Fuzzy Logic Control for Parallel Hybrid Vehicles:

Toyota Prius

By:Jason SilverNazim MuftiJames TownsendElikplim Tutsi Dornor

Instructor : Riadh Habash T.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] 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.

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.

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

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 0 -100kW

– 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

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