Power System and Controller Design for Hybrid Fuel Cell Vehicles

Transcription

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 Department of Chemical and Biological Engineering

2 Outline Introduction High Level System Modeling Hybrid Optimization Case Study Conclusion and Future Work

3 Hybridization Fuel Cell Vehicle Power Bus i arm i i i ap R arm Fuel Cell E R E R ap E ap E arm L arm w arm

4 DC-DC Converters Power Bus i arm i i a i i a i ap i aap R arm Fuel Cell E DC-DC Converter R E DC-DC Converter R ap E ap DC-DC Converter E arm L arm w arm k k k ap

5 Control of Components Power Bus i arm i i a i i a i ap i aap R arm Fuel Cell E DC-DC Converter R E DC-DC Converter R ap E ap DC-DC Converter E arm L arm w arm k k k ap

6 Servo-Loops with PI Controllers PI (sp) P + - PI (sp) P + - PI (sp) P ap + - k P k P k ap P ap Vehicle Power System

7 Supervisory Control High Level Controller (sp) P mot PI (sp) P + - PI (sp) P + - PI (sp) P ap + - k P k P k ap P ap Vehicle Power System

8 Outline Introduction High Level System Modeling Hybrid Optimization Case Study Conclusion and Future Work

9 High Level System Modeling High Level Controller (sp) P mot PI (sp) P + - PI (sp) P + - PI (sp) P ap + - k P k P k ap P ap Vehicle Power System

10 Power Request Compliance P mot (sp) PI k High Level Controller P P + - PI + - PI P k P k ap Vehicle Power System P ap + - P ap

11 Battery High Level System Model d E dt P

12 Constraints on Energy Capacity d E dt P E E E E 0 E e m ˆ

13 Constraints on Battery Power d E dt P E E E P P P E 0 E e m P C e m P C e m ˆ rate,c ˆ rate,d ˆ

14 Super Cap. High Level System Model d E dt P E E E P P P E 0 E P C e m P C e m ˆ em rate,c ˆ rate,d ˆ

15 Fuel Cell Power Constraints d P dt P P P P P P 0 ˆ p m

16 C Power Time-Derivative Constraints d P dt P P P P P P P P P 0 ˆ p m, P rate c ˆ C p m rate, d P ˆ C p m

17 High Level System Model d P dt P d E dt P d E dt ap P ap P P P P P P P P P E E E P P P E E E ap ap ap ap ap ap

18 Power Loss due to Heat Loss I 2 Rˆ P loss A

19 Power Loss Parameter Definition I 2 Rˆ P loss A I P / V A aˆ m 2 ˆ / ˆ l V a R

20 Power Loss: Function of Mass P loss l P 2 m

21 High Level System Model (Accounting for Power Losses) d P dt P d E loss P d P E loss ap P P dt dt P loss l P 2 m P loss P 2 l m

22 Disturbance Modeling High Level Controller (sp) P mot PI (sp) P + - PI (sp) P + - PI (sp) P ap + - k P k P k ap P ap Vehicle Power System

23 Power to Motor (kw) Speed (mph) Drive Cycle Data Characterization time (sec) time (sec)

24 Speed (mph) High Frequency Characteristics P mot P high time (sec)

25 Speed (mph) Low Frequency Characteristics P P P mot low high time (sec)

26 Speed (mph) Medium Frequency Characteristics P P P P mot low med high time (sec)

27 High Freq. Disturbance Model (Driven by High Freq. White Noise) P P P P mot low med high P a P b n high high high high high

28 Med. Freq. Disturbance Model (Driven by Med. Freq. White Noise) P P P P mot low med high P a P b n high high high high high P a P b n med med med med med

29 Low Freq. Disturbance Model (Driven by Low Freq. White Noise) P P P P mot low med high P a P b n high high high high high P a P b n med med med med med P a P b n low low low low low

30 P High Level System Model (with Disturbance Model Driven by White Noise) Hybrid Fuel Cell Vehicle Model: P P P P P P P P P P P mot Disturbance Model: Pmot Plow Pmed Phigh E P P loss P P P E E E 2 loss P P lm E P P loss ap P P P E E E P a P b n P a P b n P a P b n ap ap ap ap ap 2 loss P P lm high high high high high med med med med med low low low low low

31 Outline Introduction High Level System Modeling Hybrid Optimization Case Study Conclusion and Future Work

32 Back-off Selection Optimization Expected Dynamic Operating Region (EDOR) Constraints Backed- off Operating Point (BOP) Optimal Steady- State Operating Point (OSSOP)

33 Controller Tuning (Additional Feature of Optimization) EDOR s due to different controller tunings BOP with less profit BOP with more profit

34 Hybrid Optimization Problem { cˆ m cˆ m cˆ m } Such that: Operation of comp. within constraints Meet motor power demand

