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Parameters identification and discharge capacity prediction of Nickel–Metal Hydride battery based on modified fuzzy c-regression models

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Abstract

The battery in the electric vehicles provides the electrical energy necessary to power all electrical and electronic components and main-drive electric motor. So, an accurate estimation of discharge capacity to predict the battery’s end of life is of paramount importance and critical for safe and efficient energy utilization, especially for battery management systems. The resistor–capacitor (RC) equivalent circuit model is commonly used in the literature to model battery. However, a battery is a chemical energy storage system, and then the RC model will therefore be extremely sensitive to the presence of vagueness of information due to that some parameters cannot be directly accessed using sensors. In this paper, we propose a new design methodology for estimating simultaneously the model and the discharge capacity of a Nickel–Metal Hydride (Ni–MH) battery. A modified fuzzy c-regression model algorithm is used to construct a prediction model for a small Ni–MH battery pack. Then, the model, so developed, is used to estimate the discharge capacity of the battery and to predict its remaining useful life. The validity of the proposed method is experimentally verified. According to experimental results, the proposed method can achieve satisfactory results with no more than a 2% error rate for the training and test data sets.

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Abbreviations

\( f_{i}(\mathbf x _{k},{\theta }_{i}) \) :

i-th regression model of the k-th input

\( {\varvec{\sigma }}_{ik}\) :

Standard deviations

\( \varvec{\nu }_{ik}\) :

Fuzzy sets centers

U :

Partition matrix of the membership degrees

\(\delta _{i}\) :

Scale parameter

\(\mu _{ik}\) :

Fuzzy membership degree of each input–output data pair belonging to the ith cluster

\(\mu _{*k}\) :

Fuzzy membership degree of the noise cluster

\(\phi \) :

Cross-correlation function

\(\mathbf x _{k}\) :

System input

c :

Number of rules

\(D_{ik}\) :

Distance (error measure) between the value predicted by the model and the real output

E :

Terminal voltage (V)

I :

Input current (A)

\(I_{o} \) :

Terminal current (A)

m :

Fuzzy weighting exponent

M :

Dimension of input vector

N :

Data number

\(y_{k}\) :

Desired output

\({\varvec{\theta }}_{i}\) :

Parameter vector of the corresponding output model

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Authors and Affiliations

  1. Laboratoire d’Ingenierie des Systèmes Industriels et des Energies Renouvelables, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, 5 Avenue Taha Hussein, BP, 56 Bâb Manara, 1008, Tunis, Tunisia

    Moez Soltani, Achraf Jabeur Telmoudi & Abdelkader Chaari

  2. Equipe des Hydrures Métalliques, laboratoire de Mécanique Matériaux et Procédés, Ecole Nationale Supérieure d’Ingénieurs de Tunis, ENSIT, University of Tunis, Av. Taha Hussein, 1008, Tunis, Tunisia

    Yassine Ben Belgacem

Authors
  1. Moez Soltani
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  2. Achraf Jabeur Telmoudi
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  3. Yassine Ben Belgacem
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  4. Abdelkader Chaari
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Correspondence to Moez Soltani.

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Soltani, M., Telmoudi, A.J., Belgacem, Y.B. et al. Parameters identification and discharge capacity prediction of Nickel–Metal Hydride battery based on modified fuzzy c-regression models. Neural Comput & Applic 32, 11361–11371 (2020). https://doi.org/10.1007/s00521-019-04631-w

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