Electronic Design
Ford Uses Math Models to Solve Torque Challenges

Ford Uses Math Models to Solve Torque Challenges

Despite the evolution of car automobile design over the years, some fundamental issues still remain. One of these is the torque hole, or the hesitation that drivers feel during an upshift when there is a momentary drop in transmission torque output followed by a rise in torque. This has been plaguing automatic transmissions since the 1940s.

Apparently, the problem may have finally been solved, thanks to torque hole filling (THF), a Ford-developed methodology. THF uses a combination of mathematical algorithms, computer-aided engineering (CAE) tools, and transmission control technologies to sync the transmission and engine to transfer and smooth out the torque during an upshift to create a smoother driving experience for the customer. The 2012 Ford Focus will be one of the first cars to utilize the Ford Powershift dry-clutch six-speed automatic transmission, which will enable a consistently smooth shift feel with minimum calibration effort and no incremental hardware cost.

The person responsible for overcoming this challenge is Dr. Davor Hrovat, a Ford Technical Fellow in Controls Research. Although it was commonly believed in the automotive industry that the torque hole was unavoidable in automatic transmissions, in the mid 1980s Dr. Hrovat used the results of a power shift research project to anticipate the advent of electronic throttle control and use this enabler to coordinate the powertrain intervention for a “torque hole” free shift-event. His ideas appeared promising, but the technology needed to implement them wasn’t fully mature yet.

“Although the team has known what was needed to create smoother shifts, the actual implementation had to wait for drive-by-wire technologies, electronic throttle control, and processing power to catch up to transform this inventive idea into a reality,” said Chris Teslak, Ford Research Technical Expert, who credits THF project technical leaders Yuji Fujii and Eric Tseng, along with other core technical experts/engineers Jahan Asgari, Tom Brown, Chad Griffin, Don Levens, and Brad Riedle, for helping to bring THF to fruition under Hrovat’s guidance.

The mathematical algorithm responsible for this innovation involves a first-principle-based dynamic model using Bond graph methodology, a very efficient technique for modeling multi-domain physical systems, based on Dr. Hrovat’s ideas. This model was further developed and refined to include the interaction effects with modern powertrains such as the Ecoboost engine, and enhanced to provide superior robustness to various sources of variability and disturbance.

According to Teslak and Tseng, the algorithm makes use of a number of inputs from sensors, such as shaft speeds and accelerator pedal position, as well as a host of calculated inputs, like engine and clutch torques. From these inputs, a desired output shaft torque trajectory is calculated for a given shift event. This trajectory and pre-determined clutch torque profile are then input into the algorithm, which then determines the best coordination between engine torque and clutch torque to deliver the desired output shaft torque profile.

With the help of computer-aided mathematical modeling, simulation, and analysis of engine speeds, the Ford team logged 6000 man hours of testing in only two years in order to get the THF concept ready for production. Then, the system was tested in real life with an actual vehicle. According to Tseng and Teslak, in the vehicle, they used an in-house developed rapid prototyping tool for making fast algorithm changes. At each stage of testing, the results were used to validate the model. The entire process was iterative, allowing additional opportunities for refinement of the model and control algorithm.

In internal engineering evaluations using a Powershift prototype, THF improved shift quality ratings by up to 2 points on a scale of 1 to 10 in comparison to baseline shifts with conventional controls. When questioned about whether it’s possible to further improve this mathematical model, Tseng and Teslak responded that “While the current mathematical model has captured the dominant/critical dynamics of the system, there are always opportunities to improve model fidelity and bandwidth through adding more details (e.g., component models of clutch and engine dynamics). We are continuously pursuing additional opportunities for improvement and refinement of both the physical model and control algorithm.”

Ford Motor Co.


TAGS: Automotive
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