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

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] retrieved on March 7, 2007http:// – 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 – Used to better understand hybrid vehicle modelling. Authors design a dynamic car model and powertrain model.

References [4] C. C. Lee, “Fuzzy logic in control systems,” IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 404–435, – 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, – 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.

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

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)

Fuzzy Logic Controller Designed using Sugeno Controller in Simulink Fuzzy Toolbox

Fuzzy Logic Controller

Inputs

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

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

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

Charge Decision Block

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

Energy Management System Block

Top Level Design

Initial Inputs

Top Level Design System Outputs

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