Abstract:

In this paper, we propose a state of charge(SOC) estimation of lithium-ion batteries found in robots based on a fractional order model to better estimate battery percentage. Using evolutionary algorithms(EA) we aim to create a program that uses the estimated values of the battery percentage to determine the most efficient route for the robot to take. The reliability of the battery management system is directly affected by the accuracy of state of charge (SOC) estimation, so an approach utilising an Extended Kalman Filter(EKF)  has been chosen to facilitate the desired precision of the estimation.