Distributed Control, iEMS, and On-line Battery … · Battery Modeling on FREEDM SystemsBattery...

Distributed Control, iEMS, and On-line Battery Modeling on FREEDM SystemsBattery Modeling on FREEDM Systems

Mo-Yuen Chow, Ph.DDepartment of Electrical and Computer Engineering

North Carolina State UniversityyRaleigh, North Carolina

OverviewBrief descriptions of our FREEDM and ATEC projects

• Time-Sensitive Distributed Controls on FREEDM Systems (withTime Sensitive Distributed Controls on FREEDM Systems (with Ziang Zhang)

• Intelligent Energy Management System (iEMS) for PHEVs in Municipal Parking Deck (with Wencong Su)Municipal Parking Deck (with Wencong Su)

• Comprehensive Online Dynamic Battery Modeling for PHEV Applications (with Hanlei Zhang)P iti th FREEDM h d• Positions on the FREEDM research roadmaps

Consensus algorithms for a time-sensitive distributed controlled FREEDM Systemcontrolled FREEDM System

• Graph theory• Convergence rate• Time delay

Summary and future works

Future Renewable Electric Energy Delivery and Management Systems Center

2

Our current FREEDM and ATEC projects

TimeTime sensitive distributed controls on FREEDMsensitive distributed controls on FREEDM

projects

TimeTime--sensitive distributed controls on FREEDM sensitive distributed controls on FREEDM SystemsSystems• Ziang Zhang• Xichun YingXichun Ying

Intelligent Energy Management System for PHEVs at A Intelligent Energy Management System for PHEVs at A Municipal Parking DeckMunicipal Parking Deck• Wencong Su• Xu Yang

H l i Zh• Hanlei Zhang

Comprehensive Online Dynamic Battery Modeling for Comprehensive Online Dynamic Battery Modeling for PHEV ApplicationsPHEV ApplicationsPHEV Applications PHEV Applications • Hanlei Zhang• Wencong Su


The interactions among the three projectsthree projects

iEMS

Power allocationBattery Model

Real-Time Monitoring and ControlEmbedded

DAQ


4

gDAQDAQSoC, SoH, SoF

Digital Testbed: IEM Demonstration Grid connected•Dynamic pricing•Renewable/storage/loadDi it l T tb d

•Power sharingIslandingRenewable/storage/load Digital Testbed

& Demonstration•Frequency regulation

hnol

ogy

Y2.E.C5

Y2.E.C8 SCADASystem

ImprovedSCADASystem

Y2.E.C4

ablin

g Te

c

SST Prototype Deliver three pieces

Gen-IISST design

Y2.E.C6

nce Y2.F.C3

Ena Y2.F.C1 Simulink

ModelRTDSModel

Additional Controls

Distributed O i i dOptimized

enta

l Sci

en

Y2.F.C515kV SiC

DistributedControl

Framework

Optimized Control

pSystemDesignY2.F.C4

Fund

ame

Y2.F.C8

15kV SiCMOSFET

/Diode

Packageddevice

Y2 F C9 11600V GaNMOSFET

Packageddevice

Future Renewable Electric Energy Delivery and Management Systems CenterYEAR 3YEAR 2Sept.’09 Sept.’ 10 Sept.’11

Y2.F.C9-11 MOSFET device

PHEV TestbedMotor Drive TestEnergy ManagementOptimized SupplyY2.D.C1 PHEV Vehicle

PHEV Roadway Testetc.Y2.D.C2

TestBed

Y2.D.C3

Digital Testbed

PHEVParking Deck

EnergyManagement

hnol

ogy

Y2.E.C7 DESD

ablin

g Te

c

ChargerY2.E.C9

Y2 E C10DESD

Y2.E.C1

Ena Y2.E.C10

nce

Battery ModelBattery

Equivalent CircuitModel

Y2.F.C7 Capacity Fade Mechanismen

tal S

cien

Y2.F.C6High-Power Long-Life Batteries

Optimized BatteryCellsFu

ndam

e

Future Renewable Electric Energy Delivery and Management Systems CenterYEAR 3YEAR 2Sept.’09 Sept.’ 10 Sept.’11

FREEDM System: a very smart Smart Gridsmart Smart Grid

Goal of Smart Grid: Intelligent power delivery with optimal efficiency,effectiveness, power quality, resilience, reliably, availability, etc.

