Smart Battery: Hardware design, modeling, intelligent estimation and control

Welcome to Smart Battery: Hardware design, modeling, intelligent estimation and control

Description: Lithium-ion batteries have revolutionized energy storage across various applications, particularly in the rapidly growing field of e-mobility. As the demand for safer, more reliable, and intelligent energy storage systems increases, the concept of “Smart Battery (SB)” has emerged as a promising solution. This comprehensive five-day course delves into the cutting-edge world of smart battery technology, combining hardware design with battery modeling and artificial intelligence (AI)-based state estimation and control.

The course begins by exploring the integration of power electronics and intelligent control into the cells. This is done by using a half-bridge circuit connected across the cell terminals. The course introduces the operation of the SB with the integrated half bridge circuit while also giving a detailed overview of the state-of-the-art battery management systems, chargers/ charging methods. This discussion evolves into the advantages of the SB in making smart BMS and energy efficient charging methods and lifetime improvement. The design of the SB, optimal device selection, PCB design for different geometries of the cells (prismatic, pouch and cylindrical ) will be discussed. The SB also has intelligent control and the course introduces the communication architecture and controller selection for the SB management systems. Simulation exercises in Simulink/Plecs/LTSpice will be used as tools to understand and appreciate the SB concept and hardware architecture.

Building on this hardware knowledge, the course then transitions into the realm of modeling for Lithium-ion batteries, and artificial intelligence applications in their state estimation. Due to the complex electrochemical reaction of the battery, the battery performance parameters show strong nonlinearity with aging. Therefore, as the main technologies in BMS, accurate modeling, battery state estimation, lifetime prediction and balancing remain challenges. AI technologies possess immense potential in inferring battery state, and can extract aging information (i.e., health indicators) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Therefore, this course aims to introduce the application of AI in Smart Battery modeling and state estimation. Especially, students will explore battery modeling methods including equivalent circuit model and electrochemical model, and various AI algorithms in estimating and predicting crucial battery parameters such as state of charge, state of health, state of temperature, and remaining useful life. Data preparation, preprocessing, and AI model training and selection, multidimensional balancing and state control will be covered.

This course will combine both theoretical lecturers, exemplary introduction, and hands-on exercises. Multiple tools like MATLAB/Simulink, Plecs, LTSpice, and Python will be used. The students are expected to gain knowledge of establishing smart battery systems as well as the trend in next-generation BMS design. By the end of the course, students will have a comprehensive understanding of both the hardware and software aspects of smart batteries, enabling them to contribute to the development of more reliable and intelligent energy storage solutions for the future of e-mobility and beyond.

Prerequisites: Fundamental understanding of characteristics of Li-ion batteries, and familiar with programming using MATLAB, Python and any circuit simulator such as Plecs or Spice. Note: the course language is English.

Learning objectives: 

1. Analyze state-of-the-art battery management systems (BMS) for automotive applications and the advantages of SB technology in enhancing smart BMS.
2. Explore design considerations for SB, including device selection and PCB design for different Li-ion cells and modules.
3. Understand wireless communication architecture and controller selection in SB management systems. 
4. Apply simulation tools (Simulink, Plecs, LTSpice) to model and evaluate SB concepts and hardware architecture.
5. Understand battery models including equivalent circuit model and physical model, while comparing their fidelity and computational efficiency.
6. Gain knowledge in battery digital twin technology and online impedance estimation, and learn how to design optimal control strategies for achieving effective balancing.
7. Apply AI for battery state estimation, including state of charge (SOC), state of health (SOH), state of temperature (SOT), and remaining useful life (RUL) prediction.
8. Gain hands-on experience with simulation tools (Simulink/Plecs) and programming environments (Python, MATLAB) for smart battery modeling and AI-based state estimation.
9. Analyze and compare different battery state estimation algorithms (e.g., model-based, AI-based, and physics aware).

Form of evaluation: Students are expected to simulate smart battery and it’s integration with power electronics converters, solve a few exercises, and deliver an individual report with solutions and comments.

Organizer: Professor Remus Teodorescu ret@energy.aau.dk, Postdoc Xin Sui, xin@energy.aau.dk

Lecturers: Professor Remus Teodorescu AAU Energy, Postdoc Xin Sui AAU Energy, Postdoc Roberta Di Fonso AAU Energy, PhD candidate Nicolai André Weinrich, Postdoc Yicun Huang Clamers University of Technology

ECTS: 4.0

Date: 23,24,25, 26,27 November 2026

Format:  In-person attendance only.

Location : AAU Energy, Pontoppidanstræde 111, room 1.015, 9220 Aalborg East, Denmark

Deadline for enrolment: 3 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.