Hardware implementation of an algorithm based on kalman filtrer for monitoring low capacity Li-ion batteries

Abstract

In this paper, we introduce an algorithm based on an adaptive Kalman filter algorithm for estimating the state of charge of low capacity Li-ion batteries. Using the first order model with a static characterization, good results have been reached and the algorithm converges even with random initial SoC values and has represented no cumulative error drawbacks. This algorithm has been validated, simulated and implemented on a hardware platform based on a microcontroller for an online SoC estimation for multimedia application.