Features• Self healing property• Delivery of high power quality• Delivery of high power quality• Customized power usages• Effective and efficient energyy

systems• Immunity to cyber attack

224/7 il bilit• 224/7 availability• Seven sigma reliability• …


7

Time-sensitive distributed networked control systems (TS D-NCS)control systems (TS D NCS)

Enabling and enpowering individuals and small groups of sensors, actuators and controllers go global easily andcontrollers go global easily and seamlessly.Unique character – the newfound power for individuals (sensors, actuators,

t ll ) t ll b t / tcontrollers) to collaborate/cooperate globally to solve local challenging problems (that cannot be solved otherwise)Provide optimized system performance with low cost through distributed information utilizationsEnable real time monitoring control andEnable real-time monitoring, control and operation globally with distributed local informationCould usher in an amazing era of

it i ti d ll b tiprosperity, innovation, and collaboration, by integrating distributed sensors, actuators, and controllers around the world.


Central Control vs. Distributed Control

Puppet

vs.

School of fish

Central Control Distributed Control [1]

System Puppets and Puppeteer School of fish

Controller Puppeteer (Single) Fish (Multiple)

Information available to the

Puppeteer know the position of every part of puppet

Each fish only know the position of neighbors (Local)

controllery p p pp

(Global)g ( )

Control Goal Keep certain pattern of style and moving around

Keep certain pattern of shape and moving around


9• Iain D. Couzin, Jens Krause, Nigel R. Franks and Simon A. Levin, “Effective leadership and decision-making in

animal groups on the move”, Nature 433, 513-516 (3 February 2005)• …

Central control vs. distributed control -2

Central Controlled System Distributed Controlled System

P C t l l ith i l ti l R li d th t ti l b d fPros • Control algorithm is relatively simple

• …

• Relieved the computational burden for a single controller

• Ease of heavy data exchange demand• Single point of failure will notSingle point of failure will not

necessarily affect the others• Controllers do not need the entire

system state information• …

Cons • Computational limitation of central controller

• Only part of the system states are available to each distributed controller

• Communication limitation of central controller

• Single point of failure will affect the entire system

• Normally need complex algorithms and designs

• …the entire system

• …Usages Normally more appropriate for

systems with simple controlNormally more appropriate for large-scale systems need sophisticated control


systems with simple control systems need sophisticated control

10

Time-sensitive distributed network control systems challengescontrol systems challenges

Time-sensitive applications/ Time delay issuesU

U

Hard real-timeSoft real-time

Hard real-time control

Soft real-time control Umax

U( )

U: utility functionT : hard deadline: actual time Non real-time control

Resource constraints (e g bandwidthUmin

tT

Resource constraints (e.g., bandwidth,

generation)/ resources allocation issues

Data-sensitive applications/ Security issues

…

- M.-Y. Chow, S. Chiaverini, and C. Kitts, "Guest Editorial on Focused Section on Mechatronics in Multi Robot Systems," IEEE Transactions on Mechatronics, vol. 14, pp. 133-140, April 2009, pp. 133-140.

- R A Gupta and M -Y Chow "Networked Control Systems: Overview and Research Trends " forth coming accepted


11

- R. A. Gupta and M.-Y. Chow, Networked Control Systems: Overview and Research Trends, forth coming, accepted for publication in IEEE Transactions on Industrial Electronics, October 2009.

- …

Time-sensitive Distributed Controls on FREEDM SystemsSyste s

RA: Ziang Zhang (John)

Phase I : Consensus Algorithms g g ( )

Department of Electrical and Computer EngineeringNorth Carolina State University

Raleigh, North Carolina


12

What is consensus?

