Browsing by Author "Megnafi, Hicham"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
- Battery management system for mobile robots based on an extended Kalman filter approchPublication . Chellal, Arezki Abderrahim; Lima, José; Gonçalves, José; Megnafi, HichamRobots 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 veri ed with laboratory-based experiments. The acquired data is then processed either online or of ine, 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.
- Design of an embedded energy management system for li–po batteries based on a dcc-ekf approach for use in mobile robotsPublication . Chellal, Arezki Abderrahim; Gonçalves, José; Lima, José; Pinto, Vítor H.; Megnafi, HichamIn 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
- Dual coulomb counting extended kalman filter for battery SOC determinationPublication . Chellal, Arezki Abderrahim; Lima, José; Gonçalves, José; Megnafi, HichamThe importance of energy storage continues to grow, whether in power generation, consumer electronics, aviation, or other systems. Therefore, energy management in batteries is becoming an increasingly crucial aspect of optimizing the overall system and must be done properly. Very few works have been found in the literature proposing the implementation of algorithms such as Extended Kalman Filter (EKF) to predict the State of Charge (SOC) in small systems such as mobile robots, where in some applications the computational power is severely lacking. To this end, this work proposes an implementation of the two algorithms mainly reported in the literature for SOC estimation, in an ATMEGA328P microcontroller-based BMS. This embedded system is designed taking into consideration the criteria already defined for such a system and adding the aspect of flexibility and ease of implementation with an average error of 5% and an energy efficiency of 94%. One of the implemented algorithms performs the prediction while the other will be responsible for the monitoring.
- Novel SOC monitoring approach for lithium batteriesPublication . Chellal, Arezki Abderrahim; Lima, José; Gonçalves, José; Megnafi, HichamThe key element in storage based systems remains the ability to monitor, control and optimise the performance of one or more modules of these batteries, the type of device performing this task is often referred to as a Battery Management System (BMS). A BMS is a basical units of electrical energy storage systems, a variety of already developed algorithms can be applied to define the main states of the battery, among others: state of charge (SOC), state of health (SOH) and state of functions (SOF) that allow real-time management of the batteries. All research in the field of Extended Kalman Filter (EKF) based BMS is based on bench-scale experiments using powerful softwares, such as MATLAB, for data processing and controllers such as dSPACE. So far, the constraint of computational power limitation is not really addressed in the majority of scientific papers dealing with this subject. This paper proposes an approach to implement an extended Kalman filter linked to a Coulomb counting method, this method called DCC-EKF will allow a better quality monitoring of the battery.