Battery State of Charge Estimation Using Kalman Filter
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Resource Overview
This project implements a Kalman filter-based Battery State of Charge (SOC) estimation system, containing two main components: a MATLAB Simulink simulation model and a dedicated Kalman filter algorithm implementation.
Detailed Documentation
This project provides a Kalman filter implementation for estimating battery State of Charge (SOC). The package includes two key files: a MATLAB Simulink simulation model that demonstrates the battery system dynamics, and a separate Kalman filter algorithm file that implements the SOC estimation logic. The Kalman filter algorithm typically incorporates battery model equations, state prediction, and measurement update steps to recursively optimize SOC estimates. By utilizing this program, you can achieve more accurate SOC estimations, which significantly improves battery utilization efficiency and extends battery lifespan through better charge/discharge management. The Simulink model allows users to simulate various operating conditions and validate the filter's performance under different scenarios.
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