Control & Energy Systems Research Group

Design and Development of Intelligent Battery Pack for Range Extension of Electric Scooter

Project Code: PSF-CRP/TUBITAK-IV/EV/C-COMSATS (26)

Team Members

• Dr. Ali Arshad Uppal (PI, Pakistan)
• Dr. Qadeer Ahmed (Co-PI, USA)
• Dr. Ahmad Yar (RA)
• Mr. Afaq Ahmed (RA)
• Mr. Sohail Ahmed (RA)
• Mr. Azmatullah (PhD Student)
• Ms. Sheharbano (PhD Student)

Background

The electric vehicle (EV) market is steadily growing, with projections estimating more than 672 million EVs by 2050. The lithium-ion battery pack is the core component governing EV range and safety.

A major challenge limiting EV range is cell imbalance, which can cause premature battery pack shutdown based on the weakest cell. Active Cell Balancing (ACB) provides an effective solution by redistributing energy among cells, enhancing safety, performance, and driving range.

Modern EV battery packs rely on a Battery Management System (BMS) for safe and efficient operation. A key component of the BMS is the cell equalization circuit. This research aims to extend EV range using SoC-based Active Cell Balancing.

Objectives

• Selecting an appropriate Active Cell Balancing Network (ACBN)
• Developing a mathematical battery cell model including SoC, thermal dynamics, and terminal voltage
• High-fidelity modeling of ACBN including balancing currents and power losses
• Design of a robust, range-maximizing Nonlinear Model Predictive Controller (NMPC)
• Simulation-based validation of EV range extension
• Prototype development for real-time implementation

• Accurate modeling of balancing currents and power losses often ignored in existing studies
• Unified battery cell modeling suitable for both local balancing and global range optimization
• Development of range-aware NMPC for active cell balancing

• Advancement of battery control research through hybrid data-driven and model-based techniques
• Improved EV safety, reliability, and performance under diverse operating conditions
• Reduction in emissions, energy consumption, and battery degradation
• Enhanced EV affordability and driving range

1. Model-Based Quantitative Analysis of a Capacitive Cell Balancing Technique using SoC Estimation
2. Power-Loss-Aware Nonlinear Model Predictive Control Design for Active Cell Balancing

Machine Learning Based Control-Oriented Modeling of Underground Coal Gasification (UCG) Process

Team Members

• Dr. Ali Arshad Uppal (PI)
• Mr. Afaq Ahmed
• Dr. Syed Bilal Javed

Research Contributions

• Nonlinear control-oriented modeling of multivariable UCG process using machine learning
• Development of CAVLAB – a MATLAB-based control-oriented simulator for UCG
• CAVLAB simulator for academic and research use