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Communication Dans Un Congrès Année : 2020

Short-Circuit Fault Diagnosis and State Estimation for Li-ion Battery using Weighting Function Self-Regulating Observer

Jianwen Meng
Moussa Boukhnifer
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  • PersonId : 1362687
Tianzhen Wang

Résumé

A wrong estimation of Li-ion battery state of charge (SOC) can seriously affect the safety, the availability and the reliability of electrical-powered systems particularly for embedded applications. One of the main cause is the occurrence of non-detected battery soft short-circuit (SC). Hence we propose to develop an observer that can simultaneously estimate the SOC and the SC current. To deal with the open-circuit voltage (OCV)-SOC non-linear characteristic, we have used the Takagi-Sugeno fuzzy model to decompose the characteristic into segments for different SOC ranges. The membership functions are optimised with a genetic algorithm. Further on each segment a proportional-integral observer is designed by solving Linear Matrix Inequalities (LMI). Thanks to the slowly inherent varying SOC, a weighting function self-regulating mechanism is used along with Takagi-Sugeno fuzzy model to merge the g observers. Intensive numerical simulations using real cell parameters show that this structure is able to accurately estimate the SOC and the short circuit current at an early stage.
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hal-04505715 , version 1 (15-03-2024)

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Jianwen Meng, Moussa Boukhnifer, Demba Diallo, Tianzhen Wang. Short-Circuit Fault Diagnosis and State Estimation for Li-ion Battery using Weighting Function Self-Regulating Observer. 2020 Prognostics and Health Management Conference (PHM-Besançon), May 2020, Besancon, France. pp.15-20, ⟨10.1109/phm-besancon49106.2020.00010⟩. ⟨hal-04505715⟩
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