Accurate state of charge prediction for lithium-ion batteries in electric vehicles using deep learning and dimensionality reduction

State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review

How, D. N. T., Hannan, M. A., Lipu, M. S. H., & Ker, P. J. (2019) · IEEE Access · doi:10.1109/access.2019.2942213 · Cited by 823 · 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

State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms

Venkatesan, C., Patil, C. K., Karthick, A., Dharmaraj, G., Rahim, R., & Ghosh, A. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12010038 · Cited by 364 · see citing works

A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter

Tian, Y., Lai, R., Li, X., Xiang, L., & Tian, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114789 · Cited by 357 · see citing works

State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach

Tian, J., Xiong, R., Shen, W., & Lu, J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116812 · Cited by 356 · see citing works

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.3004294 · Cited by 337 · see citing works

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

Movassagh, K., Raihan, A., Balasingam, B., & Pattipati, K. R. (2021) · Energies · doi:10.3390/en14144074 · Cited by 268 · see citing works

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques

Hannan, M. A., Lipu, M. S. H., Hussain, A., Ker, P. J., Mahlia, T. M. I., Mansor, M., Ayob, A., Saad, M. H. M., & Dong, Z. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-61464-7 · Cited by 244 · 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

OCV-SOC-Temperature Relationship Construction and State of Charge Estimation for a Series– Parallel Lithium-Ion Battery Pack

Yu, Q., Huang, Y., Tang, A., Wang, C., & Shen, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3252164 · Cited by 213 · see citing works

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Hannan, M. A., How, D. N. T., Lipu, M. S. H., Mansor, M., Ker, P. J., Dong, Z. Y., Sahari, K. S. M., Tiong, S. K., Muttaqi, K. M., Mahlia, T. M. I., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98915-8 · Cited by 196 · see citing works

A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation

Guo, S., & Ma, L. (2023) · Energy · doi:10.1016/j.energy.2022.125872 · Cited by 162 · see citing works

State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter

Xu, Y., Hu, M., Zhou, A., Li, Y., Li, S., Fu, C., & Gong, C. (2019) · Applied Mathematical Modelling · doi:10.1016/j.apm.2019.09.011 · Cited by 152 · see citing works

Review on the State of Charge Estimation Methods for Electric Vehicle Battery

Zhang, M., & Fan, X. (2020) · World Electric Vehicle Journal · doi:10.3390/wevj11010023 · Cited by 149 · see citing works

State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Wei, M., Ye, M., Li, J. B., Wang, Q., & Xu, X. (2020) · IEEE Access · doi:10.1109/access.2020.3031340 · Cited by 148 · see citing works

State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning

Liu, Y., Li, J., Zhang, G., Hua, B., & Xiong, N. N. (2021) · IEEE Access · doi:10.1109/access.2021.3057371 · Cited by 148 · see citing works

State of charge estimation for lithium-ion battery based on Gaussian process regression with deep recurrent kernel

Xiao, F., Li, C., Fan, Y., Yang, G., & Tang, X. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106369 · Cited by 146 · see citing works

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

Zhang, D., Zhong, C., Xu, P., & Tian, Y. (2022) · Machines · doi:10.3390/machines10100912 · Cited by 131 · see citing works

An Immune Genetic Extended Kalman Particle Filter approach on state of charge estimation for lithium-ion battery

Jiang, Z., Shi, Q., Wei, Y., Hanlin, W., Bingzhao, G., & He, L. (2021) · Energy · doi:10.1016/j.energy.2021.120805 · Cited by 117 · see citing works

An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries

Liu, X., Li, K., Wu, J., He, Y., & Liu, X. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102655 · Cited by 114 · see citing works

State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network

Xiong, F., Chen, J., Zhang, Z., Miao, S., & Zhu, Q. (2021) · Energy · doi:10.1016/j.energy.2021.121360 · Cited by 111 · see citing works

State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Hossain Lipu, M. S., Hannan, M. A., Hussain, A., Ayob, A., Saad, M. H. M., & Muttaqi, K. M. (2020) · Electronics · doi:10.3390/electronics9091546 · Cited by 84 · see citing works

State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter

He, Z., Li, Y., Sun, Y., Zhao, S., Lin, C., Pan, C., & Wang, L. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.102593 · Cited by 81 · see citing works

