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State of charge estimation for Li-ion battery based intelligent algorithms

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Abstract

State of charge (SOC) is a crucial index for a battery’s energy assessment. Its estimation is becoming an increasing challenge in order to assure the battery's safety and efficiency. To this end, many methods can be found in the scientific literature with various accuracy and complexity. However, accurate SOC is highly dependent on the adopted methodology. This paper investigates five methods for estimating battery SOC for lithium-ion (Li-ion) manufacturers. For this purpose, five methods were selected and then used in practice, including the modified Coulomb counting method, the extended Kalman filter, the neural network (NN), and two other techniques based on machine learning known as the support vector machines and the K-nearest neighbor algorithm (KNN), respectively. A detailed analysis based on statistical assessment is performed on an experimental test that covers multiple cycles of charge and discharge modes. The KNN method proved to be more accurate than the EKF approach, which is extensively used for estimating the SOC of Li-ion batteries. The algorithm demonstrated great predictive accuracy, with most predictions having a relative error near zero and a maximum error value of roughly 0.26%. All of the findings validate the methodology's reliability and efficiency.

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Acknowledgements

The authors sincerely acknowledges for the material support provided by the laboratory team in the Renewable Energy Equipment (EER) division of the UDES/CDER Solar Equipment Development Unit located in Bousmail/Tipaza.

Funding

This research did not receive any specific grant from funding agencies in public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Centre de Développement des Energies Renouvelables, CDER, 16340, Algiers, Algeria

    Aicha Degla & Madjid Chikh

  2. Laboratory of Communication Equipment and Photovoltaic Energy Conversion, National Polytechnic School, Hassen Badi, El Harrache, Algeria

    Mahdi Mzir & Youcef Belabed

  3. Unité de Développement des Equipements Solaires, UDES, Centre de Développement des Energies Renouvelables, CDER, 42004, Tipaza, Algeria

    Madjid Chikh

  4. Commissariat aux Energies Renouvelables et à l’Efficacité Energétique, CEREFE, 16000, Algiers, Algeria

    Madjid Chikh

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  1. Aicha Degla
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Contributions

The originality of this paper is concerning both theoretical and experimental techniques; it is described as follows: Experimental study of off-grid PV system with Li-ion battery storage is done. Comparative study between five SOC algorithms is carried out. Detailed statistical analysis for a Li-ion battery is discussed. Taylor diagram is performed for the analysis.

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Correspondence to Aicha Degla.

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The work described above is approved by all authors and explicitly of the responsible authorities where the work was carried out: the Photovoltaic Energy Application Laboratory, Renewable Energy Equipment Division of the UDES/CDER Solar Equipment Development Unit, located in Bousmail/TIPAZA. In addition, this work will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher, if accepted.

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Degla, A., Chikh, M., Mzir, M. et al. State of charge estimation for Li-ion battery based intelligent algorithms. Electr Eng 105, 1179–1197 (2023). https://doi.org/10.1007/s00202-022-01728-9

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