ConsensusConsensus [1]

A school of fish

Goal: swimming towards oneChorus

ConsensusConsensus

Goal: swimming towards one same direction Goal: Synchronize the melody

[1] Larissa Conradt and Timothy J Roper “Consensus decision making in animals” Trends in Ecology & Evolution Volume


13

[1]. Larissa Conradt and Timothy J. Roper, Consensus decision making in animals , Trends in Ecology & Evolution, Volume 20, Issue 8, August 2005, Pages 449-456.

How can consensus be reached?

Consensus Network

Independent Physical SystemsEach of them follow their own dynamic

A sufficient condition for reach consensus: If there is a directed spanning tree* exists in the communication network, then consensus can be reached. [1]

*Spanning tree: a minimal set of edges that connect all nodes


14[1] Wei Ren Randal W. Beard Ella M. Atkins , “A Survey of Consensus Problems in Multi-agent Coordination”, 2005 American Control Conference June, 2005. Portland, OR, USA

Consensus algorithm based modeling

Adjacency matrix of a finite graph G on n vertices is the n × n matrix where the entry aij is the number of edges from vertex i to vertex j aij =0 representthe entry aij is the number of edges from vertex i to vertex j, aij =0 represent that agent i cannot receive information from agent j

C bl d li0 1 1⎡ ⎤⎢ ⎥

Example:

iξ, 1,...,i i i nξ ξ= =

Consensus problem modelingLocal information stateFirst-order system

1 0 11 1 0

A ⎢ ⎥= ⎢ ⎥⎢ ⎥⎣ ⎦

Adj t iConsensus algorithm:

Scalar From Matrix Form

i i Adjacency matrix

Continuous

Discrete1

( ), 1,...,n

i ij i jj

a i nξ ξ ξ=

= − − =∑ nLξ ξ= −

[ 1] [ ] 1n

k d k iξ ξ+ ∑

Where Ln is the Laplacian matrix associated with A,

Discrete[ 1] [ ]nk D kξ ξ+ =

1

[ 1] [ ], 1,...,i ij jj

k d k i nξ ξ=

+ = =∑


n p ,and Dn is Row-stochastic matrix associated with A.

15

Consensus algorithm performance

2Continuous-time Consensus

Consensus performance with different network topology - Step inputs as load references

0 5

1

1.5

k)

4 -1 -1 -1 -1 -1 1 0 0 0 -1 0 1 0 0

1 0 0 1 0L

⎡ ⎤⎢ ⎥⎢ ⎥

= ⎢ ⎥⎢ ⎥

p

-0.5

0

0.5ξ(k

SST1SST2SST3SST4SST5ref

-1 0 0 1 0 -1 0 0 0 1

⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

Al b i ti it λ 10 5 10 15 20 25 30 35 40

-1

k

1.5

2Continuous-time Consensus

2 -1 -1 0 0⎡ ⎤⎢ ⎥

Algebraic connectivity λ2 = 1

0.5

1

ξ(k)

SST1

-1 1 0 0 0-1 -1 2 0 00 0 -1 2 -10 0 0 -1 1

L

⎡ ⎤⎢ ⎥⎢ ⎥

= ⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦

0 5 10 15 20 25 30 35 40-1

-0.5

0

SST1SST2SST3SST4SST5ref

⎣ ⎦

Algebraic connectivity λ2 ≈ 0.38

Al b i ti it λ Th l b i ti it f

Future Renewable Electric Energy Delivery and Management Systems Center 16

0 5 10 15 20 25 30 35 40k

Algebraic connectivity λ2 : The algebraic connectivity of a graph G is the second-smallest eigenvalue of the Laplacian matrix of G.

