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State of Health Monitoring of Batteries using an edge computing Approach

Sufian Saeed

State of Health Monitoring of Batteries using an edge computing Approach.

Rel. Edoardo Patti. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2021


There is a great importance of batteries in the functioning of many devices. Vehicles occupy a major spot among them. This thesis emphasizes of a smart electronic device for 12V lead-acid batteries. The deduced name of device is BAT-UP! It is able to sample the battery sample and provide it to the final user. Apart from this, the device is capable of providing State Of Health and some parameters such as State of Charge and Time To Criticality. The State Of Health shows the ageing of battery and it is produced using the open circuit voltage function. The second is a prediction of time that when the battery may start to work under critical voltage threshold, and is obtained through reconstruction of voltage profile with RLS estimator. This work was aimed to improve the industrial project which was already initiated by brain technologies. A working prototype was initiated for this purpose having some future adjustments in it. A simplified version of the automotive V-cycle workflow were the first steps. The built-up of algorithm strategy and model based development in simulink followed it. The software which was required for proper functioning of hardware was made by the code for bluetooth low energy (BLE) transmission. The model was deployed in the firmware by auto generated code by simulink. Some hardware testing was done with a simple setup which was very helpful for reviewing it before hand. A bluetooth mobile app was built for testing the communication between BLE and human machine interface. The tests were conducted on a car battery tp test the results realistically and surprisingly the results were up-to mark as expected. By altering battery models using Bat-up Devices, SOH data was created and gathered from 20 distinct batteries. By comparing globally past data in real time, this data has been used to generate warning indicators to the user of a battery that is not working according to design. We can avoid the life of batteries from being ruined in real time by employing this methodology.

Relators: Edoardo Patti
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 78
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: Brain technologies
URI: http://webthesis.biblio.polito.it/id/eprint/20512
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