__________________________________________________________________________________

UK-Saudi Electric Vehicles Enhanced Education and Research
Network
A report on the workshop carried out at Brunel University during the period from 24 – 30
November 2022
The purpose of this workshop is to provide a more practical and interactive approach toeducation and research in the area of Electric Vehicles between UK and KSA. Four Students
and two academics from KSA attended the workshop as well as 5 academics and 10
students from the UK. Two industrial invited speakers and six academic invited speakers
have also attended the work shop. The full programme of the work shop is attached in the
appendix.
During a workshop, participants are were introduced to the topic through several
presentations. Following this, they are then given hands-on exercises to put the learning
into practice. The workshop facilitator provided support and guidance along the way. Threeindustrial visits were organised during the workshop (Industrial visit to Brill Power Battery
Charging; Industrial visit to Braintree GRIDSERVE-EV Forecourt; and a visit to London EVshow).
Day 1 activities included a welcome talk to all delegates by Dr Darwish, followed by a brief
introduction by the head of department (Professor Gareth Taylor). Dr Eydhah Almatrafi,
KSA has then presented a brief introduction about King Abdulaziz University, Rabigh.
Project progress was then presented by Dr Darwish. Applications of AI in EV Systems waspresented by Professor Abbod (Brunel) followed by industrial presentation on
Semiconductors in Autonomous Vehicles by Dr Alaa Alani (Sondrel Ltd, UK). In theafternoon a presentation on Hydrogen Powered Combustion Engine as a range extender forElectric Vehicles was given by Dr Xinyan Wang, Brunel. A Research-Led Teaching in Electric
Vehicle Systems talk was delivered by Dr Chun Sing Lai, Brunel. Professor Amr Metwally(Grand Canyon University, Phoenix AZ, USA) has then given a presentation on Regenerative
Breaking in EV. There was a networking discussion of all delegates at the end of the firstday. Day 1 presentations are shown in the appendix.

__________________________________________________________________________________

Day 2 activities started with a talk by Professor Farrag (Glasgow Caledonian University, UK)
on E-Scooter for Sustainable Societies followed by industrial speaker from Jerry Stokes(Executive Chairman, GRIDSERVE) on ‘Overview of the GRIDSERVE “Sun-to-Wheel” Ecosystem’. Dr Damien Frost, Brill Power, UK has then given presentation on Battery
Management System in EV. In the afternoon of the second day all delegates visited Brill
Power Battery Charging in Oxford. Presentations and some photos are are shown in the
appendix.
Day 3 activities was mainly an industrial visit to Braintree GRIDSERVE-EV Forecourt, where
all participants saw a real EV Forecourt located in Braintree. Listened to presentations andhave a very informative tour and had the opportunity to ask questions. The appendix
contains some of the photos during the visit.
Day 4 activities included a talk “Towards Connected Autonomous Vehicles Technology for
Future Smart Cities Applications by Professor M. Shafik, University of Derby.

The talk was

followed by simulation session on EV driving range modelling and followed by practicalsession at Brunel electronics lab on EV management system. Activities and presentations of
day 4 are given in the appendix.
Day 5 included a visit to the London EV show (one of the biggest EV shows in the world). On
return to Brunel the consortium had a networking discussion on the requirements ofCurriculum Development, and recommendation on engineering education and smart green
technologies with the development of new EV courses for engineering programmes. The
appendix contains some photos of day 5 activities.
The workshops covered a wide range of topics, including skills development within the EV
industry, personal training during the simulation and practical sessions, and education
development required for future engineers in both KSA and UK.
In conclusion, the workshop provided an effective way for individuals from KSA and UK tolearn and develop new skills while interacting with others and sharing ideas. Some of thebenefits, including increased engagement, hands-on learning, networking opportunities, anda chance to develop new solutions to challenging problems within the EV industry.

__________________________________________________________________________________

Appendix A: Full programme

UK-Saudi Electric Vehicles Enhanced Education and Research
Network
Proposed Programme for the “UK-Saudi Electric Vehicles Enhanced Education andResearch Network” workshop to be held at Brunel University, UK during the period
from 24 – 30 November 2022
All Brunel events will be held in Michael sterling 057, Brunel University
Thursday 24 November:
9:00 – 9:30

Registration and coffee

9:30 – 9:40

Welcome to delegates (Dr Mohamed Darwish, Brunel)

9:40 – 10:00

Brief introduction about Brunel (Professor Gareth Taylor, Head of Department,
Brunel)10:00 – 10:15 Brief introduction about King Abdulaziz University, Rabigh (Dr Eydhah Almatrafi, KSA)
10:15 – 10:30 Project progress (Dr Mohamed Darwish, Brunel)
10:30 – 11:15 Applications of AI in EV Systems (Professor Maysam Abbod, Brunel)
11:15 – 12:15 Semiconductors in Autonomous Vehicles (Dr Alaa Alani, Sondrel Ltd)
12:15 – 13:30 Open discussion/networking and Lunch break
13:30 – 14:15 Hydrogen Powered Combustion Engine as a range extender for Electric Vehicles (DrXinyan Wang, Brunel)
14:15 – 15:00 Research-Led Teaching in Electric Vehicle Systems (Dr Chun Sing Lai, Brunel)
15:00 – 16:00 Regenerative Breaking in EV: What is it and how it works? (Professor Amr Metwally,
Grand Canyon University, Phoenix AZ, USA)
16:00 – 17:00 Networking and tea break
18:00 – 20:00 Workshop dinner (Lancaster Hotel, Brunel)
Friday 25 November:
9:00 – 10:00

E-Scooter for Sustainable Societies (Professor M. E. Farrag, Glasgow Caledonian
University)

10:00 – 11:00 An Overview of the GRIDSERVE “Sun-to-Wheel” Eco-system, Jerry Stokes (Executive
Chairman, GRIDSERVE).

__________________________________________________________________________________
11:00 – 12:00 Battery Management System in EV by Dr Damien Frost, Brill Power, UK
12:00 – 13:00 Lunch break
13:00 – 17:00 Industrial visit to Brill Power Battery Charging (23 Park End Street, Oxford, OX1 1HU)

Saturday 26 November:Free Day

Sunday 27 November:
A social Visit to Windsor (all day)Monday 28 November:
9:00 – 17:00

Industrial visit to Braintree GRIDSERVE-EV Forecourt (Edison Wy., Braintree CM77
7AP)

Tuesday 29 November:
9:00 – 10:00

Industry 4.0, Drive and Fly by Wire: Towards Connected Autonomous Vehicles
Technology for Future Smart Cities Applications (Professor M. Shafik, University of
Derby)

10:00 – 10:30 Feedback discussion and tea break
10:30 – 12:30 Simulation session – EV Driving Range Modelling (Dr Chun Lai)
12:30 – 14:00 Lunch
14:00 – 17:00 Practical Lab session (Dr Xinyan Wang, Brunel)

Wednesday 30 November:
9:00 – 14:00

A visit to London EV show (ExCeL London, One Western Gateway, Royal Victoria
Cock, London, E16 1XL). To attend the EV show you need to register at:
https://londonevshow.com/

14:00 – 17:00 Curriculum Development, Workshop discussion / recommendation on engineering
education and smart green technologies with the development of new EV courses
for engineering programmes (All)__________________________________________________________________________________

Appendix (Presentation Slides and Some Photos for the activities)

UK-Saudi Electric Vehicles
Enhanced Education andResearch Network(Dr Mohamed Darwish)

Brunel University
24 – 30 November 2022

Thursday 24 November:
9:00 – 9:30

Registration and coffee

9:30 – 9:40

Welcome to delegates (Dr Mohamed Darwish, Brunel)

9:40 – 10:00

Brief introduction about Brunel (Professor Gareth Taylor, Head of Department,
Brunel)UK-Saudi Electric Vehicles

10:00 – 10:15 Brief introduction about King Abdulaziz University,
Rabigh (Dr Eydhah
Almatrafi,and
KSA)
Enhanced
Education
10:15 – 10:30 Project progress (Dr Mohamed Darwish, Brunel)

Research Network

10:30 – 11:15 Applications of AI in EV Systems (Professor Maysam Abbod, Brunel)
11:15 – 12:15 Semiconductors in Autonomous Vehicles (Dr Alaa Alani, Sondrel Ltd)
12:15 – 13:30 Open discussion/networking and Lunch break
13:30 – 14:15 Hydrogen Powered Combustion Engine as a range extender for Electric Vehicles (DrXinyan Wang, Brunel)
14:15 – 15:00 Research-Led Teaching in Electric Vehicle Systems (Dr Chun Sing Lai, Brunel)
15:00 – 16:00 Regenerative Breaking in EV: What is it and how it works? (Professor Amr Metwally,
Grand Canyon University, Phoenix AZ, USA)
16:00 – 17:00 Networking and tea break
18:00 – 20:00 Workshop dinner (Lancaster Hotel, Brunel)
Friday 25 November:
9:00 – 10:00

E-Scooter for Sustainable Societies (Professor M. E. Farrag, Glasgow Caledonian
University)

10:00 – 11:00 An Overview of the GRIDSERVE “Sun-to-Wheel” Eco-system, Jerry Stokes (Executive
Chairman, GRIDSERVE).
11:00 – 12:00 Battery Management System in EV by Dr Damien Frost, Brill Power, UK
12:00 – 13:00 Lunch break
14:00 – 17:00 Industrial visit to Brill Power Battery Charging (23 Park End Street, Oxford, OX1 1HU)

Saturday 26 November:Free Day

Sunday 27 November:
A social Visit to Windsor (all day)Monday 28 November:
9:00 – 17:00

Industrial visit to Braintree GRIDSERVE-EV Forecourt (Edison Wy., Braintree CM77
7AP)

Tuesday 29 November:
9:00 – 10:00

Industry 4.0, Drive and Fly by Wire: Towards Connected Autonomous Vehicles
Technology for Future Smart Cities Applications (Professor M. Shafik, University of
Derby)

10:00 – 10:30 Feedback discussion and tea break
10:30 – 12:30 Simulation session – EV Driving Range Modelling (Dr Chun Lai)
12:30 – 14:00 Lunch
14:00 – 17:00 Practical Lab session (Dr Xinyan Wang, Brunel)

Wednesday 30 November:
9:00 – 14:00

A visit to London EV show (ExCeL London, One Western Gateway, Royal Victoria
Cock, London, E16 1XL). To attend the EV show you need to register at:
https://londonevshow.com/

14:00 – 17:00 Curriculum Development, Workshop discussion / recommendation on engineering
education and smart green technologies with the development of new EV courses
for engineering programmes (All)Progress of the project so far:
− Initial meeting /delivered agreed project plan
− 3 further meetings− A research paper published and presented in UPEC 2022
(Forecourt Electric Vehicles Charging Hubs – UK and Saudi
Research and Education Collaboration)− Workshop (Brunel University)Industrial Support:− GRIDSERVE− Brill Power− HVSS Ltd

Academic Support:− Glasgow Caledonian University (Prof. Farrag)− University of Derby (Prof. Shafik)− Cardiff University (Prof. Liana Cipcigan)− Cranfield University (Prof. Patrick Luk)
− Birmingham University (Dr Pietro Tricoli)

The project deals with how a
successful model of electric vehicles(EV) forecourts in the UK can be
implemented into KSA for supporting
research, knowledge, and innovationin emerging EV technologies.

7

Example of a layout

Stringconverter

DC/AC
Inverter

Smart
Grid

Charging
Unit~

Battery
Storage8

Future EV Charging Infrastructure
In UK and Saudi Arabia9EV- Curriculum Development
at Brunel

Brunel University EV conversion project10EV- Curriculum Development
at Brunel

Brunel University EV conversion project11EV- Curriculum Developmentin general

Research Collaboration
Projects Related to the EV Forecourts- Optimisation of energy storage devices within
the forecourt for maximum efficiency.
- Grid connection of the forecourt.
- Business modelling of EV forecourt.

Research Collaboration
Projects Related to the EV Industry- Battery management system.
- End of life electric motors.

- Wireless charging.
- Mitigating three-phase power imbalance with

EV and grid battery.
- Privacy and anonymity in energy transactions.

Research Collaboration
Projects Related to Information and Data
Exchange- Transmission System Operator (TSO)- Distribution System Operator (DSO)- Electricity retailer data and information exchange.

- Grid to Vehicle (G2V) and Vehicle to Grid (V2G)- Real-time power flow optimization with exchange ofelectricity price and power flow data within different
entities should be standardised.

Research Collaboration
Projects Related to Information and Data
Exchange- Transmission System Operator (TSO)- Distribution System Operator (DSO)- Electricity retailer data and information exchange.

- Grid to Vehicle (G2V) and Vehicle to Grid (V2G)- Real-time power flow optimization with exchange ofelectricity price and power flow data within different
entities should be standardised.