35 Power and Energy Constraints of Battery E P C E rate,c E eˆ m E 0 P P C E rate,d

36 Constraints: Function of Mass E P C E rate,c E eˆ m P

37 Aspect Ratio: Function of C-Rate E P C E rate,c E eˆ m P

38 Optimal Steady State Operating Point (Battery Power and Energy at Zero) E P

39 Optimal Steady State Operating Point (Time Averaged Slope of Power of FC is Zero) P ` Δ P

40 Optimal Steady State Operating Point (Zero Power & Energy of Battery and Slope of FC) P E ` Δ P P

41 Expected Dynamic Operating Regions (EDOR s) P E ` Δ P P

42 Backed-Off Operating Points (BOP s) P E ` Δ P P

43 Backed-Off Operating Points (with Power Loss) P E ` Δ P P

44 Hybrid Optimization Problem (Steady State Perspective) P { cˆ m cˆ m cˆ m } st.. Δ P P P P P o m m m m m v o P P lm 0 0 P 0 lm P P l m 0 P 0 0 l m T AX BY AX BY Go mvg Go mvg1 w i i x u T T x u i D X D X D Y X D Y 0 i i P E E 2 i P P P P E E 0 i i P 1 2 E 3 4 P 5 6 E P P P P P P E E P P P

45 Hybrid Optimization Problem (Expected Dynamic Operating Regions) E, { cˆ m cˆ m cˆ m } st.. P, P P P P o m m m m m v o P P lm 0 0 P 0 lm P P l m 0 P 0 0 l m T AX BY AX BY Go mvg Go mvg1 w i i x u T T x u i D X D X D Y X D Y 0 i i P E E 2 i P P P P E E 0 i i P 1 2 E 3 4 P 5 6 E P P P P P P E E P P P

46 Hybrid Optimization Problem (Power Loss due to Heat Loss) { cˆ m cˆ m cˆ m } st.. P P P P o m m m m m v o P P lm 0 0 P 0 lm P P l m 0 P 0 0 l m T AX BY AX BY Go mvg Go mvg1 w i i x u T T x u i D X D X D Y X E, D Y 0 i i P E E 2 i P P P P E E 0 i i P 1 2 E 3 4 P 5 6 E P P P P P P E E P P P P,

47 Hybrid Optimization Problem (System Constraints) { cˆ m cˆ m cˆ m } st.. P P P P o m m m m m v o P P lm 0 0 P 0 lm P P l m 0 P 0 0 l m T AX BY AX BY Go mvg Go mvg1 w i i x u T T x u i D X D X D Y X D Y 0 i E, i P E E P 2 i P P P E E P P 0 i i P 1 2 E 3 4 P 5 6 E P P P P E E P P P P,

48 Hybrid Optimization Problem (Global Search Algorithm for Reverse-Convex Inequality) { cˆ m cˆ m cˆ m } st.. P P P P o m m m m m v o P P lm 0 0 P 0 lm P P l m 0 P 0 0 l m T AX BY AX BY Go mvg Go mvg1 w i i x u T T x u i D X D X D Y X D Y 0 i i P E E 2 i P P P P E E 0 i i P 1 2 E 3 4 P 5 6 E P P P P P P E E P P P

49 Outline Introduction High Level System Modeling Hybrid Optimization Case Study Conclusion and Future Work

50 Energy Storage Components Technology Lithium Battery Super-Capacitor Cost $59/kg 93 C-rate 0.5 hr Voltage 3.3 V 2.3 Current Density 0.66 ma/cm Resistive Density 0.1 Ω-cm Specific Area 915x 10 7 cm 2 /kg 1100x 10 2 Power Density 100 W/kg 110,000 Energy Density 342 kj/kg 246 Appetecchi & Prosini (2005) Portet, Taberna, Simon, Flahaut, & Laberty-Robert (2005)

51 Fuel Cell Component Technology Cost Polymer Electrolyte Membrane $300/kg ΔC rate 10 hr -1 Power Density 1 W/kg Murphy, O. J.; A. Cisar;, E. Clarke (1998) Low-cost light weight high power density PEM fuel cell stack, Electrochimica Acta, vol 43, pp

52 E (kj) Super-Cap Supervisory Simulation 700 Super Capacitor 60 SuperCap, kw time (hr) P (kw)

53 P (kw) Fuel Cell Supervisory Simulation Fuel Cell P (kw/s) x Fuel Cell Power(kW) time(hr)

54 E (kj) Battery Supervisory Simulation Battery 5 Battery Power, kw P (kw) time (hr)

55 Hybrid Power Simulation 50 SuperCap, kw Battery, kw Fuel Cell Power(kW) time(hr)

56 Conclusions and Diussion 1.Ni-MH 2.Ni-Cd 3.Ni-Zn 4.Li-Ion Battery Characteristics 1.`PEMFC 2.SOFC Fuel Cell Characteristics 1.Carbon Nanotubes 2.Graphene 3.Activated Carbon 4.Aerogels Super Cap Characteristics * * Global Optimization of hybrid vehicle Optimal Size of each component Optimal Controller $ Min Capital Costs Optimal Vehicle Drive Cycle

57 Future Work 1.Ni-MH 2.Ni-Cd 3.Ni-Zn 4.Li-Ion Battery Technologies Best Technology 1.PEMFC 2.Others Fuel Cell Technologies 1.Carbon Nanotubes 2.Graphene 3.Activated Carbon 4.Aerogels Super Cap Technologies * * Global Optimization of hybrid vehicle Optimal Size of each component Optimal Controller $ Min Cost MPG Optimal Vehicle Drive Cycle Min Oper. Cost via Fuel Efficiency

58 Acknowledgements Argonne National Laboratory Department of Chemical and Biological Engineering at Rachid Ae and Chih-Ping Lo