Intelligent Grid Interfaced Vehicle Eco charging iGIVE

energy, power & intelligent control

Intelligent Grid Interfaced Vehicle Eco‐charging ‐iGIVE

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Prof Kang Li, Prof Yusheng Xue, Prof Shumei Cui, Prof Patrick Luk

Queen’s University BelfastCranfield University

Harbin Institute of TechnologyState Grid Electric Power Research Institute

20‐21 April 2016


Project overview2

EP/L001063/1 NSFC51361130153


Aim: to develop an intelligent grid interfaced vehicle eco‐charging(iGIVE) system for more reliable, more flexible and efficient, and moreenvironmental friendly smart gird solutions for seamless integration ofdistributed low‐carbon intermittent power generation and largenumber of EVs.

Challenges: Real‐time estimation of SOC, SOH, etc On‐board charging apparatus piecemeal and non‐systematic Environmental acceptance: harmonics, EMI, etc. Dispatch for coordinated EV charging/discharging Behaviours of different actors affect whole system, e.g. reliability Information platform

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Project overview


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WP1) Development of energy and information flow framework for iGIVEWP2) Power flow control‐based battery modelWP3) Bi‐directional traction drive charging systemWP4) Model for environment friendly EV battery chargingWP5) Optimal dispatching strategy for EV charging and dischargingWP6) Holistic system model for the impact of EV actors

Project overview


48 journal papers, 35 conference papers, 1 special issue, 2 conference proceedings

2 conferences (LSMS2014 & ICSEE2014 and Control 2016), and 3 workshops

8 project meetings, 11 invited seminars, 2 keynotes 1 new joint EV lab 3 follow‐on grants Industrial collaborations: Wrightbus, Yutong, Nari

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Output Summary


Joint EV lab at QUB

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7

DC Bus

Wireless charging

Li‐ion Battery Pack

10kW Resister load Bank

Retrofitted EVs

15kw EA charger

Super Capacitor Pack

10.5kW EA electronic

load

Electronic bike

3kW DC/AC inverter

Other DC load


Retrofitted EVs in EV lab8

G‐Wiz electric car• DimensionsL 2.6m, W 1.3m, H 1.6m• Motor power: 6kW continuous• Weight: 400 kg excl batteries• Battery: 200AH, 48V, Lead‐acid• Range: 48 miles

Retrofit: Li‐ion battery, BMS, wireless communication, standard charging plug Electric DeLorean


Test dataHPPC tests at different temperatures: [0, 10, 23, 32, 39, 52] on LiFePO4 (10Ah)

Thermal test


Simplified Thermoelectric Battery Model

∗ ∗∗ ∗ ∗

Battery electrical model and SOC estimation.

[1] Cheng Zhang, Kang Li, Lei Pei, Chunbo Zhu. "An integrated approach for real‐time model‐based state‐of‐charge estimation of lithium‐ion batteries." Journal of Power Sources 283 (2015): 24‐36

: battery internal thermal capacity: battery shell thermal capacity: internal temperature :shell temperature: environment temperature

: heat generation rate, , heat conductive coefficients

Battery thermal model and internal temperature estimation.

[2] Cheng Zhang, Kang Li, Jing Deng. "Real‐time estimation of battery internal temperature based on a simplified thermoelectric model." Journal of Power Sources 302 (2016): 146‐154

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Battery modelling results

Part of the electrical modelling resultsThermal modelling results


SOH estimation12

State of Health (SOH) estimation method

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Degradationcycle

Temp (°C)

Measured SOHP (%)

Est.SOHP (%)

Measured SOHE (%)

Est.SOHE (%)

0 50 100.0 100.6 100.0 100.6

100 30 98.4 99.8 95.4 98.7

200 10 92.2 94.7 87.7 91.8

300 −10 71.3 70.9 75.2 76.7


Battery management system13

A battery management system has been setup to control

charging and discharging of lithium‐ion battery pack


Battery Temperature ControlConsider the developed battery thermal model, an objective function is proposed

which is a trade‐off between cooling performance (characterized by battery aging), and cooling parasitic energy consumption, i.e.,

Then different controller can be designed and optimized.

PID controller Bang‐Bang controller optimal controller.

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Battery fast charging controlBased on the developed battery thermoelectric model, new objective function is proposed for battery fast charging control

This objective function is a trade‐off between charging time, efficiency, and battery aging.

Different charging patterns can be compared.


V2G Charging ‐ Cranfield University

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Novel V2G Charging‐Traction PM motor technology

The vector control with sensorless algorithm developed based on DSP 28335.