Thank You
M. Darwish

UK – Saudi EV WS
Welcome to Brunel
University London
24th November 2022
Prof Gareth Taylor
Director, BIPS Research Centre
Head of Department
Electronic and Electrical Engineering

Department ofElectronic andElectricalEngineering
BrunelInterdisciplinary
Power Systems(BIPS) ResearchCentreBrunel University London Campus

LONDON

Brunel University London

2

Overview of Brunel University London• Established as a University in 1966
• Contemporaries include Bath, Loughboroughand Warwick• ~2,500 Staff• 1,300 Academic + 1,200 Support• ~16,000 Students• ~11,000 UG + ~3,500 Master’s + ~1,500 Doctoral• Students from >130 countries (40-50%
Postgraduate)• Leading research intensive university in theUK

Brunel University London

3

Annual Research Grant Income
40000000

35000000

30000000

25000000

20000000

Value £

15000000

10000000

50000000
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17▪ 13th largest research income from Innovate UK (>100)▪ 34th largest EPSRC portfolio in the UK▪ Two prestigious Innovative Manufacturing ResearchCentres (IMRCs) from EPSRC (~20 in UK)
Brunel University London

4

Environment, Health & Societies

Health & Life Sciences

Engineering, Design &Physical Sciences

Research
Institutes

Business, Arts &Social Sciences

Materials & Manufacturing

Brunel Educational ExcellenceCentreProfessional Development Centre

Ground-breaking
Interdisciplinary
research

Energy Futures

Colleges

Research Support& Development
OfficeTeaching and disciplinary
research

Environment, Health & Societies

Health & Life Sciences

Engineering, Design &Physical Sciences

Research
Institutes

Business, Arts &Social Sciences

Materials & Manufacturing

Brunel Educational ExcellenceCentreProfessional Development Centre

Ground-breaking
Interdisciplinary
research

Energy Futures

Colleges

Research Support& Development
OfficeTeaching and disciplinary
research

College of Engineering, Design andPhysical Sciences - CEDPS
Currently four Engineering Departments
within CEDPS college• Chemical Engineering• Civil Engineering• Electronic and Electrical Engineering (EEE)• Mechanical and Aerospace Engineering

Brunel University London

7

Electronic and Electrical Engineering(EEE)
Electronic and Electrical Engineering at Brunel is highly
regarded both nationally and internationally• Ranked in top 4 Universities in London (~20) over the last
three years• Included in top 30% of UK Universities over the last 3
years• Included in top 200 Universities internationally in bothteaching and research
Brunel University London

Electronic and Electrical Engineering(EEE)
All Electronic and Electrical Engineering academic staff areresearch active in the following internationally recognised
research areas
•
•
•
•
•
•

5G and Beyond
Digital Media & Technology
Electrical Power Systems
Electronic Systems
Industrial and Applied AI
Sensors and Instrumentation

Brunel University London

BIPS Research Centre
Director: Prof Gareth Taylor (EEE)▪ Originally established at Brunel in 1991

▪ Prof Sir Michael Sterling and Prof Malcolm Irving▪ Interdisciplinary Power Systems Research▪ Institute of Energy Futures▪ Smart Grids and Renewable Energy Systems▪ Current Research Centre Membership▪ EEE (10): Taylor; Li; Abbod; Darwish; Zobaa;
Pisica; Huang, Lai, Zhang, Ekwue (RAEng VP)▪ CEDPS (2): Date, Louvieris; CBASS: Manika▪ Researchers: 4 RAs/RFs, 30 Doctoral Researchers

Key Expertise
•
•
•
•
•
•
•
•
•

Power Transmission & Distribution Systems
Power Electronics & Power Quality
DC Transmission & Distribution
Digitalisation of Future Power Systems
Renewable Energy Systems
Decarbonisation of Power Systems
Smart Grids
Energy Economics & Power Markets
Power System Informatics & Communications

Thank you for your time
Any questions?

Email: Gareth.Taylor@brunel.ac.uk

Brunel University London

King Abdulaziz University
Rabigh –Faculty of Engineering
24 November 2022

Universities Role
▪ 5 Saudi universities in the 200 list of best
world universities▪ Build effective international collaboration forknowledge transfer, research, and students
training▪ Quality education with national andinternational accreditation▪ Filling gaps with local market▪ Generate income (self finance)

University Mission▪ Community Responsibility▪ Knowledge Development▪ ResearchRabigh Branch –KAU
▪ About 7000 students▪ Faculty of Business▪ Faculty of Computing & Information Technology▪ Faculty of Medicine▪ Faculty of Science & Arts

▪ Faculty of Applied Medical Sciences▪ Faculty of Applied Studies▪ Faculty of Engineering

Faculty of Engineering-Rabigh (FER)▪ Established in 2009
▪ To develop the industrial community in Rabigh and its vicinities .

▪ Six undergraduate programs:
1. Architectural Engineering▪ Three graduate programs:

2. Civil Engineering

1. Chemical Engineering

3. Chemical Engineering

2. Electrical Engineering

4. Electrical Engineering

3. Mechanical Engineering

5. Industrial Engineering
6. Mechanical Engineering

FER Main Objectives▪ National and international accreditation for all

programs▪ Developing graduate programs▪ National and international collaboration▪ Additional technical training programs forstudents▪ Center of expertiseHuman Resources‫إحصائيات طالب الكلية‬‫‪200‬‬‫‪200‬‬‫‪180‬‬‫‪160‬‬‫‪120‬‬‫‪126‬‬‫‪112‬‬‫‪69‬‬‫‪79‬‬‫‪71‬‬‫‪77‬‬‫‪59‬‬‫‪56‬‬‫‪50‬‬‫‪44‬‬‫‪33‬‬‫‪27‬‬‫‪58‬‬‫‪56‬‬‫‪56‬‬‫‪56‬‬‫‪62‬‬‫‪50‬‬‫‪80‬‬‫‪65‬‬‫‪54‬‬‫‪60‬‬‫‪39‬‬‫‪40‬‬‫‪13‬‬‫‪12‬‬‫‪120‬‬‫‪100‬‬‫‪85‬‬‫‪68‬‬‫‪52‬‬‫‪127‬‬‫‪117‬‬‫‪91‬‬‫‪86‬‬‫‪140‬‬

‫‪4‬‬

‫‪0‬‬

‫‪0‬‬

‫‪20‬‬‫‪0‬‬‫‪2021‬‬‫‪2021‬‬‫‪120‬‬‫‪12‬‬‫‪27‬‬‫‪56‬‬‫‪52‬‬‫‪69‬‬‫‪86‬‬‫‪2020‬‬‫‪2020‬‬‫‪126‬‬‫‪13‬‬‫‪33‬‬‫‪71‬‬‫‪68‬‬‫‪91‬‬‫‪112‬‬‫‪Graduates = 582‬‬‫‪2019‬‬‫‪2019‬‬‫‪127‬‬‫‪4‬‬‫‪39‬‬‫‪44‬‬‫‪50‬‬‫‪79‬‬‫‪85‬‬‫‪2018‬‬‫‪2018‬‬‫‪200‬‬‫‪0‬‬‫‪56‬‬‫‪56‬‬‫‪58‬‬‫‪59‬‬‫‪77‬‬‫‪2017‬‬‫‪2017‬‬‫‪117‬‬‫‪0‬‬‫‪65‬‬‫‪54‬‬‫‪62‬‬‫‪50‬‬‫‪56‬‬‫‪Current Students = 422‬‬‫الطالب المستجدين بالكلية‬‫قسم الهندسة المعمارية‬‫قسم الهندسة المدنية‬‫قسم الهندسة الكيميائية وهندسة المواد‬‫قسم الهندسة الكهربائية‬‫قسم الهندسة الصناعية‬‫قسم الهندسة الميكانيكية‬‫الخريجون‬‫الشركة الوطنية لإلسكان‬‫أرامكو السعودية‬‫الشركة العربية لالسمنت‬‫الشركة السعودية للكهرباء‬‫اسمنت الصفوة‬‫سابك‬‫شركة اي ام سي االلمانية‬‫جامعة الملك عبدالعزيز‬‫الشركة السعودية لمعدات الديزل‬‫عبداللطيف جميل‬‫شركة سفاري‬‫المؤسسة العامة لتحلية المياه‬‫شركة تطوير المباني‬‫وزارة الدفاع‬‫المعامل‬‫والورش‬‫الهندسة الكهربائية‬‫الهندسة الكيميائية وهندسة المواد‬‫• هندسة المواد‬‫• التحكم وهندسة التفاعالت‬‫• إنتقال الحرارة‬‫• العمليات الطبيعية‬‫• األبحاث المتقدمة‬‫• ميكانيكا الموائع‬‫• العمليات الطبيعية‬‫• الدوائر الكهربائية‬‫• التحكم واإللكترونيات‬‫• القوى واآلالت الكهربائية‬‫الهندسة الميكانيكية‬‫• التبريد والتكييف‬‫• ميكانيكا الموائع‬‫• الورش‬‫الهندسة المدنية‬‫• الهندسة الجيوتقنية‬‫• التشييد والخرسانة‬‫• المساحة والنقل‬‫• الهيدروليكا‬‫• الهندسة البيئية‬‫• ماكينة اإلختبار العامة‬‫قسم الهندسة الصناعية‬‫• التصنيع بالحاسب‬‫• التعليم التفاعلي‬‫• بيئة العمل وهندسة الجودة‬Accomplishments

Student Chapters▪ Institution of Civil Engineering (ICE)▪ Material Advantage chapter▪ American Institute of Chemical Engineers▪ Institute of Electrical and Electronics Engineers▪ Industrial Engineering and Operations Management▪ American Society of Mechanical Engineers‫انجازات الطالب‬‫• عبدهللا الشهري وعبدهللا الدهاسي‪ :‬مخرج الطوارئ للمصاعد‬‫• علي الغامدي‪ :‬حامل السكاكين األمن‬‫• معرض جنيف الدولي لإلختراعات‪ :‬ميدالية برونزية من معرض جنيف الدولي‬‫• جائزة تايوان لالختراعات‬‫لالبتكارات ‪ ،‬ميدالية من بعثة رومانيا ‪ ،‬ميدالية من بعثة تايوان ‪ ،‬ميدالية من بعثة‬‫• جائزة الجمعية اللبنانية للمبتكرين‬‫تايالن‬‫• تمثيل الجامعة معرض وزارة التعليم لإلبتكارات في الرياض‪ :‬سلطان‬‫ياقوت وتركي البالدي‬‫• داخل الجامعة‪:‬‬‫• الملتقى العلمي الثامن‪:‬‬‫•‬‫المركز الثاني والثالث في محور‬‫االبتكارات‬‫• المركز االول والثالث في محور‬‫• الملتقى العلمي العاشر‪ :‬المركز األول‬‫االبحاث‬‫في محور اإلبتكارات‬‫• الملتقى العلمي التاسع‪:‬‬‫• مسابقة ابتكاري‪ :‬المركز الرابع والخامس‬‫•‬‫المركز الثاني والثالث في محور‬‫• الملتقى العلمي األول برابغ (‪:)2020‬‬‫االبتكارات‬‫• جائزة الريشة الذهبية‪ ،‬المركز األول‬‫• المركز الثاني والثالث في محور‬‫االبحاث‬‫والثاني والثالث في محور البحث العلمي‪،‬‬‫المركز األول والثاني في محور اإلبتكار‪،‬‬‫وجائزة الوسام الذهبي‬‫فوز فريق كلية الهندسة برابغ لكرة القدم على‬‫المركز األول ببطولة دوري كليات جامعة‬‫الملك عبدالعزيز فرع رابغ‪.‬‬‫المشاركة في فريق الجامعة (‪ 3‬طالب) في الدورة الرياضية الثامنة‬‫لجامعات ومؤسسات التعليم العالي بدول مجلس التعاون الخليجي‬‫المركز الثاني في‬‫السباحة‬Collaboration‫▪ إتفاقية مع رشكة تكنيا يج إم يب إش‬‫▪ إتفاقية مع إملعهد إلعايل لتقنيات إملياة‬‫وإلكهرابء‬▪ High Institute for Elastomer (HIEI)▪ Universiti Teknologi Malaysia (UTM):

Students Internship Summer Program▪ Institute of Membrane Technology(ITM-CNR), at University of Calabria Italy▪ Indiana University–Purdue University,
Indianapolis, IN, USA▪ Universitat Rovira i Virgili (URV), SpinQuality&
Accreditaton

NCAAA & ABET Accreditations

Graduate Studies&
Scientific Research‫الدراسات العليا والبحث العلمي‬‫الماجستي األكاديمية‬‫برامج‬‫ر‬‫ماجستي الهندسة الكيميائية‬‫▪‬‫ر‬‫ماجستي الهندسة الكهربائية‬‫▪‬‫ر‬‫ماجستي الهندسة الميكانيكية‬‫▪‬‫ر‬‫الدراسات العليا والبحث العلمي‬‫المجموع = ‪ 33‬طالب دراسات عليا للعام ‪1443‬‬‫الدراسات العليا والبحث العلمي‬‫الدراسات العليا والبحث العلمي‬‫الماجستي النوعية‬‫برامج‬‫ر‬‫ماجستي هندسة الطاقة المستدامة‬‫▪‬‫ر‬‫ماجستي هندسة التصنيع‬‫▪‬‫ر‬‫ماجستي اإلدارة الهندسية (تحت اإلنشاء)‬‫▪‬‫ر‬

The EndELECTRIC VEHICLE IN THE AGE OFARTIFICIAL INTELLIGENCE
MAYSAM ABBOD

CONTENTS• Electric Vehicle• Battery Management• Car Management• Traffic and Routing• Travel Planning• Network Management• Fleet Management

KEY DEFINITIONS• The term ‘electric vehicle’ or ‘EV’ covers any vehicle
that operates on an electric motor or traction motor
instead of an Internal Combustion Engine (ICE). This
includes not only cars but electric trucks, planes, trains,boats and two- and three-wheelers.

Battery Types of EVs• Powertrain technologies include full electric vehicles
and plug-in hybrids, as well as hydrogen fuel cellvehicles that convert hydrogen into electricity.

• Battery Electric (BEVs)• Electro mobility (or e-Mobility) represents the concept
of using electric powertrain technologies, in-vehicle
information, and communication technologies andconnected infrastructures to enable the electric
propulsion of vehicles and fleets.

• Hybrid Electrics (HEVs)• Plug-in Hybrids (PHEVs)• Hydrogen Fuel Cells

3

WHAT EXACTLY IS AI?
The science of AI can be simply
translated as the science of making
machines and appliances smart enoughto give them the ability to perform all
the tasks that humans do.

THE POWER OF EV BATTERIESThe scope of the global battery market is rapidly increasing. EV models like
Tesla S 100D offers 355 miles, Hyundai Kona offers 198 miles, or MG ZS EV
could offer 214 miles on average and none can be fully charged in a matter ofminutes. For instance, at Tesla supercharging station, EVs will take 75 minutes toreach full charge.

HOW AI IS ACCELERATING THE POWER OF EVBATTERIES
In every aspect of a vehicle's life, battery performance is a crucial factor toconsider. The battery accounts for 25% of the entire cost of an electric vehicle
and when it comes to EV developments, nothing is more important thanincreasing battery life. Artificial intelligence has made aspirations like partial
recharging an electric vehicle in the time it takes to stop at a petrol station, andit might help enhance other elements of battery technology as well.

BATTERY LIFE CYCLE

Machine Learning helps to explore the opportunity of battery life cycle
management. The key to improving battery life lies in the data. By blending
advanced electronics with IoT, data science and digital twin, Machine Learning
uses the power of predictive intelligence to predict battery life, identify potential
breakdown and their causes, fix errors even before they arise. All this data can be
stored in the cloud.