Consensus algorithm based modeling

Consensus test with time-delay1.5

2Continuous-time Consensus with delay=0

τ3

4Continuous-time Consensus with delay=pi/6

( ) ( )t L tξ ξ τ= − −2 1 1− −⎡ ⎤

⎢ ⎥3

Continuous-time Consensus with delay=0.5

-1.5

-1

-0.5

0

0.5

1

ξ(t)

-2

-1

0

1

2

3

ξ(t)

1 2 11 1 2

L ⎢ ⎥= − −⎢ ⎥− −⎢ ⎥⎣ ⎦

-1

0

1

2

ξ(t)

0 5 10 15 20-3

-2.5

-2

t 0τ =0 5 10 15 20

-4

-3

t

Continuous-time Consensus with delay=0 7854

0.5236sec2 n

πτλ

= ≈2 33, 3nλ λ λ= = =

1 1 0−⎡ ⎤

0 5 10 15 20-3

-2

t

0.5secτ =Continuous-time Consensus with delay=0 5

-0.5

0

0.5

1

1.5

2Continuous-time Consensus with delay=0

ξ(t) -1

0

1

2Continuous time Consensus with delay=0.7854

ξ(t)

1 1 01 1 0

0 1 1L

⎡ ⎤⎢ ⎥= −⎢ ⎥

−⎢ ⎥⎣ ⎦1

-0.5

0

0.5

1

1.5

2Continuous time Consensus with delay=0.5

ξ(t)

0 5 10 15 20-3

-2.5

-2

-1.5

-1

t

0τ =0 5 10 15 20

-4

-3

-2

t

0 7854secπτ = ≈2 1, 2λ λ= = 0 5secτ =0 5 10 15 20

-3

-2.5

-2

-1.5

-1

t

0τ = 0.7854sec2 n

τλ

= ≈2 1, 2nλ λAlgebraic Connectivity

Largest

2 :λ

:λ

A sufficient condition for convergence [1] of the consensus algorithm above is that

2 n

πτλ

<

0.5secτ =

Future Renewable Electric Energy Delivery and Management Systems Center 17

Largest eigenvalue of L

:nλ[1] Reza Olfati-Saber and Richard M. Murray, “Consensus Problems in Networks of Agents

With Switching Topology and Time-Delays”, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 49, NO. 9, SEPTEMBER 2004

Current work: Application of consensus algorithms on FREEDM systemsalgorithms on FREEDM systems

Formulate the consensus algorithms for the FREEDM systems with both continuous time models and discrete event modelstime models and discrete event modelsDesign high performance and reliable consensus algorithms for FREEDM systemsInteracting with other groups

NCSU (communication network resilience, delay, reconfiguration, NCSU green hub models,NCSU (communication network resilience, delay, reconfiguration, NCSU green hub models, distributed control algorithms – Dr. Mueller, Dr. Jiang, Dr. Baran)MST (MST FREEDM testbed, load balancing algorithms – Dr. McMillin, Dr. Crow)ASU (SST models, optimization – will establish closer interactions)


18Publication: Ziang Zhang, Mo-Yuen Chow, “Consensus Algorithms for a Distributed Controlled FREEDM System”, FREEDM Annual Conference, May 2010

Some future works of the projects

Time-Sensitive Distributed Controls on FREEDM SystemsDevelop consensus algorithms under separated power network and communication network

projects

gDevelop other distributed control algorithmsAnalyze and develop distributed controls to handle time-delayAnalyze and develop adaptive sampling strategies and distributed bandwidth allocation algorithms to handle bandwidth limitationalgorithms to handle bandwidth limitationCollaborate with other FREEDM teams to demonstrate the distributed control algorithms on the FREEDM testbeds…

Intelligent Energy Management System (iEMS) for PHEVs in Municipal Parking Deck

Integrate the comprehensive battery models from the Battery Monitoring System Development and Deployment project into the iEMS

Interact with the Distributed Control of FREEDM system project on using some ofthe developed distributed control algorithms for iEMS

Collaborate with the Bi-directional electric vehicle supply equipment project toCollaborate with the Bi directional electric vehicle supply equipment project toimplement distributed control algorithms

Use the architect developed by Optimization of the power delivery architectureproject to develop related communication network requirements and control

i t / t i t


requirements/constraints

Demonstrate iEMS algorithm on the PHEV testbed

…19

Some future works of the projects – contprojects cont.

