased significantly, as well as the total mass, which improve the safety and cut the cost. The optimization method is also applicable to any multi-parameter multi-objective mechanical part design.

Dynamic data, forces, moments and displacements are widely used parameters in a simulation environment for design and testing. These results may be obtained from field tests, laboratory measurements, and numerical simulations. The correctness of the simulation results depends strongly on the models and numerical solution techniques. This paper presents a preliminary examination of the differences between results obtained from the computer code DADS (Dynamic Analysis and Design System) [1] and the field data for the response of a military tank. The differences are analyzed by standard statistical methods in the frequency domain. The statistical tests show that DADS results differ from the measured field data and that the errors are not white noise. Moreover, the principal frequencies of the differences are identified.