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

Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs

Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.031 · Cited by 1,385 · see citing works

Critical review of state of health estimation methods of Li-ion batteries for real applications

Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van den Bossche, P. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.042 · Cited by 1,126 · see citing works

Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles

He, H., Xiong, R., & Guo, H. (2012) · Applied Energy · doi:10.1016/j.apenergy.2011.08.005 · Cited by 388 · see citing works

Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination

Waag, W., & Sauer, D. U. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.05.001 · Cited by 216 · see citing works

Improved extended Kalman filter for state of charge estimation of battery pack

Sepasi, S., Ghorbani, R., & Liaw, B. Y. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.12.093 · Cited by 187 · see citing works

Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery

Zhang, W., Shi, W., & Ma, Z. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2015.04.148 · Cited by 178 · see citing works

State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation

El Mejdoubi, A., Oukaour, A., Chaoui, H., Gualous, H., Sabor, J., & Slamani, Y. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2509916 · Cited by 170 · see citing works

A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects

Li, K., Wei, F., Tseng, K. J., & Soong, B.-H. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2779411 · Cited by 160 · see citing works

A novel Gaussian model based battery state estimation approach: State-of-Energy

He, H., Zhang, Y., Xiong, R., & Wang, C. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.04.062 · Cited by 127 · see citing works

Dynamic battery cell model and state of charge estimation

Wijewardana, S., Vepa, R., & Shaheed, M. H. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.01.072 · Cited by 43 · see citing works

An in-depth analysis of electric vehicle charging station infrastructure, policy implications, and future trends

Mastoi, M. S., Zhuang, S., Munir, H. M., Haris, M., Hassan, M., Usman, M., Bukhari, S. S. H., & Ro, J.-S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.09.011 · Cited by 523 · see citing works

A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles

Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441 · see citing works

Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications

Shrivastava, P., Naidu, P. A., Sharma, S., Panigrahi, B. K., & Garg, A. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107159 · Cited by 205 · see citing works

A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements

Li, Y., Li, K., Liu, X., Li, X., Zhang, L., Rente, B., Sun, T., & Grattan, K. T. V. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119787 · Cited by 205 · 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

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

Chen, J., Feng, X., Jiang, L., & Zhu, Q. (2021) · Energy · doi:10.1016/j.energy.2021.120451 · Cited by 193 · see citing works

Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression

Li, S., Fang, H., & Shi, B. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107542 · Cited by 190 · 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 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

A review on data-driven SOC estimation with Li-Ion batteries: Implementation methods & future aspirations

D.V.S.R., S., Badachi, C., & Green II, R. C. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108420 · Cited by 98 · see citing works

Online joint estimator of key states for battery based on a new equivalent circuit model

Liu, F., Shao, C., Su, W., & Liu, Y. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104780 · Cited by 87 · 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

A comprehensive review of hybrid battery state of charge estimation: Exploring physics-aware AI-based approaches

Sorouri, H., Oshnoei, A., Che, Y., & Teodorescu, R. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113604 · Cited by 62 · see citing works

Robust state-of-charge estimation of Li-ion batteries based on multichannel convolutional and bidirectional recurrent neural networks

Bian, C., Yang, S., Liu, J., & Zio, E. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108401 · Cited by 55 · see citing works

State of Charge Estimation for Lithium-Ion Battery Based on NARX Recurrent Neural Network and Moving Window Method

Wang, Q., Gu, H., Ye, M., Wei, M., & Xu, X. (2021) · IEEE Access · doi:10.1109/access.2021.3086507 · Cited by 52 · see citing works

A review on state-of-charge estimation methods, energy storage technologies and state-of-the-art simulators: recent developments and challenges

Oloyede, M., Akpakwu, G., Myburgh, H., De Freitas, A., & Kunatsa, T. (2024) · World Electric Vehicle Journal · doi:10.3390/wevj15090381 · Cited by 47 · see citing works

Source-Free Cross-Domain State of Charge Estimation of Lithium-Ion Batteries at Different Ambient Temperatures

Shen, L., Li, J., Zuo, L., Zhu, L., & Shen, H. T. (2023) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2023.3251568 · Cited by 39 · see citing works

Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review

Dini, P., & Paolini, D. (2025) · Batteries · doi:10.3390/batteries11030107 · Cited by 40 · see citing works

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 · see citing works

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

Rehman, A. (2024) · Power System Technology · doi:10.52783/pst.906 · Cited by 12 · see citing works

Battery Impedance Spectroscopy Embedded Measurement System

Cicioni, G., De Angelis, A., Janeiro, F. M., Ramos, P. M., & Carbone, P. (2023) · Batteries · doi:10.3390/batteries9120577 · Cited by 11 · see citing works

Numerical and experimental investigation of state of health of Li-ion battery

Sarmah, S. B., Kalita, P., Das, B., Garg, A., Gao, L., Pai, R. K., & Sarma, M. (2020) · International Journal of Green Energy · doi:10.1080/15435075.2020.1763360 · Cited by 11 · see citing works

Advanced SOH and SOC Prediction Models for Lithium-Ion Batteries: Integrating Machine Learning with Battery Management Systems

Yuan, C., Bao, Z., Li, W., Liu, Z., Zhao, X., & Jiao, K. (2025) · SAE Technical Paper Series · doi:10.4271/2025-01-7015 · Cited by 2 · see citing works