Modeling, Design, and Control of Hybrid Energy Systems and Wireless Power Transfer systems

Transcription

1 Modeling, Design, and Control of Hybrid Energy Systems and Wireless Power Transfer systems Chengbin Ma, Ph.D. Assistant Professor Univ. of Michigan-SJTU Joint Institute, Shanghai Jiao Tong University (SJTU), Shanghai, P. R. China IEEE International Workshop on Design Automation for Cyber-Physical Systems (CPSDA) June 5 th, 2016, Austin, TX USA 1

2 Outline Introduction Quantitative Analysis of HESS Energy Management of HESS Control/Design of WPT Systems Conclusions 2

3 Dynamic Systems Control Lab (2010~Pre.) 4 Ph.D., 5 M.S. 1. Battery /Energy Management 2. Wireless Power Transfer Control of Tl + + Tt 1 Jls Ks s - + ωl Motion & Tm 1+K + - K Tm Jms Ks s ωm Energy 3. Electric Vehicle Dynamics 4. Motion/Motor Control 3

4 Electrolysis Conveter Inveter Conveter New Challenges Control of Motion Speed Precision Efficiency DC System Energy AC Grid Synergy Flexibility Scalability Reliability Wind power generator Solar panel Heat Solar collector Electricity Flywheel Super Capacitor Battery Hydrogen Hydrogen Tank Fuel Cell G2V/V2G EV Plug-in EV Fuel Cell EV 4

5 Outline Introduction Quantitative Analysis of HESS Energy Management of HESS Control/Design of WPT Systems Conclusions 5

6 Battery-Ultracapacitor Test System 6

7 ESR-based Efficiency Analysis Equivalent-Series-Resistance circuit Model: 7

8 Optimal Current Distribution Even for a high energy efficiency, ultracapacitors should provide most of dynamic load current. - C. Zhao, H. Yin, Z. Yang, C. Ma: Equivalent Series Resistance-Based Energy Loss Analysis of A Battery Semi-Active Hybrid Energy Storage System, IEEE Trans. on Energy Conversion, Vol. 30, No. 3, pp , Sep

9 Efficiencies of Four Systems a) Battery-only System b) Passive HESS c) Battery Semi-active HESS d) Capacitor Semi-active System - C. Zhao, H. Yin, C. Ma: "Quantitative Efficiency and Temperature Analysis of Battery-Ultracapacitor Hybrid Energy Storage Systems", IEEE Trans. on Sustainable Energy, accepted on May 20th,

10 Comparison of Efficiencies Under various average and dynamic load currents (I l,d, I l,dp, I l,dn ), battery SOC (SOC b ) and efficiencies of dc-dc converter (h d ). h d =95% 10

11 Voltage [V] Current [A] Voltage [V] Current [A] Voltage [V] Current [A] Voltage [V] Current [A] Battery Ageing Test Dynamic Discharging Mod. Constant Discharging Temperature: 45 deg. Constant Discharging Calendar Life Time [h] Time [h] Time [h] Time [h] 60% SOC No.1 Cell No.2 Cell No.3 Cell No.4 Cell 11

12 Experimental Setup Four battery cells inside the environment chamber Environment chamber LabVIEW program to control and record data Three sets of power supply and electronic load. 12

13 Quantitative Results Realistic case with optimized size of SCs The capacity loss of the battery at 1/3 and 1C rate caused by cycling can be reduced by 28.6% and 29.0% respectively, compared with the case with no ultracapacitors. Ideal case with infinite size of SCs The capacity loss of the battery at 1/3 and 1C rate caused by cycling can be reduced by 36.3% and 39.3 % respectively, compared with the case with no supercapacitors. The resistance increase of the battery can be reduced by at least 50%, compared with the case with no ultracapacitors. - C. Zhao, H. Yin, C. Ma: "Quantitative Evaluation of LiFePO4 Battery Cycle Life Improvement Using Ultracapacitors", IEEE Transactions on Power Electronics, Vol. 31, No. 6, pp , Jun

14 Outline Introduction Quantitative Analysis of HESS Energy Management of HESS Control/Design of WPT Systems Conclusions 14

15 Control of Networked Energy Systems Flexibility, Fault-tolerance, Scalability, Reliability Intelligent Plug & Play in a dynamic environment. Strategic Decision Maker Agent Platform Multi-agent Interaction Modeling Cooler Heater Entertainmen t Battery Supercapacitor Range Extender Strategic Interaction Analysis Wireless Charing Brake Power Steering Light Traction Motor Solar Panel Technical Committee (TC) on "Energy Storage " (TCES) 15

