Presentations from the smartCEM Stakeholder Dissemination event (Newcastle pilot site), 25th September 2014. Project consortium members: Newcastle University, Gateshead College, Hyperdrive, Charge Your Car

1. Newcastle pilot site: Stakeholder Dissemination Event
Gateshead College Performance Track
(Nissan Site) 25thSeptember, 9.00am-1.30pm

2. Welcome and Introduction
Professor Phil Blythe
Director of Transport Operations Research Group, Newcastle University of Newcastle Upon Tyne
wifi: guest
password: gatesheadcollege

3. A regional perspective of EV in the North East
Neil Ellison, Independent Consultant

4. 2050: the challenges
•Climate change
-extreme weather events, flooding, storm surge
-land loss
-international regulation
•Energy costs
-fuel poverty
-costs to businesses and services
•Energy security
-costs
-supply disruptions
•Population growth
-indigenous
-future migration
•Waste management
-landfill
•Resource availability
-water
-raw materials
•Food availability & security

5. 2050: obligations and regulation
Stern Review
•80% reduction in carbon emissions
•International targets and obligations
UK Climate Change Act 2008
•Committee on Climate Change
•Carbon budgets, carbon pricing
Implications
•90% reduction in transport emissions
•no new CO2-emitting vehicles can be sold from 2040
-DfT-Ultra Low Emission Vehicles (ULEV) Strategy 2013

6. 2050: opportunities for the North East
•Renewable and sustainable energy
•Energy storage
•Load balancing of National Grid
•Energy & raw materials from waste
•Low/zero carbon carbonbuildings
•Retro-fitting of buildings for energy saving
•New & more efficient/intensive food production
•More integrated & smarter transport systems
•Low carbon vehicles
-manufacture
-take-up
-servicing, maintenance, recycling
-charging solutions & energy management

7. 2050: pathways
Stockton’s ‘Green Vision’
•Statement of ambition
•Identifying the key areas, risks and opportunities
•Set out milestones and route maps
-business case for adoption of electric cars across Council services
•Identify who can achieve what
•Key partnerships

8. Delivering the vision in the North East
Create a prosperous & sustainable low carbon economy
•Commercial opportunities
-long-term investments
•Maximise economic gain
•Minimise economic loss
•Maximise commercial advantage
-regulatory frameworks
-pump-priming with subsequent commercialisation
-partnerships
Partnerships
•North East Local Enterprise Partnership
•Tees Valley Local Enterprise Partnership
-deliver low carbon economy
-maximise national and European funding for investment
-help create sustainable businesses

9. Neil Ellison
ipponken53@gmail.com

10. The SmartCEM ProjectPromoting Electric Vehicles Across Europe
Simon Edwards (Newcastle University)
UK Pilot Site Dissemination Event
25thSeptember 2014

11. Today’s Event
•Dissemination event for the smartCEM project’s Tyne and Wear pilot site
•Demonstrate smartCEM common APP and connected services to project review team
•Presentations and competition for external stakeholders

12. What is smartCEM?
NAME
Smart Connected Electro Mobility
ACRONYM
smartCEM
PROGRAM
CIP-Pilot actions (Competitiveness and Innovation)
ACTIVE
2012-2015
CONSORTIUM
27 partners
SITES
4 pilot sites
BUDGET
4,920,005 €
FUNDING
2,460,000€

13. Objectives of smartCEM
1.Toenhanceuseracceptanceandconfidenceinelectricvehicles(EVs)
2.Toevaluatetheextenttowhichtransportefficiencycanbeoptimised
3.TodevelopasuiteofservicesaccessedthroughacommonAPP
4.Toidentifybarriersandaddressalldeploymentelements
5.Tosupportpan-Europeaninteroperability,e.g.betweendifferentsystemsandvehicles
6.Topavethewayforwideracceptanceofelectro-mobilityinalltypesofroadtransport
7.Topromoteintegrationofnewschemes,e.g.car-sharingwithinpublictransport
8.TopromotethesmartCEMservicestomorecitiesandkeystakeholders

