Comparative Analysis of PI, PID, and PSO-PID Controllers for Charging Lithium-Ion Batteries

Abstract

The growing demand for efficient and reliable energy storage solutions has made the optimization of battery charging systems a critical area of research, particularly in the context of electric vehicles and renewable energy integration. Lithium-ion batteries, owing to their high energy density and long cycle life, have emerged as the preferred choice for such applications. However, ensuring that these batteries are charged in a manner that is both fast and safe requires a well-designed control strategy at the heart of the charging system. This paper presents a comparative analysis of three current control strategies — Proportional-Integral, Proportional-Integral Derivative, and a meta-heuristic optimized variant of the latter — applied to a lithium-ion battery pack. Two charging circuit topologies are considered to broaden the scope of the evaluation. Each control strategy is assessed on the basis of how accurately it tracks the desired charging current, how effectively it regulates output voltage, how uniformly the battery state of charge progresses ,throughout the process. Simulations are carried out in a controlled software environment, and the results reveal a clear performance hierarchy among the three controllers These outcomes highlight the practical value of incorporating computational optimization techniques into battery management system design and provide meaningful guidance for engineers working on charging infrastructure for electric mobility and stationary energy storage platforms.

Introduction

The text discusses the design and evaluation of advanced control strategies for lithium-ion battery charging systems, focusing on improving charging efficiency, stability, and battery safety. Lithium-ion batteries are widely used in electric vehicles and energy storage systems due to their high energy density and reliability, but achieving optimal charging performance remains challenging, especially under dynamic operating conditions.

The study compares three controller types used in Battery Management Systems (BMS): PI, PID, and PSO-optimized PID controllers. While PI controllers are simple and widely used, they suffer from phase lag and poor transient performance. PID controllers improve response by adding derivative action, but they are sensitive to noise and difficult to tune manually. To overcome these limitations, Particle Swarm Optimization (PSO) is used to automatically tune PID gains, resulting in more accurate and stable current tracking.

Two charging methods are analyzed: Multi-Step Constant Current (MSCC), which gradually reduces charging current for safety, and Boost (fast charging), which delivers high initial current for rapid charging followed by a tapering phase. A MATLAB/Simulink framework is used to simulate and compare controller performance across these charging scenarios.

Results show that the PSO-PID controller consistently outperforms PI and PID controllers, achieving better current tracking, reduced ripple, faster settling time, and improved transient response in both charging topologies. Although all controllers maintain similar voltage regulation and state-of-charge (SOC) evolution, PSO-PID provides the most precise and stable control behavior.

Conclusion

The growing demand for smarter and more efficient battery charging solutions has brought the role of feedback controller design into sharp focus, particularly in electric vehicle and energy storage applications where performance and reliability cannot be compromised. This study conducted a simulation-based comparison of three current control strategies applied to a lithiumion battery pack across two charging topologies, revealing a clear and consistent performance hierarchy among the controllers evaluated. The conventional controllers, though functional, demonstrated inherent limitations rooted in the boundaries of classical tuning methods. The PSO-optimised controller, leveraging the well-established global search capability of Particle Swarm Optimisation a technique now widely applied across power systems, robotics, and intelligent energy management consistently delivered superior current regulation, more stable voltage response, and significantly higher power conversion efficiency across every operating condition tested. These findings confirm that incorporating nature-inspired optimisation into battery management controller design is not merely an academic exercise but a practically meaningful advancement with real implications for charging system performance.

Copyright

Copyright © 2026 E. Surya Vamshidhar Rao, T. Murali Krishna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.