Newcastle pilot site: Stakeholder Dissemination Event

Gateshead College Performance Track

(Nissan Site)25th September, 9.00am- 1.30pm

Welcome and Introduction

Professor Phil Blythe

Director of Transport Operations Research Group, Newcastle University of Newcastle Upon Tyne

wifi: guestpassword: gatesheadcollege

A regional perspective of EV in the North East

Neil Ellison, Independent Consultant

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

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

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 carbon buildings• 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

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

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

Neil Ellison

ipponken53@gmail.com

The SmartCEM ProjectPromoting Electric Vehicles Across Europe

Simon Edwards (Newcastle University)

UK Pilot Site Dissemination Event

25th September 2014

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

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 €

Objectives of smartCEM1. To enhance user acceptance and confidence in electric vehicles

(EVs)2. To evaluate the extent to which transport efficiency can be

optimised3. To develop a suite of services accessed through a common APP4. To identify barriers and address all deployment elements5. To support pan-European interoperability, e.g. between different

systems and vehicles6. To pave the way for wider acceptance of electro-mobility in all

types of road transport7. To promote integration of new schemes, e.g. car-sharing within

public transport8. To promote the smartCEM services to more cities and key

stakeholders

The green STIG EV Challenge ….

Green EV Challenge - Route

smartCEM solution – Common Services

EV-navigation: Improving routing and guidance specific to electric vehicles

EV-efficient driving:Making driving style more efficient

EV-trip management:Making the trip more

efficient through journey optimization

EV-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

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

Pilot Sites

Available vehicles (scooters): 45Available charging locations: 140

Available vehicles (car sharing): 4Available vehicles (hybrid bus): 5Available charging points: 14

Available vehicles (cars): 12Available charging points: 1158

Available vehicles (municipal vans): 10Available charging points: 31

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

Site key features

• Three partners - Newcastle University, Gateshead College, Hyperdrive

• The site comprises the Tyne and Wear region (1 million population)

• Based on dense network of charging stations, now over 1000 in the region (3kw; 7kw; 50kw)

Core services

• smartCEM common APP

• EV charging station management

• EV efficient driving (post trip)

• EV policy tool

• EV navigation

Common APP

The smartCEM Common APP is available inAndroid

The user who installs and runs this applicationon a smartphone or tablet will be able to:

• Access the list of available smartCEMservices

• Obtain and run the applications thatimplement the smartCEM services

Services are not provided directly by thesmartCEM Common APP. It offers a GUI throughwhich the user can see which services areavailable, install those, and then launch thededicated applications/websites that providethe actual smartCEM services

CS Management

Charge Your Car (CYC):

• Single national charging stationmanagement system

• It enables station owners to connect tothe network, making their posts visibleto all EV drivers via the CYC live statusmap

• For drivers, the CYC Lifetime Card (RFID)provides access to all charging stationson the network

• CYC App is the first App that lets EVdrivers find and use charging stations

CYC Main Features

• The world’s first App that lets you use charging stations

• Mix of free-to-use and pay-to-use charging stations

• One-click search facilities to view map or lists of CS

• Search charging stations by town, postcode or point code

• Filter charging stations by connector type (slow or rapid)

• Live status of charging stations

• Plan a route to a charging stations

• Start, end and pay for a charging session

• Bookmark favourite charging stations

• Latest news and information – new CS, etc.

• Helpdesk telephone support

• Activity history

• Personalised online account with payment history

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

EV Policy Tool

• Analytical tool that is targeted at service providers and cityauthorities

• It is a decision support tool for managing networks: forexample it can provide analysis of queuing at charging stationsat peak periods, enabling service providers to pushinformation to drivers looking to recharge to avoid the queues

• Also potentially beneficial for freight operators and fleetmanagement

• Ultimately it may elicit understanding of the interactionbetween travel and energy planning as a cooperative electro-mobility challenge

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)

User Feedback

EV users

• Survey of EV acceptance, range anxiety, smartCEM acceptance

• Survey of EV efficient driving

– Circulated to smartCEM participants

• Survey of Charging Station Management

– Circulated to CYC members

Non-EV users

• Survey to understand the wider market challenges of increasinguser uptake

– Circulated to university students and employees

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

Newcastle University

Simon Edwards

Graeme Hill

Gateshead College

Alexandra Prescott

Alisha Peart

Hyperdrive

Tony Green

Stephen Irish

www.smartcem-project.eu/

‘Electric Vehicles: an owner driver’s perspective’

Joe Mallon, Electric Vehicle Enthusiast

Perspective of Private Owner

• Nissan Leaf (2 no.) over about 2 years

•Mitsubishi Outlander PHEV for 1 week

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

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 organisation and 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 centres etc)

