Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

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TimeGAN-Based Diversified Synthetic Data Generation Following BERT-Based Model for EV Battery SOC Prediction: A State-of-The-Art Approach

Mohanty, P. K., Jena, P., & Padhy, N. P. (2025) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2025.3534165 · Cited by 19

Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm

Hou, J., Wang, X., Su, Y., Yang, Y. J., & Gao, T. (2022) · Applied Sciences · doi:10.3390/app12147332 · Cited by 17

SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm

Song, Y., Lu, J., Zhang, H., & Liu, G. (2025) · Energy Science & Engineering · doi:10.1002/ese3.1992 · Cited by 16

State-of-Charge Estimation Using Triple Forgetting Factor Adaptive Extended Kalman Filter for Battery Energy Storage Systems in Electric Bus Applications

ElMenshawy, M. S., Massoud, A. M., & Guglielmi, P. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3514704 · Cited by 16

A cycle-aware and physics-informed framework for battery remaining useful life prediction

Chen, Y., Wu, Y., & Li, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-28505-5 · Cited by 16

Robust and data-efficient battery state of charge estimation via transfer learning-enhanced physics-informed neural networks

Hong, S., Kim, H., Kim, J., & Baek, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.120671 · Cited by 16

Overcoming limited battery data challenges: A coupled neural network approach

Herle, A., Channegowda, J., & Prabhu, D. (2021) · International Journal of Energy Research · doi:10.1002/er.7081 · Cited by 16

Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

Lee, K., Han, J., & Kim, K. (2022) · The Plant Pathology Journal · doi:10.5423/ppj.nt.04.2022.0062 · Cited by 16

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

Kazmi, S. N. A., Ulasyar, A., Khattak, A., & Zad, H. S. (2022) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312221128526 · Cited by 16

A Recurrent Neural Network with Long Short-Term Memory for State of Charge Estimation of Lithium-ion Batteries

Li, C., Xiao, F., Fan, Y., Yang, G., & Zhang, W. (2019) · 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785770 · Cited by 29

State of Charge Estimation using Recurrent Neural Networks with Long Short-Term Memory for Lithium-Ion Batteries

Bockrath, S., Roßkopf, A., Koffel, S., Waldhör, S., Srivastava, K., & Lorentz, V. (2019) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2019.8926815 · Cited by 45

A Novel State of Charge Estimation Method of Batteries Using Recurrent Neural Networks

Cheng, A., & Wang, Y. (2018) · Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018) · doi:10.2991/ncce-18.2018.199 · Cited by 2

A State-of-Charge Estimation Method based on Bidirectional LSTM Networks for Lithium-ion Batteries

Zhang, Z., Xu, M., Ma, L., & Yu, B. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305394 · Cited by 28

State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method

Chung, D., Ko, J.-H., & Yoon, K.-Y. (2022) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-021-00954-8 · Cited by 85