BATTERY LIFE CYCLE - MONITORING
AI is contributing significantly in the overall battery management in electronics. It
has been there for some time now in smartphones where AI helps to increase thecharge of a battery, reduce time to charge and also improves the life of thebattery. For instance, AI keeps a check on the temperature of the battery andresponds with safety measures in case of overheating, etc. Similarly, it’s helping
optimize the energy efficiency in EVs as well.

BATTERY LIFE CYCLE - MANAGEMENT
Artificial intelligence is analysing battery usage, recharge data and constructing
a mathematical model to optimize the fast-charging capabilities withoutcompromising the life span of the vehicles, which is improving battery
performance and battery life cycle management, including driving range andcharging time, and life span of the vehicles. It can also help accelerate thebattery field research by speeding up the search for better materials andtesting.

CAR MANAGEMENT• Power utilisation• Traction control• Collision detection• Charge management• Driving Conditions• Self Driving

SMART CARS
• Sense the road
• Control the speed• Avoid collision• Check the traffic

SELF DRIVINGCARSAUTONOMOUS CARSAUTONOMOUS CARSSCENE LABELLING USING DEEP LEARNINGTRAFFIC MANAGEMENT
AI is used in road traffic management to help analyse real-time data fromvarious means of transportation, including cars, buses and trains. AI analyses thisinformation for patterns that could indicate safety risks. This information is thenused to suggest ways to mitigate these risks and reduce the number of accidents
that occur.

TRAVEL PLANNING• Adaptive traffic control system (ATCS)• Automated vehicles• Intelligent parking planning• Safety and Emergency Situations• Transit Planning – Intelligent Transportation Systems• Urban PlanningV2G MANAGEMENT
Vehicle-to-grid (V2G) is one of the future key technologies for the smart grid.
Electric vehicles (EV) are potential power consumers that can play a crucial roleby delivering the power back to the grid in order to meet the power demand.
However, the V2G network has some crucial security and privacy challenges.

EV AND THE GRIDEV FLEET MANAGEMENT
EV fleet management considers the big issues of fleet electrification and electricvehicle charging requirements. The volume of vehicles and distances travelledmust be analysed to determine the number of depot and/or “on the road”
charge points required and the distance between them – whether owned andoperated by the fleet owner, public charge points, or home-based charge points
for lighter vehicles. This directly addresses the issue of range anxiety, ensuring
vehicles have access to charge points when and where they need them, whether
on the road or at the depot, so they can complete their missions.

OPTIMISING CAR CONFIGURATIONSThere are thousands of possible combinations of features that could be chosen
to configure a car. Powerful machine learning algorithms find the optimal
combinations of features (body type, powertrain, options package etc.) to place
on a car dealership lot, in order to minimise turn time and maximise revenue.

FUTURE OF ARTIFICIAL INTELLIGENCE IN EV
INDUSTRY
With various applications such as autonomous driving, user behaviour monitoring,and smart navigation systems, AI is playing a key role in the EV Industry. It canbe used for safety applications:• Predictive maintenance of equipment• driver behaviour monitoring• vehicle security.
Different companies are using AI in their vehicles, some use this technology tochange current transportation systems with self-driving services, while some use it
for strengthening the battery power of their EVs.

AI ROLE IN EV INDUSTRY• Maximising the charge utilisation• Reducing the charging time
• Increasing the life of the battery• Optimizing the use of battery in all electronics including lights• Optimising the car speed• Optimising acceleration• Safety and security

AI TO EASE EV ELECTRICITY DEMAND
With an influx of investment being poured into electric vehicle (EV)charging infrastructure, the number of EVs and EV fleets is on the rise.

• How to manage EV charging while preventing blowouts, asset wear andtear and power outages.
There is an equation that needs to be formulated to best approach EV
grid integration in light of the expanding electrification.

EV DEMAND FORECASTING
By predicting demand ahead of time for new and existing car models, carmanufacturers and dealerships can plan ahead and optimise operations andmarketing.

CONCLUSION• EV is developing fast and new technologies are introduced every year.
• AI can help refining these technologies and improve the performance of EV• Integration of EV with the electric grid is the future direction.

Semiconductors inAutonomous Vehicles
24 November 2022

Alaa Alani PhD MIET CEng
Sondrel Plc.
UKDisclaimer
The opinions expressed in this presentation and on the following slides aresolely those of the presenter and not necessarily those of Sondrel(Holdings) Plc or its subsidiaries.
Sondrel Plc does not guarantee the accuracy or reliability of theinformation provided herein

A. Alani 2022

Semiconductors in Autonomous Vehicles Electronics in Cars
 Historical data Electronic systems
 Cost of electronic parts
 Three categories
 Semiconductors
 Evolution of microchips
 Semiconductor regions/countries
 The Middle East Autonomous Vehicles
 Sensors and systems


Camera, Radar, LiDAR

 Challenges
 Levels of driving automation

 Summary

A. Alani 2022

Semiconductors in Autonomous Vehicles

Electronics in Cars

A. Alani 2022
Semiconductors in Autonomous Vehicles

Historical Data
 Electronic systems have become an increasingly large

component of the cost of an automobile
 From only around 1% of its value in 1950 to around
30% in 2010.
 Modern electric cars rely on power electronics for themain propulsion motor control, as well as managing
the battery system.
 Future autonomous cars will rely on powerfulcomputer systems, an array of sensors, networking,and satellite navigation
A. Alani 2022

Semiconductors in Autonomous Vehicles

Historical Data
 The earliest electronic systems available as factory installations werevacuum tube car radios in 1930’s.
 The development of semiconductors after WWII greatly expanded
the use of electronics in automobiles in the 1960’s onward (e.g.,solid-state diodes alternator, transistorised ignition system, etc.)
 In the 1980-90’s, most car manufacturers started using
microprocessors in ECUs for engine management
 In the 2000’s first attempts of self-drive vehicles powered by
microchips from Renesas, Mobileye, Nvidia, Intel, etc. began Since 2014, car makers such as Google, Apple, Tesla, Toyota andothers entered the race in self-drive cars
 In parallel, most self-drive cars are being powered by electric engines
using an ever increasing number (>2000) of semiconductor chips
A. Alani 2022

Semiconductors in Autonomous Vehicles

Electronic Systems
 Transmission control

 Electronic tachometer

unit (TCU) Adaptive cruise control(ACC) Alternators
 Automatic headlightdimmers Electric fuel pumps
 Electronic fuel-injection
 Electronic ignition control

 Sequential turnA. Alani 2022

signals, speed indicators
 Tyre-pressure monitors Voltage regulators
 Windshield wiper control
 Electronic Skid
Prevention (ESP), Heating, ventilation, andair conditioning (HVAC)

Semiconductors in Autonomous Vehicles

Cost of Electronic Parts
Cost %
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
1970
A. Alani 2022

1980

1990

2000

2010

Semiconductors in Autonomous Vehicles

2020

2030

Three Categories
Estimates say that there are roughly around 1,000+
chips in a non-electric vehicle and twice as much in anelectric one

Three Categories: Safety, Security, and Access Powertrain and Electrical Systems
 Comfort, Infotainment, and Connectivity

A. Alani 2022

Semiconductors in Autonomous Vehicles

Three Categories
SafetySecurity

AccessAir bag

Door lock

Fob

Parking
Assistance

Collision
avoidance
Breaking
assistance

AlarmPowertrain
EngineLighting

Fuel injection

Diagnostic
systemKeyless

Tracking
Hybridelectric
control
Transmission
control

A. Alani 2022

Electric
systems

Semiconductors in Autonomous Vehicles

signallingThree Categories
ComfortA. Alani 2022

Infotainment

Connectivity

Wind/mirror
control

Audio/video
controlsystemWiFi

Seat heating

Driverdisplays

Bluetooth

Climatecontrol

Navigation

Over the air
s/w update

Semiconductors in Autonomous Vehicles

Semiconductors
 Devices contain hundreds of millions transistors

integrated in tiny chips used in consumer goods,
telecom, computers, space, robots, cars, etc.
 Advanced nodes 16nm, 12nm, 7nm, 5nm, 3nm
 Global semiconductor market size is ~$500 billions
 Design
 Manufacturing Assembly TestA. Alani 2022
Autonomous Vehicles

Semiconductors inEvolution of Microchips
Electronic Devices

Single Board Computer
Computers

A. Alani 2022
Semiconductors in Autonomous Vehicles

Semiconductor Regions/Countries
 Malysia: electronic exports $30bn in 2013
 Thailand: Electronic industry is valued at $100bn mainly

automotive electronic parts
 The Philippines: Electronics shipments totalled$37.57bn in 2018
 Singapore: Electronic industry is valued at $37.4bn in2017 Israel: 5 Fabs and more than 15 fabless companies;
20,000 employees with annual revenues of $5bn
 Brazil: Semi Revenues $552.8m in 2019
A. Alani 2022
Autonomous Vehicles

Semiconductors inSemiconductor Regions/Countries

A. Alani 2022
Autonomous Vehicles

Semiconductors inSemiconductor Regions/Countries

A. Alani 2022
Autonomous Vehicles

Semiconductors in

The Middle East Silicon Oasis was established in Dubai started Pilot Design Environment

to establish the Dubai Circuit Design (DCD) centre in 2001
 Abu Dhabi established Advanced Technology Investment Company(ATIC) in 2008 which invested in AMD to create Global Foundry which is
100% owned by Abu Dhabi since 2012
 Masdar Institute of Science and Technology in Abu Dhabi collaborated
with Global Foundry on 28nm SLP low-power bulk CMOS technology in2014
 Egypt started tech start-up in 1993 with Anacad. In 2004, two chip design
companies emerged to cater for customers in the US, Europe and Asia.
Currently, 17 fab-less companies form the semiconductor sector in Egypt
 Morocco was the first Arab country to enter semiconductor industry
established by ST Micro – A French company in 2003. Acquired bySondrel 2015
A. Alani 2022
Autonomous Vehicles

Semiconductors inAutonomous Vehicles

A. Alani 2022
Semiconductors in Autonomous Vehicles

Properties
 An autonomous car is a vehicle capable of sensing its

environment and operating without human
involvement.
 Autonomous cars rely on sensors, actuators, complex
algorithms, machine learning systems, and powerfulprocessors to execute software.
 Autonomous cars create and maintain a map of theirsurroundings based on a variety of sensors situated indifferent parts of the vehicle.

A. Alani 2022

Semiconductors in Autonomous Vehicles

Examples

A. Alani 2022

Semiconductors in Autonomous Vehicles

Sensors and Systems
 Video Cameras: detect traffic lights
 read road signs
 track other vehicles
 look for pedestrians

A. Alani 2022

 Radar Sensors:
 monitor the position of
nearby vehicles
 LIDAR Systems
 measure distances detect road edges
 identify lane markings

Semiconductors in Autonomous Vehicles

RADAR

Radar chip with antennas on package

 Radar sensors are an integral part ofadvanced driver assistance systems(ADAS) in modern cars.
 They are used for:
 blind spot detection (BSD) lane change assistance (LCA)
 collision mitigation (CM) parking aid (PA) rear cross traffic alert (RCTA) features

 Operate at 3 frequency bands
 24GHz 60GHz 77-79GHz

Fury radar chip by Texas Instruments
A. Alani 2022

Semiconductors in Autonomous Vehicles

LiDAR
 LiDAR is an acronym for Light

Detection and Ranging. In
LiDAR, laser light is sent from asource (transmitter) andreflected from objects in thescene.
 The reflected light is detectedby the system receiver and thetime of flight (TOF) is used todevelop a distance map of theobjects in the scene.
 Implemented in a microchip of
10x10mm or smaller containing>125 million transistors

A. Alani 2022

Semiconductors in Autonomous Vehicles

LiDAR on a Chip

LIDAR Hottest topic in optics and photonics
 Spinning car-top sensors or solid-state scanners(InGaAs) 0.1 angular degree resolution
 Works n low-lighting conditions
 Suffers from adverse weather conditions
 Time-of-flight versus FMCW TofF: determines distance from flight time of a laser pulse
 FMCW: extracts time and velocity from frequency shift ofreturning light
A. Alani 2022

Semiconductors in Autonomous Vehicles

A. Alani 2022

Semiconductors in Autonomous Vehicles

Surround Views and Detection

Long-range radar
Short/medium-range radar
UltrasoundOptical cameras
Lidar
A. Alani 2022

Semiconductors in Autonomous Vehicles

Challenges
 Lidar and Radar: High complexity and cost
 Weather Conditions
 Traffic Conditions and Laws

 Country Regulation
 Accident Liability
 Artificial vs. Emotional Intelligence https://www.synopsys.com/automotive/what-is-autonomous-car.html

A. Alani 2022

Semiconductors in Autonomous Vehicles

Levels of Driving Automation

A. Alani 2022

Semiconductors in Autonomous Vehicles

Summary
 Modern cars increasingly rely on electronics
 Semiconductor chips are used in cars for safety,

security, access, comfort, infotainment andconnectivity
 Sensors are classified into three systems: cameras,
Radars and Lidars all work in synergy
 Tremendous efforts are being made to overcome
challenges and reduce risk Future cars will have higher levels of driving
automation
A. Alani 2022

Semiconductors in Autonomous Vehicles

Thank You

A. Alani 2022

Semiconductors in Autonomous Vehicles

Notes Two optical challenges in particular: One is the need for compact, economical, high-peak-power lightsources capable of producing nanosecond pulses.
 Equally demanding requirement is for effective collection optics anddetectors that can deal with extremely low photon budgets. “Only asmall fraction of emitted photons will return to the detector” even in
the best case.
 On the light-source side, the most familiar distinction is between 1550-

nm sources, which can operate at higher power (and, thus, longer
ranges) without compromising eye safety, and more economical 905nm systems, which “have been there forever” but must operate at lower
power and ranges for eye safety.
 As with lasers, each detector type—from avalanche photodiodes tosilicon-based single-photon avalanche detectors—has its weaknesses
and strengths. “There’s not a ‘perfect’ detector”. “There are a lot ofchoices out there, and we’re still making choices today.”
A. Alani 2022
Autonomous Vehicles

Semiconductors inHydrogen Powered Combustion Engine as a Range Extender forElectric Vehicles

