There is currently a high level of uncertainty surrounding the evolution of personal transportation. A variety of new types of vehicle powertrains have been proposed or implemented, including alternative fuels, hybrid electric vehicles, and fully electric vehicles. It is also possible, as shown by Mechtenberg , to combine multiple fuels and batteries to design this 36 mode hybrid vehicle. The hybrid vehicle presented here features multiple modes of operation with a wide range of possible combinations of fuel and battery usage. While the many degrees of freedom offered by this hybrid vehicle design present an opportunity to operate under a variety of different conditions, it also presents a control challenge, as the vehicle’s control system must decide how best to use the various modes available, given the driver’s optional selection and the current status of the vehicle. In this paper, we discuss the various modes of operation, degree of driver involvement in their selection, and automatic switching between various options. The optimal control is found for various different driving cycles, based on the objective of maximizing the efficiency of the powertrain, and it is shown that this type of hybrid vehicle can operate efficiently under a variety of different scenarios. This model is built upon Wagner and Papalambros’ engine optimization  and Ahn’s continuously variable transmission model .
- Dynamic Systems and Control Division
Control of a 36 Mode Hybrid Vehicle With Driver Option Selection: Incorporating Urban, Suburban, and Highway Driving
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Mechtenberg, AR, & Peters, DL. "Control of a 36 Mode Hybrid Vehicle With Driver Option Selection: Incorporating Urban, Suburban, and Highway Driving." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 137-144. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8692
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