Open circuit tested. No‐load condition tested with

the maximum speed at 1280rpm (the designed value is 1250rpm, very close agreement to the test result).

Load‐condition with 10Nm at 1000rpm tested.

Dual‐use traction‐charging PMSM motor

energy, power & intelligent control17

V2G Charger Design in Parking Lots and Impacts

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2V 4V

5V 7V

6V 8V

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5C 7C

6C 8C

3 Phase PWM AD/DC Bi‐directional DC/DC Converter

Li-ion battery modelling



High Voltage Side Voltage THD High Voltage Side Current Harmonics THD

High Voltage Side Current

放电过程充电机所占比例(%)

THD

高压侧电压

(%)

0 10 20 30 40 50 60 70 80 90 100放电过程充电机所占比例(%)

THD

高压

侧电

流(%)

0

5

10

15

20

25

30

35

40

N =10N =9N =8N =7

放电过程充电机所占比例(%)

高压侧

电流幅值(A)


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Development of wide power range and high performance bi‐directional charger

iGi

C

1L 2Lcu

gu1i 2i

Current weighting control structure based on LCL filter

Bidirectional

AC-DC

Bidirectional isolationDC/DC

Ac connector

L

N

V2G dc connector

+-

PFC V2G control

Source

Sampling Drive Drive

Prtection

Communication

PC

Battery

+

-

Control

Bidirectional V2G control system


-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5-1.5

-1

-0.5

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0.5

1

1.5

D

Po*

d=1d=0.5d=1.5

Power curves

PWM‐SPS soft switch

range


V2G Charger Design for Vehicles and Impacts

Single Phase PWM AD/DC LCL Filter with a small resistor

Bi-directional DC/DC Dual Active Bridge

load

ACi

ACU

2L 1L

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DCI

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105

106

-270

-225

-180

-135

-90

Phas

e (d

eg)

Bode DiagramGm = Inf , Pm = 90 deg (at 278 rad/s)

Frequency (rad/s)

LCL滤波

改进的LCL滤波

L型滤波

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C1

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6.5

Po(kW)

THD

(%)

控制流加法电权PR +控制流加法电权PI +控制PI控制PR

THD using different control


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Interface and Integration of Microgrid and EVs


Hierarchical Management for Integrated Community Energy System

Group-based DR management• Thermostatically controlled loads• EVs considering eco-charging• EVs and TCLs coordination.

Integrated optimal power flow• Heat, gas, and electricity flows• Three-phase electrical system• Microgrids and distribution networks.


Flowchart of the three-phase DR.

Three‐phase Demand Response Dispatch


A hybrid experimental‐economics‐based simulation platform

A questionnaire design method proposed. Probabilisticcharacteristics of potential EV users’ purchase and traveldecisions extracted from questionnaires

A multi‐agent model was built to reflect multi‐dimensionaljoint probability distribution of the respondents’ willingness

A verification method designed – potentially used for modelingof other group decision behaviors

A framework to study the Interactivity of EV and power gridoperation proposed


archives

SCADAinquiry

experi.sim.

mathmodel

Stat. data caus. data behav. data

stat. anal caus. anal

Multiagentsmodelling

experi. econ. interact. hybrid simul.real person

caus.find

knowl.ext.

knowl.ext.

raising eff.

adaptivity

cros. fielddecis. sup.

Multi‐source heterogeneous data

Collection, analysis, knowledge extraction and decision support of big data


• Difficulties of experimental economics to study group behaviors– The number of participants is limited– Repeated trials may be poorly comparable with each other

• Extracting correlation inf. from big data to model group behavior – Historical data, real‐time data acquisition, questionnaire– Matching probability distribution of respondents’ behaviors – Verifying the validity of multi‐agent by comparing the multi‐agent Monte‐

Carlo simulation results and the questionnaire results

Modeling and Simulation Methods of EV Users’ Behaviors

Dynamic Simulation

Real Participants Computer Agents

Knowledge Extraction

Information Collection

Decision Support

HMI Data

Transfer

System Models

Decision Modes

• A hybrid experimental‐economics‐based simulation– Using computer agents to

replace a certain group of experts

– Using real participants to play several key roles


Behavior‐involved Projects Studied by Using Hybrid Simulations

Mathematical Models

Experimental Economics Simulations Platform

Multi-agentsActual participants

19. 04. 2016 XUE Yusheng, SGEPRI, China 27


ActualSystem

Disturbance

Researcher

BehavioralAgents

HumanParticipants

SimulationPlatform

Mathematical Model

Hybrid Simulation

Multi-agent

Hybrid SimulationPlatform

Hybrid simulations including game behaviors

Mathematical Model

Mathem. Model

Mathematical Model

SimulationPlatform

SimulationPlatform

Computer Technology

HumanPartic.

