| Title |
Advanced state of charge estimation using deep neural network, gated recurrent unit, and long short-term memory models for lithium-Ion batteries under aging and temperature conditions / |
| Authors |
El Fallah, Saad ; Kharbach, Jaouad ; Vanagas, Jonas ; Vilkelytė, Živilė ; Tolvaišienė, Sonata ; Gudžius, Saulius ; Kalvaitis, Artūras ; Lehmam, Oumayma ; Masrour, Rachid ; Hammouch, Zakia ; Rezzouk, Abdellah ; Ouazzani Jamil, Mohammed |
| DOI |
10.3390/app14156648 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2024, vol. 14, iss. 15, art. no. 6648, p. 1-31.. ISSN 2076-3417 |
| Keywords [eng] |
lithium-ion battery ; GRU neural network ; LSTM neural network ; state of charge ; deep learning ; electrical vehicle |
| Abstract [eng] |
Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2024 |
| CC license |
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