State of charge estimation for lithium-ion batteries based on cross-domain transfer learning with feedback mechanism

Yang, Y., Zhao, L., Yu, Q., Liu, S., Zhou, G., & Shen, W. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108037 · Cited by 79 · see citing works

Review of lithium-ion battery state of charge estimation

Li, N., Zhang, Y., He, F., Zhu, L., Zhang, X., Ma, Y., & Wang, S. (2021) · Global Energy Interconnection · doi:10.1016/j.gloei.2022.01.003 · Cited by 73 · see citing works

Intelligent state of charge estimation of lithium-ion batteries based on L-M optimized back-propagation neural network

Zhang, G., Xia, B., & Wang, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103442 · Cited by 61 · see citing works

Design and investigation of PV string/central architecture for bayesian fusion technique using grey wolf optimization and flower pollination optimized algorithm

Hemalatha, S., Albert, J. R., Banu, G., & Indirajith, K. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117078 · Cited by 54 · 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

Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Xu, P., Li, J., Sun, C., Yang, G., & Sun, F. (2021) · Electronics · doi:10.3390/electronics10020122 · Cited by 35 · see citing works

Robust State of Charge estimation for Li-ion batteries based on Extended State Observers

Sandoval-Chileño, M. A., Castañeda, L., Luviano‐Juárez, A., Gutiérrez-Frías, O., & Vázquez-Arenas, J. G. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101718 · Cited by 30 · see citing works

State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles under Extreme Operating Temperatures Based on an Adaptive Temporal Convolutional Network

Miao, J., Tong, Z., Tong, S., Zhang, J., & Mao, J. (2022) · Batteries · doi:10.3390/batteries8100145 · Cited by 28 · see citing works

State of Charge Estimation of Lithium Batteries in Electric Vehicles Using IndRNN

Venugopal, P., Vigneswaran, T., & Reka, S. S. (2021) · IETE Journal of Research · doi:10.1080/03772063.2021.1906770 · Cited by 27 · see citing works

State of Charge Estimation of Li-Ion Battery Based on Adaptive Sliding Mode Observer

Wang, Q., Jiang, J., Gao, T. Y., & Ren, S. (2022) · Sensors · doi:10.3390/s22197678 · Cited by 24 · 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

Urgent needs for second life using and recycling design of wasted electric vehicles (EVs) lithium-ion battery: a scientometric analysis

Zahoor, A., Kun, R., Mao, G., Farkas, F., Sápi, A., & Kónya, Z. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-33979-3 · Cited by 19 · see citing works

A survey of machine learning applications in advanced transportation systems: Trends, techniques, and future directions

Zhang, Y., Zhang, S., & Dinavahi, V. (2025) · eTransportation · doi:10.1016/j.etran.2025.100417 · Cited by 18 · see citing works

A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles

Tripp-Barba, C., Aguilar-Calderón, J. A., Urquiza-Aguiar, L. F., Zaldívar-Colado, A., & Noriega, A. D. R. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16020057 · Cited by 13 · see citing works

Recent progress and advances of high-entropy polyanionic cathodes in lithium-ion and sodium-ion batteries

Zhao, Q., Dou, S. X., Liu, H.-K., Hu, H., Zhou, L., & Chen, M. (2025) · Chemical Communications · doi:10.1039/d5cc02296g · Cited by 13 · see citing works

Dynamic Load Forecasting in EV Charging Systems Using Deep Neural Networks

Dipto, D. R., Rahman, M. T., Shib, S. K., Shufian, A., & Islam, R. (2025) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest63960.2025.10914382 · Cited by 9 · see citing works

State of charge estimation for the lithium-ion battery based on fractional-order multi-dimensional Taylor network

Yu, W., Zhang, Z., Yan, Z., Zhu, W., Guan, Q., & Tan, N. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113564 · Cited by 8 · see citing works

Review on data-driven state of health estimation methods for lithium-ion batteries

Wang, S., Zhou, J., Li, L., Zhang, L., Fernández, C., & Blaabjerg, F. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.240783 · Cited by 7 · see citing works

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

Sethupathi, M. G., & Azhagiri, M. (2024) · SN Computer Science · doi:10.1007/s42979-024-03426-8 · Cited by 3 · see citing works

