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Technical Paper

Characterizing the Effect of Automotive Torque Converter Design Parameters on the Onset of Cavitation at Stall

2007-05-15
2007-01-2231
This paper details a study of the effects of multiple torque converter design and operating point parameters on the resistance of the converter to cavitation during vehicle launch. The onset of cavitation is determined by an identifiable change in the noise radiating from the converter during operation, when the collapse of cavitation bubbles becomes detectable by nearfield acoustical measurement instrumentation. An automated torque converter dynamometer test cell was developed to perform these studies, and special converter test fixturing is utilized to isolate the test unit from outside disturbances. A standard speed sweep test schedule is utilized, and an analytical technique for identifying the onset of cavitation from acoustical measurement is derived. Effects of torque converter diameter, torus dimensions, and pump and stator blade designs are determined.
Technical Paper

Computationally Efficient Reduced-order Powertrain Model of a Multi-mode Plug-in Hybrid Electric Vehicle for Connected and Automated Vehicles

2019-04-02
2019-01-1210
This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle with velocity and elevation inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (Eco AND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources. Although overall estimation accuracy is less than neural network and high-fidelity models, emphasis on runtime minimization with reasonable estimation accuracy enables energy optimization of CAV without a need for computationally expensive server-based models.
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