State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Untitled work

(2016) · Cited by 11,679 · see citing works

Learning long-term dependencies with gradient descent is difficult

Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542 · see citing works

State estimation for advanced battery management: Key challenges and future trends

Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857 · see citing works

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018) · Journal of Power Sources · doi:10.1016/j.jpowsour.2018.06.104 · Cited by 818 · see citing works

Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Shrivastava, P., Soon, T. K., Idris, M. Y. I. B., & Mekhilef, S. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.06.040 · Cited by 704 · see citing works

State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network

Yang, F., Li, W., Li, C., & Miao, Q. (2019) · Energy · doi:10.1016/j.energy.2019.03.059 · Cited by 543 · see citing works

Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., Karim, T. F., & How, D. N. T. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124110 · Cited by 378 · see citing works

Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm

Hannan, M. A., Lipu, M. S. H., Hussain, A., Saad, M. H. M., & Ayob, A. (2018) · IEEE Access · doi:10.1109/access.2018.2797976 · Cited by 289 · see citing works

Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries

Bian, C., He, H., & Yang, S. (2020) · Energy · doi:10.1016/j.energy.2019.116538 · Cited by 284 · see citing works

State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Sun, D., Yu, X., Wang, C., Zhang, C., Huang, R., Zhou, Q., Amietszajew, T., & Bhagat, R. (2021) · Energy · doi:10.1016/j.energy.2020.119025 · Cited by 242 · see citing works

Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles

Hong, J., Wang, Z., Chen, W., Wang, L. Y., & Qu, C. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101459 · Cited by 217 · see citing works

Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine

Sheng, H., & Xiao, J. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.01.145 · Cited by 202 · see citing works

An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit

Li, C., Xiao, F., & Fan, Y. (2019) · Energies · doi:10.3390/en12091592 · Cited by 180 · see citing works

State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks

Fasahat, M., & Manthouri, M. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228375 · Cited by 174 · see citing works

State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture

Bian, C., He, H., Yang, S., & Huang, T. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227558 · Cited by 143 · see citing works

An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery

Zhang, S., Guo, X., & Zhang, X. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101980 · Cited by 142 · see citing works

Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter

Zhu, R., Duan, B., Zhang, J., Zhang, Q., & Zhang, C. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115494 · Cited by 123 · see citing works

State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications

Sassi, H. B., Errahimi, F., & Es-Sbai, N. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101978 · Cited by 62 · see citing works

Building feature space of extreme learning machine with sparse denoising stacked-autoencoder

Cao, L., Huang, W., & Sun, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.02.096 · Cited by 58 · see citing works

Face recognition via Deep Stacked Denoising Sparse Autoencoders (DSDSA)

Görgel, P., & Simsek, A. (2019) · Applied Mathematics and Computation · doi:10.1016/j.amc.2019.02.071 · Cited by 56 · see citing works

Robust state of charge estimation based on a sequence-to-sequence mapping model with process information

Ma, L., Wang, Z., Yang, F., Cheng, Y., Lu, C., Tao, L., & Zhou, T. (2020) · Journal of Power Sources · doi:10.1016/j.jpowsour.2020.228691 · Cited by 28 · see citing works

Research of stacked denoising sparse autoencoder

Meng, L., Ding, S., Zhang, N., & Zhang, J. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2790-x · Cited by 23 · see citing works

A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM

Ren, X., Liu, S., Yu, X., & Dong, X. (2021) · Energy · doi:10.1016/j.energy.2021.121236 · Cited by 375 · see citing works

SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output

Chen, J., Zhang, Y., Wu, J., Cheng, W., & Zhu, Q. (2023) · Energy · doi:10.1016/j.energy.2022.125375 · Cited by 362 · see citing works

State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries

Yu, H., Zhang, L., Wang, W., Li, S., Chen, S., Yang, S., Li, J., & Liu, X. (2023) · Energy · doi:10.1016/j.energy.2023.127846 · Cited by 96 · see citing works

Joint Estimation of State of Charge and State of Energy of Lithium-Ion Batteries Based on Optimized Bidirectional Gated Recurrent Neural Network

Chen, L., Song, Y., Lopes, A. M., Bao, X., Zhang, Z., & Lin, Y. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3291501 · Cited by 85 · see citing works

An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery

Chen, J., Lu, C., Chen, C., Cheng, H., & Xuan, D. (2022) · Applied Sciences · doi:10.3390/app12052305 · Cited by 42 · see citing works

Deep learning powered rapid lifetime classification of lithium-ion batteries

Fei, Z., Zhang, Z., Yang, F., & Tsui, K.-L. (2023) · eTransportation · doi:10.1016/j.etran.2023.100286 · Cited by 36 · see citing works

Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries

Yao, J., & Kowal, J. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100585 · Cited by 30 · see citing works

Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20 · see citing works

Performance Comparison of Three Types of Autoencoder Neural Networks

Tan, C. C., & Eswaran, C. (2008) · Asia International Conference on Modelling & Simulation (AMS) · doi:10.1109/ams.2008.105 · Cited by 29 · see citing works

A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload

Du, J., Song, L., Zheng, T., et al. (2020) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa.2019.00041

The Learning Effect of Different Hidden Layers Stacked Autoencoder

Xu, Q., Zhang, C., Zhang, L., et al. (2016) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2016.280 · Cited by 21 · see citing works

Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions

Utkin, L. V., DROBINTSEV, P. D., Kovalev, M. S., & Konstantinov, A. V. (2021) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct50888.2021.9347612 · Cited by 5 · see citing works

Assisted Diagnosis Research Based on Improved Deep Autoencoder

Ke, Z.-H., Duan, Y.-K., Tian, Y., et al. (2017) · ITM Web of Conferences · doi:10.1051/itmconf/20171203043