Disturbance Disturbance

Researcher

Disturbance Disturbance

Researcher

Researcher

Disturbance Disturbance DisturbanceDisturbance

Researcher

19. 04. 2016 XUE Yusheng, SGEPRI, China 28


DSMES supports dynamic interactive simulations of cross‐domain to study interactions among physical power system,power market, emission trading, etc.

Dynamic Simulation platform for Macro‐Energy Systems (DSMES)

19. 04. 2016 XUE Yusheng, SGEPRI, China

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Experimental Economics Research on EV Purchase Willingness

Verification of multi‐agent model accuracy

Agent‐1 ...

Ranking of factors’ importance

Factors affecting purchase decisions

Psychological thresholddistribution of a single factor

Extract information

The joint distribution of all factors’ psychological thresholds

Agents’ probabilistic model based on the joint distribution

Tested EV types

Agent‐n

StatisticsQuestionnaire‐1 ... Questionnaire‐n

Statistics

Hybrid simulation with true participants and verified agents

Questionnaires

Passed

Extract information

Extract information

Creating agents


Flow Chart for Generating Multi‐Agents Reflecting the Purchase Willingness of EV Potential Users

i = 1

Mapping the distribution of factors’ importance

N

Y

Comparing the simulation results

i < the total number of agents

Mapping the distribution of each factor’s threshold

Multi‐agents modeling

Decision of the ith agent

i = i+1

Extracting deep information fromanswer sheets

Generating individual agents as many as needed

Questionnaires data


PMU Telecoms Framework

ApplicationInertial EV response


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Wind Power Integration

Time Source Receiver(GPS)

Data Acquisition(GPS Disciplined)

Signal Processing(Embedded PC)

Data Representation

(IEEE / IEC Std.)

Input Signals

Output Synchrophasor


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Electric Vehicles with Smart Grid

f

df/dt

P

t

t

t

charge

discharge

2-3 s

t=0 s

0

10%

Simulation of inertial response


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Wind and Gas Integration

• Ramping carried out per unit generated• Wind resulted in an increase from 54 MW/GWh to 57 MW/GWh• Flexibility is key for the future generation portfolio• Overall total gas generation costs fell 13% with the inclusion of wind• This fall is masked by less generation• Similar costs on a per unit basis

25

35

45

55

65

75

85

95

01/01/2011 11/04/2011 20/07/2011 28/10/2011

Ramp pe

r Unit G

enerated

(M

W/G

Wh)

Wind No Wind

40

45

50

55

60

65

70

31/12/2010 10/04/2011 19/07/2011 27/10/2011

Cost per Unit G

enerated

(Eur/G

Wh)

Wind No Wind


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Wind and storage• Counters wind variability

– Reduces the requirement of thermal generation to fulfil residual demand• Increases system flexibility

– Fast acting response to system status• Can provide arbitrage opportunities

– Peak Load Shaving– Charge low, Discharge High– Unit given a SRMC of 150 €/MWh

• Reduce wind power curtailment– Reduction of 6 GWh to 1.7% of total available capacity


Add: No.19 Chengxin Avenue, Nanjing 211106, Jiangsu Province, China

Tel: +86-25-81093060 E-mail: [email protected]

http://www.mpce.info http://www.springer.com/40565/

First SCI indexed in China on Energy and Power


“Shaping low carbon energy future”UK‐China workshop on

28‐31 August 2016, Great Hall, Lanyon Building, Belfast, UK

Keynotes1. Energy efficiency in intelligent manufacturing ‐ Prof Cheng Wu

2. Smart grid and integration with electric vehicles ‐ Prof Yusheng Xue

3. Battery management and wireless charging ‐ Prof Chunbo Zhu

4. Modelling and analysis of integrated energy systems ‐ Prof Kang Li

5. Planning, governance, and low carbon economy ‐ Prof Geraint Ellis

6. Cyber security ‐ Prof Maire O’Neill,

This 3‐day workshop is featured with keynote speeches, industrial exhibitions andknowledge transfer training, technical presentations and posters, panel discussions,break‐out brain‐storming, academic visits, formation of consortium and specific taskgroups, and networking events and social visits


Thank you!

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http://igive.wikidot.com/http://www.i‐give.org.uk/

Documents

Intelligent Grid Interfaced Vehicle Eco charging iGIVE