Power System and Controller Design for Hybrid Fuel Cell Vehicles Syed K. Ahmed  Donald J. Chmielewski Department of Chemic...
Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul>...
Hybridization Fuel Cell Vehicle
DC-DC Converters
Control of Components
Servo-Loops with PI Controllers
Supervisory Control
Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul>...
High Level System Modeling
Power Request Compliance
Battery High Level System Model
Constraints on Energy Capacity
Constraints on Battery Power
Super Cap. High Level System Model
Fuel Cell Power Constraints
FC Power Time-Derivative Constraints
High Level System Model
Power Loss due to Heat Loss
Power Loss Parameter Definition
Power Loss: Function of Mass
High Level System Model (Accounting for Power Losses)
Disturbance Modeling
Drive Cycle Data Characterization
High Frequency Characteristics
Low Frequency Characteristics
Medium Frequency Characteristics
High Freq. Disturbance Model (Driven by High Freq. White Noise)
Med. Freq. Disturbance Model (Driven by Med. Freq. White Noise)
Low Freq. Disturbance Model (Driven by Low Freq. White Noise)
High Level System Model (with Disturbance Model Driven by White Noise) Hybrid Fuel Cell Vehicle Model: Disturbance Model:
Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul>...
Back-off Selection Optimization
Controller Tuning (Additional Feature of Optimization)
Hybrid Optimization Problem <ul><li>Such that: </li></ul><ul><ul><li>Operation of comp. within constraints </li></ul></ul>...
Power and Energy Constraints of Battery
Constraints: Function of Mass
Aspect Ratio: Function of C-Rate
Optimal Steady State Operating Point (Battery Power and Energy at Zero)
Optimal Steady State Operating Point (Time Averaged Slope of Power of FC   is Zero)
Optimal Steady State Operating Point (Zero Power & Energy of Battery and Slope of FC)
Expected Dynamic Operating Regions (EDOR’s)
Backed-Off Operating Points (BOP’s)
Backed-Off Operating Points (with Power Loss)
Hybrid Optimization Problem (Steady State Perspective)
Hybrid Optimization Problem (Expected Dynamic Operating Regions)
Hybrid Optimization Problem (Power Loss due to Heat Loss)
Hybrid Optimization Problem (System Constraints)
Hybrid Optimization Problem (Global Search Algorithm for Reverse-Convex Inequality)
Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul>...
Energy Storage Components Appetecchi & Prosini (2005) |  Portet, Taberna, Simon, Flahaut, & Laberty-Robert (2005) Technolo...
Fuel Cell Component Murphy, O. J.; A. Cisar; , E. Clarke (1998) ”Low-cost light weight high power density PEM fuel cell st...
Super-Cap Supervisory Simulation
Fuel Cell Supervisory Simulation
Battery Supervisory Simulation
Hybrid Power Simulation
Conclusions and Discussion
Future Work
Acknowledgements <ul><li>Argonne National Laboratory </li></ul><ul><li>Department of Chemical and Biological Engineering a...
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Power System and Controller Design for Hybrid Fuel Cell Vehicles

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The notion of a hybrid fuel cell vehicle is promising in the sense that size and cost reductions for the fuel cell unit can be achieved by offloading much of the peak power requirements to an auxiliary energy storage device (either a battery or super-capacitor). However, to maximize the efficacy of such a system, a power management coordination unit (or controller) will need to be designed. Additionally, the design of this controller and its ability to meet drive cycle requirements will be dramatically influenced by earlier decisions concerning the size of the fuel cell and energy storage devices. Thus, the objective of this work is to develop a scheme that simultaneously designs the controller while sizing the fuel cell and energy storage units. Such an approach, in which system design decisions are driven by the capabilities of the closed-loop system, represents a paradigm shift in hybrid vehicle design.

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  • -Fuel Cell Powered vehicle -fuel cell solely too expensive -hybridize -example: accelerating( motor demand, power from all sources) -example: decelerating (regenerative braking, )
  • -regulate with dc and switches
  • -regulate with dc and switches
  • Develop PI controllers to make servo loops Assume that the they work perfectly Also we need to find out the motor power demand.. From a profile
  • Develop PI controllers to make servo loops Assume that the they work perfectly Also we need to find out the motor power demand.. From a profile
  • Again we assume the setpoint can meet the demand instantenously Drop the ….
