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Fuzzy Logic Control for Parallel Hybrid Vehicles: Toyota Prius

Fuzzy Logic Control for Parallel Hybrid Vehicles: Toyota Prius. By: Jason Silver Nazim Mufti James Townsend Elikplim Tutsi Dornor. Instructor : Riadh Habash . T.A. : Fouad F. Khalil . References.

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Fuzzy Logic Control for Parallel Hybrid Vehicles: Toyota Prius

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  1. Fuzzy Logic Control for Parallel Hybrid Vehicles: Toyota Prius By: Jason Silver Nazim Mufti James Townsend Elikplim Tutsi Dornor Instructor : Riadh Habash T.A. : Fouad F. Khalil

  2. References • [1] Niels J. Schouten, Mutasim A. Salman, and Naim A. Kheir, “Fuzzy Logic Control for Parallel Hybrid Vehicles” in IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 10, NO. 3, MAY 2002 • Basis of our system design., Provided us with the rules and conditions for the fuzzy logic controller and the power controller. • [2] http://www.toyota.ca, retrieved on March 7, 2007 • Used to retrieve specifications of the Toyota Prius. • [3] B. K. Powell, K. E. Bailey, and S. R. Cikanek, “Dynamic modeling and control of hybrid electric vehicle powertrain systems,” IEEE Contr. Syst. Mag., pp. 17–33, Oct. 1998. • Used to better understand hybrid vehicle modelling. Authors design a dynamic car model and powertrain model.

  3. References • [4] C. C. Lee, “Fuzzy logic in control systems,” IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 404–435, 1990. • Used to help in the design of the fuzzy logic controller. Authors implement a fuzzy logic controller in a control system. • [5] B. M. Baumann, “Intelligent control strategies for hybrid vehicles usingneural networks and fuzzy logic,” Master’s thesis, Dept. Elect. Eng.,Ohio State Univ., Columbus, 1997. • Used to help in the design of the hybrid vehicle and fuzzy logic controller. Authors developed a fuzzy logic control technique for the powertrain of a hybrid vehicle.

  4. Parallel Hybrid Vehicle (PHV) • Electric Motor (EM) and Internal Combustion Engine (ICE) combined in parallel • Advantages • Very efficient • Environmentally friendly • Quiet • Disadvantages • Lower performance • Expensive • Requires complicated control system

  5. Our Control System • Designed with the specifications of a Toyota Prius. • Methods: • Pseudo Feedback (Jason Silver & Elikplim Tutsi Dornor) • Fuzzy Logic (Nazim Mufti & James Townsend) • Energy Management System (Elikplim Tutsi Dornor & Nazim Mufti) • Simulink Implementation (James Townsend & Jason Silver)

  6. Fuzzy Logic Controller • Designed using Sugeno Controller in Simulink Fuzzy Toolbox

  7. Fuzzy Logic Controller

  8. Fuzzy Logic Controller • Inputs

  9. Fuzzy Logic Controller • Outputs • Generated Power (Pgen) • This value depends on the inputs above • Ranges from 0 to 40 kW • ScalingFactor • Depends on State of Charge (SOC) only • Ranges from 0-1

  10. Pseudo Feedback • Needed to generate inputs for the Charge Decision Block • Input • Throttle (taken in as Pdriver) • Ranges from 0 -100kW • Electric Motor Speed (Wem) • Ranges from 0 – 1000 rad/s • Output • Pem (EM power)

  11. Charge Decision • Decides whether SOC should increase or decrease • Decrease: EM operation as motor • Increase: EM operation as generator • Input • Pem (from Pseudo Feedback) • Output • Dynamic SOC

  12. Charge Decision Block

  13. Energy Management System • Generated Power and Scaling Factor come from the FLC • Pdriver comes directly from the initial driver inputs • The system delegates power % between ICE and EM, using specs of the Prius

  14. Energy Management System Block

  15. Top Level Design

  16. Top Level Design • Initial Inputs

  17. Top Level Design • System Outputs

  18. Conclusion • From the graph above it is shown that the controller successfully delegates power to the EM and ICE efficiently • SOC remains optimal • Limitations included lack of information in the power controller design

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