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Energy Management of Li-Po Batteries in the Mobile Robotics Domain.

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dc.contributor.author Chellal, Arezki Abderrahim
dc.date.accessioned 2022-01-09T10:09:01Z
dc.date.available 2022-01-09T10:09:01Z
dc.date.issued 2021
dc.identifier.uri http://hdl.handle.net/STDB_UNAM/244
dc.description.abstract The 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 EKF to predict the SOC in small systems such as mobile robots, where computational power in some application is severely lacking. To this end, this work proposes an implementation of 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. One of the implemented algorithms performs the prediction, while the other will be responsible for the monitoring. en_US
dc.language.iso en en_US
dc.publisher Dr. Hicham Megnafi, Prof. Dr. José Lima, Prof. Dr. José Gonçalves en_US
dc.subject Prediction Algorithm - Battery Management System - Extended Kalman Filter - Coulomb Counting Algorithm - Engineering applications. en_US
dc.title Energy Management of Li-Po Batteries in the Mobile Robotics Domain. en_US
dc.type Thesis en_US


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