Comprehensive Online Dynamic Battery Modeling for PHEV ApplicationsDevelop online model parameter identification algorithms to properly identify theDevelop online model parameter identification algorithms to properly identify the model characteristic parameters in real timeDesign appropriate State of Charge (SoC), State of Health (SoH), and State of Function (SoF) measures to infer proper battery performances serving the iEMS( ) p p y p gand other FREEDM projectsCollaborate with other ATEC teams to implement the algorithms on the actual PHEV testbedCollaborate with the distributed control group to provide proper battery bank real-time models…

Continue and expand interactions with other FREEDM and ATEC teamsEnhance the interactions with industrial partnerspReach out to new companies…


20

Acknowledgements: These works were partially supported by the National Science Foundation (NSF) under Award Number ( )EEC-08212121.


21

Comprehensive Online Dynamic Battery Modeling

Objective• Develop models and algorithms that can adapt to different types of batteries, their actual

conditions and their operating environments based on the in situ measurements on the batteryconditions, and their operating environments based on the in-situ measurements on the battery.• Design appropriately State of Charge, State of Health and State of Function measures to infer

proper battery performances.

Motivation• Battery states information helps to

enable optimal control over thebattery charging/discharging process,providing essential information for

Battery Monitoring System

providing essential information foriEMS to allocate power optimally.

SoC: State of ChargeSoC: State of Charge

Temperature, humidity, etc.Voltage & currentLithium Ion Battery

Challenges• Battery relaxation effect

B tt h t i ff t

gSoH: State of HealthSoF: State of Function

gSoH: State of HealthSoF: State of Function

Intelligent Energy Management System(iEMS)

Battery ModelBattery Model

• Battery hysteresis effect• Environmental effect, such as

temperature, humidity, etc.• Aging effect


Related Works in Battery Model Area

Battery Models Pros Cons

Input‐output mapping [1] • Easy to obtain model parameters • Can not reflect system dynamics

EIS‐based model [2]• Can be accurate in finding the model

parameters• Special instrument is required• Battery must be tested offlinep y

Transient response mapping [3] • Predict battery SoC• Reflect the battery dynamics partially

• vt (iL) dynamics modeling need to be improved

• No hysteresis effect considered

Require research on:Dynamic online model [4‐6]

(our model)• Accurate under dynamic load condition• Suitable for online PHEV application

Require research on:• Relaxation and hysteresis effect modeling• Online parameter identification algorithm

development

References[1] O. Tremblay, L. Dessaint and A. Dekkiche, “A generic battery model for the dynamic simulation of hybrid electric vehicles”, in Proceedings of Vehicle Power and

Propulsion Conference, VPPC 2007. IEEE, pp. 284-289.[2] S B ll M Th l R D k d E K d "I d b d i l ti d l f it d Li i b tt i f l t i li ti " IEEE[2] S. Buller, M. Thele, R. Doncker and E. Karden, "Impedance-based simulation models of super-capacitors and Li-ion batteries for power electronic applications," IEEE

Transactions on Industry Applications, vol. 41, 2005, pp. 742-747.[3] M. Chen and G. Rincon-Mora, “Accurate electrical battery model capable of predicting runtime and I-V performance,” IEEE Transactions on Energy Conversion, vol. 21,

2006, pp. 504-511.[4] H. Zhang and M. Chow, "Comprehensive dynamic battery modeling for PHEV applications," in Proceedings of Power & Energy Society General Meeting, IEEE, 2010.[5] H. Zhang and M. Chow, “Comprehensive dynamic battery model serving a municipal parking deck intelligent energy management system (iEMS),” submitted to the

second FREEDM Annual Conference 2010


second FREEDM Annual Conference, 2010.[6] H. Zhang and M. Chow, “Dynamic battery model including battery relaxation and hysteresis effect for PHEV applications,” submitted to the 36th Annual Conference of

the IEEE Industrial Electronics Society, IEEE IECON10, 2010.

