A machine learning approach for state-of-charge estimation of Li-ion batteries

Heba Yahia Youssef; Latifa A. Alkhaja; Hajar Humaid Almazrouei; Ali Bou Nassif; Chaouki Ghenai; Mohammad A. AlShabi

doi:10.1117/12.2632624

6 June 2022 A machine learning approach for state-of-charge estimation of Li-ion batteries

Heba Yahia Youssef, Latifa A. Alkhaja, Hajar Humaid Almazrouei, Ali Bou Nassif, Chaouki Ghenai, Mohammad A. AlShabi

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 1211329 (2022) https://doi.org/10.1117/12.2632624
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

The state-of-charge (SOC) of Li-ion batteries is an important parameter for regulating and ensuring the safety of batteries, especially for Electric Vehicle applications, where accurate SOC estimation is important for remaining driving range prediction. The SOC is conventionally obtained through different indirect measurement methods that are either impractical or inaccurate. Data-driven approaches for SOC estimation are becoming more popular as an alternative to the conventional estimation methods due to their accuracy and low complexity. In this work, we apply 4 machine learning algorithms: Multiple Linear Regression (MLR), Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forest (RF) to predict the SOC using voltage, current, and temperature measurements from mixed driving cycles datasets, with 50000 instances. Out of the 4 models, the Random forest model performed the best with an MAE of only 0.82%.

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Heba Yahia Youssef, Latifa A. Alkhaja, Hajar Humaid Almazrouei, Ali Bou Nassif, Chaouki Ghenai, and Mohammad A. AlShabi "A machine learning approach for state-of-charge estimation of Li-ion batteries", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 1211329 (6 June 2022); https://doi.org/10.1117/12.2632624

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