  • … . the sp notation
  • We have maximum energy as a func of mass And the power max/min also as a func of mass Related to energy density by C-rate
  • We have maximum energy as a func of mass And the power max/min also as a func of mass Related to energy density by C-rate
  • We have maximum energy as a func of mass And the power max/min also as a func of mass Related to energy density by C-rate
  • We have maximum energy as a func of mass And the power max/min also as a func of mass Related to energy density by C-rate
  • Similarly for FC: 1. FC cannot go negative Max power function of size … we can see…
  • Similarly for FC: 1. FC cannot go negative Max power function of size … we can see…
  • … . We also add another concept.. Concept of loss:
  • Assumed to be heat loss associated with internal resistance of the battery, need this in terms of mass,
  • Assumed to be heat loss associated with internal resistance of the battery, need this in terms of mass,
  • Assumed to be heat loss associated with internal resistance of the battery, need this in terms of mass,
  • complete
  • Determine the motor demand, Many complex ways to model the disturbance, We plan on using argonne’s simulator  psat : Powertrain Systems Analysis Toolkit, power Just started with a simple deduction of the..
  • Power profile -we deduced three areas
  • -start/stop
  • Low, frequency characteristic of whole day
  • Medium, Short trips to grocery store, bestbuy
  • Put our disturbance in terms of whitenose, N, a high frequency model
  • Use whitenoise to drive the model
  • complete
  • -the controller and optimization brings these ellips closer the most profit area, -different tunings changes EDORs
  • Illustrate this optimization
  • Ss And limits
  • Ss And limits
  • Ss And limits
  • Ss And limits
  • Ss And limits
  • Ss And limits
  • Ss And limits
  • Ss And limits
  • Add Esc
  • Links the the ss and the dynamics , keep the ellipse within the limits
  • Everythin except the box are linear or convex LMI constriants and can be solved, reverse-convex, a global solution procedure is used to resolve this
  • main source at small time Average loss is 2 Kw
  • Makes up for losses by super –cap time plots (30-40 hour wavelenght), low freq … battery
  • Barely picked 1 kW Picked for slower than supercap time
  • Time plots See how each component is utilized, Super cap @ high freq Battery @ med freq And FC @ low freq
  • Multiple Technologies Multiple Drive Cycles Optimal Vehicle Be able choose which component, from a list/database of components Maximize Fuel Efficiency Validation using a Argonne National Laboroties PSAT, ANL&apos;s Powertrain Systems Analysis Toolkit
  • Multiple Technologies Multiple Drive Cycles Optimal Vehicle Be able choose which component, from a list/database of components Maximize Fuel Efficiency Validation using a Argonne National Laboroties PSAT, ANL&apos;s Powertrain Systems Analysis Toolkit
  • Multiple Technologies Multiple Drive Cycles Optimal Vehicle Be able choose which component, from a list/database of components Maximize Fuel Efficiency Validation using a Argonne National Laboroties PSAT, ANL&apos;s Powertrain Systems Analysis Toolkit
  • Power System and Controller Design for Hybrid Fuel Cell Vehicles

    1. 1. Power System and Controller Design for Hybrid Fuel Cell Vehicles Syed K. Ahmed Donald J. Chmielewski Department of Chemical and Biological Engineering Illinois Institute of Technology Presented at the AIChE Annual Meeting November 8-13, 2009
    2. 2. Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul><ul><li>Case Study </li></ul><ul><li>Conclusion and Future Work </li></ul>
    3. 3. Hybridization Fuel Cell Vehicle
    4. 4. DC-DC Converters
    5. 5. Control of Components
    6. 6. Servo-Loops with PI Controllers
    7. 7. Supervisory Control
    8. 8. Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul><ul><li>Case Study </li></ul><ul><li>Conclusion and Future Work </li></ul>
