Design of an Embedded Energy Management System for Li–Po Batteries Based on a DCC-EKF Approach for Use in Mobile Robots

Abstract

In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires higher computing power; thus, the most advanced BMS algorithms reported in the literature are developed and verified by laboratory experiments using PC-based software. The objective of this paper is to describe the design of an autonomous and versatile embedded system based on an 8-bit microcontroller, where a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) algorithm for State of Charge (SOC) estimation is implemented; the developed prototype meets most of the constraints for BMSs reported in the literature, with an energy efficiency of 94% and an error of SOC accuracy that varies between 2% and 8% based on low-cost components.

A review on energy efficiency in autonomous mobile robots

“…Two notable studies have made significant contributions to power management systems in AMRs, focusing on BMSs and innovative power source designs, respectively. Chellal et al (2021aChellal et al ( , 2021b proposed a BMS based on the EKF and an Embedded Energy Management System for Li-Po batteries using a DCC-EKF approach for energy management in mobile robots. These algorithms achieved high energy efficiency and provided more accurate remaining battery capacity predictions for mobile robots without relying on external devices to process data.…”