• The Driving experience of the LEAF was very positive

Need for 4 x 4

Is this an acceptable App

How I found transition

Northern Regional Meeting, York

Public Health & NHS

NHS Sustainability Day Event 28-03 -14

EVs & Home charging

What do you do with a sunken cable

Being ICE’d

Missing Charger Posts

EV Forums

http://www.leaftalk.co.uk/forumdisplay.php/76-

Batteries-and-Charging-Stations

https://speakev.com/

Demonstration: smartCEM Policy Tool

Konstantinos Gkiotsalitis, Research Associate,

Intelligent Transport Systems Division,

NEC Laboratories Europe

Demonstration: smartCEM Policy Tool

Konstantinos Gkiotsalitis, Research Associate,

Intelligent Transport Systems Division,

NEC Laboratories Europe

Agenda

• Background

• Problem Addressed by EV Policy Tool

• Initial Framework

• Optimization Technique

• Modified Framework/workflow

• Results

• Next Steps

Background

• Acceptance of EVs is hindered by limitedbattery capacity

• Improper route planners lead to wastage ofenergy

• Dynamic unplanned events like traffic jamshave a higher impact on EV’s

• Important issues for EVs in the foreseeablefuture rely on accurate prediction of :

– remaining cruising range,

– energy-aware routing

– Location of optimized charging stops

• concurrent and frequent recharging demandlead to high waiting time at the chargingstops

Home

Planned Route

Planned Charging

Points Traffic Congestion

Delayed Charging

Points

1

3

2

Problem Addressed by EV Policy tool

• Charge planners, are not equipped to dealwith concurrency

• Traditional “plan-execute” scheduling, doesnot cope with dynamicity

• Dynamicity due to:

– Driver behavior: vary the dischargingpatterns, new charging demand, chargingtime and location changes

– External conditions: traffic congestion,weather

• Negative impact of dynamicity:

– Reduced revenues (additional trips)

– Reduced customer satisfaction

– Increased waiting time at charging stations

– Inaccurate range predictions

– Impact on EV user acceptance

EV Policy Tool Framework• EV Policy tool is a multi-objective platform that:

– Analyzes and optimizes route and charging plans

– Re-computes routes and charging schedules in real-time

– Provides an indicator to the gain in OPEX for bothEV user and Charging spot operator

• Solution

– Static input in planning phase: capacity, timewindows, constraints (e.g. resource conflicts)

– Scheduler computes base charging schedule:handles complexity

– Dynamic input in execution phase: traffic jam, jobchanged (time/space)

– New plan computed. Takes into account variability(routes/customer inconsistency)

EV Policy Tool

Scheduler(complexity)

Re-scheduler(real-time)

Dynamic

Input

Static

input

Base Plan

Re-computed planPlanning

Phase

Execution

Phase

1

23

4

I II

High Level Workflow

Stage 1: Data Analytics and Planning

Optimization Goals

Optimization of

Route planData Analytics

Stage 2: Dynamic Re-Scheduling

InputFleet size, CS location, demand, constraints

Online Optimization

Output

Dynamic Factors

Optimization Goals

Optimized Route with reduced

Concurrency and Gain

EV Policy tool

Current Mobility

EV- Policy Tool Stage 1 and Stage 2

• Stage 1– With relevant fleet data EV Policy tool can:

• Identify gaps in the initial plan and potential gain

• Optimize plan to reduce the impact of dynamic factors

– Relevant data needed to optimize :

• Charging demand

• Initial route plan

• Number of Vehicles

• Reaction of any dynamic event.

• Stage 2– Handle demand variation and optimize charging schedule

– Optimization algorithms based on Genetic algorithms and slack timeutilization

Stage 1 – Data Analytics (On Going)

• With relevant fleet data EV Policy tool can:– Identify gaps in initial plan and potential gain

– Optimize plan to reduce dynamicity impact

• Varying the slack time for different slots

– Improves tolerance to dynamicity and

– maximizes the number of chargingrequests that can be accommodated

• Slack time also updated during executionto optimize operational time

Home

Route Logs

Data Analytics

Inconsistent routes due to

dynamicity

Stage 1

Better Planning

Stage 2 – Optimization (Completed)• Scheduler: key enhancements to Genetic Algorithm

• Re-scheduler: algorithm that plans new jobs based on dynamicity risk

# NLE Enhancement to GA for Vehicle Routing & Scheduling Advantage

1 Multi-parent cross-over (CO) builds routes from multi sub-routes More routes analyzed per iteration

2 Locally optimized CO picks best sub-routes Faster progress inside single iteration

3 Graph-aware CO evaluates sub-routes against constraints Earlier dropping of invalid population

4 On-line learning adopts crossover to select the optimal population Prevents local optimum traps

Mu

ch f

ewer

it

erat

ion

s

Determine Slot Inconsistency

Derive slack times New plan

1 2+ 3 4

Routes and customers records

Depth of Discharge

• The maximum DoD is recorded for vehicles which remain in the simulation for themaximum time and have longer route.