    9. 9. High Level System Modeling
    10. 10. Power Request Compliance
    11. 11. Battery High Level System Model
    12. 12. Constraints on Energy Capacity
    13. 13. Constraints on Battery Power
    14. 14. Super Cap. High Level System Model
    15. 15. Fuel Cell Power Constraints
    16. 16. FC Power Time-Derivative Constraints
    17. 17. High Level System Model
    18. 18. Power Loss due to Heat Loss
    19. 19. Power Loss Parameter Definition
    20. 20. Power Loss: Function of Mass
    21. 21. High Level System Model (Accounting for Power Losses)
    22. 22. Disturbance Modeling
    23. 23. Drive Cycle Data Characterization
    24. 24. High Frequency Characteristics
    25. 25. Low Frequency Characteristics
    26. 26. Medium Frequency Characteristics
    27. 27. High Freq. Disturbance Model (Driven by High Freq. White Noise)
    28. 28. Med. Freq. Disturbance Model (Driven by Med. Freq. White Noise)
    29. 29. Low Freq. Disturbance Model (Driven by Low Freq. White Noise)
    30. 30. High Level System Model (with Disturbance Model Driven by White Noise) Hybrid Fuel Cell Vehicle Model: Disturbance Model:
    31. 31. Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul><ul><li>Case Study </li></ul><ul><li>Conclusion and Future Work </li></ul>
    32. 32. Back-off Selection Optimization
    33. 33. Controller Tuning (Additional Feature of Optimization)
    34. 34. Hybrid Optimization Problem <ul><li>Such that: </li></ul><ul><ul><li>Operation of comp. within constraints </li></ul></ul><ul><ul><li>Meet motor power demand </li></ul></ul>
    35. 35. Power and Energy Constraints of Battery
    36. 36. Constraints: Function of Mass
    37. 37. Aspect Ratio: Function of C-Rate
    38. 38. Optimal Steady State Operating Point (Battery Power and Energy at Zero)
    39. 39. Optimal Steady State Operating Point (Time Averaged Slope of Power of FC is Zero)
    40. 40. Optimal Steady State Operating Point (Zero Power & Energy of Battery and Slope of FC)
    41. 41. Expected Dynamic Operating Regions (EDOR’s)
    42. 42. Backed-Off Operating Points (BOP’s)
    43. 43. Backed-Off Operating Points (with Power Loss)
    44. 44. Hybrid Optimization Problem (Steady State Perspective)
    45. 45. Hybrid Optimization Problem (Expected Dynamic Operating Regions)
    46. 46. Hybrid Optimization Problem (Power Loss due to Heat Loss)
    47. 47. Hybrid Optimization Problem (System Constraints)
    48. 48. Hybrid Optimization Problem (Global Search Algorithm for Reverse-Convex Inequality)
    49. 49. Outline <ul><li>Introduction </li></ul><ul><li>High Level System Modeling </li></ul><ul><li>Hybrid Optimization </li></ul><ul><li>Case Study </li></ul><ul><li>Conclusion and Future Work </li></ul>
    50. 50. Energy Storage Components Appetecchi & Prosini (2005) | Portet, Taberna, Simon, Flahaut, & Laberty-Robert (2005) Technology Lithium Battery Super-Capacitor Cost $59/kg 93 C-rate 0.5 hr -1 360 Voltage 3.3 V 2.3 Current Density 0.66 mA/cm 2 100 Resistive Density 0.1 Ω -cm 2 0.4 Specific Area 915x 10 7 cm 2 /kg 1100x 10 2 Power Density 100 W/kg 110,000 Energy Density 342 kJ/kg 246
    51. 51. Fuel Cell Component Murphy, O. J.; A. Cisar; , E. Clarke (1998) ”Low-cost light weight high power density PEM fuel cell stack,” Electrochimica Acta, vol 43, pp 3829-3840 Technology Polymer Electrolyte Membrane Cost $300/kg ΔC rate 10 hr -1 Power Density 1 W/kg
    52. 52. Super-Cap Supervisory Simulation
    53. 53. Fuel Cell Supervisory Simulation
    54. 54. Battery Supervisory Simulation
    55. 55. Hybrid Power Simulation
    56. 56. Conclusions and Discussion
    57. 57. Future Work
    58. 58. Acknowledgements <ul><li>Argonne National Laboratory </li></ul><ul><li>Department of Chemical and Biological Engineering at Illinois Institute of Technology </li></ul><ul><li>Rachid Amine and Chih-Ping Lo </li></ul>

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