A new frontier has arisen that demands an increase in sustainable energy solutions, highlighting the importance of effective battery solutions. This research aims to develop a novel solution for a battery management system using artificial intelligence (AI) to improve charging time, energy efficiency, and longevity of battery cells. Previous battery management methods exist, such as PID and fuzzy logic controllers; however, they often fall short in battery efficiency and adaptability between different systems. This research aims to fill these gaps by leveraging AI techniques. The methodology saw the development of a neural network-based controller, implemented in Simulink, to actively balance cells by adjusting variable resistors. This system was directly compared to traditional methods of PID and fuzzy controllers in Simulink through simulations capable of measuring response time, accuracy, and steady-state error during charge and discharge cycles. Model validation was planned against physical testing, though limitations arose due to resource constraints. Preliminary results indicated that the AI controller had the potential to outperform the existing methods, particularly in reducing steady-state error and decreasing balancing time. However, this model had to undergo immense tuning and optimization to finally reach its full potential, which showed extremely promising dominance over traditional methods. Overall, the AI approach to battery management systems has shown promising advancements in this field, offering more adaptable and efficient solutions. This research has identified areas for further improvements, particularly in exploring additional AI methodologies to further enhance system performance. Highlights • Developed an AI-based battery controller: Introduced a novel neural network control algorithm for active and passive cell balancing, enhancing efficiency and longevity. • Traditional comparative analysis: Conducted a thorough comparison of AI, PID, and fuzzy logic controllers, demonstrating the gaps and improved performance of AI in reducing response time and improving efficiency. • Simulink implementation: Successfully implemented both a battery model and an AI control system into Simulink, showcasing and allowing for real-world application in battery management systems. • Identified future advancement: Highlighted the need to further improve this system and exploration of other AI methods to enhance performance across varying battery types.

Design of an Artificial Intelligence-Based Controller for Stationary Battery Packs

章节
发表日期：

2026年

查看全文

Joe Anderson,
Aydin Azizi

摘要

A new frontier has arisen that demands an increase in sustainable energy solutions, highlighting the importance of effective battery solutions. This research aims to develop a novel solution for a battery management system using artificial intelligence (AI) to improve charging time, energy efficiency, and longevity of battery cells. Previous battery management methods exist, such as PID and fuzzy logic controllers; however, they often fall short in battery efficiency and adaptability between different systems. This research aims to fill these gaps by leveraging AI techniques. The methodology saw the development of a neural network-based controller, implemented in Simulink, to actively balance cells by adjusting variable resistors. This system was directly compared to traditional methods of PID and fuzzy controllers in Simulink through simulations capable of measuring response time, accuracy, and steady-state error during charge and discharge cycles. Model validation was planned against physical testing, though limitations arose due to resource constraints. Preliminary results indicated that the AI controller had the potential to outperform the existing methods, particularly in reducing steady-state error and decreasing balancing time. However, this model had to undergo immense tuning and optimization to finally reach its full potential, which showed extremely promising dominance over traditional methods. Overall, the AI approach to battery management systems has shown promising advancements in this field, offering more adaptable and efficient solutions. This research has identified areas for further improvements, particularly in exploring additional AI methodologies to further enhance system performance. Highlights • Developed an AI-based battery controller: Introduced a novel neural network control algorithm for active and passive cell balancing, enhancing efficiency and longevity. • Traditional comparative analysis: Conducted a thorough comparison of AI, PID, and fuzzy logic controllers, demonstrating the gaps and improved performance of AI in reducing response time and improving efficiency. • Simulink implementation: Successfully implemented both a battery model and an AI control system into Simulink, showcasing and allowing for real-world application in battery management systems. • Identified future advancement: Highlighted the need to further improve this system and exploration of other AI methods to enhance performance across varying battery types.