14. The green STIG EV Challenge ….

15. Green EV Challenge -Route

16. smartCEM solution –Common ServicesEV-navigation:Improving routing and guidance specific to electric vehicles EV-efficientdriving: Making driving style more efficientEV-trip management: Making the trip more efficient through journey optimizationEV-charging station management:Making more efficient use of charging infrastructure
smartCEM
SERVICES & COMMON ARCHITECTURE
EV sharing management: Making the use of electric vehicles more efficient

17. smartCEM solution –Common Architecture
STRONG INTERRELATIONSHIP BETWEEN SERVICES & WEB ACCESS
RESPONDS TO EXISTING INITITIVES AND MOBILITY REQUIREMENTS
MANAGE ALL SERVICES INTO A SINGLE PLATFORM
RESPOND TO EUROPEAN AND GOVERNMENT INITIATIVES
smartCEM
SERVICES & COMMON ARCHITECTURE

18. Pilot Sites
Available vehicles (scooters): 45
Available charging locations: 140
Available vehicles (car sharing): 4
Available vehicles (hybrid bus): 5
Available charging points: 14
Available vehicles (cars): 12
Available charging points: 1158
Available vehicles (municipal vans): 10
Available charging points: 31

19. Tyne & Wear Pilot Site
Objectives
•To increase the uptake of EVs among private motorists
•To improve the environment through facilitating informed travel choices and improved driver behaviour
Sitekeyfeatures
•Threepartners-NewcastleUniversity,GatesheadCollege,Hyperdrive
•ThesitecomprisestheTyneandWearregion(1millionpopulation)
•Basedondensenetworkofchargingstations,nowover1000intheregion(3kw;7kw;50kw)
Core services
•smartCEMcommon APP
•EV charging station management
•EV efficient driving (post trip)
•EV policy tool
•EV navigation

20. Common APP
ThesmartCEMCommonAPPisavailableinAndroid
Theuserwhoinstallsandrunsthisapplicationonasmartphoneortabletwillbeableto:
•AccessthelistofavailablesmartCEMservices
•ObtainandruntheapplicationsthatimplementthesmartCEMservices
ServicesarenotprovideddirectlybythesmartCEMCommonAPP.ItoffersaGUIthroughwhichtheusercanseewhichservicesareavailable,installthose,andthenlaunchthededicatedapplications/websitesthatprovidetheactualsmartCEMservices

21. CS Management
ChargeYourCar(CYC):
•Singlenationalchargingstationmanagementsystem
•Itenablesstationownerstoconnecttothenetwork,makingtheirpostsvisibletoallEVdriversviatheCYClivestatusmap
•Fordrivers,theCYCLifetimeCard(RFID) providesaccesstoallchargingstationsonthenetwork
•CYCAppisthefirstAppthatletsEVdriversfindandusechargingstations

22. CYC Main Features
•Theworld’sfirstAppthatletsyouusechargingstations
•Mixoffree-to-useandpay-to-usechargingstations
•One-clicksearchfacilitiestoviewmaporlistsofCS
•Searchchargingstationsbytown,postcodeorpointcode
•Filterchargingstationsbyconnectortype(sloworrapid)
•Livestatusofchargingstations
•Planaroutetoachargingstations
•Start,endandpayforachargingsession
•Bookmarkfavouritechargingstations
•Latestnewsandinformation–newCS,etc.
•Helpdesktelephonesupport
•Activityhistory
•Personalisedonlineaccountwithpaymenthistory

23. EV Efficient Driving
EV Efficient Driving provides post-trip feedback and advice to drivers through an online service which includes energy efficient driving (km/Kwh), acceleration profiles (hard and light), idling time, regenerative braking, and driving tips

