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Real-Time Battery Conditions Estimation: Energetic framework definition and algorithm implementation for the real-time determination of the batteries’ SoC and SoH

Stefano Pregnolato

Real-Time Battery Conditions Estimation: Energetic framework definition and algorithm implementation for the real-time determination of the batteries’ SoC and SoH.

Rel. Giuseppe Carlo Calafiore. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2019

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Abstract:

This academic work is part of the BAT-MAN research and development industrial project owned by brain Technologies, sponsored by the regional contribution POR FESR 2014-2020 (European fund for the regional development) and whose main goal is the realisation of an electronic device capable of detecting and forecasting, in real-time, the working conditions of a Lead-Acid battery. Entering the team as Algorithm and Control Engineer, I’ve been in charge of analysing the problem, defining experimental campaigns and creating the algorithm for the real-time batteries’ states estimation. The work can be divided into three major sections: 1)Energetic Framework definition 2) Battery modelling 3) Model-based Solution The definition of a rigorous Energetic Framework, that mathematically describes the main quantities necessary to define the state of a battery (SoC, SoH, etc.) and the energy exchanges, was the first solid milestone on which building all the reasoning. Then, a suitable battery model was built in order to define a strategy for the final model-based algorithm, always balancing between computational effort, robustness, required precision and effectiveness. The final solution, implemented in Mathworks environment (Matlab, Simulink, Simscape, Stateflow) was eventually exported with the automatic code generation and the Software Team has been responsible for the micro-controller integration in the first real prototype.

Relatori: Giuseppe Carlo Calafiore
Anno accademico: 2019/20
Tipo di pubblicazione: Elettronica
Numero di pagine: 131
Soggetti:
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-25 - INGEGNERIA DELL'AUTOMAZIONE
Aziende collaboratrici: Brain technologies
URI: http://webthesis.biblio.polito.it/id/eprint/12495
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