Course detail

Welcome to Neutral Networks in Batteries

Description: Artificial intelligence (AI) is transforming science and engineering, with neural networks (NNs) at the forefront of breakthroughs in fields such as natural language processing, computer vision, and generative media. In parallel, lithium-ion and emerging battery technologies are being deployed on a massive scale to power electric transportation, store renewable energy, and support a low-carbon future. These trends converge in a critical research area: the use of advanced AI methods to enhance battery performance, reliability, and safety.

This course provides a focused introduction to neural networks—ranging from foundational architectures to advanced deep neural networks (DNNs) and physics-informed neural networks (PINNs)with a special emphasis on their applications in battery applications. Participants will learn how these techniques can be used for battery state estimation, lifetime prediction, and safety management.

Prerequisites: No prior background in artificial intelligence is required, as the course will provide fundamental introductions to the key concepts. Basic familiarity with Python programming is welcome and will be advantageous for participating in hands-on exercises.

Learning objectives: The course covers both theoretical foundations and practical applications of neural networks (NNs), deep neural networks (DNNs), and physics-informed neural networks (PINNs), with a focus on their use in battery modeling and state estimation. Participants will also gain experience with Python programming and NVIDIA Modulus (formerly known as NVIDIA Physics-Informed Neural Networks/NeMo) for implementing these models.

Form of evaluation: Students will work in groups to complete all assigned exercises and will submit a final report no later than four weeks after the course ends.

Organizer: Professor Remus Teodorescu, ret@energy.aau.dk

Lecturers: Professor Remus Teodorescu AAU Energy, Assistant Professor Yunhong Che AAU Energy, Postdoc Yusheng Zheng AAU Energy

ECTS: 3.0

Date: 15.-18. December 2026

Place: Aalborg University

City: Aalborg

Maximal number of participants: 30

Open for enrolment: 15. August 2026

Deadline for enrolment: 27. November 2026

Important information concerning PhD courses: There is 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 the 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 of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

You may find more information in our FAQ: https://phd.moodle.aau.dk/local/page/faq 

For inquiries not described in the FAQ, please contact the PhD administration at phdcourses@adm.aau.dk. When contacting us please state the course title and course period. Thank you.