24. EV Policy Tool
•Analyticaltoolthatistargetedatserviceprovidersandcityauthorities
•Itisadecisionsupporttoolformanagingnetworks:forexampleitcanprovideanalysisofqueuingatchargingstationsatpeakperiods,enablingserviceproviderstopushinformationtodriverslookingtorechargetoavoidthequeues
•Alsopotentiallybeneficialforfreightoperatorsandfleetmanagement
•Ultimatelyitmayelicitunderstandingoftheinteractionbetweentravelandenergyplanningasacooperativeelectro- mobilitychallenge

25. EV Navigation
Implemented in two ways:
•CYC navigation
•PTV navigation connected to Bluedash(a Bluetooth-enabled communication between the vehicle’s CAN and a smartphone)

26. User Feedback
EVusers
•SurveyofEVacceptance,rangeanxiety,smartCEMacceptance
•SurveyofEVefficientdriving
–CirculatedtosmartCEMparticipants
•SurveyofChargingStationManagement
–CirculatedtoCYCmembers
Non-EVusers
•Surveytounderstandthewidermarketchallengesofincreasinguseruptake
–Circulatedtouniversitystudentsandemployees

27. Practical Demonstrations
•On the Road -CS Management
–Phone-in approximately 10.30am
•EV-Efficient Driving (post trip) -Lunch time
•EV Policy Tool -10.00am
•Bluedash –11.20am

28. Newcastle UniversitySimon EdwardsGraeme HillGateshead CollegeAlexandra PrescottAlisha PeartHyperdriveTony GreenStephen Irishwww.smartcem-project.eu/

29. ‘Electric Vehicles: an owner driver’s perspective’
Joe Mallon, Electric Vehicle Enthusiast

30. Perspective of Private Owner
•Nissan Leaf (2 no.) over about 2 years
•Mitsubishi Outlander PHEV for 1 week

31. How & When I converted to EV
•Opportunity for EV “Switch” 6 month trial in 2012
•Interview process at Nissan Test Track
•Wife took part (she had left arm disability from lympodemia)
•Established over 6 months lease LEAF’s viability
•We each were driving ICE cars one of which with 70k plus mileage
•In spring of 2013 my daughter passed her test and needed car for work so we were looking for another one
•Around about May 2013 new Gen 2 Sunderland manufactured came on market and price of Gen 1 LEAFs dropped by about £10k so I was able to pick up 2013 reg with only about 800 mileage
•This month my existing old Suzuki 4 x 4 with 80k mileage going to cost more to MOT than it was worth so traded it in for Outlander PHEV

32. Why I converted to EV
•Sustainability tendencies for over 40 years
•Started career as architect specifying green products
•Retired last month as national sustainability lead for NHS organisationand aware of air quality public health benefits
•Promoting Cleaner Air & Low Carbon Transport so wanted to practice what I preached
•About 4 years ago started to see business articles about synergies between domestic Solar PV & EV ownership
•I had 3kwp solar panels on my house before the EV Switch trial and after it had a domestic charger in place which I agreed to keep
•The trial demonstrated we had an excellent EV infrastructure in place in the NE (free parking in our town centresetc)
•The Driving experience of the LEAF was very positive

33. Need for 4 x 4

34. Is this an acceptable App

35. How I found transition

36. Northern Regional Meeting, York

37. Public Health & NHS

38. NHS Sustainability Day Event 28-03 -14

39. EVs & Home charging

40. What do you do with a sunken cable

41. Being ICE’d

42. Missing Charger Posts

43. EV Forums
http://www.leaftalk.co.uk/forumdisplay.php/76- Batteries-and-Charging-Stations
https://speakev.com/

44. Demonstration: smartCEM Policy Tool
Konstantinos Gkiotsalitis, Research Associate,
Intelligent Transport Systems Division,
NEC Laboratories Europe

45. Demonstration: smartCEM Policy Tool
Konstantinos Gkiotsalitis, Research Associate,
Intelligent Transport Systems Division,
NEC Laboratories Europe

46. Agenda
•Background
•ProblemAddressedbyEVPolicyTool
•InitialFramework
•OptimizationTechnique
•ModifiedFramework/workflow
•Results
•NextSteps

