Browsing by Author "Bouchikh, Mohamed Amine"

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    Optimized AI-based real-time state of charge (SOC) estimation of lithium-ion batteries.
    (Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2024) Bouchikh, Mohamed Amine; Tidjani, Mohamed Redha; Touzout, Walid (Supervisor)
    Lithium-ion (Li-ion) batteries are highly valued for their ability to extend battery lifespan and enhance power energy density due to their chemical properties. The battery State of Charge (SOC) is a crucial parameter for monitoring battery health and estimating its lifespan, indicating how much longer the battery can be used and when it needs to be charged. Therefore, accurate SOC predictions are essential to prevent overcharging or over-discharging, and can be determined using conventional methods or data-driven approaches. In this report, we present a novel approach for SOC estimation of Li-ion batteries using optimized machine learning-based SOC estimation in both charging and discharging modes. The models are trained, tested, and optimized using a prognostic Li-ion battery dataset provided by the National Aeronautics and Space Administration (NASA) with five main inputs : load voltage, load current, measured voltage, measured current, and primarily battery temperature, the report is key for preferring data-driven approaches over conventional methods. Initially, nine different methods were evaluated : LASSO, K-Neighbor Regressor, CAT Boost Regressor, Extra Tree Regressor, Random Forest Regressor, XGB Regressor, Decision Tree Regressor, and Gradient Boosting Regressor, These methods were assessed in terms of their accuracy and the evaluation metrics R2, MAE, RMSE, with Four approaches showing promising results according to state-of-the-art applications namely : Extra Tree, XGB, DTR and Gradient Boot Regressors. Moreover, the novelty of this report involves hyperparameter tuning, including learning rate, maximum tree depth, number of trees and more, to find the optimal parameters for the three best models. Thus, GridSearchCV optimization method demonstrated significant improvement in terms of model evaluation metrics. Finally, the best approach (Extartree regressor) for charging and discharging was deployed onto an ESP32 microcontroller with an OLED interconnected with current, voltage, and temperature sensors for real-time battery SOC display and monitoring.