AI in Battery Management

Short description

With the rapid evolution of electric vehicles, renewable energy storage, and portable electronics, ensuring the reliability, safety, and lifetime of battery systems has become a critical challenge. This special session will focus on advancements in Battery Modelling, Diagnostics, Prognostics and Optimal Charging covering multi-physics modeling, real-time diagnostics, and predictive analytics to enable next-generation battery management.

List of topics

• Electrochemical-thermal-mechanical coupling for battery state estimation
• Multi-physics modeling and degradation-aware digital twins
• Embedded sensing and EIS for real-time diagnostics
• AI-driven battery health assessment: interpretable and physics-informed approaches
• Ultra-fast charging optimization

Chair

Xin Sui
e-mail: xin@energy.aau.dk
Aalborg University, Denmark

Co-Chairs

Remus Teodorescu
e-mail: ret@energy.aau.dk
Aalborg University, Denmark

Roberta di Fonso
e-mail: rdf@energy.aau.dk
Aalborg University, Denmark

Program Committee

• TBA