Dr Xinyan Wang
UKRI Future Leaders Fellow

Centre for Advanced Powertrain and Fuels (CAPF)
Brunel University London

24th November 2022

Dr Xinyan Wang
UKRI Future Leaders Fellow
Centre for Advanced Powertrain and Fuels
Research interests:- Advanced combustion technologies- Dedicated zero/low carbon fuel engines
Office:- Novel fuel designs

Howell Building 124- Advanced hybrid electric engine systems

Email:

Research grants (£1.9m):

Xinyan.Wang@brunel.ac.uk-

UKRI Future Leaders Fellowship on novel engine and fuels-

EPSRC on ammonia/hydrogen combustion-

Industrial project on hydrogen enginesTel:+44 (0)1895 265903

Contents
1. Challenges and opportunities of moving towards net zero transport
2. Propulsion and fuel technologies driving the net zero transition
3. Summary

Contents
1. Challenges and opportunities of moving towards net zero transport
2. Propulsion and fuel technologies driving the net zero transition
3. Summary

Challenges and opportunities of moving towards net zero
❑ Net zero targets drive the future development of all sectors

Carbon emissions
Millionton

Source: University of Reading; http://www.daoxinhe.com/a/xinwendongtai/xingyedongtai/72.html

Challenges and opportunities of moving towards net zero
❑ Net zero targets drive the future development of all sectors
UK carbon budgets

Challenges and opportunities of moving towards net zero
❑ Transport is an important but challenging area for decarbonisation
China

9%

27%

Source: Lancaster University & Small World Consulting; http://finance.sina.com.cn/esg/ep/2021-11-30/doc-ikyakumx1025810.shtml

Challenges and opportunities of moving towards net zero
❑ Transport is an important but challenging area for decarbonisation
UK new car sale

Challenges and opportunities of moving towards net zero
❑ Transport is an important but challenging area for decarbonisation
UK new car sale
• The market share of hybrid increase
faster than BEV.
• Petrol-powered vehicles, including
mild hybrids (MHEVs), remain
Britain’s most popular powertrain,accounting for 58.3% of all newcars registered in 2021, with dieselpowered cars including MHEVs
making up 14.2% of the market,
followed by BEVs at 11.6%, HEVs
at 8.9% and PHEVs at 7.0%.
• 88.4% of new vehicles still have
ICEs

Challenges and opportunities of moving towards net zero
❑ Transport is an important but challenging area for decarbonisation
China: New Energy vehicle market is dominated by pure electric vehicles

Challenges and opportunities of moving towards net zero
❑ Transport is an important but challenging area for decarbonisation
Heavy Good Vehicles?

28T
320 km mileage
overnight charging

LCA

Challenges and opportunities of moving towards net zero
❑ Transport is an important but challenging area for decarbonisation
What about these?

Challenges and opportunities of moving towards net zero
Applications

Market shares

Engine based powertrain

Air• Wide applications• High emissions (23%)

CarbonEmission
Zero

Water
Low
• Zero Exhaust emission• Lower market share• Limited applications

Road

Fuel-cell, battery electric system

Now

Short TermLong Term

HighChallenges and opportunities of moving towards net zero
Applications

Market shares

Water

Conventional
Engine/Fuels

Air

CarbonEmission

?

Zero

Low

?
High

Road
Fuel-cell, battery electric systems
Now

Short TermLong TermChallenges and opportunities of moving towards net zero❑ What are the opportunities
Hydrogenbased fuels(H2, ammonia,
methanol, etc)

Optimisedengines

Electric systems

Contents
1. Challenges and opportunities of moving towards net zero transport
2. Propulsion and fuel technologies driving the net zero transition
3. Summary

Propulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels

Life cycle analysis (LCA) of carbon emissions

Source: FEV 201817

Propulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
Source: FEV

Combustion
systems

Pure electric
systems

Propulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
Hydrogen••

Fuel Cells
Combustion systems

Propulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
HydrogenPropulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
Hydrogen
Attributes

H2 Engine

H2 Fuel Cell (FC)CO2 emission

zero

zero

Hydrogen quality

Low purity

High purity

Powertrain system Engine, Hybrid Electric

FC+ batteries

Cost

Similar to Diesel/NG engines

Substantially expensive

Overall efficiency

Baseline(42%), Advanced (45%), Dedicated(50%)

FC –battery-motor (55% x 80%x80%)FC-motor (55%x 80%)

Performance

Instant cold-start and shut-downWarm-up period, slow response

Pollutant Emission Zero Impact NOx (< 0.5 μg/m3 vs 18μg/m3
ambient)

Zero

Durability

Similar to NG engines

PEM (6000 vs 30000 hours for
HDV)

Social /Economic
impacts

Protect Existing infrastructure and employment

New manufacturing, precious
metalsPropulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
HydrogenSource: AVL

Propulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
Ammonia: an excellent hydrogen carrier
Advantages

Disadvantages

Hydrogen-Zero carbon
Optimal combustion + high calorific value-

Huge investment in storage
Long time to set up infrastructure

Ammonia-

Zero carbon
Existing infrastructure-

Poor combustion, low efficiency

Transport
HydrogenAmmonia

Renewable Energies

exhaustWater

direct use

•
•
•
•
•
•

Carbon-free
Optimised combustion
Higher efficiency (with H2)Using existing infrastructure
Faster implementation
Applicable for other sectors

Propulsion and fuel technologies driving the net zero transition❑ Low/zero carbon fuels
E-fuels (e-methanol, e-methane, etc.)

Transport

Sources: Marco Chiodi, FKFS

Propulsion and fuel technologies driving the net zero transition❑ Electric techniques to improve ICEs further
Hybrid and range extender systems▪ High energy density of fuels – long range▪ Eliminate/reduce external charging – increased flexibility▪ Load leveller – improved ICE efficiency▪ Usage of electric machine – improved efficiency and torque characteristics▪ Regeneration ability – improved overall efficiency▪ Start-stop function - reduced emissions and fuel consumption▪ (Plug-in hybrid – used external electricity supply with lower running cost)Propulsion and fuel technologies driving the net zero transition❑ Electric techniques to improve ICEs further
Hybrid and range extender systems

Propulsion and fuel technologies driving the net zero transition❑ Electric techniques to improve ICEs further
Hybrid and range extender systems

Range Extender:Challenges of moving towards net zero
❑ Electric techniques to improve ICEs further
Dedicated range extender (Compact 2-stroke engine)
Fuel-Flexible Engine:

Dedicated Engine Generator:

by adapting to hydrogen fuel.

by integrating electric generator.

ToyotaTransportChallenges of moving towards net zero
❑ Electric techniques to improve ICEs further
Dedicated engine generator (Compact 2-stroke engine)
Key Features:• 2-stroke operation• Uniflow scavenging
o Best scavenging efficiency
o Minimised short-circuitingo Even thermal load
• Overhead exhaust valves• VVA compatibility• Direct injection• Boosted operation

R&D work:• Determination of B/S ratio , Journal of Automobile
Engineering, 2017.
• Scavenge port designs, SAE 2016-01-1049• Scavenge plenum designs, SAE 2017-01-1031• VVA optimization, International Journal of Engine
Research, 2017; IMechE: ICE conference 2017
• Fuel injection strategies, International Journal ofEngine Research, 2017; SAE 2018-01-0272.

• Combustion chamber design, International Journalof Engine Research, 2020.
• Boosting system optimisation, SAE 2020-01-2007• Review and potential analysis, Engineering, 2019.

1. Research background❑ Electric techniques to improve ICEs further
Dedicated range extender (Compact 2-stroke engine)16
10 12 1480 100

140

W/O

2

0.2

W/T

120

0

0.1

1

1.5

X. Wang and H. Zhao (2019). "A High-Efficiency Two-Stroke Engine Concept: The Boosted Uniflow Scavenged
Direct-Injection Gasoline (BUSDIG) Engine with Air Hybrid Operation." Engineering 5 (3): 535-547.

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Differences in fuel properties require advanced combustion techniques to improve hydrogenengine efficiency
Fuel properties
Laminar flame speed (cm/s)
Flammable range (f/a ER)• Pre-chamber ignition• Dual fuel combustion• Low temperature combustion

Hydrogen291
0.1-7.1

Ammonia70.63-1.4

Methane37
0.5-1.7

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Pre-chamber ignition
Maserati "Nettuno" engine

32

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Pre-chamber ignition@ 720 CA

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Pre-chamber ignition

35mm

35mm

15mm

TKE: 015mm

50 m2/s2

EGR: 00.1

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Pre-chamber ignition
The nozzle needs to be carefully designed, as small nozzle design restrict the gas exchange, while large
nozzle reduce the pressure difference.
@ 715 CA

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Low temperature combustion

MAZDA Spark controlled compression ignition (SPCC)

36

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Low temperature combustion (SI-CAI hybrid combustion)••

Richer mixture around
spark plug to induce
flame propagation
DI is used to optimise
the fuel stratification••

Lean mixture aroundthe chamber with
auto-ignition mode
PFI is used to provide
lean homogeneous
mixture.

• Stabilize the flame propagation• Reduce the PRR by subsequent auto-ignition• Additional control methods offered by PFI/DI strategy
Wang, X., Zhao, H. and Xie, H., 2016. Effect of dilution strategies and direct injection ratios on stratified flame ignition (SFI) hybrid combustion in a PFI/DI gasoline
engine. Applied Energy, 165, pp.801-814. Wang, X., Zhao, H. and Xie, H., 2015. Effect of piston shapes and fuel injection strategies on stoichiometric stratified flame
ignition (SFI) hybrid combustion in a PFI/DI gasoline engine by numerical simulations. Energy Conversion and Management, 98, pp.387-400.

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Low temperature combustion (SI-CAI hybrid combustion)

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Low temperature combustion

Source: Bosch

Lean burn hydrogen engine produce near zero Nox.

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Dual fuel combustion
Direct injection of highreactivity fuel (diesel,
biodiesel, etc)

Port injection of low
reactivity fuel (NG,methanol, hythane,hydrogen, ammonia)

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Dual fuel combustion (diesel + methane with 20% hydrogen)

23% CO2 reduction

K. Longo, X. Wang , H. Zhao. The impact ofDiesel-hythane dual-fuel combustion onengine performance and emissions in aheavy-duty engine at low-load condition.
THIESEL Conference on Thermo-and Fluid
Dynamics of Clean Propulsion Powerplants.
13-16 Sep 2022. Valencia Spain.

Propulsion and fuel technologies driving the net zero transition❑ Advanced combustion techniques for low/zero carbon fuels
Dual fuel combustion (diesel + methane)

Piston design

TKE

Shen, Z., et al., Numerical investigation of natural gas-diesel dual-fuel engine with different piston geometries andradial clearances. Energy, 2021. 220: p. 119706. Shen, Z., et al. Numerical Investigation of Diesel-Spray-Orientated
Piston Bowls on Natural Gas and Diesel Dual Fuel Combustion Engine. 2020. SAE Technical Paper 2020-01-0311.

Contents
1. Challenges and opportunities of moving towards net zero transport
2. Propulsion and fuel technologies driving the net zero transition
3. Summary

Summary• The combustion system represented by the internal combustion engine is atechnology that is inclusive and keeps pace with the times. It does not
exclude any emerging technologies, but can absorb their advantages andintegrate them into technologies that are more in line with the requirements ofthe times and serve the public.
• The hydrogen range extender can make use of existing engine and battery
technologies while addressing problems of both, showing great potential.
• The emission reduction of the transportation system cannot be solved by thetransportation system itself. It also needs to coordinate with other related
fields, such as power generation, clean energy acquisition, low-carbon andzero-carbon fuel production and other fields.

45

Thank you very much.
Q&A
Office: Howell Building 124
Email: Xinyan.Wang@brunel.ac.uk
Tel: +44 (0)1895 265903

Research-Led Teaching in Electric
Vehicle Systems
Dr Chun Sing Lai
Lecturer, Department of Electronic and Electrical Engineeringchunsing.lai@brunel.ac.uk“UK-Saudi Electric Vehicles Enhanced Education andResearch Network” Workshop
24th-30th November 2022

Content• Background• MSc Electric Vehicle Systems• Research Projects in EV Systems• Doctoral Research Projects

2

Background• Joined Brunel in Jan 2020 as a Lecturer• Module Leader for EE1618 Devices and Circuits, taught
at Chongqing University of Posts and Telecommunication(CQUPT)• Course Director for MSc Electric Vehicle Systems• Member of Brunel Interdisciplinary Power Systems(BIPS) Research Centre• His current interests are in power system optimisation,energy system modeling, data analytics, electric vehicle
systems, hybrid powertrains optimisation, and energy
economics for renewable energy and storage systems

3

Motivation of Transport Electrification in the UK• Transport accounts
for around 20% of
global CO2
emissions• Electric vehicles
can be powered bygreen electricity•

Gov.UK Ref:
www.gov.uk/government/n
ews/government-takeshistoric-step-towards-netzero-with-end-of-sale-ofnew-petrol-and-dieselcars-by-2030
4

Key Technology Enabler: Electrical Energy
Storage and Batteries• Large-scale energy storage cost is plummeting

Lai, C.S. et al., 2017. A comprehensive review on large-scale photovoltaic system with applications of5electrical energy storage. Renewable and Sustainable Energy Reviews, 78, pp.439-451.

