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The torque converter and torque converter clutch are critical devices governing overall power transfer efficiency in automatic transmission powertrains. With calibrations becoming more aggressive to meet increasing fuel economy standards, the torque converter clutch is being applied over a wider range of driving conditions. At low engine speed and high engine torque, noise and vibration concerns originating from the driveline, powertrain or vehicle structure can supersede aggressive torque converter clutch scheduling. Understanding the torsional characteristics of the torque converter clutch and its interaction with the drivetrain can lead to a more robust design, operation in regions otherwise restricted by noise and vibration, and potential fuel economy improvement.

Electronic technologies continue to provide ever-increasing options in computational capabilities. This in turn, enables the use of advanced signal processing techniques that can allow sensors or even actuators to be less complex or possibly eliminated. Alternatively, additional information could also possibly be extracted from existing sensors. This paper will discuss one such example: a virtual mass airflow sensor. The engine airflow system is a multi-variable nonlinear system and as such, use of a virtual mass airflow sensor represents many automotive systems. In this paper we'll discuss the impact of using a virtual mass airflow sensor on the rest of the powertrain control system. We'll discuss the impact in terms of vehicle performance, the impact on the downstream algorithms that use the virtual signal and the impact to the electronic hardware.

Recent advances in ANN (Artificial Neural Network) technology enable new methods to be developed in sensor technology. There are a large number of cases where there exists a causal relationship between one or more inputs and a physical quantity, but where an easily implemented analytical relationship between the inputs and the output can not easily be found. In such cases, machine learning techniques, such as artificial neural networks, are able to model that functional relationship. However, using conventional computing hardware, these methods, while theoretically attractive, are too computationally intensive for field deployment in real-time systems. Using a hardware implementation of an artificial neural network architecture, these computational restrictions can be eliminated.

Signal delivery is the means of translating a physical parameter from a sensor measurement to the application software in the electronic controller. Signal delivery is also translating a digital word from the application software to an actuator response. In both cases, there are many transform functions along the path that will introduce noise, error, and non-linearity. This paper will discuss the importance of understanding the error and sensitivity to variation that signal delivery analysis provides. The analysis will direct design change to improve control system robustness as well as decisions for failure events.