ETUDE ET DIAGNOSTIC DES BATTERIES DEDIES AUX SYSTEMES PHOTOVOLTAIQUES
| dc.contributor.author | Maamar, SOUAIHIA | |
| dc.date.accessioned | 2020-09-20T07:45:24Z | |
| dc.date.available | 2020-09-20T07:45:24Z | |
| dc.date.issued | 2020-07-04 | |
| dc.description.abstract | The increasing demand for Photovoltaic energy has led to technological advancements in the field of battery technology. State of charge (SOC) estimation is a fundamental function of the battery management system, which is a key to modelling, managing the Lithium-ion battery system. Numerous methods have been developed to estimate the SOC based on the terminal voltage and current measurements of battery. The purpose of this thesis is to establish a robust mapping between open circuit voltage (OCV) and SOC, beside that developing a performed algorithm for SOC estimation with less parameters based on simple electrical circuit model (ECM). An algorithm is capable to track SOC with high precision, take in consideration of low memory and flexible with initial uncertainties. To solve the previous problem, an adaptive extended Kalman filter (EKF) have been adopted and compared with a sliding mode observer (SMO). The results show better speed tracking performance at dynamic and steady state. However, the SMO algorithm provides a better performance, acceptable estimations errors, robustness in different tests compared to the Kalman filter | fr_FR |
| dc.identifier.uri | http://hdl.handle.net/123456789/1294 | |
| dc.language.iso | en | fr_FR |
| dc.publisher | Bachir BELMADANI | fr_FR |
| dc.subject | State of charge | fr_FR |
| dc.subject | Battery management system | fr_FR |
| dc.subject | Kalman filter | fr_FR |
| dc.subject | Sliding mode | fr_FR |
| dc.subject | Lithium-ion battery | fr_FR |
| dc.title | ETUDE ET DIAGNOSTIC DES BATTERIES DEDIES AUX SYSTEMES PHOTOVOLTAIQUES | fr_FR |
| dc.title.alternative | (Study and diagnosis of batteries dedicated to photovoltaic systems) | fr_FR |
| dc.type | Thesis | fr_FR |