MSc EVS Programme Aim• Aim of this MSc programme is to produce
postgraduates with advanced and targetedknowledge and skills relevant to growing sectorof electric vehicle engineering• The graduate will have specific knowledge andexpertiseinelectronicandelectricalengineering subjects:
Ø

Ø
Ø
Ø
Ø
Ø

Power electronics and drives
Data communication systems
Electrical power systems
Sensors and instrumentation
Control and intelligent systems
Embedded systems
6

Current status update• Programme approved by Senate on 19th May 2022
• Programme web page available to public in end-June 2022
• 8 applicants for the programme in Sept 2022 intake• 2 students are enrolled (1 UK and 1 Chinese student)• 2nd Industry Advisory Panel meeting: 17th November 2022
• 10 applicants to date for the programme in Sept 2023
intakeCurrent Modules
Compulsory Modules

Term

EE5631 Advanced Vehicular Systems Technology
EE5630 Advanced Embedded Systems
Engineering
EE5633 Smart Grid Operation and Management
EE5632 Power Electronics and Drives
EE5625 Engineering Ethics and Sustainability
EE5518 Energy Economics and Power Markets
EE5500 Project / Dissertation – 60 Credits

AutumnAutumnProgramme Team/
Module Leader
Dr C S Lai
Dr H Meng

SpringSpringSpringAutumnThroughout

Prof G Taylor
Dr M Darwish
Dr D Smith
Prof G Taylor
Dr T Itagaki

Optional Modules (select 2)

Term

EE5622 Communication Network Technologies

Autumn

EE5623 Design for Internet of Things
EE5624 Applied Sensors, Instrumentation andControl
EE5627 Artificial Intelligence System Techniques

AutumnAutumnProgramme Team/
Module Leader
Prof H AlRaweshidy
Dr L Gan
Dr I Pisica

AutumnProf M Abbod• All modules are 15-credits unless statedModules in 2023/24
Compulsory Modules

Term

EE5631 Advanced Vehicular Systems Technology
EE5518 Energy Economics and Power Markets
EE5633 Smart Grid Operation and Management
EE5632 Power Electronics and Drives
EE5625 Engineering Ethics and Sustainability
EE5500 Project / Dissertation – 60 Credits

AutumnAutumnSpringSpringSpringThroughout

Optional Modules (select 2)

Term

EE5628 Embedded DSP for Communications
EE5612 Communication Networks Security
EE5622 Communication Network Technologies

SpringSpringAutumn

EE5630 Advanced Embedded Systems Engineering Autumn
EE5623 Design for Internet of Things
Autumn
EE5624 Applied Sensors, Instrumentation andAutumnControl• All modules are 15-credits unless stated

Programme Team/
Module Leader
Dr C S Lai
Prof G Taylor
Prof G Taylor
Dr M Darwish
Dr D Smith
Dr T Itagaki
Programme Team/
Module Leader
Dr N Boulgouris
Dr T Itagaki
Prof H AlRaweshidy
Dr H Meng
Dr L Gan
Dr I Pisica

EE5631 Advanced Vehicular Systems Technology

10EE5632 Power Electronics and Drives

11Dedicated Laboratory Equipment

Battery
Management
SystemElectrical
Machinesand Drives

MSc Electric Vehicle Systems

https://www.brunel.ac.uk/study/postgraduate/electric-vehicle-systems-msc

13

Industry Advisory Panel
Sector
VehicleManufactures

Organisation
Renault Group

ToyotaBMS Manufacturer Brill Power Ltd
Substation/DNOs/Ot eClassics
hers
HVSS High Voltage
Substation Services
Ltd
National Grid ESO
Scottish andSouthern Electricity
Networks
UK Power Networks

Contact Person
Dr Javier IbanezGuzman
Dr Pujitha Gunaratne
Dr Damien Frost
Mr Joseph Salama
Mr Sina Etminan
Mr Benjamin Bonjesi
Mr Maciej Fila
Mr Tim Manandha
14

IAP Individual Commitment• Two meetings per annum: online (first term,Nov) and in person (third term, July)• Poster presentation in July (combined with SEP
this academic year)• Field trips• Industry led projects• Awards and bursaries

15

Project: Data-driven Approach for OptimalDistribution Network Operation With Rapid Charging
Infrastructure and Large-scale Battery• Develop an innovative toolkit to enable
management of distribution networks

active• To maximise and examine the profitability of largescale battery in distribution networks• Project duration: 1st Feb 2021 - 31st March 2022
• PI: Dr C S Lai; Co-I: Prof G A Taylor

16Project: Data-driven Approach for OptimalDistribution Network Operation With Rapid Charging
Infrastructure andLarge-scale Battery
Multi-agent energy management system
Distribution network

Renewable
power
generation

Wholesale
electricity
marketRenewable
generationEV
charging
stationEnergystorage
agent

Large-scale battery
systemLegendPower flow
Information flow

17

Project: Data-driven Approach for OptimalDistribution Network Operation With Rapid Charging
Infrastructure and Large-scale Battery
1. Lai, C.S., et al. 2021, July. Operational challenges to accommodatehigh penetration of electric vehicles: a comparison between UK andChina. In 2021 IEEE 15th International Conference on Compatibility,
Power Electronics and Power Engineering (CPE-POWERENG).
2. Lai, C.S., et al. 2021, April. A distributed transaction method for
mitigating three-phase imbalance by scheduling electric vehicle
charging. In 2021 6th Asia Conference on Power and ElectricalEngineering (ACPEE).
3. Huang, L. et al. A distributed optimization model for mitigating threephase imbalance with electric vehicles and grid battery. Accepted on
7th May 2022 by Electric Power Systems Research (IF: 3.414)
4. Lai C.S. et al. “Profit maximization for large-scale energy storage
systems to enable fast EV charging infrastructure in distribution
networks”, Major revision, Energy (IF:7.147)
18

Doctoral Research Projects Supervisions
1. Ms L Duan, Solar-Hydrogen-Storage Integrated EV
Charging Station. 01/10/21-now. Second Supervisor: Dr X
Zhang
2. Mr MME Farrag, EV Powertrain Optimisation with
Supercapacitor and Battery Systems. 01/10/21-now.
Second Supervisor: Dr M Darwish
3. Mr X Ren, Design and Implementation of Intelligent Energy
Management Systems for Autonomous EVs. 09/2022-now.
Second Supervisor: Prof G Taylor
4. Mr GA Blankson, Interoperable High Frequency and Low
Frequency Impedance Matching for EV Wireless Power
Transfer. 01/01/22-now. Principal Supervisor: Dr M Darwish
5. MEng Group Project, ICE to EV conversion: A Feasibility
Study. 2021/22. Principal Supervisor: Dr M Darwish
19

Solar-Hydrogen-Storage Integrated Electric
Vehicle Charging Station
Minimise the CAPEX and OPEX of EV charging station.

20

EV Powertrain Optimisation with Supercapacitor
and Battery Systems• To study the technical and economic viability of a
hybrid energy storage system for EVs comprising of
the battery pack and a super-capacitor.
• To examine the electrical and thermal characteristic of
BMS.
• Increase the lifetime of the EVs battery and reduce thestress on its materials.
=
Battery=
Bi-directional DC – DC Converter

DC
Link

=
Motor

DC – AC
Converter=SC=
Bi-directional DC – DC Converter

21Design and implementation of intelligent energy
management systems for autonomous vehicle
electrification• Inter-vehicle communication model in eco-driving based
EMS scenario and explore how much each type of data
error from GPS and V2X will affect the agent's decisionmaking• Distributed Federated Reinforcement Learning-based
eco-driving strategy so that solve the problem of privacy
problem when collecting massive data from reality.
• Eco-Driving based EMS from a most economical view invariation price scenario rather than energy consumption.

22

Summary• Teaching of Electric Vehicle Systems is challenging
1. Multi-disciplinary
2. Teaching content
3. Emerging area
• MSc dissertation projects can be based on real-lifescenarios/challenges

23Thank You
Q/A24

Power Converters andDrivesELECTRIC BRAKINGOF DC MOTORSWhy Braking• To stop the electric motor.

• To sustain the speed of the drive systemat the desired value.
• To prevent the motor from over-speeding.
• To hold the motor shaft at a certain
position regardless to the applied load
torque.

Regenerative
Braking

1. Regenerative Braking
This method is used when the load on the motor has over-haulingcharacteristic as in1) the lowering of the cage of a hoist or2) the downgrade motion of an electric train
Fm

Fm



F
F
Fl

Fl

1. Regenerative Braking• When the motor torque causes the machine to run at a speed >no-load speed, but without changing its direction of rotation,
the motor is in regenerative braking.
Fm

Fm



F
F
Fl

Fl

1. Regenerative Braking• Regeneration takes place when the back EMF of themachine (Ea) becomes grater than the applied voltage
Va.
• This happens when the overhauling load acts as a prime
mover and so drives the machine as a generator.
• Consequently, direction of Ia and hence of armature
torque is reversed and speed falls until E becomes
lower than V.

1. Regenerative Braking• It is obvious that during the slowing down of
the motor, power is returned to the line which
may be used for supplying another train on an
upgrade, thereby relieving the powerhouse ofpart of its load.

SpeedPower Flow

Power Flow

Load


G

Tload

Load

Tm

Tm


M

Tload

Torque

2 =

Vt
Ra
3 =
−
Tl 3
2
K ( K )Vt −Ea 3 Tl 3
I a3 ==RaK

Vt −Ea 2
Tl 2
Ia2 =
=
=0
Ra
K

Vt = Ea 2

Speed

Vt  Ea 33

2

T(down)

Vt
K

Vt
Ra
1 =
−
T
2 l1
K
( K )

Vt − Ea1 Tl
I a1 ==RaK
Vt  E1
1

T(up)TorqueSpeed3
2

T(up)T(down)Operating
point

Load
TorqueTerminal
VoltageArmature
current

1

2

3

0

1

0

Speed

Field

TorqueEa

Comments

Motor1 < oEa1 < Vt

2 = oEa2 = Vt

No Load3 > oEa3 > Vt

Generator

ADVANCED ELECTRICAL MACHINES AND
ENERGY CONVERTERS

INDUCTION MOTOR
BRAKING27

Regenerative
Braking28

Regenerative Braking➢ Regenerative braking occurs when the motor speed
exceeds the synchronous speed.
➢ This may happen when the load torque drives the electricmotor beyond it synchronous speed.
➢ In this case, the load is the source of the energy and theinduction machine is converting the mechanical power
into electrical power which is delivered back to theelectrical system.
➢ The maximum torque developed by the induction motor
on generating operation (regenerative braking) will reacha higher value than that on motoring operation29

SpeedS<0S=0ns − n
s=
ns

S>0
Td =

V 2 R'2



' 2
R2 
2 


s s R1 ++ X eq



s





Torque30

31➢ In more general drive systems, the induction machines
may operate in the first or second quadrant (as motor or
generator).
➢ An example is shown in the following figure, the load
torque is considered constant but reversible.
➢ The reference operating point represents a motor
operation where the motor speed is less than thesynchronous speed.
➢ When the load torque changes its direction from T1 to
T2, the motor operates in the second quadrant and the
speed of the motor exceeds its synchronous speed.
➢ Keep in mind that the motor still rotates in the original
direction.
32

Regenerative braking
Speed

1
2

T2

T1

Torque33➢ Another example of regenerative braking is shown in thefollowing figure:➢ The figure shows two characteristics for two different
values of v/f control➢ The load torque is assumed to be constant andunidirectional, and the operating point is 1
➢ If v/f control is applied to reduce the speed of the motor,
the operating point moves to 2, and eventually settles atpoint 3 in the first quadrant.
➢ However, during the transition from point 2 to point 3,
the motor operates in the second quadrant underregenerative barking.
34

Regenerative braking during speed
reduction
Speed
f1, V1
1
2

3
f2, V2Tl

Torque35

INDUCTION GENERATORS• The induction generator is nothing more than an
induction motor driven above its synchronous speedby an amount not exceeding the full load slip the unit
would have as a motor.
• Assuming a full load slip of 3%, a motor with asynchronous speed of 1200 rpm would have a fullload speed of 1164 rpm. This unit could also be
driven by an external prime mover at 1236 rpm foruse as an induction generator.

36Regenerative Braking with
InverterInverter required to allow kinetic energy in load to be recovered

Energy recovered is fed back into the mains

This methods saves energy and money

37

GRIDSERVE: Sun-to-Wheel

UK-Saudi EV Enhanced Education and Research Network
November 2022

GRIDSERVE’s purpose is to #deliver
sustainable energy and move theneedle on climate change

GRIDSERVE Sun-to-Wheel Eco-system

Feedstock(Sunlight)

Refining(Battery Storage)

Transport(Electricity Grids)

Charging(Electric Forecourts &
Hubs)

Wheel

(Electric
Vehicles)Sun-to-Wheel

1 Acre of Solar generates enough energy every year to drive an EV 1,000,000 miles.
Providing Energy Security, Fighting Climate Change, Improving Air QualityYORK
HYBRIDSOLAR FARM

SOLAR:

35 MWp
STORAGE:

30 MWhAfter years of research, design andinnovation we decided to build theworld’s most technically advanced solar
farm.​
In just five months, we made it happen.
Welcome to York, the first of its type, atruly subsidy free hybrid solar farm.​HULL
HYBRIDSOLAR FARM

SOLAR:

26 MWp
STORAGE:

10 MWhSimilar to York, Hull uses a single axis tracking systemwith bifacial modules and​ energy storage. It supplies
all of the council’s electricity needs and will save themmore than £2 million a year.​CLAY HILLHYBRIDSOLAR FARM

SOLAR:

10 MWp
STORAGE:

6 MWh

Clayhill, located near Flitwick in Bedfordshire, wasacquired to #deliver our Sun to Wheel model, a net
zero carbon, completely sustainable electric vehicle
charging ecosystem.​This project is the beginning of our ambition toprovide UK drivers dependable, low cost, clean
energy from solar power.​ The good news? It can be
replicated.​

GRIDSERVE Sun-to-Wheel Eco-system

Feedstock(Sunlight)

Refining(Battery Storage)

Transport(Electricity Grids)

Charging(Electric Forecourts &
Hubs)

Wheel

(Electric
Vehicles)Sun-to-Wheel

1 Acre of Solar generates enough energy every year to drive an EV 1,000,000 miles.
Providing Energy Security, Fighting Climate Change, Improving Air Quality

Network Highlights

GRIDSERVE Electric Hubs

• 130,000 charging sessions per month• Net Zero Carbon EV Charging- Motorway Electric Hubs

• Flexible charging solutions addressing

Backbone of the GRIDSERVE Electric Highway,
serving the UK’s motorways and Strategic Road
Network (SRN)

different customer charging needs:
DC / AC, CCS / CHAdeMO• More than 160 state-of-the-art Motorway- Destination Electric Hubs

• Ease of use: Contactless payment, open

Maximizes regional & local network coveragethrough partnerships with leading retailers and site
owners in convenient destination locations• Convenient, dependable, reliable using

GRIDSERVE Electric Forecourts

Network & Electric Hub sites operational

easy access for all with no membership,
no App, no RFID card
leading ABB chargers backed by AA andin-house customer support 24/7/265• Fast DC charging: all chargers 50kW to350kWState of the art community-based charging
infrastructure to primarily serve local areas, inparticular people who do not have access to homecharging. Includes an integrated retail convenience
experience, wi-fi and site dependent facilities
including convenience supermarket, coffee, hotfood, meeting rooms, fitness facilities

GRIDSERVE Electric Super Hubs 2022
Super Hubs

Between 6 and 12 x 350kW Chargers
Easy to use and reliable
All with Contactless payment
Can add over 100 miles range in 15 minutes
Co-located with full range of services – food,coffee, washrooms, rest area

The first phase of a UK-wide network of Ultra-fast chargers
Already providing a fast and reliable network for highmileage, long-distance EV drivers in addition to the
GRIDSERVE Medium Power chargers located across the UK
motorway network
Located on roads and routes with exceptional traffic flow
MANY MORE to be installed in 2023 and beyond!

