Fuzzy Logic Control for Parallel Hybrid Vehicles: Toyota Prius

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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
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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. 1733, Oct. 1998.
• Used to better understand hybrid vehicle
  modelling. Authors design a dynamic car model and
  powertrain model.

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References

• 4 C. C. Lee, Fuzzy logic in control systems,
  IEEE Trans. Syst., Man, Cybern., vol. 20, pp.
  404435, 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, Masters 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.

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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

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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)

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Fuzzy Logic Controller

• Designed using Sugeno Controller in Simulink
  Fuzzy Toolbox

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Fuzzy Logic Controller
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Fuzzy Logic Controller

• Inputs

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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

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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)

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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

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Charge Decision Block
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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

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Energy Management System Block
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Top Level Design
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Top Level Design

• Initial Inputs

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Top Level Design

• System Outputs

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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