Data Centric Engineering for Electrification

Course structure

The course will run over three days, from Tuesday to Thursday. We will guide learning through lectures, lab-based simulations in IVHM, AVEC, and DARTeC , and industrial case studies, which will enable strong interaction between academics, industry specialists, and delegates so that by the end of the course, delegates have not only a good appreciation of data-centric engineering and its role in their businesses, but also a network of contacts including leading experts in the field.

What you will learn

Overall, this course is intended to help increase the proficiency of the workforce involved in designing, developing, and maintaining advanced electrical and electronic systems across the automotive and wider transportation industry. It will improve through-life management capability of UK automotive, rail, energy, aerospace (UAS systems), and defence industries engaged in realising the electrification of complex systems with strong skills base. Eventually, the course will improve understanding and hence optimal and wider application of data-centric engineering methods and techniques to remediate complex systems’ design, availability and maintenance problems.

On successful completion of this short course, you will be able to:

• Understand and appreciate the concept of data-centric engineering for electrification, its wider applications and business as well as environmental significance for future technologies.
• Leverage the technologies (such as the intelligent sensors, digital twins, and reasoners) that are enabling the capture of an ever-greater volume of data from heterogeneous sources and design safer, secure, and resilient electrified systems. 
• Deploy tools and techniques (including ML and AI applications) to analyse data and gain insights for enhancing robustness and hence the overall system performance.
• Apply IVHM methodologies (encompassing sensors’ optimisation and instrumentation) in conjunction with data-centric engineering techniques to through-life management of electrification in energy and wider transportation industries. 
• Compose electrification transformation plans and strategies more realistically within their areas of expertise for the development of novel data-engineered and data-driven systems, such as more-electric and self-conscious platforms.
• Devise data-centric engineering processes to enable more realistic end-of-life predictions for critical battery technologies and optimise integrated battery systems’ condition monitoring and health management.   

Core content

• Data-centric engineering for electrification – principles, strategies, and methodologies.
• Digital technologies and data-centricity.
• Predictive analytics.
• Intelligent sensing and power electronics reliability.
• Data mining and visualisation.
• Big data processing and integrated vehicle health management (IVHM) for electrified systems.
• Prognostics and health management of power electronics and battery systems. 

Timetable

Day 1: concepts and principles 

1. Introduction to data-centric engineering.
2. Power electronics and electrification: prognostics and health management (PHM).
3. Big data and digital technologies.
4. IVHM and data-centric modelling.
5. Intelligent diagnostics and prognostics for power devices.
6. Automotive electrification – powertrain case study and simulation.

Day 2: design, reasoning, and processing 

1. Data-centric engineering techniques for electrification.
2. Reasoning technologies and decision support.
3. Sensors, instrumentation and big data processing.
4. Design for IVHM and OSA-CBM architecture.
5. Health management development process.
6. Digital twin for power electronics: condition monitoring and prognostics simulation.

Day 3: analytics  

1. Data mining and visualisation.
2. Digital twin as an analytical tool.
3. Data uncertainty quantification and heterogeneity.
4. Data-driven computational predictive analytics.
5. Data-centric engineering for electrification - 1: more-electric aircraft.
6. Data-centric engineering for electrification - 2: battery systems.

Who should attend

This course will benefit individuals working in the area of electrification such as design, research and development and implementation, including:

• Engineers from all disciplines including systems engineers and system specialists.
• Project managers and project management professionals.

The course is also suitable for those who are new to the area as well as those that do have some knowledge and/or experience. 

 

Speakers

• Dr. Suresh Perinpanayagam (Senior Lecturer in Intelligent Systems).
• Prof. James Brighton (Professor of Automotive Engineering).
• Prof. Ian K Jennions (Technical Director IVHM Centre).
• Dr Daniel Auger (Reader in Electrification, Automation and Control).
• Dr. Stephen King (Senior Lecturer in Advanced Analytics).
• Dr. Andrew Wileman (Research Fellow in Data-centric Engineering and Power Electronics).
• Dr. Sohaib Aslam (Research Fellow in Power Electronics Reliability).

Concessions

20% discount for Cranfield alumni.

10% discount when registering three or more delegates from the same organisation at the same time.

Accommodation options and prices

This is a non-residential course. If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information regarding our accommodation on campus can be found here.

Alternatively you may wish to make your own arrangements at a nearby hotel.

 

 

Location and travel

Cranfield University is situated in Bedfordshire, close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes away respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

Cranfield University
College Road
Cranfield
Bedford 
MK43 0AL