GRIDSERVE Sun-to-Wheel Eco-system

EXETER
12 x 350kW chargers

RUGBY
12 x 350kW chargers

Note: Photos taken during the commissioning phase and prior to opening to the public – these are exceptionally busy sites!

GRIDSERVE Braintree Electric Forecourt®

GRIDSERVE Electric Forecourt® at Braintree

EV Showcase – Already Featured
5MW Battery Storage

Semi / 18-Wheeler / HGV Charging Lane

6 Tesla Charging Bays

12 DC 350kW Chargers
12 DC 90kW Chargers
6 AC 22kW Chargers

Retail Partners

Solar Canopies

Additional Customer Parking

Queuing lane
Source: Company information.

17

GRIDSERVE Norwich Electric Forecourt®

The Next GRIDSERVE Electric Forecourts®
Gatwick Airport

Liverpool – Edge Lane

NevendonStevenage

Bromborough

PlymouthNext-gen High Power Charging
Within the last 12 months we have seen a surge in lightcommercial electric vehicles regularly charging at ourBraintree and Norwich Electric Forecourts® . Vehicles likethe Ford E-Transit pictured. The main reason is that theeconomics today are more than favourable: electric lightcommercial vehicles are now fundamentally better thantheir diesel-powered counterparts on price, performanceand practicality.

The new charger is able to offer dynamic dual-charging meaningthat two vehicles will be able to plug in and charge simultaneously,
and the EV charger will dynamically distribute the power between
both vehicles.
The chargerl design is more user-friendly. LED lighting will now
reveal when a charger is available or in use, a new pulley system
ensures the retractable 5m charging cable is manoeuvrable andkeeps pathways clear, while enlarged digital screens will provide all
your EV charging information. The addition of a second, portraitshaped screen means additional interactivity is possible.

GRIDSERVE Sun-to-Wheel Eco-system

Feedstock(Sunlight)

Refining(Battery Storage)

Transport(Electricity Grids)

Charging(Electric Forecourts &
Hubs)

Wheel

(Electric
Vehicles)Sun-to-Wheel

1 Acre of Solar generates enough energy every year to drive an EV 1,000,000 miles.
Providing Energy Security, Fighting Climate Change, Improving Air Quality

GRIDSERVE Sun-to-Wheel Eco-system
High correlation between generation and consumption on the GRIDSERVE Electric Highway

Solar
Generation

MWhEV
Charging

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of Day

GRIDSERVE Sun-to-Wheel Eco-system

Feedstock(Sunlight)

Refining(Battery Storage)

Transport(Electricity Grids)

Charging(Electric Forecourts &
Hubs)

Wheel

(Electric
Vehicles)Sun-to-Wheel

1 Acre of Solar generates enough energy every year to drive an EV 1,000,000 miles.
Providing Energy Security, Fighting Climate Change, Improving Air Quality

Electric Vehicle Leasing
GRIDSERVE offers electric vehicle leasing packages
to customers that can include charging and otherbenefits.

GRIDSERVE Sun-to-Wheel Eco-system

Feedstock(Sunlight)

Refining(Battery Storage)

Transport(Electricity Grids)

Charging(Electric Forecourts &
Hubs)

Wheel

(Electric
Vehicles)Sun-to-Wheel

1 Acre of Solar generates enough energy every year to drive an EV 1,000,000 miles.
Providing Energy Security, Fighting Climate Change, Improving Air Quality

The GRIDSERVE Partner Network

GLOBAL MARKETOPPORTUNITY

Maximising success & scale-up capabilities,
Providing GRIDSERVE world class tech ecosystem to partners‘Sun-to-Wheel-as-a-Service’ (SWaaS)

Partner Program offerings include:
Network Creation

All Products & Systems

Financing Options

Knowledge &Training

Sales &
MarketingCompetitive
Advantages

Infrastructure
DeliveryTurn-key TechnicalSupport
Grow withGRIDSERVE

Our Partner Network program offers full DEPCOMM access to our product suite allowing third-party partners
to capitalise on GRIDSERVE’s market leading net zero charging solutions and technology platforms within theirown networks and geographies. This approach also turns would-be competitors, into revenue generating
committed partners.

34

Market Leading Flexible Solutions?
Latest Technology

Best Service Levels

We are a leading technology integrator
working primarily with ABB and Tritium.
We integrate our additional software,components and technology to deliver asuperior customer experience,
evidenced by our 5-star ratings.

EV charging technology is still in its infancy
and needs constant support. We areobsessed with customer satisfaction andservice levels. We have our own operation &maintenance teams continuously analysing
data, monitoring equipment, diagnosing
and fixing issues, and delivering 24x7
support.

Sustainable Energy

Additional Customers

We are technology leaders in integrating
solar PV energy and energy storage,
providing complementary benefits
including lower cost net zero carbon
energy, buffering grid connections andgenerating additional income throughgrid services. These are available throughour Sun-to-Wheel service.

In addition to delivering, operating andmaintaining the chargers, we focus on
maximising utilisation. We promote thechargers on our rapidly growing, EV
Charging Network, and we sell electricvehicle deals to people in the vicinity ofeach charger, particularly those who can’t
charge at home.

International Partner

CultureGRIDSERVE are interested in partneringwith like-minded companies globally. Our
principal objective is to #deliver EV
charging networks powered by renewableenergy to help move the needle on climatechange for the benefit of all.

GRIDSERVE’s businesses are hugely
ambitious and highly adaptive market
leaders, with a strong family heritage. With
partners who share these values we canscale quickly and successfully together.

GRIDSERVE Sun-to-Wheel Eco-system

Feedstock(Sunlight)

Refining(Battery Storage)

Transport(Electricity Grids)

Charging(Electric Forecourts &
Hubs)

Wheel

(Electric
Vehicles)Sun-to-Wheel

1 Acre of Solar generates enough energy every year to drive an EV 1,000,000 miles.
Providing Energy Security, Fighting Climate Change, Improving Air Quality

NorwichElectric
Forecourt®: Interesting
VisitorGRIDSERVE
Sun-to-Wheel
Eco-system

Braintree Electric Forecourt®: Interesting Visitors

GRIDSERVE – Leading the Charge
EVIES awards

Charge Point Network of
the Year

IJ Global-ESG Europe Awards

Best sustainabletransport transaction

Forecourt Trader Awards

Best Alternate Fuel Provider

Best Forecourt Innovation

Let’s work together to #deliver
sustainable energy and move theneedle on climate change.

Distributed conversion and EV Projects
Damien Frost, CTO & Co-Founder
UK-Saudi Electric Vehicles Enhanced Education and Research Network
25 November 2022

Who?
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 20222

3

Oxford spin-out
developing intelligent battery
management, monitoring,and control technology.

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Who we are

Market focus on Electric Vehicles and energy Storage
Systems, with a global reach.

•
•
•
•

Founded in 2016
HQ in Oxford, UK
32 FTEs
Markets: energy storageand electromobility

Brill PowerFounding Team

Christoph Birkl, DPhil,
CEO, Director

Carolyn Hicks, MASc, MBA,
CFO, Director

The founding members met whilst
pursuing their PHD’s in battery
degradation, battery modelling andpower electronics at the University of
Oxford. It started with a goal of reducing
battery waste by means of innovating
BMS design to accurately study andpredict battery degradation over time.
The rigidity of conventional BMS designs
became an obstacle and our visiondeveloped to, not only include and solve
the battery waste issue but also to
introduce the means so that every cell isused to its full potential.
Simply put, in a few short years, Brill
Power changed the landscape of BMS
technology and innovation.

Damien Frost, DPhil, CTO

Adrien Bizeray, DPhil, Chief
Data Scientist

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

4

Brill PowerFounding Team

Christoph Birkl, DPhil,
CEO, Director

Carolyn Hicks, MASc, MBA,
CFO, Director

The founding members met whilst
pursuing their PHD’s in battery
degradation, battery modelling andpower electronics at the University of
Oxford. It started with a goal of reducing
battery waste by means of innovating
BMS design to accurately study andpredict battery degradation over time.
The rigidity of conventional BMS designs
became an obstacle and our visiondeveloped to, not only include and solve
the battery waste issue but also to
introduce the means so that every cell isused to its full potential.
Simply put, in a few short years, Brill
Power changed the landscape of BMS
technology and innovation.

Damien Frost, DPhil, CTO,
Director

Adrien Bizeray, DPhil, Chief
Data Scientist

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

5

Brill Power Timeline

2014

Prototype complete
3rd patent application
Shell New Energy
Challenge
Pre-seed roundIUK Investment Acc. grant
Faraday Challenge grant

2016
2015

1st patent application
Presentation at IEEE
GHTC, USGreenhouse preaccelerator

EEF 7 grant
2 Faraday Challenge Grants
3rd prize in LG Chem
Competition
TDAP Admission
Pilot delivery
Team grown to 8
1st Patent Grant

2020

2018
2017

Incorporation of
Brill Power Ltd
Proof of concept
GHTC grantTeam grown to 24
Expanded office space.
Product launched.
12 R&D projects
completed

2019
First Pilot projectagreed2nd iteration of BrillMS
2 new employees

2022
2021

Team grown to 15
Seed Round
8 Projects Underway
Over £4 million in
funding committed(private and government
grant)
Adapting to Covid 19

Team grown to 30+£8.6million Series A
Ongoing productdevelopment

6

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Oxford Uni Innovation
Fund
Climate KIC accelerator
2nd patent application

Integrate power electronics
into battery packs toincrease their performance
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

8

The Problem

Performance:
Limited by weakest cell

Current flow

Lifetime:
Limited by weakest cell

Centralised control

Cost:
High due to replacements/oversizing

Sustainability:
Low due to battery waste from
replacements

All cells exposed to same current

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

The weakest link issue: batteries are only as good as their weakest cells

9Balancing Options

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

The Problem
10Balancing Options

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

The Problem
11

The Solution

Performance:
Up to 46% more energy

Lifetime:

Current flow

Distributed control

Up to 60% longer lifetime

Cost:
Low - no replacements/oversizing

Sustainability:
High – no unnecessary battery waste

Cell current proportional to capacity

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

The BrillMS: intelligent control for maximal lifetime, performance and safety

12Our Solution
13

The BrillMS: intelligent control for maximal lifetime, performance and safety• The outputs of the Modules are connected inseries• Each module is responsible for the control ofthe power electronics and ensuring the safetyof the electrochemical energy storage
elements

MasterMCU
Vout

ModuleBMS• The output of the pack has voltage andcurrent regulation• The master is the gateway to the packand controller of electrical distribution
system (EDS).

MCU

PWM
m1

Module

MCU
BMS

PWM
mM

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• Each Module contains powerelectronics

CAN RS485

Charge rates
14

Faster charge rates with Brill Power BMS

250kW rapid charge

4.2

4.4

4

4.2

3.8

3.6
3.4

Cell A

3.2

Voltage (V)Voltage (V)3.7kW home charge

Cell B

3

3.8
3.6
3.4

Cell A

3.2

Cell B

3

0

5

10

15

20

0

100

5

10

15

20

100

80
60
40

Cell A

20

75% SOC80

15% faster

SOC (%)SOC (%)

460

15% faster

40
Cell A

20

Cell B

0

Cell B

00

5

10Time (h)

15

20

0

Data based on Simulink battery simulation

5

10
Time (min)1520

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Charge rates are accelerated by compensating for differences in cell capacities(example: 15% difference between weakest and average cell)

Brill Power battery software• High-accuracy state estimation algorithms:• state of charge• state of health• power capability• Unique real-time resistance measurement• State of health and end of life prediction models• Full access to low-level cell data for improved warranty
and insurance• Cloud based battery intelligence platform:• Real-time display of battery status and key
operating metrics across asset portfolio• Access to historic battery data
• Prediction of performance and expected EoL• IoT communication and security based on
Microsoft Azure

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Software solutions for various battery management functions

15

Brill Power battery analytics
16

Cloud-based battery analytics platform
List of asset groups

List of batteries

Module level details explorer• Reliable IoT
communication, networking
and provisioning supportedby Microsoft Azure services• Secure user identification
based on Microsoft Azure
Active Directory

Dashboard view
• Battery pack status and key
metrics across a portfolio ofbattery assets• Comparison of high-level
current and historical telemetry
data across different battery
assetsData viewer – Real Timeand Long term

Performance
Metrics viewer

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

IoT communication and Cloud
InfrastructureChallenges and Motivation
1. Electrochemical Cells are Small…but they are the best solution we have

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

17

Challenges and Motivation18

2. 454 Wh per 1 Wh of Li-ion energy storage1• Take any cradle to grave study with apinch of salt

• Now getting 10x return

We must use each cell until
it is completely depleted

90%
80%
70%
60%
50%
0

100

200

300

400

500

Cycle number
1.

C. J. Barnhart and S. M. Benson, “On the importance of reducing the energetic and material demands of electrical energy storage,” Energy Environ. Sci., vol. 6, pp. 1083–1092,
2013. [Online]. Available: http://dx.doi.org/10.1039/C3EE24040A

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• Need 500 cycles just to break even

Residual energy storage capacity• This one if from 2013100%

Challenges and Motivation…but there are many active players in the market

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

3. Adding cost to an expensive component

19

Challenges and Motivation
4. BABE* Project (Not our name…)

Warranties

Size

Range (km)Mileage
Guarantee

Age
Guarantee

SoH
Guarantee

Est. Replacement
Cost

Nissan Leaf

24 kWh135

60,000

5

70%

£5k

Nissan Leaf

30 kWh172

100,000

8

70%

£6k

Chevrolet Bolt

60 kWh520

100,000

8

60%

£12k

Renault ZOE

22 KWh240

60,000

5

70%

£5k

Renault ZOE

41 KWh400

60,000

5

70%

£6k

Data from 2018

*Battery management control system for Advanced Battery Engineering 

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

21

Challenges and Motivation
4. BABE* Project (Not our name…)

22• User submitted, publicly available data
• 201x 24 kWh Nissan Leafs• 82x 30 kWh Nissan Leafs (newer model)• 1,302 SoH data points• Red dot is the warranty• Older model performed as designed• Newer model was not• Distance travelled not recorded, which
will play a factor.