47. Background
•AcceptanceofEVsishinderedbylimitedbatterycapacity
•Improperrouteplannersleadtowastageofenergy
•DynamicunplannedeventsliketrafficjamshaveahigherimpactonEV’s
•ImportantissuesforEVsintheforeseeablefuturerelyonaccuratepredictionof:
–remainingcruisingrange,
–energy-awarerouting
–Locationofoptimizedchargingstops
•concurrentandfrequentrechargingdemandleadtohighwaitingtimeatthechargingstops
Home
Planned Route
Planned Charging
Points
Traffic Congestion
Delayed Charging
Points
1
3
2

48. Problem Addressed by EV Policy tool
•Chargeplanners,arenotequippedtodealwithconcurrency
•Traditional“plan-execute”scheduling,doesnotcopewithdynamicity
•Dynamicitydueto:
–Driverbehavior:varythedischargingpatterns,newchargingdemand,chargingtimeandlocationchanges
–Externalconditions:trafficcongestion, weather
•Negativeimpactofdynamicity:
–Reducedrevenues(additionaltrips)
–Reducedcustomersatisfaction
–Increasedwaitingtimeatchargingstations
–Inaccuraterangepredictions
–ImpactonEVuseracceptance

49. EV Policy Tool Framework
•EVPolicytoolisamulti-objectiveplatformthat:
–Analyzesandoptimizesrouteandchargingplans
–Re-computesroutesandchargingschedulesinreal- time
–ProvidesanindicatortothegaininOPEXforbothEVuserandChargingspotoperator
•Solution
–Staticinputinplanningphase:capacity,timewindows,constraints(e.g.resourceconflicts)
–Schedulercomputesbasechargingschedule: handlescomplexity
–Dynamicinputinexecutionphase:trafficjam,jobchanged(time/space)
–Newplancomputed.Takesintoaccountvariability(routes/customerinconsistency)
EV Policy Tool
Scheduler
(complexity)
Re-scheduler
(real-time)
Dynamic
Input
Static
input
Base Plan
Re-computed plan
Planning
Phase
Execution
Phase
1
2
3
4
I
II

50. High Level Workflow
Stage 1: Data Analytics and Planning
Optimization Goals
Optimization of Route plan
Data Analytics
Stage 2: Dynamic Re-Scheduling
Input
Fleet size, CS location, demand, constraints
Online Optimization
Output
Dynamic Factors
Optimization Goals
Optimized Route with reduced
Concurrency and Gain
EV Policy tool
Current Mobility

51. EV-Policy Tool Stage 1 and Stage 2
•Stage1
–WithrelevantfleetdataEVPolicytoolcan:
•Identifygapsintheinitialplanandpotentialgain
•Optimizeplantoreducetheimpactofdynamicfactors
–Relevantdataneededtooptimize:
•Chargingdemand
•Initialrouteplan
•NumberofVehicles
•Reactionofanydynamicevent.
•Stage2
–Handledemandvariationandoptimizechargingschedule
–OptimizationalgorithmsbasedonGeneticalgorithmsandslacktimeutilization

52. Stage 1 –Data Analytics (On Going)
•WithrelevantfleetdataEVPolicytoolcan:
–Identifygapsininitialplanandpotentialgain
–Optimizeplantoreducedynamicityimpact
•Varyingtheslacktimefordifferentslots
–Improvestolerancetodynamicityand
–maximizesthenumberofchargingrequeststhatcanbeaccommodated
•Slacktimealsoupdatedduringexecutiontooptimizeoperationaltime
Home
Route Logs
Data Analytics
Inconsistent routes due to dynamicity
Stage 1
Better Planning

