Abstract
The estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems. SoC is one of the most critical parameters that must be determined in real-time to ensure the reliable and safe operation of Li-ion batteries. SoC is a non-measurable parameter, but its value can be derived from other measurable parameters, such as current, voltage, and temperature. Unlike most studies available in the literature, this paper presents a comparative study between two machine learning methods: the Random Forest Regressor (RFR) and the Multi-layer Perceptron (MLP) to accurately estimate the SoC of lithium-ion batteries from data collected under Matlab/Simulink software from a \(LiCoO_2\) battery cell, taking into account the effect of the operating temperature on the battery, and under different current charge/discharge profiles. The results indicate that the Random Forest regressor model is reliable in estimating the SoC with a coefficient of determination of 0.99, a mean error value of less than 0.5%, and a maximum error value of less than 1.83%. In contrast, the MLP yields a mean error value of less than 0.8%, and a maximum error value of less than 1.87%, demonstrating the accuracy and robustness of the Random Forest regressor model for SoC estimation.
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El Fallah, S., Kharbach, J., Rezzouk, A., Ouazzani Jamil, M. (2023). Robust State of Charge Estimation and Simulation of Lithium-Ion Batteries Using Deep Neural Network and Optimized Random Forest Regression Algorithm. In: Masrour, T., Ramchoun, H., Hajji, T., Hosni, M. (eds) Artificial Intelligence and Industrial Applications. A2IA 2023. Lecture Notes in Networks and Systems, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-031-43520-1_4
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