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• Nissan Leaf Study 2018

Projects
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

23

Brill Power technology demonstration
24

Various EV demo projects completed

Dual chemistry EV demo system using highenergy/high-power Li-ion cells. Specifications: 48
V, 850 A, 6.5 kWhPozibot
EV demo system. Specifications: 48 V, 500 A

Retrofitting Electric Vehicles forExtended Lifetime (REVEL)EV demo system with AMTE Power li-ion cells andBrill Power BMS
Specifications: 48 V, 192 A, 5.5 kWh

Project Partners

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Projects
Hybrid Vehicle battery Optimisation (HBO)Project 1: Pozibot
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

25

Pozibot
Goals

26• Business• Transforming accountancy, insurance
and legal services with AI and data

• Hardware• Create a high current pack• 25 A packs to date
• 500 A pack for Pozibot• Interface with an ECU• Learn about automotive CAN and its
requirements• Implement on our hardware• Working with external hardware
partner, Delta Motorsports

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• How to insure batteries?

Road to 500 A

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Pozibot
27

The completed pack

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Pozibot
28

Pozibot
29

The completed packAir cooled
modules

Electrical Distribution
SystemPack terminals

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Liquid cooled Cells

Fan for Module
cooling

Conventional Battery Pack

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Life Analysis
30

Project 2: HBO
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 202231

Hybrid Battery Optimisation• Maximise the performance of an
EV battery pack over its SoC
range• Main problem: battery packs are
de-rated at low SoC• Lower SoC -> Lower voltages on cells• Lower voltages on cells -> highcurrents are required• Higher currents -> larger voltage dropon cells• Voltage drop on cells -> hit lower
voltage limit sooner.

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Goals

32

Conventional pack

Power

Samsung 48X Cells
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Hybrid Battery Optimisation
33

Hybrid Battery Optimisation
34

Hybrid Battery Pack

Voltage determined byconventional power cells

Power

LG M50 Cells

Grepow Cells

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Controlled current

Hybrid packs can reduce the amount of derating
Hybridisation reduces average power de-rating in all cases.

35

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

A 55kW LGM50-Grepow hybrid system out-performs a single chem 55kW 48X system.

Estimated degradation

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Estimation of the degradation of 48X cells in a single-chem pack vs 48X cells in a hybrid(48X – Grepow) according to two degradation models.

36

Hybrid Battery Optimisation• Take the learnings from the
Pozibot Project and create a more
integrated design• Liquid cooled electronics• Cooling circuit integrated with cells• Electronics more integrated into
the pack• Vertical stack-up for
manufacturability

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Pack Integration37

Hybrid Battery Optimisation• Take the learnings from the
Pozibot Project and create a more
integrated design• Liquid cooled electronics• Cooling circuit integrated with cells• Electronics more integrated into
the pack• Vertical stack-up for
manufacturability

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Pack Integration

38

Hybrid Battery Optimisation• Take the learnings from the
Pozibot Project and create a more
integrated design• Liquid cooled electronics• Cooling circuit integrated with cells• Electronics more integrated into
the pack• Vertical stack-up for
manufacturability

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Pack Integration

39

Hybrid Battery Optimisation• Hybridisation allows packs to keep delivering high powers even at low state of charge.
• System design and testing were subject to constraints on component availability and development/test time.
Removing these constraints, the performance of the hybrid system can be significantly improved.

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

HESS Performance Testing40Hybrid Battery Optimisation
Conclusions

41• Hybrid packs not advantageous• No high performance needed• lower efficiency reduces range• Premium EVs & highway driving• Low-end single-chem battery packs do not have enough power• High-end single-chem battery packs have to derate peaks due to low SoC but can meet demand• Hybrid packs can meet power pulses longer, but reduced range• Conclusion:• Hybrids not useful for low-end use• Hybrids can improve acceleration at mid-SoC• Hybrids can be good cost-compromise to use cheaper cells while getting similar performance• Keep an eye on efficiency or range is compromised• Could use high-end 48X-Grepow hybrid but then the cost advantage goes away

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• City driving

Future of Automotive
UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

42

Current
43

You need a lot• EVs require a lot of current• 125 kW peak
• 350 V Battery pack• 357 A

• Tesla Model S• 750 kW peak

0000 AWG – 107 mm2
195 A continuous
60 °C ambient• 407 V Battery pack

• 1,843 A• Ouch• Solutions?
• V=I2R• Make V bigger

10 AWG – 5.26 mm2
30 A Continuous
60 °C ambient
Household wiring for 30 A circuits

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• BMW i3

Current
44

You need a lot• EVs require a lot of current• 125 kW peak
• 350 V Battery pack• 357 A

• Tesla Model S• 750 kW peak
• 407 V Battery pack

• 1,843 A• Ouch• Solutions?
• V=I2R• Make V bigger• Challenges with existing infrastructure

560 kW peak; 800 V Battery pack; 700 A

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• BMW i3

Will distributed power electronics be main stream?

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

Distributed Power Electronics?
45

Interesting Resources
Tear down videos of vehicles

46• Weber Auto

• Munro Live• https://www.youtube.com/c/MunroLive

UK-Saudi EV Reseach Nework | CONFIDENTIAL | Copyright © Brill Power Ltd. | November 2022

• https://www.youtube.com/@WeberAuto

Thank you!
Get in touch: damien.frost@brillpower.com

Industry 4.0, Drive and Fly by Wire:
Towards Connected Autonomous Vehicles
Technology for Future Smart Cities
Applications
Mahmoud Shafik BEng (HONS) CEng MSc PhD FHEA MIET MASME
Professor AC in Intelligent Mechatronics Engineering and Digital Technology
Principal Investigator
Editor-In-Chief of International Journal of Robotics and Mechatronics
Website: http://ojs.unsysdigital.com/index.php/ijrm

College of Science and Engineering, University of Derby, UK
Email: mshafik@derby.ac.uk & Webpage: http://www.derby.ac.uk/staff/mahmoudshafik

Sensitivity: Internal

Overview▪ Introduction▪ Challenges and Barriers to SME’s Growth!
▪ Industry 4.0 & IoE & Big Data & 5G and AI!

▪ Drive and Fly by Wire
▪ Safety, Reliability, and Battery Technology!

▪ Connected Vehicles▪ Autonomous and Hover Vehicles▪ Data Security and Cybersecurity!

▪ Snapshots of ongoing Research Programmes!
▪ Summary and Discussions!

Sensitivity: Internal

Challenges and Barriers to SME’s Growth▪ Take Up of New Technologies▪ Innovation and Knowledge Transfer▪ Accessing Skills and Training▪ Access to Finance▪ Exporting▪ Regulatory Issues▪ Energy Cost and Security of Supply!

▪ COVID-19▪ … etc.

Sensitivity: InternalIndustry 4.0

https://lvivity.com/what-is-industry-4-0

Sensitivity: InternalIndustry 4.0

Smart Manufacturing!

https://gblogs.cisco.com/ca/2017/09/01/industry-4-0-11-questions-answered/

Sensitivity: InternalIndustry 4.0 Revolution
Shifting our Thinking!!!

Transform Industry and Business into New Era!
https://paul4innovating.com/2018/01/27/shifting-our-thinking-within-the-fourth-industrial-revolution/

Sensitivity: Internal

Big Data & IoT & IoE & 5G & ICT and Beyond!

ICT Generation Helps to Transform Lives into Digital Era!
http://www.eurekamagazine.co.uk/design-engineering-features/interviews/recent-developmentshave-brought-the-expected-roll-out-timetable-for-5g-forward/168100/

Sensitivity: InternalArtificial Intelligence!

Intelligent Systems and Self-learning Machinery!
https://wiredelta.com/simple-guide-to-machine-learning-and-ai/

Sensitivity: InternalIndustry 4.0, 5G and Beyond!

Transform Lives into Digital Era - Connected Devices & Selflearning Machinery!
https://meridiancoms.com/new-5g-network-coming-soon/

Sensitivity: InternalIndustry 4.0, 5G and Beyond!

•https://www.libelium.com/libeliumworld/top_50_iot_sensor_applications_ranking/Sensitivity: InternalDrive and Fly by Wire:
Towards Connected Autonomous Vehicles
Technology for Future Smart Cities
Applications

Sensitivity: InternalDrive and Fly by Wire: Electric andAutonomous Vehicles!
The global market of drive by wire was valued at USD 23.99 billion in2020 and is projected to reach USD 37.66 billion by 2028, growing at acompound annual growth rate (CAGR) of 5.8% from 2022 to 2031.

Global Market Drive by Wire 2021 – 2028!

https://www.verifiedmarketresearch.com/product/drive-by-wire-market/

Sensitivity: InternalDrive and Fly by Wire: Electric andAutonomous Vehicles!

Typical Applications:
▪
▪
▪
▪
▪
▪

Steer-By-Wire
Brake-by-Wire
Park-by-wireIntegrated vehicle dynamics
Cam-less engines
Integrated starter alternator…etc.

OEM Driven:
▪
▪
▪
▪
▪
▪
▪

Advanced safety features
Reliability
Fuel economy
Manufacturing flexibility
Design freedom
Cost
…etc.

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!

https://www.sciencedirect.com/science/article/pii/S0951832019305034

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!
High
Integrity
Actuation
Intelligence for Steer-by-Wire!

with

Embedded

Programme was aiming to:develop a fault-tolerance actuation system to meet
the key safety requirements, and at the same time
improve the reliability at reduced cost!

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!
Driver Module

Steering Module

System Architecture of Steer – by - Wire for EV and AV Technology

https://www.notateslaapp.com/news/1053/tesla-may-implement-steer-by-wire-next-year

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!

System Architecture of Steer – by - Wire for EV and AV Technology

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!
▪ Subsystem Level Vs. Component level▪ Advanced Safety Features▪ Reliability▪ Redundancy Level▪ ISO▪ Cost▪ … etc.

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!
Programme key innovation:
New power control configuration and integrated control
method to enable the independent control of individual
motor phases so that remaining phases will not beaffected if any one (or more) phase fails to function (e.g.,
due to failure of power switch, motor winding … etc)
A robust and effective method to detect critical faults in thesystem, taking full advantage of the symmetrical structureof electrical motors.

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!
Power supply
42V / 12V

Control (ECU)

Control (ECU)

Fault-tolerance Bus (e.g., TTP/C)…
14 switches

Power switch…

…

Sensors & interfaces(steering forces, position/angle)

Actuators

High Integrity Actuation System

System architecture showing the interface of each module to the TTP/C and actual roadwheel Steer (X)-BY-Wire

Sensitivity: Internal

Steer and Fly by Wire: Safety and Reliability
Fault-tolerance
Bus (e.g.,
TTP/C)

Power supply
42V / 12V

IP

IP

Control (ECU)

IP

IP

Control (ECU)

IP

IP

Power
Switch Unit

Motor

IP

Power
Switch Unit

4-PhasesIP

DAS

DC Voltage
sensor unit

IP

DAS

Motor
current
sensor unit

IP

DAS

Motor
current
sensor unit

IP

DAS

Motor
current
sensor unit

IP

DAS

Motor
current
sensor unit

Control (ECU)

Three
Redundancey
level

Motor position
sensor unit

DAS

IP

Motor position
sensor unit

Motor position
sensor unit

Bus CPU

Power
Switch Unit
Motor

Power
Switch Unit

System architecture showing the interface of each module to the TTP/C and actual roadwheel Steer (X)-BY-Wire

Sensitivity: Internal

Society of Automotive Engineers (SAE)
Communication Classes
Three communication system classes▪ Class A▪ System with low speed networks▪ Soft Real Time Systems▪ Class B▪ System with high speed networks, but without safetyCritical requirements▪ Class C▪ System with safety critical requirements [125 − 200C o ]▪ Hard real-time system!

Sensitivity: Internal

Steer and Fly by Wire: Electric andAutonomous Vehicles!
▪ Subsystem Level Vs. Component level▪ Advanced Safety Features▪ Reliability▪ Redundancy Level▪ Cost … etc.

▪ TTP/C Vs FlexRay Data Bus▪ Data rate
▪ Fault tolerant▪ Safety▪ Cost …etc.

Sensitivity: Internal

TTP – Principles and Mechanism
TTP/C –Time Triggered Protocol▪ Developed by TTTech which was formed from Universityof Vienna▪ TTP involves a continuous communication of allconnected nodes via a redundant bus▪ Operates according to Time Division Multiple Access(TDMA) standard▪ Each node connected has a pre-allocated time slot that isused to send a message on the bus.
▪ Each node consists of a TTP/C controller, Controller
Network Interface and a Host CPU

Sensitivity: Internal

TTP – Principles and Mechanism• FTU: Fault Tolerant Unit
TTP/C Message data static structure and message dispatching table

Sensitivity: Internal

TTP – Principles and Mechanism▪ On the cluster level, node failures and communication
failures can be masked by replicating the nodes andgrouping them into Fault-Tolerant Units (FTUs)▪ Message transmission is replicated in both the spacedomain, by using two buses, and the time domain, bysending the messages twice on each bus▪ Each node is considered to fail silent!
▪ Features:▪
▪
▪
▪

25 Mbits/Sec per channel
Redundancy
Safety and
Fault Tolerance!

Sensitivity: Internal

TTP IP Power Node
• Communication Start-up

How it all begins…..

Sensitivity: Internal

TTP IP Power Node
Listen▪ After initialisation, the controller
starts to listen on the network toestablish if
there is any
communication.
▪

If communication is available it
integrates into network,▪

if not it goes into “cold-start”

Got
I-FrameYes
Ready

Sensitivity: Internal

No

Listen in
the busNoTime
expired

Yes
Cold
Start

TTP IP Power Node
Cold
Start

▪

▪

In
cold
start
controller sends an Iframe, then tries toreceive a response for
one TDMA round.
If that fails, it retries
until it either gets a
responseor
runs
out of tries.

