Balancing plays a vital role in improving the performance and reliability of electric vehicle (EV) batteries. My research focuses on developing an AI-based control strategy that enables real-time, adaptive balancing of charge and temperature across battery cells.
Unlike conventional model-based methods, the controller learns directly from operational data, determining when to insert or bypass individual cells to maintain optimal pack conditions. This self-learning capability allows the system to respond dynamically to changes in driving patterns, charging behavior, and temperature variations.
Through simulation and hardware-in-the-loop validation, the AI-based control demonstrates faster charge equalization, improved thermal uniformity, and greater robustness under real-world conditions. This research contributes to the development of intelligent and sustainable EV battery systems — where data-driven control ensures longer life, enhanced safety, and higher efficiency for the next generation of electric mobility.
As electric vehicles (EVs) continue to advance, the efficiency and reliability of their battery systems have become crucial for achieving longer driving range, improved safety, and extended lifetime. One of the key challenges lies in maintaining uniform operating conditions across all cells within a battery pack. Even minor variations in state of charge or temperature can lead to uneven aging, capacity loss, and performance degradation. Overcoming these issues requires control strategies that can adapt and respond intelligently to real-time operating conditions.
My research focuses on developing AI-based balancing control that enables a battery pack to regulate itself autonomously during operation. Unlike conventional model-based algorithms or static rule sets, this controller learns directly from data in real time. It continuously observes each cell’s behavior and decides when to insert or bypass a cell to maintain balance in both energy and temperature. This adaptive approach ensures that every cell operates efficiently, preventing overstress, overheating, and premature degradation.
The algorithm is designed to be adaptive, lightweight, and robust, capable of running on embedded battery management hardware. Rather than depending on a precise mathematical model, it dynamically adjusts its control policy according to driving conditions, charging profiles, and environmental variations. This allows it to manage the complex, nonlinear, and uncertain dynamics typical of real-world EV operation.
Results from extensive simulations and hardware-in-the-loop experiments show that the AI-based controller achieves faster equalization, smoother temperature regulation, and improved overall stability compared with conventional model-based methods. The approach not only enhances pack uniformity and reliability but also increases energy efficiency and extends the battery’s usable life.
By integrating this AI-driven control into the battery management layer, the research paves the way for smarter, more resilient, and sustainable electric powertrains. A well-balanced pack lasts longer, performs more consistently, and ensures safer operation under all conditions.
This work supports the broader vision of next-generation EV systems that can learn, adapt, and optimize themselves in real time — reducing energy waste and contributing to a cleaner and more intelligent mobility ecosystem.
Arman Oshnoei
Aalborg University – RWTH Aachen