16 Non-Cooperative Current Control Game Three energy devices act as agents to play a game Engine-generator: lower the fuel consumption; Battery pack: extend the cycle life; UC pack: maintain the charge/discharge capability. Ultracapacitor is an assistive energy storage device. Two degree-of-freedoms: battery and generator 16

17 Utility Functions and Nash Equilibrium The preferences of the engine-generator (EG) unit, the battery and UC packs, are quantified by their respective utility functions. The currents at the Nash equilibrium provide a solution that balances the different preferences of the players. EG unit and UC pack Bat. and UC packs 17

18 Test bench 18

19 Results under Real Test Cycles - H. Yin, C. Zhao, M. Li, C. Ma, M. Chow: "A Game Theory Approach to Energy Management of An Engine- Generator/Battery/Ultracapacitor Hybrid Energy System", IEEE Trans. Industrial Electronics, Accepted on Jan. 26 th,

20 Fault Tolerance in Energy Management Game theory-based energy management is expected to be superior in fault tolerance. The control strategy can be reconfigured when failure happens. Battery Pack Load Failure Happens Enginegenerator Enginegenerator Battery Pack 50% of Load UC Pack UC Pack Normal Operation Fail! Limp home mode 20

21 Other Ongoing Directions Battery management system: hardware, states estimation, and control algorithms Energy flow modeling and control between electric vehicles and smartgrids. Modeling EV Charging Model and Adaptive Correction Distributed Modeling of Energy Flow Strategy Nash Equilibrium among EVs Stackelberg Equilibrium between EVs and Grids Application Intelligent and Dynamic Management of Energy Flow 21

22 Outline Introduction Quantitative Analysis of HESS Energy Management of HESS Control/Design of WPT Systems Conclusions 22

23 System-level Optimizations/Control Optimal load tracking for high efficiency Robust design of system parameters Autonomous power distribution and control in multi-receiver systems Power level: 20 W System Efficiency: 84% (k=0.1327) 23

24 Optimal Load for High Efficiency Optimal loads Lm PA Pf Rectifier DC/DC converter PL Load RL 24

25 Improved Charging Efficiency Wireless charging efficiency improvement with a fixed coil relative position. 18% 43.4% - M. Fu, C. Ma, X. Zhu: A Cascaded Boost-Buck Converter for Load Matching in 13.56MHz Wireless Power Transfer", IEEE Trans. on Industrial Informatics, IEEE Transactions on Industrial Informatics, Vol. 10, No. 3, pp , Aug

26 Experiment Setup The experimental WPT system. (a) Overall system. (b) Relative position of coils. (c) Power sensor. (d) I/V sampling board. (e) Cascaded DC/DC converter. 26

27 Hill-climbing Tracking of Optimal Load A varying load resistance A varying coil position Fig. 1 Tracking of optimal load resistances with a varying R l. Fig. 2 Tracking of optimal load resistances with a varying k. - M. Fu, H. Yin, X, Zhu, C. Ma: Analysis and Tracking of Optimal Load in Wireless Power Transfer Systems, IEEE Trans. on Power Electronics, Vol. 30, No. 7, pp , Jul

28 Robust Optimization and Design Instead of active control, the system parameters are optimized to improve the robustness against a varying operating condition. Robustness Index 28

29 Experimental Results Variation in coil distance Load=15 Ohm Load=30 Ohm Load=45 Ohm Variation in load d=15 cm d=30 cm d=45 cm - M. Liu, Y. Qiao, S. Liu, C. Ma: "Analysis and Design of A Robust Class E^2 DC-DC Converter for Megahertz Wireless Power Transfer", IEEE Trans. on Power Electronics, accepted on May 16th,

30 Multiple-Receiver WPT System 30

31 Outline Introduction Quantitative Analysis of HESS Energy Management of HESS Control/Design of WPT Systems Conclusions 31

32 Conclusions A fundamental transition is occurring from control of motion to control of energy. System-level analysis, optimization, and implementation of control are crucial. Major interests of DSC lab: Battery management system: hardware and various algorithms Modeling and control of networked energy systems (hybrid energy systems, alternative energy systems, vehicle-to-grid systems) Optimal design and control of WPT systems (new sensor, tunable components, control and design methodology) Autonomous power distribution among multiple receivers/devices 32

33 Thank You Presented by Dr. Chengbin Ma Web: Lab: 33