No

Send
I-frameGot
Response?

Yes

No

One round
Done?

Retries left?

Yes

No

Yes
ActiveSensitivity: Internal

Listen in
tstartuplistenTTP IP Power NodeReady

▪ Once a controller isready, it waits for its next
sending slot and▪ if the host is alive, it
becomes active.

No
My
Slot?
Yes
No
Host alive?

Yes
Alive

Sensitivity: Internal

PassiveTTP IP Power Node
▪ Start up mode: start-up of cluster communication and application SW▪ Application mode: normal operation of cluster

START
TTP

Init
HW + SW

Check
Membership.

changeto appl.
mode

Application software runningN-frames
I-frames

I-frames`round 1round n-1

Start up mode

Sensitivity: Internalround nround 1round 2

Application mode

TTP IP Power Node
▪ Message Transfer
TASK A

m1

m2

FT-CNI

f1

FT-COM

f2

TTP-CNI

Chan A
Chan B

Sensitivity: Internal

m1
m1

m2
m2

TTP Controller and Interface

TTP/C communication interface and TTP/C communication structure

Sensitivity: Internal

TTP Controller and Interface▪ TTP Controller – Schematic Layout
TTP Interrupt

HOST CPU

What
is
this?

CNI in ‘DRAM’MEDL in ‘ROM’

Protocol
Processor
BG

Physical Layer (not part of
TTP specification)

TTP/C
MEDL – Message Description List
CNI – Communication Network Interface
BG – Bus Guardian

Sensitivity: Internal

TTP Controller and Interface
TTP Interrupt

HOST CPU
StatusData

CNI in ‘DRAM’MEDL in ‘ROM’

Protocol
Processor
BG

H C-State

Frame Data

CRC

TTP/C
MEDL – Message Description List
CNI – Communication Network Interface
BG – Bus Guardian

Sensitivity: Internal

TTP Controller and Interface
In detail….

CNI RAM

TTP/C
BUS

StatusH

C-StateData11010110101000101011000101001100010
1

Frame Data

The frame-to-memory mapping is done in the MEDL

Sensitivity: Internal

CRC

TTA Application Design
FT – COM Layer:
Responsible for transmitting information from between the applicationand the Communication Network Interface (CNI) and is the interface
between TTP-OS and the CNI.
It packs and unpacks messages into frames and does all demanding
work necessary to handle things like:▪ Message packing and unpacking▪ Message compression and decompression of bit messages▪ Redundancy management for two comms. Channels▪ Message stability handling▪ Replica-deterministic agreement▪ Byte order▪ Synchronisation of CNI access, and▪ Message status handling

Sensitivity: Internal

TTA Application Design
TTP-OS▪ Interface between the hardware of the host controller and theapplications Running on the host.
Application Software

OS
FTL

HW

Sensitivity: Internal

TTP-FT-COM

TTP-OS
TTP
Controller

CPUTTP Cluster Tools Interface
The cluster nodes contain an MPC 555 CPU thathas the following interfaces:▪ PWM output▪ Digital input & output, and▪ ADC input

Sensitivity: InternalTTP/C Data Bus
There are two approaches:▪ Off-Shelf System [TTECH Vienna-Austria]▪ Development life cycle▪ Cost!
▪ Develop our own TTP/C▪ Hardware▪ Components▪ Software▪ Algorithm▪ Features & Criteria▪ Modules

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Steer and Fly by Wire: Safety, Reliability and
Cost!
Facts and Statistics
Component Level:▪ The total number of IP for TTP/C bus required are 14 IP’s [will be
more according to the redundancy level]▪ The cost of each IP is approx. £25/1KU▪ TTP/C Microcontroller is approx. €8/1KU▪ The number of the DAS are 6 DAS▪ STM 32 is approx. $3-4/1KU or▪ DSP C28XX is approx. $5/1KU▪ The cost of each DAS is approx. £15/1KU
The total cost for system interface on this case will be approx. £410

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Steer and Fly by Wire: Safety, Reliability and
Cost!
Facts and Statistics
Component Level:▪ The total number of IP for TTP/C bus required are 10 IP’s [will be
more according to the redundancy level]▪ The cost of each IP is approx. £25/1KU▪ TTP/C Microcontroller is approx. €8/1KU▪ The number of the DAS are 6 DAS▪ STM 32 is approx. $3-4/1KU or▪ DSP C28XX is approx. $5/1KU▪ The cost of each DAS is approx. £15/1KU
The total cost for system interface on this case will be approx. £340

Sensitivity: InternalIndustrial Applications: Solid State Devices MOSFET& IGBT Industrial Applications

Three Main Functional Blocks are Integrated into a Single Device that Drives a Three-phase
Motor
https://www.ecnmag.com/article/2012/04/selecting-best-three-phase-bldc-motor-design-techniqueSensitivity: InternalPower Switch Circuit Module

Sensitivity: InternalPower Switch Circuit Module

Sensitivity: InternalTTP/C and Power Switch Circuit Module

TTP/C Power NodeSensitivity: InternalPower Switch Unit

Steer and Fly by Wire: Electric andAutonomous Vehicles!
TTP/C Power Node for whole
Drive by wire Vehicle:▪ 11 TTP Power Node
▪ 3 Nodes for Steeringsystem
[Tripleredundancy]
▪ 2
Nodes
for
force
feedback[Tworedundancy]▪ 4 Nodes for the brake▪ 2 Nodes for Padel [Tworedundancy]

Sensitivity: Internal

Time Triggered Protocols
TTP/C – Present:▪ Hamilton Sundstrand has selected
controller for Boeing 787 Dreamliner

TTechs’s

TTP▪ TTP from TTTech are used in Nord-Micros’s cabinpressure control system on the Airbus A380 mega-airline

https://www.tttech.com/wp-content/uploads/TTTech-Nord_Micro-Casestudy-A380.pdf

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Time Triggered Protocols▪ TTP/C▪ 25 Mbits/Sec per channel▪ Redundancy, safety and fault tolerance▪ Costly▪ Supports only static mode Comms▪ …etc.
▪ FlexRay▪ 10 Mbits/Sec per channel▪ Redundancy, safety and fault tolerance▪ Seems to win in the automotive market▪ Cheaper▪ …etc.

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Time Triggered Protocols FlexRay
Flex Ray -Current▪ Currently on the BMW X5.

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Steer and Fly by Wire: Electric andAutonomous Vehicles!

Steer – by - Wire System Architecture for EV and AV Technology

https://www.sciencedirect.com/science/article/pii/S0951832019305034

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Steer and Fly by Wire: Electric andAutonomous Vehicles!

Schematic of the Fail operational Steer-by-Wire System Architecture of for EV
Technology

https://www.sciencedirect.com/science/article/pii/S0951832019305034

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Steer and Fly by Wire: Electric andAutonomous Vehicles!

Schematic of the Fail operational Steer-by-Wire System Architecture of for EV
Technology

https://www.sciencedirect.com/science/article/pii/S0951832019305034

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Steer and Fly by Wire: Electric andAutonomous Vehicles!

Schematic of the Fail operational Steer-by-Wire System Architecture of for EV
Technology

https://www.sciencedirect.com/science/article/pii/S0951832019305034

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TESLA ELECTRIC VEHICLE TECH
Schematic of a Tesla Model S

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https://evannex.com/blogs/news/how-does-an-electric-car-workhttps://www.youtube.com/watch?v=3SAxXUIre28#action=shareCollision Avoidance Platform Solution forAutonomous Vehicles

Autonomous Vehicles systems architecture overview and various sources of INS and GPS

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Collision Avoidance Platform Solution forAutonomous Vehicles

Collision Avoidance System architecture overview of the Autonomous Vehicles prototype

https://link.springer.com/article/10.1007/s42452-020-2383-2

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Collision Avoidance Platform Solution forAutonomous Vehicles

Collision Avoidance System SRF05 ultrasonic sensor beam patternhttps://link.springer.com/article/10.1007/s42452-020-2383-2

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Collision Avoidance Platform Solution forAutonomous Vehicles

Algorithm flowchart of Collision Avoidance System

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Collision Avoidance Platform Solution forAutonomous Vehicles

Collision Avoidance System Voting programme flowchart

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Collision Avoidance Platform Solution forAutonomous Vehicles

Collision avoidance system including FOD, BOD and NV circuit diagram

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Collision Avoidance Platform Solution forAutonomous Vehicles

Tesla Collision at Road Intersection: 3D Vision systems able to perceive, reason,
move in 3D, and learn from experiences at lower cost!
https://electrek.co/2016/07/01/understanding-fatal-tesla-accident-autopilot-nhtsa-probe/https://www.youtube.com/watch?v=X3hrKnv0dPQ
https://www.youtube.com/watch?v=zRnSmw1i_DQ – AV

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https://www.youtube.com/watch?v=7KpGluGduLk – Prediction

3D Machine Vision System using Machine
Learning for Connected Vehicles
Applications
Wide angle

NormalTele

Field of View in 2D

Focal plane

Depth of Field

Impossible to focus Out of focus

Minimum object distanceIn focus

Out of focus

Depth of field

3D Vision systems able to perceive, reason, move in 3D, and learn fromexperiences at lower cost!

Sensitivity: Internal

3D Machine Vision System using Machine
Learning for Connected Vehicles
Applications

3D Vision systems able to perceive, reason, move in 3D, and learn fromexperiences at lower cost!

Sensitivity: InternalA Tracking Platform Solution for Autonomous
Vehicles Localization in Smart Cities, Using
Machine Learning

Autonomous Vehicles systems architecture overview and various sources of INS and GPS

Sensitivity: InternalA Tracking Platform Solution for Autonomous
Vehicles Localization in Smart Cities Using
Machine Learning
▪

▪

▪
▪

▪

Navigation data for AV is based on
information obtained from multiple sources:
INS, GPS, dynamic maps, LIDAR, RADAR,
vision systems …etc.
Continuousandaccurate
localization
involves is critical to safe implementation ofdriverless systems.
GPS/INS is key in continuous localization,
albeit with their unique setbacks.
This programme is focused on theimplementation of ML algorithm to offset the
deficiencies of traditional GPS/INS fusion.
This
researchwork
focuses
its
implementation on “GPS shadow areas”.

Sensitivity: Internal

GPS/INS (Fusion)

ML Algorithm

INS(GPS absent)

Navigation solution

Position, Velocity,
Orientation

Next Generation of Battery and BMS
Technology for AV

Challenges that need to be accounted for while understanding the physics of operationand degradation of NIB

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Next Generation of Battery and BMS
Technology for AV

Illustration of the Hierarchical Bayesian Network that captures the ontology of degradation influencing
factors at the cell, module and pack level through probabilistic knowledge graphs

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Next Generation of Battery and BMS
Technology for AV

BMS Main Function Flow Diagram

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Next Generation of Battery and BMS
Technology for AV

Solutions Proposed By EATRON in a R&D Stagehttps://eatron.com/ai-points-the-way-to-better-ev-battery-management/

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Next Generation of Battery and BMS
Technology for AV

Application of Digital Twin in Smart Battery Management Systems (Wang et al.,2021)

Sensitivity: InternalIndustry 4.0, Steer and Fly by Wire:
Towards Connected Autonomous Vehicles
Technology for Future Smart Cities
Applications

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Platform Solution for Connected Vehicles
Applications
PATIENT 4

CENTRAL BACKEND (2)PATIENT 1
CLOUDCOMPUTINGRESULTS
MULTI MODALAI PATTERN
RECOGNITIONSYSTEMCENTRAL INFORMATION
HUB

QUERY

MEDICALRESEARCHERPATIENT 2DATARETRIEVAL AND
SUMMARYSYSTEMRESULTSQUERY

PATIENT 3GP

CENTRAL BACKEND (3)PATIENT n

Schematic View of Connected Vehicles Network System Architecture

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Platform Solution for Connected Vehicles
Applications
System Architecture:
1. Blockchain!!!
2. Cyber Security Platform
Machine Learning, Deep
Cognitive
3. Edge Computing
4. Cloud Computing
5. Big Data & AI
6. IoT & IoE …etc.!

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Solution using
Learning, andIndustry 4.0, Steer and Fly by Wire:
Towards Connected Autonomous andHover Vehicles Technology for Future
Smart Cities Applications

Sensitivity: InternalFly by Wire: Towards Hover Vehicles for
Future Smart Cities Applications

Hover Vehicles for Future Smart Cities Applications – Tokushima University

Sensitivity: InternalFly by Wire: Towards Hover Vehicles for
Future Smart Cities Applications

Hover Vehicles for Future Smart Cities Applications – Tokushima University

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Sensitivity: InternalPutting Learning and Teaching into Practice!
Sensitivity: InternalPutting Learning and Teaching into Practice!
Sensitivity: InternalPutting Learning and Teaching into Practice!
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Towards Connected Autonomous and Hover
Vehicles - Ways Forward!
▪ It is very much believed that industry 4.0 and ICT will havea great impact on future traffic and transportation
services and facilities within smart cities that will be fully
equipped to ensure connected vehicles and services meetthe end-user needs and expectation.
▪ It is the use of Smart Sensors Wireless Network
Technology, IoE, Big Data, 5G, and AI that will have thesupreme prospective, in the coming decades torevolutionise
the concept of Intelligent Traffic And
Transportation Services.

Sensitivity: InternalSummary and Discussions!
▪ Excellence and Business Growth:– Innovation and Knowledge Transfer– Finding Pioneering Solutions– Skills and Training– Retain Clients– New Business– New Clients– Spin-Out Companies– Income Generations

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Thank You!
Mahmoud Shafik BEng (HONS) CEng MSc PhD FHEA MIET MASME
Professor AC in Intelligent Mechatronics Engineering and Digital Technology
Principal Investigator
Editor-In-Chief of International Journal of Robotics and Mechatronics
Website: http://ojs.unsysdigital.com/index.php/ijrm

College of Science and Engineering, University of Derby, UK
Email: mshafik@derby.ac.uk & Webpage: http://www.derby.ac.uk/staff/mahmoudshafik
ReserachGate: https://www.researchgate.net/profile/Mahmoud_Shafik
LinkedIn: https://www.linkedin.com/in/mahmoud-shafik-ph-d-0602377/

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