Changfu Zou

Phone: +46 (0)31-772 3392

Hörsalsvägen 11, Göteborg 41296, Sweden

I am a Professor with the Energy Systems and Optimal Control (eSOC) group, Automatic Control unit, Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.

I was a Researcher in 2017-2018, and became an Assistant Professor in early 2019 and an Associate Professor in 2022, all with the Dept. Electrical Engineering, Chalmers. I obtained the PhD (under the supervision of Prof Chris Manzie and Prof Dragan Nesic) from the University of Melbourne, Australia, and the B.E. degree from Beijing Institute of Technology, China. I was a visiting student researcher at the University of California Berkeley, USA.

My research focuses on advanced modelling and automatic control of energy storage systems, particularly batteries. Many of my works are in collaboration with industrial partners, such as Volvo Cars, Volvo Trucks, Polestar, Scania, ABB, and Zeekr Europe. As the PI, I have received funding from the Swedish Research Council (incl. the Starting Grant and the Project Grant), the European Commission (e.g., for projects BatCon, MoreSafe, SmartHEM, ULICBat, ProBat, TEAMING), the Swedish Energy Agency (incl. 4 projects within the Vehicle Strategic Research and Innovation Program), Swedish Foundation for International Cooperation in Research and Higher Education, Swedish Electromobility Center, Batteries Sweden (BASE), etc. Five of my team members have been awarded the prestigious Marie Skłodowska-Curie Individual/Postdoctoral Fellows, and the Fellowship is among Europe's most competitive and prestigious research and innovation fellowships.

Data and code

Battery aging dataset: We generated the largest known dataset of its kind from 215 commercial lithium-ion batteries of the same type controlled individually at the same operating conditions until >25% capacity fade. All the data and associated code are shared [here], and a detailed description of the experiment is given in the [article].
A Python tutorial about Physics-Informed Neural Networks (PINN) and Model-Integrated Neural Networks (MINN) is given at [Link]. MINN is our proposed physics-based learning framework than can learn the physics-based dynamics of general non-autonomous systems consisting of partial differential-algebraic equations (PDAEs). The obtained architecture systematically solves an unsettled research problem in control-oriented modeling, i.e., how to obtain optimally simplified models that are physically insightful, numerically accurate, and computationally tractable simultaneously.

Recruitment

For MSCA postdoctoral fellowships (MSCA-PF), please contact me before June every year. MSCA-PF is recognized as one of Europe's most prestigious and competitive funding programs. These fellowships are highly valued for providing researchers with advanced training, interdisciplinary and intersectoral experience, and the opportunity to define their own research agendas in any field.
We are looking for another PhD student to do research on robotics. Here is the application link, and the deadline falls on 12 November, 2025.

Updates

📘 New Publication

Nov. 2025: Our Preview titled “Multi-Frequency Excitation Enables One-Second Battery Diagnostics across the Lifecycle Chain”, led by Shengyu, was accepted for publication in the Cell Press journal Joule.

Oct. 2025: Our article titled “A Fast Fixed-Point Solution Framework for the P2D Model of Lithium-Ion Batteries”, led by Yang, was published in the journal Journal of Power Sources.

This work presents a computationally efficient fixed-point framework that accelerates physics-based battery simulations and enables real-time electrochemical modelling for digital-twin applications.

Sep. 2025: Our article titled “Mathematical Modeling for Reconfigurable Battery Systems with Parallel–Series Connections”, led by Quan and Albert, was accepted for publication in IEEE Transactions on Control Systems Technology.

This paper establishes a unified mathematical model for dynamically reconfigurable battery packs, providing a foundation for various model-based applications, such as optimal control, lifetime extension, and reliability enhancement in electric vehicle systems.

🏛️ Promotions/appointments

Nov. 2025: After obtaining his doctoral studies at Tsinghua University and the University of California at Berkeley, Dr. Shengyu Tao joined our group as a Postdoctoral Researcher. He will continue his research on advancing battery intelligence.

Sep. 2025: My promotion to Professor was approved by our Department Evaluation Committee (IB) and the Chalmers Faculty Appointment Committee (AK).

Aug. 2025: After five productive years with us as an MSCA Fellow and researcher, Dr. Yang Li joined Wuhan University as a Professor.

💰 Grants

Our application “Data-Efficient Learning for Mobile Manipulation Tasks Using Multi-Modal Data” has been funded by the Knut and Alice Wallenberg Foundation as a WASP Academic PhD Project.

Our project on battery fast charging has been funded by Batteries Sweden, in collaboration with Uppsala University, Volvo Trucks, ABB, and Husqvarna.

🏅Awards

We received the 2nd-class Prize Paper Award from IEEE Transactions on Transportation Electrification for our article "Knee-point-conscious battery aging trajectory prediction based on physics-guided machine learning".

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