Algorithm-oriented benchmarking of deep learning and hybrid architectures for robust SOC estimation in electric vehicle batteries

Demirci, O., & Taşkın, S. (2026) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2026.102286 · Cited by 3 · see citing works

State-of-charge estimation for batteries based on temporal distribution characterization and matching transfer learning framework

Xu, H., Yu, T., Cheng, S., Wu, X., & Hu, Y. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116198 · Cited by 2 · see citing works

Neuroplastic Fuzzy GCN-LSTM: A Hybrid Graph-based Deep Learning Model for Accurate Battery SoC Prediction

Munish Kanna, S., & Narmadha, G. (2025) · Journal of Trends in Computer Science and Smart Technology · doi:10.36548/jtcsst.2025.3.007 · Cited by 2 · see citing works

Promote the Electric Vehicle-Driven Automotive Industry Based on the Effective Scale of Hybrid Green Energy Resources

Liu, B. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425500120 · Cited by 1 · see citing works

Driving-Pattern-Based Ensemble Clustering for SOC Prediction in Battery-Swapping Electric Two-Wheeled Vehicles

Choi, M., Ko, G.-J., Boongoen, T., Iam-On, N., Zu, S., & Cheong, T. (2025) · IEEE Access · doi:10.1109/access.2025.3626273 · Cited by 1 · see citing works

A Novel Integrated CNN-BiGRU-Attention Algorithm For Accurate State of Charge Prediction of Power Batteries

Li, M., Hong, J., Zhang, L., Pei, J., Xie, S., & Wei, J. (2026) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3214/1/012054 · Cited by 1 · see citing works

Enhancing State of Charge Prediction of Lithium-Ion Batteries Through Linear Polynomial Regression – Support Vector Machine Modeling with Temperature Varying and Open Circuit Voltage CompensationPreprint

Koukoyi, E., Wang, S., Li, X., Tao, J., Dablu, B. E., Fernández, C., & Blaabjerg, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5258861

Transient Voltage Stability Margin Evaluation of Novel Power System Based on SDAE-BiLSTM

Jin, W., Xiang, X., Lv, X., Lin, T. T., Li, J., & Li, C. (2025) · doi:10.1109/powercon66300.2025.11294471

A Deep Learning Approach for Electric Vehicles State of Charge Forecasting Based on Battery Metrics, Weather and Geospatial DataPreprint

Abid, M., & Tabaa, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6142671

State of Charge Estimation in Li-Ion Batteries Using Data-Driven Techniques for EVs

S, B. H., Tejaswini, S., Kumar, N. K., & Ramesh, N. (2026) · International Conference on Emerging Trends in Mobile Computing and Sustainable Informatics (ICEMCSI) · doi:10.1109/icemcsi67638.2026.11603226

Joint multi-state estimation of lithium-ion batteries using a temporal convolutional network combined with a bidirectional gated recurrent unit network

Li, A., Li, J., Li, W., Tong, S., Lu, W., Zhu, Y., & Liu, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124555

A comprehensive review of supercapacitor modelling and SOC, SOH, and RUL estimation techniques for energy management systems

Sachan, S., Viumdal, H., Sharma, R., Øyvang, T., & Mishra, S. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109765

Electric Vehicle State-of-charge Prediction using Deep LSTM Network Model

Mohanty, P. K., Jena, P., & Padhy, N. P. (2022) · IEEE International Conference on Power Electronics, Drives and Energy Systems · doi:10.1109/pedes56012.2022.10080466 · Cited by 10 · see citing works

Prediction of State of Charge in Electric Vehicle Lithium-ion Batteries using Deep Learning

Sugashini, R., & Mangaiyarkarasi, S. P. (2024) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict61591.2024.10718483

Li-Ion Battery State of Charge Prediction for Electric Vehicles Based on Improved Regularized Extreme Learning Machine

Zhang, B., & Ren, G. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj14080202 · Cited by 14 · see citing works

State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model

Alhazmi, R. M. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15090385 · Cited by 42 · see citing works

Data Driven Estimation of Electric Vehicle Battery State of Charge Informed By Multi-Physics Modeling

Ragone, M., Yurkiv, V., Ramasubramanian, A., Kashir, B., & Mashayek, F. (2020) · ECS Meeting Abstracts · doi:10.1149/ma2020-02211598mtgabs