State of Charge Estimation on Lithium-Ion Batteries Using Particle Swarm Optimization Method

Abstract

Lithium-ion battery management is crucial as their use grows in devices and electric vehicles. A key aspect is State of Charge (SoC) estimation, which indicates the battery's charge level at any given time. This research aims to develop a method that can provide accurate SoC estimates for Li-ion batteries using the Particle Swarm Optimization (PSO) method. In this research, a 12V 8.4 Ah Lithium-Ion battery was used as a test subject, utilizing a voltage sensor, ACS712 sensor, and LM35 temperature sensor to measure key parameters such as voltage, current, and temperature. The PSO approach was chosen because of its ability to find optimal solutions in complex search spaces, such as SoC estimation in batteries. Through a combination of the PSO algorithm and data generated from sensors, it is hoped that the SoC estimates produced can improve battery usage efficiency, extend service life, and increase the performance of systems that depend on batteries. PSO can provide more accurate predictions with smaller errors, both in terms of the RMSE value of 0.0391 and the MAPE value of 12.028%. The high accuracy of 87.972% of PSO also shows that this method is reliable for applications that require precise SoC predictions. It is hoped that the results of this research can become a basis for further research in the field of battery management and metaheuristic algorithm optimization. After all, this research aims to enhance battery management systems and deepen understanding of PSO-based SoC estimation.

Keywords

• lithium-ion
• sensor
• state of charge
• particle swarm optimization
• battery management