Title: Adaptive parameters identification of lithium-ion batteries with adaptive linear neuron and state-of-charge estimation based on open circuit voltage

Authors: Ghania Aggoun; Djaffar Ould Abdeslam; Rachid Mansouri

Addresses: Faculty of Sciences and Applied Sciences, Electrical Engineering Department, University Akli Mohand Oulhadj-Bouria, Rue Drissi Yahia, Bouira-10000, Algeria ' IRIMAS Laboratory, University of Haute Alsace, 68093, Mulhouse, France ' L2CSP Laboratory, Mouloud Mammeri University, BP 17 RP, Tizi-Ouzou, Algeria

Abstract: The state of charge (SOC) is a critical parameter of a Li-ion battery. An accurate online estimation of the SOC is important for forecasting the electric vehicle driving range. A good estimation of the SOC results from a good identification of the battery parameters. Reducing the algorithm complexity is important to improve the accuracy of SOC estimation results. We propose in this work an original structure of an adaptive linear neuron (ADALINE) to estimate the SOC. The ADALINE provides the weighted sum of the inputs based on an online identification of the open-circuit voltage (OCV). The advantage of this approach is its adaptable capability and the speed of execution (fast training) as well as the possibility of interpreting these weights. The simulation results indicate that the proposed method can ensure an acceptable accuracy of SOC estimation for online application with a maximum error being less than 5%.

Keywords: SOC; state-of-charge; equivalent circuit model; parameter identification; ADALINE; adaptive linear neuron; state observer design; OCV; open-circuit voltage.

DOI: 10.1504/IJMIC.2020.115395

International Journal of Modelling, Identification and Control, 2020 Vol.36 No.1, pp.66 - 77

Received: 30 Apr 2020
Accepted: 03 Aug 2020

Published online: 27 May 2021 *

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