Implementation of the State of Charge Estimation with Adaptive Extended Kalman Filter for Lithium-Ion Batteries by Arduino

Abstract

This study considers the use of Arduino to achieve state of charge (SOC) estimation of lithium-ion batteries by adaptive extended Kalman filter (AEKF). To implement a SOC estimator for the lithium-ion battery, we adopt a first-order RC equivalent circuit as the equivalent circuit model (ECM) of the battery. The parameters of the ECM will be identified through the designed experiments, and they will be approximated by the piecewise linear functions and then will be built into Arduino. The AEKF algorithm will also be programed into Arduino to estimate the SOC. To verify the accuracy of the SOC estimation, some lithium-ion batteries are tested at room temperature. Experimental results show that the absolute value of the steady-state SOC estimation error is small.