A Nonlinear TSNN Based Model of a Lead Acid Battery | Zendy

El Mehdi Laadissi | Zendy; Anas El Filali | Zendy; Malika Zazi | Zendy

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A Nonlinear TSNN Based Model of a Lead Acid Battery

Author(s) -

El Mehdi Laadissi,

Anas El Filali,

Malika Zazi

Publication year - 2018

Publication title -

bulletin of electrical engineering and informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.251

H-Index - 12

ISSN - 2302-9285

DOI - 10.11591/eei.v7i2.675

Subject(s) - nonlinear autoregressive exogenous model , lead–acid battery , nonlinear system , artificial neural network , battery (electricity) , autoregressive model , control theory (sociology) , benchmark (surveying) , engineering , automotive industry , computer science , artificial intelligence , power (physics) , mathematics , control (management) , physics , geodesy , quantum mechanics , aerospace engineering , econometrics , geography

The paper studies a nonlinear model based on time series neural network system (TSNN) to improve the highly nonlinear dynamic model of an automotive lead acid cell battery. Artificial neural network (ANN) take into consideration the dynamic behavior of both input-output variables of the battery charge-discharge processes. The ANN works as a benchmark, its inputs include delays and charging/discharging current values. To train our neural network, we performed a pulse discharge on a lead acid battery to collect experimental data. Results are presented and compared with a nonlinear Hammerstein-Wiener model. The ANN and nonlinear autoregressive exogenous model (NARX) models achieved satisfying results.

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