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Wet clutches are complex hydrodynamic devices used in both conventional and electrified drivetrain systems. They couple or de-couple powertrain components for applications such as automatic shifting, engine disconnect and torque vectoring. Clutch engagement behaviors vary greatly, depending on design parameters and operating conditions. Because of their direct impact on vehicle drivability and fuel economy, a predictive CAE model is desired for enabling analytical design verification processes. During engagement, a wet clutch transmits torque through viscous shear and asperity contact. A conventional Coulomb’s model, which is routinely utilized in shift simulations, is inadequate to capture non-linear hydrodynamic effects for higher fidelity analysis. Extensive research has been conducted over the years to derive hydrodynamic torque transfer models based on 1D squeeze film or 3D CFD. They are typically coupled with an elastic asperity contact model for mechanical torque transfer.

Simulations conducted on-board in a vehicle control module can offer valuable information to control strategies. Continued improvements to on-board computing hardware make online simulations of complex dynamic systems such as drivetrains within reach. This capability enables predictions of the system response to various control actions and disturbances. Implementation of online simulations requires model initialization that is consistent with the physical drivetrain state. However, sensor signals and estimated variables are susceptible to errors, compromising the accuracy of the initialization and any future state predictions as the simulation proceeds through the numerical integration process. This paper describes a drivetrain modeling and analysis method that accounts for initialization errors, thereby enabling accurate simulations of system behaviors.

A wet clutch couples or decouples gear elements to alter torque paths in an automatic transmission system. During the gear shifting event, the clutch torque is directly transmitted to the output shaft. Hence, clutch torque heavily influences the dynamics of the transmission. In order to evaluate the behavior of the transmission early and efficiently, the development process increasingly relies on high-fidelity transmission system simulations with added complexity. However, a wet clutch continues to be modeled using Coulomb’s friction in a typical shift simulation. Its linear framework does not physically represent non-linear hydrodynamic effects due to the presence of oil layer during clutch engagement. To make up the lack of physics, Coulomb’s clutch model often requires extensive tuning to match actual shift behaviors.

A new generation of a planetary-gear-based automatic transmission system is designed with an increasing number of ratio steps. It requires synchronous operation of one or more wet clutches, to achieve a complex shift event. A missed synchronization results in drive torque disturbance which may be perceived by vehicle occupants as an undesirable shift shock. Accurate knowledge of clutch behaviors in an actual vehicle environment is indispensable for achieving precise clutch controls and reducing shift calibration effort. Wet clutches are routinely evaluated on an industry-standard SAE#2 tester during the clutch design process. While it is a valuable tool for screening relative frictional behaviors, clutch engagement data from a SAE#2 tester do not correlate well with vehicle shift behaviors due to the limited reproducibility of realistic slip, actuator force profiles, and lubrication conditions.

A step-ratio automatic transmission is a system of planetary gear sets, wet clutches, hydraulic control system and torque converter to provide the flexibility in gear ratio selection. Gearshifting is realized by the engagement and disengagement of clutches which are commanded by control strategy through the hydraulic actuators. A complex interaction between components results in transient drive shaft torque, affecting shift quality. In particular, it is difficult to achieve fast upshift without inducing a large inertia torque spike due to changing speed ratios. A deep understanding of the system kinematics and dynamics becomes critical to control clutches for fast and smooth gearshifting. This article performs detailed analytical study to explain the upshift behaviors of a 10-speed automatic transmission by deriving the system’s governing equations. These equations show insights of working principles of the transmission and provide a new method to improve shift quality.

A variety of vehicle controls, from active safety systems to power management algorithms, can greatly benefit from accurate, reliable, and robust real-time estimates of vehicle mass and road grade. This paper develops a parallel mass and grade (PMG) estimation scheme and presents the results of a study investigating its accuracy and robustness in the presence of various noise factors. An estimate of road grade is calculated by comparing the acceleration as measured by an on-board longitudinal accelerometer with that obtained by differentiation of the undriven wheel speeds. Mass is independently estimated by means of a longitudinal dynamics model and a recursive least squares (RLS) algorithm using the longitudinal accelerometer to isolate grade effects. To account for the influences of acceleration-induced vehicle pitching on PMG estimation accuracy, a correction factor is developed from controlled tests under a wide range of throttle levels.

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.

A direct trajectory optimization approach is developed to assess the capability of a GTDI-DCT Powertrain, with a Gasoline Turbocharged Direct Injection (GTDI) engine and Dual Clutch Transmission (DCT), to satisfy stringent drivability requirements during launch. The optimization is performed directly on a high fidelity black box powertrain model for which a single simulation of a launch event takes about 8 minutes. To address this challenging problem, an efficient parameterization of the control trajectory using Gaussian kernel functions and a Mesh Adaptive Direct Search optimizer are exploited. The results and observations are reported for the case of clutch torque optimization for launch at normal conditions, at high altitude conditions and at non-zero grade conditions. The results and observations are also presented for the case of simultaneous optimization of multiple actuator trajectories at normal conditions.

Dynamic vehicle loads play critical roles for automotive controls including battery management, transmission shift scheduling, distance-to-empty predictions, and various active safety systems. Accurate real-time estimation of vehicle loads such as those due to vehicle mass and road grade can thus improve safety, efficiency, and performance. While several estimation methods have been proposed in literature, none have seen widespread adoption in current vehicle technologies despite their potential to significantly improve automotive controls. To understand and bridge the gap between research development and wider adoption of real-time load estimation, this paper assesses the accuracy and performance of four estimation methods that predict vehicle mass and/or road grade.

Progress in the design and application of the magneto-elastic torque sensor to automotive drivetrain systems has taken the technology from the concept level to the point where it is considered production feasible. The latest generation of the sensors shows promising results regarding both the capabilities and applications to powertrain controls. Sensor designs, electronics and packaging layout are maturing. Well-defined component specifications and requirements are becoming available. The sensor utilities for real-time shift analysis and friction element control are established through vehicle-level investigation to demonstrate the production feasibility of the technology for transmission torque sensing.

This paper presents the characterization of the random noise in driveline output shaft torque measurements that is commonly induced by road disturbances. To investigate the interaction between the shaft torque and road side excitation, torque signals are measured using a magnetoelastic torque sensor, as well as a conventional strain gauge sensor, under various types of road surfaces and conditions such as unevenness. A generalized de-trending method for producing a stationary random signal is first conducted. Statistical methods, in particular the probability density function and transform technique, are utilized to provide an evident signature for identifying the road excitation effect on the vehicle output shaft torque. Analysis results show how the road surface can act as a disturbance input to the vehicle shaft torque.

The design discipline of Design For Six Sigma (DFSS) has been applied to many areas of product development and manufacturing. As DFSS application has recently been extended to upfront automotive engineering areas such as research and advanced development, more robust and optimized technologies can be achieved in the pre-production stage, reducing cost, exhibiting superior quality and performance, and shortened development cycle. This paper describes the application of the DFSS process, Define, Characterize, Optimize, and Verify (DCOV) to develop an automotive technology that begins from the conceptual phase and continues up through the implementation phase. The role of DFSS in the automotive industry is to provide a framework for more rigorous upfront engineering. It provides guidelines to a more effective and efficient development of new technologies.