Dynamic battery model including battery relaxation and hysteresis effect

RelaxationRelaxation effecteffect modelingmodeling

• Use 2-norm and infinity norm to quantify the modelaccuracy 1

2n⎛ ⎞

• Use series connected RC parallel circuits to model thebattery relaxation effect

( )

22

21

1 2

max , , ... , .

n

ii

n

e

e e e=

∞

⎛ ⎞= ⎜ ⎟⎝ ⎠

=

∑e

e ( )1 2, , , n∞

Fig. 1. The equivalent circuit of a battery cell.

e2

e

• Heuristically, more RC circuits provides better modelaccuracy

olta

ge (v

)

rren

t (A

)

Vo

Cur

Fig. 3. Modeling error with different RC circuit number.

• On the other hand, more RC circuits also increase model computational complexity


Fig. 2. Relaxation effect modeling with one RC circuit and two RC circuits. • We need to balance between accuracy and complexity according to the application requirement

Automatic Remote Battery Charge/Discharge Web Based Workstation

• Real time controlled battery charge discharge experimentsElectronic Electronic

L dL d Po er S pplPo er S pplMultimeterMultimeter

• Automatic battery charge discharge with user defined load profile

• Friendly GUI interface to real time battery measurements• Basic platform for online model parameter identification

with in-situ battery measurements

LoadLoad Power SupplyPower Supply

Testing BatteryTesting Battery

with in situ battery measurements

Fig. 1. The Lithium-ion battery cell and the testing instruments.

Fig. 3. Graphic user interface of the battery charge/discharge workstation.Fig. 2. Electrical connection and communication links.

Related publications1. H. Zhang and M. Chow, "Comprehensive dynamic battery modeling for PHEV applications," in Proceedings of Power & Energy Society

General Meeting, IEEE, 2010.2. H. Zhang and M. Chow, “Comprehensive dynamic battery model serving a municipal parking deck intelligent energy management system


(iEMS),” in Proceedings of the second FREEDM Annual Conference, 2010.3. H. Zhang and M. Chow, “Dynamic battery model including battery relaxation and hysteresis effect for PHEV applications,” submitted to the

36th Annual Conference of the IEEE Industrial Electronics Society, IEEE IECON10, 2010.

Intelligent Energy Management System for PHEVS at a Municipal Parking Deck

•Power Allocated•Rate of charge•Other control messages

User Profile EntryRA: Wencong SuElectrical and Computer EngineeringN th C li St t U i it

a u c pa a g ec

gNorth Carolina State UniversityRaleigh, North Carolina

Objective•Available Power•Pricing

UtilityData Acquisition

(DSP, FPGA etc.)

Objective•To develop an Intelligent Energy ManagementSystem (iEMS) architecture to achieve theoptimal power allocation to PHEVs at a

•Time of availability•Type of charge

Communication Medium:

p pmunicipal parking deck and also allow forVehicle-to-Grid (V2G) technology.

iEMSoptimization

•Type of charge•Pricing preferences•Current state of charge•Power consumed•…..

Wi-Fi, Bluetooth, Satellite etc.Challenge

• A large variation of the arrival and departmentti f PHEV i t PHEV ki d ktime of PHEVs into a PHEV parking deck

• The number of PHEVs in a parking deck at a time has a large variation with limited amount power supplied from utilities.• Need Low cost and effective communications with sufficient bandwidth to pass information among PHEVs and thecontrollers to effectively perform the charging and discharging• Need effecti e optimal charging/discharging controller algorithms to ork seamlessl ith tilities and PHEV c stomers


• Need effective optimal charging/discharging controller algorithms to work seamlessly with utilities and PHEV customersunder large uncertainties, and make decisions in real-time with limited bandwidth to communicate among all the entities

Intelligent Energy Management System for PHEVS at a Municipal Parking Deck a u c pa a g ec