53. Stage 2 –Optimization (Completed)
•Scheduler: key enhancements to Genetic Algorithm
•Re-scheduler: algorithm that plans new jobs based on dynamicity risk
#
NLEEnhancement to GA for VehicleRouting & Scheduling
Advantage
1
Multi-parent cross-over(CO) builds routes from multi sub-routes
Moreroutes analyzed per iteration
2
Locally optimized CO picks best sub-routes
Faster progress inside single iteration
3
Graph-aware CO evaluatessub-routes against constraints
Earlier droppingof invalid population
4
On-line learning adopts crossoverto select the optimal population
Prevents local optimum traps
Much fewer iterations
Determine
Slot Inconsistency
Derive slack times
New plan
1
2
+
3
4
Routes and customers records

54. Depth of Discharge
•ThemaximumDoDisrecordedforvehicleswhichremaininthesimulationforthemaximumtimeandhavelongerroute.
•Onanaverage,theDoDofvehiclesintheurbannetworkisaround2%to5%.
•PeakDoDupto14%duringtheirjourneywithinanurbannetwork.

55. Results to remove concurrency (Newcastle Use case)
4*4 with 50 OD’s
4*4 with75 OD’s
4*4 with100 OD’s
4*4 with 125 OD’s
4*4 with150 OD’s

56. Preliminary Results for Reggio Emilia Use Case
•PreliminaryResultsof(10%DemandVariation):
–Distancetravelledreducedby20%comparedtoreschedulingitthenextday
–Re-planningthemissedcustomerswithothersetofcustomersleadstowastedcapacityandadditionalroutes
~20% Reduction
Wasted capacity
Additional Routes
Base Operational Cost (Initial plan)

57. Approach
•EVPolicyToolisbasedonrouteinconsistency
–DefinedasdeviationfromthenormalprocessduetoEVdriverbehaviourandexternalfactors(traffic,weather,etc)
•High-levelflow
–GrouptheEVUsersintimeslots(customerorientedoroperationoriented)
–Computeslotinconsistencyfactor
–Allotappropriateslacktimetoeachslot

58. Dynamic Rescheduling
•Handledemandvariationandoptimizethedeliveryschedule
•Continuouslymonitorforanyinconsistencyintheinitialplan
•Ifvehicleencountersaninconsistentroute(e.g.C3inthefigure)
–Toolexecutesadynamicreassignmentalgorithm
–Algorithmfindsanappropriateslotwithoptimalcostforchargingstop(C3inthefigure)
–Duringthere-assignmentphase,thealgorithmensuresthatthestaticcustomers(C4andC5inthefigure)withacommittedtimeslotstimearenotaffected

59. Status
•To be done in WP6
–Stage 1 preliminary analysis being done using open data and to be verified with real data
•Started in WP2
–Stage 2 is mature and has been evaluated for NC,
•Refinement ongoing

60. Demo Time!!! Thank you for your time!

61. Coffee break 10.35am -11.00am
Following the break:
•In Classroom: H&S briefing [pre test track drive]
(all delegates signed up for test drive)
•Reception area: ‘An Electric car called Trev’, Robert Llewellyn’ film
(all delegates NOT signed up for test drive)

62. Health & Safety Briefing
Peter Carey
Performance Track Technician
Gateshead College

63. Performance Track

64. SmartCEM -Bluedash
Simon Edwards (UNEW), Tony Green (HYPERDRIVE)
UK Pilot Site Dissemination Event
25thSeptember 2014

65. What is Bluedash?
•BlueDash™(www.dquid.com)isaunitwhichcanbeinstalledinanycartoaccesson-boardvehicledataandtransmititviaBluetoothtoanonboarddevice(smartphoneortablet)
•ThedriverwillinterfacedirectlywiththeapplicationrunningontheonboarddeviceandwillhavenocontactwiththeBluedashunit
•TheunitreadsvehicledataviatheCANbus.Onthetouchscreenoftheonboarddevice,itispossibletovisualisevehicleperformance,fuelconsumptionandemissions
•BluedashisequippedwithGPSforpositioningdataandGPRSfordatacommunications

