Subject Area
Electrical, Electronics Engineering
Abstract
This research introduces a novel approach to fast-charging control by integrating physics-based electrochemical models into Battery Management Systems (BMS) for real-time applications. The proposed system leverages simplified electrochemical models to dynamically regulate high charging currents, mitigating adverse effects of lithium plating, a significant barrier to achieving fast charging goals.
A methodology is introduced for calibrating the electrochemical battery model, significantly reducing testing time and dependence on resource-intensive equipment. By leveraging synthetic data generation and optimization techniques, this approach streamlines the calibration process while maintaining model fidelity. The calibrated model enables precise battery performance predictions, even under challenging fast-charging conditions.
Building on the calibrated model, the study develops an age-aware dynamic control framework that optimizes charging without compromising battery lifespan. This controller integrates adaptive algorithms that respond to real-time battery states, including State of Charge (SoC), State of Health (SoH), while minimizing side reactions such as Solid Electrolyte Interphase (SEI) growth and lithium plating. Comprehensive experimental validation under diverse conditions, including temperatures, high C-rates, and real-world applications such as electric vertical take-off and landing (eVTOL), confirms the effectiveness and practicality of the approach.
The dissertation further introduces an AI-enabled digital twin that couples the real-time electrochemical model with data-driven estimators trained on partial and variable-length discharge data. This digital twin continuously updates key health indicators, such as capacity and internal resistance, and feeds them back to the model and controller, enabling adaptive, age-aware adjustment of fast-charging limits over the battery lifetime. The overall framework demonstrates a significant advancement toward safe, efficient, and real-time implementable fast charging, achieving rapid charging targets while reducing degradation and extending battery life.
Degree Date
Spring 5-16-2026
Document Type
Dissertation
Degree Name
Ph.D.
Department
Electrical Engineering
Advisor
Mahesh Krishnamurthy
Number of Pages
240
Format
".pdf"
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Qasem, Mohammad Talal Saleh, "AI-Enabled Dynamic Fast-Charging Control with Age-Aware BMS for Enhanced Safety and Efficiency in Li-ion Batteries" (2026). Electrical Engineering Theses and Dissertations. 91.
https://scholar.smu.edu/engineering_electrical_etds/91