Priority Based Allocation Formulation Motivation: Would like all vehicles SoC high to prevent starvation of any vehicle

∑∑Charger

Capacity required, ( )r iC k

( )iI k

' ( )iV k

( )ip kCharger

Capacity required, ( )r iC k

( )iI k

' ( )iV k

( )ip k

where

and p: allocated power for each car at time k,i.e., pi(k) for i∈[1, …, N]

min ( )p

J k

, ( ) (1 ( ))r i i iC k SoC k C= − ⋅

( )( ) ( )i ij i

J k w k SoC k j= − +∑∑ , ( )a iC k( )iSoC k

, ( )a iC k( )iSoC k

wi(k): the priority assigned for to vehicle i at time step kCurrently, we assign priorities based on capacity required and time remaining:where α1 and α2 are weighting coefficients. 1 , 2

1( ) ( )( )i r iw k C k

T kα α= +

Simulating the

1 , 2, ( )i r i

r iT k

Data Acquisition

Simulating the time of plug in

Completed3

Vehicle Information2

Notify Plug in1

TransportDelay

Pulse for triggering plug -in Trigger Acq Acquired Information

plug_in_trigger

Charge

Completion

Plugin

Data _trigger

Begin _charge

Completed

update

DataAcqIDiscrete event decisions

Comparison of Allocation Strategies

100

120

t (%

)

Equal PriorityAssgnmt

State Machine for Station I

UpdateI5 VA

4

Chart

Battery_Charger subsystem I

Begin

Current

P_allc

P

SOC

Completed

VA

Update

CompletedI

SOCI

PI

InputRate and Allocated

Power

1

H b id S t 0

20

40

60

80

SoC

at P

lug-

out Assgnmt

Dynamic PriorityAssgnmtOptimal Allc for SoCMaximization


Charging with allocated current

Continuous Dynamics

Hybrid System 0Car A Car B Car C Car D Car E

Vehicle

Intelligent Energy Management System for PHEVS at a Municipal Parking Deck

Highlights:

Have prototyped iEMS algorithms on a PHEV Municipal Parking Deckin

a u c pa a g ec

Have prototyped iEMS algorithms on a PHEV Municipal Parking DeckinMatlab/SimulinkHave developed a Graphical User Interface in Labview to conceptualize thesystem operationHave investigated the communication network with ZigBee

Current Work and Expected Milestones:

Further developing iEMS architecture along with implementation in FREEDMand ATEC demonstration testbed in Matlab/Simulink and Labview.Simulating real-world parking deck scenarios with random vehicles arrivals,

initial PHEVs states, time of availability using Monte Carlo method.Integrating the iEMS and demand side management programs into the existingIntegrating the iEMS and demand side management programs into the existingtestbed to alleviate the peak load demand.

Related Publication:1) P Kulshrestha L Wang M Y Chow and S Lukic “Intelligent Energy Management System Simulator for PHEVs at Municipal Parking Deck1) P. Kulshrestha, L. Wang, M.-Y. Chow, and S. Lukic, “Intelligent Energy Management System Simulator for PHEVs at Municipal Parking Deck

in a Smart Grid Environment,” in Proceedings of IEEE Power and Energy Society General Meeting, Calgary, Canada, 2009. (invited)2) P. Kulshrestha, K. Swaminathan, M.-Y. Chow, and S. Lukic, “Evaluation of ZigBee Communication Platform for Controlling the Charging of

PHEVs at a Municipal Parking Deck,” in Proceedings of IEEE Vehicle Power and Propulsion Conference, Dearborn, Michigan, U.S.A, Sept 7-11,2009.

3) W. Su, M.-Y. Chow, “An Intelligent Energy Management System for PHEVs Considering Demand Response,” in Proceedings of 2010 FREEDM


Annual Conference, Tallahassee, Florida, U.S.A, (Submitted)4) W. Su, M.-Y. Chow, “Evaluate Intelligent Energy Management System for PHEVs Using Monte Carlo Method,” (Draft)

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