Enhancing Battery Management Systems: Innovations in State-of-Charge Estimation
DOI:
https://doi.org/10.37628/ijem.v9i2.1075Abstract
A Battery Management System (BMS) is crucial for managing the electronics of rechargeable batteries, ensuring their safe and efficient operation. This is particularly important for lithium-ion (Li-ion) batteries, which are preferred in electric vehicles and energy storage systems due to their high energy density, low self-discharge rates, long cycle life, and broad operating temperature ranges. The primary functions of a BMS include monitoring the state of charge (SOC), managing battery health, and ensuring safety by preventing conditions such as overcharging, over-discharging, and overheating. In this project, the focus is on developing a combined method for SOC estimation and passive equilibrium control, specifically targeting lithium cobalt oxide (LiCoO2) batteries. Accurate SOC estimation is vital for optimizing battery performance and longevity. Traditional methods for SOC estimation can be inaccurate due to the nonlinear and time-varying characteristics of Li-ion batteries. To address this, the project utilizes a Kalman filter, a robust algorithm known for its ability to provide accurate SOC estimates even in the presence of measurement noise and model uncertainties. The project involves the design of an experimental BMS platform that integrates both software and hardware components. The hardware setup includes sensors and control circuits to monitor and manage the battery pack, while the software component involves programming the BMS to implement the Kalman filter for SOC estimation and passive equilibrium control. MATLAB software is used for simulating the SOC estimation process, providing a virtual environment to test and refine the BMS algorithms before deployment. This combined approach aims to enhance the safety, reliability, and efficiency of LiCoO2 battery packs, contributing to the broader goals of advancing electric vehicle technology and energy storage solutions. The integration of accurate SOC estimation and effective equilibrium control within a BMS framework represents a significant step forward in battery management technology.References
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