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Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack

Youssef Cheddadi(1*), Omar Diouri(2), Ahmed Gaga(3), Fatima Errahimi(4), Najia Es-Sbai(5)

(1) Sidi Mohamed Ben Abdellah University, Faculty of Sciences and Technology, Laboratory of Renewables Energy and Intelligent Systems, Morocco
(2) Sidi Mohamed Ben Abdellah University, Faculty of Sciences and Technology, Laboratory of Renewables Energy and Intelligent Systems, Morocco
(3) Sidi Mohamed Ben Abdellah University, Faculty of Sciences and Technology, Laboratory of Renewables Energy and Intelligent Systems, Morocco
(4) Sidi Mohamed Ben Abdellah University, Faculty of Sciences and Technology, Laboratory of Renewables Energy and Intelligent Systems, Morocco
(5) Sidi Mohamed Ben Abdellah University, Faculty of Sciences and Technology, Laboratory of Renewables Energy and Intelligent Systems, Morocco
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v10i2.11957

Abstract


State of charge (SOC) estimation is an important parameter that has to be determined by the battery management system, especially in electric vehicle and smart grid applications. SOC is an unmeasured parameter. Therefore, it must be inferred from other measured quantities from each battery such as discharge current, battery terminal voltage and temperature. There are various methods to estimate the battery’s SOC: Modeless approaches, Data driven nonlinear models and Model based observers. In this work, we adopted the second approach. An optimal feed forward-neural-network (FFNN) based battery model was suggested to simulate the complete dynamic electrical features of the battery and estimate accurately its SOC. Different charge/discharge current profiles are taken into account during training steps to improve the neural network model robustness and estimation accuracy. The obtained results show that FFNN, which is trained with an importance sampling data, is an accurate estimator for SOC.
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Keywords


Lithium-Ion Battery; SOC Estimation; Feed-Forward Neural Network; Battery Management System; Importance Sampling; Training Algorithm; Electric Vehicle; Micro-Grid

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References


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