• On an average, the DoD of vehicles in the urban network is around 2% to 5%.

• Peak DoD up to 14% during their journey within an urban network.

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

Preliminary Results for Reggio Emilia Use Case

• Preliminary Results of (10 % Demand Variation):

– Distance travelled reduced by 20% compared to rescheduling it the next day

– Re-planning the missed customers with other set of customers leads to wasted capacity andadditional routes

~20%

Reduction

Wasted

capacity

Additional

Routes Base Operational Cost (Initial plan)

Approach

• EV Policy Tool is based on route inconsistency

– Defined as deviation from the normal process due to EV driver behaviour andexternal factors (traffic, weather, etc)

• High-level flow

– Group the EV Users in time slots (customer oriented or operation oriented)

– Compute slot inconsistency factor

– Allot appropriate slack time to each slot

Dynamic Rescheduling

• Handle demand variation and optimizethe delivery schedule

• Continuously monitor for anyinconsistency in the initial plan

• If vehicle encounters an inconsistentroute (e.g. C3 in the figure)

– Tool executes a dynamic reassignmentalgorithm

– Algorithm finds an appropriate slot withoptimal cost for charging stop(C3 in thefigure)

– During the re-assignment phase, thealgorithm ensures that the staticcustomers (C4 and C5 in the figure) witha committed time slots time are notaffected

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

Demo Time!!!

Thank you for your time!

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)

Health & Safety Briefing

Peter Carey

Performance Track Technician

Gateshead College

Performance Track

SmartCEM - BluedashSimon Edwards (UNEW), Tony Green (HYPERDRIVE)

UK Pilot Site Dissemination Event

25th September 2014

What is Bluedash?

• BlueDash™ (www.dquid.com) is a unit which canbe installed in any car to access on-board vehicledata and transmit it via Bluetooth to an on boarddevice (smartphone or tablet)

• The driver will interface directly with theapplication running on the on board device andwill have no contact with the Bluedash unit

• The unit reads vehicle data via the CANbus. Onthe touch screen of the on board device, it ispossible to visualise vehicle performance, fuelconsumption and emissions

• Bluedash is equipped with GPS for positioningdata and GPRS for data communications

Bluedash in the UK

• In smartCEM the Bluedash™ unit sends data fromthe vehicle to a server. Data is then fed to thesmartCEM application installed on an Android 4.4.4-based on board device (in this case a Nexus 5smartphone)

• Software (EV Listener) is installed on the phone,along with the SmartCEM Portal (Common APP) andsmartNavigator

• Battery status, ‘engine’ power (kW), ‘mileage’ (kms),temperature

EV Listener Interface

PTV Navigator

PTV Navigator

Contact:

Leandro Guidotti leandro.guidotti@unimore.it

Dorin Palanciuc Dorin.Palanciuc@teamnet.ro

Tony Green tony.green@hyperdriveinnovation.com

How efficiently did the Green STIG drive?

Graeme Hill

TORG

Newcastle University

How Efficiently did the Green STIG drive?

(and what is efficiency anyway?)

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?

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

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”

The Journey of the Stig

Efficiency

Regeneration

Acceleration

Idling

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!)

Track Driving Efficiency

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

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

Power

• Finally this can be compared with power

• Generally, the lower power usage (including regeneration) is associated with deceleration

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?

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

Green EV STIG Competition winner

Dr Colin Herron

and

THE STIG

EV LEADER BOARD

MOST EFFICIENT 2m: 16 s

2.13Rachel Forsyth-Ward

2.156Wim Boredes

2.162Dirk Kok2.163Fernando Zubilliga

2.19Stafanos Gouvras

2.2Brendan Prior

2.22Steve Spink

2.27Guido Di PasQuale

2.27Tankut Acarman

2.29Marzena Skubij

2.32Joe Mallon

2.34Andrew Fenwick- Green

2.36Martin Forster

2.45Konstantinos Gkiotsalitis

Closing remarks

Dr Colin Herron, Managing Director,

Zero Carbon Futures

Newcastle University

Gateshead College & Zero Carbon Futures

Hyperdrive Charge Your Car

Simon Edwards Alisha Peart Tony Green Alexandra Prescott

Graeme Hill Stephen Irish