El Fallah, S.; Kharbach, J.; Vanagas, J.; Vilkelytė, Ž.; Tolvaišienė, S.; Gudžius, S.; Kalvaitis, A.; Lehmam, O.; Masrour, R.; Hammouch, Z.;
et al. 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. Appl. Sci. 2024, 14, 6648.
https://doi.org/10.3390/app14156648
AMA Style
El Fallah S, Kharbach J, Vanagas J, Vilkelytė Ž, Tolvaišienė S, Gudžius S, Kalvaitis A, Lehmam O, Masrour R, Hammouch Z,
et al. 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. Applied Sciences. 2024; 14(15):6648.
https://doi.org/10.3390/app14156648
Chicago/Turabian Style
El Fallah, Saad, Jaouad Kharbach, Jonas Vanagas, Živilė Vilkelytė, Sonata Tolvaišienė, Saulius Gudžius, Artūras Kalvaitis, Oumayma Lehmam, Rachid Masrour, Zakia Hammouch,
and et al. 2024. "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" Applied Sciences 14, no. 15: 6648.
https://doi.org/10.3390/app14156648
APA Style
El Fallah, S., Kharbach, J., Vanagas, J., Vilkelytė, Ž., Tolvaišienė, S., Gudžius, S., Kalvaitis, A., Lehmam, O., Masrour, R., Hammouch, Z., Rezzouk, A., & Ouazzani Jamil, M.
(2024). 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. Applied Sciences, 14(15), 6648.
https://doi.org/10.3390/app14156648
