Modeling, Design, and Control of Hybrid Energy Systems...

Dynamic Systems Control Laboratory, UM-SJTU Joint Institute

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 5th, 2016, Austin, TX USA

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

1


Introduction

Quantitative Analysis of HESS

Energy Management of HESS

Control/Design of WPT Systems

Conclusions

Outline

2


Dynamic Systems Control Lab (2010~Pre.) http://umji.sjtu.edu.cn/lab/dsc/

3

1. Battery /Energy Management 2. Wireless Power Transfer

Control of Motion &

Energy

4 Ph.D., 5 M.S.

3. Electric Vehicle Dynamics

ωmTm’

K

1+K

sKs

Jms1

sKs

Jls1

+

-

+

+Tl

-+

ωl

+

-

Tt

Tm

4. Motion/Motor Control


New Challenges

4

Control of Motion

Energy

Wind power generator

Solar panel

Solar collector

Inv

eter

Co

nv

eter

Ele

ctro

lysi

s

Heat

Hydrogen

Super Capacitor

Battery

Hydrogen Tank

Fuel Cell

Fuel Cell EVPlug-in EVC

on

vet

er

AC GridDC System

G2V/V2G EV

Electricity

Flywheel

■ Speed ■ Precision ■ Efficiency

■ Synergy ■ Flexibility ■ Scalability ■ Reliability


Introduction




Conclusions

Outline

5


Battery-Ultracapacitor Test System

6


ESR-based Efficiency Analysis

7

Equivalent-Series-Resistance circuit Model:


Optimal Current Distribution

8

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. 1081-1091, Sep. 2015.


a) Battery-only System

b) Passive HESS

c) Battery Semi-active HESS

d) Capacitor Semi-active System

Efficiencies of Four Systems

9

- 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, 2016.


Under various average and dynamic load currents (Il,d, Il,dp, Il,dn), battery SOC (SOCb) and efficiencies of dc-dc converter (hd).

Comparison of Efficiencies

10

hd =95%


Battery Ageing Test

11

Temperature: 45 deg.

Calendar

Life

No.1 Cell No.2 Cell No.3 Cell No.4 Cell

Dynamic

Discharging

Mod. Constant

Discharging

Constant

Discharging

0 0.5 1 1.5 2 2.52.8

2.9

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

Time [h]

Voltage [

V]

0 0.5 1 1.5 2 2.5-60

-50

-40

-30

-20

-10

0

10

20

30

Curr

ent

[A]

0 0.5 1 1.5 2 2.52.9

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

Time [h]

Voltage [

V]

0 0.5 1 1.5 2 2.5-50

-40

-30

-20

-10

0

10

20

30

Curr

ent

[A]

0 0.5 1 1.5 2 2.52.9

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

Time [h]

Voltage [

V]

0 0.5 1 1.5 2 2.5-50

-40

-30

-20

-10

0

10

20

30

Curr

ent

[A]

0 0.5 1 1.5 2 2.52.9

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

Time [h]

Voltage [

V]

0 0.5 1 1.5 2 2.5-50

-40

-30

-20

-10

0

10

20

30

Curr

ent

[A]

60% SOC


Three sets of power supply and electronic load.

Experimental Setup

12

LabVIEW program to control and record data

Environment

chamber Four battery cells inside the environment chamber


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.

Quantitative Results

13

- 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. 3989-3993, Jun. 2016


Introduction




Conclusions

Outline

14


Control of Networked Energy Systems

15

Flexibility, Fault-tolerance, Scalability, Reliability

Intelligent “Plug & Play” in a dynamic environment.

Cooler HeaterEnter-tainmen

t

BrakePowerSteer-ing

LightTraction Motor

Battery

Super-capacitor

Wireless Charing

Range Extender

Solar Panel

Agent

Platform

Strategic

Decision Maker

Technical Committee (TC) on "Energy Storage " (TCES)

Multi-agent Interaction Modeling

Strategic Interaction Analysis


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


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.

Utility Functions and Nash Equilibrium

17

EG unit and UC pack

Bat. and UC packs


Test bench

18


Results under Real Test Cycles

19

- 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. 26th, 2016.


Game theory-based energy management is expected to be superior in fault tolerance.

The control strategy can be reconfigured when failure happens.

Fault Tolerance in Energy Management

20

Normal Operation

Engine-generator

Battery Pack

UC Pack

Load

Engine-generator

Battery Pack

UC Pack

50% of Load

Fail!

Failure Happens

Limp home mode


Battery management system: hardware, states estimation, and control algorithms

Energy flow modeling and control between electric vehicles and smartgrids.

Other Ongoing Directions

21

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


Introduction




Conclusions

Outline

22

Dynamic Systems Control Laboratory, UM-SJTU Joint Institute 23

Optimal load tracking for high efficiency Robust design of system parameters Autonomous power distribution and control in

multi-receiver systems

System-level Optimizations/Control

Power level: 20 W

System Efficiency: 84% (k=0.1327)


Optimal Load for High Efficiency

24

PA RectifierDC/DC

converterLoad

RL

PLPf

Lm

Optimal loads


Improved Charging Efficiency

25

Wireless charging efficiency improvement with a fixed coil relative position.

- 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. 1972-1980, Aug. 2014.

43.4%↑ 18%↑


Experiment Setup

26

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.


Hill-climbing Tracking of Optimal Load

27

Fig. 1 Tracking of optimal load resistances with a varying Rl.

Fig. 2 Tracking of optimal load resistances with a varying k.

A varying load resistance A varying coil position

- 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. 3952-3963, Jul. 2015.


Instead of active control, the system parameters are optimized to improve the robustness against a varying operating condition.

Robust Optimization and Design

28

Robustness Index


Experimental Results

29

Load=15 Ohm Load=30 Ohm Load=45 Ohm

Variation in coil distance

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, 2016.


Multiple-Receiver WPT System

30


Introduction




Conclusions

Outline

31


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

Conclusions

32


Thank You

Presented by Dr. Chengbin Ma

Email: [email protected]

Web: http://umji.sjtu.edu.cn/faculty/chengbin-ma/

Lab: http://umji.sjtu.edu.cn/lab/dsc

33

mailto:[email protected]

http://umji.sjtu.edu.cn/faculty/chengbin-ma/




Modeling, Design, and Control of Hybrid Energy Systems...

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