Battery Management System For Mobile Robots based on an Extended Kalman Filter Approch

Abstract

Robots are rapidly developing, due to the technology advances and the increased need for their mobility. Mobile Robots can move freely in unconstrained environments, without any external help. They are supplied by batteries as the only source of energy that they could access. Thus, the management of the energy offered by these batteries is so crucial and has to be done properly. Most advanced Battery Management System (BMS) algorithms reported in literature are developed and verified with laboratory-based experiments. The acquired data is then processed either online or offline, using PC-based software. This work consists of developing an on-Chip Extended Kalman Filter based BMS, which can be directly linked in a robot without having to be connected with an external device to process the data. The proposed system is implemented in a low-cost 8 bit microcontroller and results allow to validate the proposed approach.

A review on energy efficiency in autonomous mobile robots

“…Other studies explore applying AI techniques, such as reinforcement learning for energy-constrained coverage with mobile robots (Lee and Jae Jang, 2022). Additionally, several studies propose different algorithms for battery management, including replacing the battery of an automated tool using serving mobile robots (Kozyr’ et al , 2022) and designing an embedded energy management system for Li-Po batteries based on a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) approach for use in mobile robots (Chellal et al , 2021a).…”

“…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.…”

Exponential Moving Average Extended Kalman Filter for Robust Battery State-of-Charge Estimation

“…Model (8) or (9) provides the evolution of state variables of the battery equivalent circuit. However, the output or the value that is typically measured is the terminal voltage of the battery which is given by eq.…”

Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy

“…Modern technologies are also based on the Battery Management System (BMS). Algorithms are developed and verified in laboratory conditions, presenting the idea of a BMS system using the Kalman filter [21].…”