66. Bluedash in the UK
•InsmartCEMtheBluedash™unitsendsdatafromthevehicletoaserver.DataisthenfedtothesmartCEMapplicationinstalledonanAndroid4.4.4- basedonboarddevice(inthiscaseaNexus5smartphone)
•Software(EVListener)isinstalledonthephone, alongwiththeSmartCEMPortal(CommonAPP)andsmartNavigator
•Batterystatus,‘engine’power(kW),‘mileage’(kms), temperature

67. EV Listener Interface

68. PTV Navigator

69. PTV Navigator

70. Contact: Leandro Guidotti leandro.guidotti@unimore.itDorin Palanciuc Dorin.Palanciuc@teamnet.roTony Green tony.green@hyperdriveinnovation.com

71. How efficiently did the Green STIG drive?
Graeme Hill
TORG
Newcastle University

72. How Efficiently did the Green STIG drive?
(and what is efficiency anyway?)

73. What is Efficiency?
•For an IC (Internal Combustion) engined car, the efficiency is normally framed in terms of miles per gallon.
–E.g. how many miles can the vehicle travel on a gallon of fuel?
•There is a similar metric for an electric vehicle, km per kWh
–E.g. how far can a vehicle travel per kWh of energy?

74. What Affects Efficiency?
•Many things affect efficiency
–Speed
–Acceleration
–Gradient
–Temperature
–Vehicle Weight
–Wind
•Minimising the effects of each of these variables is the key to an efficient drive

75. How do you maximise efficiency?
•To maximise efficiency you can:
–Allow the EV to brake naturally, thus increasing the regeneration
–Drive at the optimum speed (not 80mph…)
–Use gradients as a natural brake
–Plan ahead to avoid excessive traffic
–Wear a jumper! (running air conditioning costs power)
•In two words “Smooth Driving”

76. The Journey of the Stig

77. Efficiency

78. Regeneration

79. Acceleration

80. Idling

81. So was the Green Stig the most efficient driver? (in the world)
•Not really….
•Although quite good with acceleration, the Stig could really have done better on:
–Regeneration (brake more smoothly Stig!)
–Idling (Stop hanging around looking at other cars Stig!)

82. Track Driving Efficiency

83. What does this look like on the track?
•Here we can see the schematic for a trip around the test track
•High power usage is in red, regeneration is in green

84. Speed and Acceleration
•The speed and acceleration (plus their relationship to each other) for the trip can be seen here
•Vehicles slow down before corners, and accelerate afterwards

85. Power
•Finally this can be compared with power
•Generally, the lower power usage (including regeneration) is associated with deceleration

86. The lap to beat!
•An earlier drive set a test lap target of
–2:16
•This lap was attempted with an efficient driving style; neither going too fast or too slow.
•Can you match it?

87. Lunch 12.00pm –1.15pm
Performance Track Competition –report to reception 5 minutes before your driving slot
Visit the exhibitions in the main Exhibition area
‘smartCEM video’ -shown in the ‘Classroom’
‘Easy thrills in a Nissan Leaf’ film –shown in reception

88. Green EV STIG Competition winner
Dr Colin Herron
and
THE STIG

89. EV LEADER BOARD
MOST EFFICIENT
2m: 16 s
2.13
Rachel Forsyth-Ward
2.156
WimBoredes
2.162
Dirk Kok
2.163
Fernando Zubilliga
2.19
Stafanos Gouvras
2.2
Brendan Prior
2.22
Steve Spink
2.27
Guido Di PasQuale
2.27
Tankut Acarman
2.29
Marzena Skubij
2.32
Joe Mallon
2.34
Andrew Fenwick-Green
2.36
Martin Forster
2.45
Konstantinos Gkiotsalitis

90. Closing remarks
Dr Colin Herron, Managing Director,
Zero Carbon Futures

91. Newcastle UniversityGateshead College & Zero Carbon Futures
HyperdriveCharge Your Car
Simon Edwards
Alisha Peart
Tony Green
Alexandra Prescott
Graeme Hill
Stephen Irish