Artificial intelligence (AI) has significantly revolutionized research activities and industrial applications in image processing and natural language processing. Likewise, the synergy of power electronics and computer science is expected to unleash great potentials in power electronic systems as well with their transition towards data-rich ones. From the power electronics perspective, this course aims to focus on two essential aspects of this interdisciplinary field, i.e., artificial intelligence and advanced data analytics. It is organized following a typical pipeline when implementing data-driven solutions in power electronics, ranging from the initial data collection to the final decision-making. As a 3-day course, it includes fundamentals, tools, applications, outlook, and hands-on exercises, which are specifically tailored for power electronic applications. Combining with several case studies where AI has shown great benefits, the attendees are expected to establish solid foundations and skills of AI and data analytics to address core challenges in data-driven applications in power electronics. 

Day 1: Fundamentals of data-driven solutions in power electronics (Shuai Zhao, Huai Wang, 8:30-16:30) 
This course will first get the attendees familiar with the background and existing development of data-driven research in the power electronic field. It will then establish the basics of two relevant aspects of data-driven solutions, i.e., AI tools and advanced data analytics. It will cover the following topics: 

·     Data-driven solutions in power electronics 

·     Artificial intelligence tools in power electronics  

·     Neural network for power electronic applications 

·     Advanced data analytics in power electronics 

Day 2: Case studies and application examples in power electronics 
(Shuai Zhao, Huai Wang, 8:30-16:30) 
With the fundamental knowledge of data-driven solutions in power electronics, several case studies and promising applications, where AI and data analytics have illustrated a great superiority compared to conventional methods, will be detailed. It will cover the following topics: 

·     Prognostics and health management (PHM) in power electronics 

·     Case study I: AI-assisted experiment design and potentials in power electronics 

·     Case study II: Non-invasive condition monitoring of power converter 

·     Case study III: Information fusion for remaining useful life prediction in power electronics 

Day 3: Frontiers of data-driven methods and team project 
(Shuai Zhao, 8:30-16:30) 
To have a further overview of this rapidly dynamic field, the development trend, cutting-edge tools, and further promising directions of this synergy field will be outlooked. Moreover, a hands-on team project will be introduced and instructed. It will cover the following topics: 

·     Frontiers of data-driven research in power electronics and beyond 

·     Team Project discussion support (based on a Nature Energy paper) 


·     Fundamentals of power electronics 

·     Fundamentals of probabilistic models and statistical analysis 

·     Experience with MATLAB/Python 

Form of evaluation: 
The course is accompanied by a hands-on team project so that the theoretical tools introduced in the course can be implemented in real applications. The project is mainly formulated following the paper ”Data-driven prediction of battery cycle life before capacity degradation”, which is published in Nature Energy in 2019. The course evaluation will be based on this team project report. 

Professor Huai Wang, 
Postdoc, Shuai Zhao 

Huai Wang, Aalborg University 
Shuai Zhao, Aalborg University 


Time: 6-8 April 2022

Place: AAU Energy, Aalborg 

Number of seats: 25

Price6000 DKK for PhD students outside of Denmark and 8000 DKK for the Industry excl. VAT

Deadline: 16 March 2022

Important information concerning PhD courses:
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.