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

A review on the key issues for lithium-ion battery management in electric vehicles

Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2012.10.060 · Cited by 4,837

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.2004.02.032 · Cited by 1,927

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

Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach

He, H., Xiong, R., & Fan, J. (2011) · Energies · doi:10.3390/en4040582 · Cited by 1,180

State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model

He, H., Xiong, R., Zhang, X., Sun, F., & Fan, J. (2011) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2132812 · Cited by 825

A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation

Hu, C. K., Youn, B. D., & Chung, J. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.08.002 · Cited by 561

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

Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.004 · Cited by 547

A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles

Xiong, R., Sun, F., Chen, Z., & He, H. (2013) · Applied Energy · doi:10.1016/j.apenergy.2013.07.061 · Cited by 529

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

Plett, G. L. (2006) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.06.003 · Cited by 390

Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering

Lee, J.-M., Nam, O., & Cho, B. H. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2007.03.072 · Cited by 332

Lithium-ion Battery State of Energy Estimation Using Deep Neural Network and Support Vector Regression

Shrivastava, P., Soon, T. K., Idris, M. Y. B., Mekhilef, S., & Adnan, S. B. R. S. (2021) · IEEE ECCE Asia Downunder · doi:10.1109/ecce-asia49820.2021.9479413 · Cited by 9

Modeling lithium-ion Battery in Grid Energy Storage Systems: A Big Data and Artificial Intelligence Approach

Miao, Y., He, X., & Gu, C. (2023) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps58381.2023.10128104 · Cited by 7

Effect of Electrode Thickness and Operating Temperature on Electrochemical Performance of Li‐Ion Batteries

Mishra, A., & Shukla, M. (2025) · Energy Storage · doi:10.1002/est2.70172 · Cited by 6

Na3VO4 Nanoparticles on Honeycomb Carbon Networks for Enhanced Lithium Storage Performance

Lu, J., Zhang, D., Li, D., Pei, C., Li, T., Xiao, T., & Ni, S. (2022) · ACS Applied Nano Materials · doi:10.1021/acsanm.1c04122 · Cited by 6

Smart sensing breaks the accuracy barrier in battery state monitoring

Bian, X., Zou, C., Fridholm, B., Sundvall, C., & Wik, T. (2025) · Energy storage materials · doi:10.1016/j.ensm.2025.104410 · Cited by 5

Simple indirect forecast of remaining discharge energy of lithium-ion battery under future complex discharge conditions

Liu, C., Hu, M., Jin, G., Fu, C., Yang, H., & Jia, Q. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.104566 · Cited by 4

A Method for Remaining Discharge Energy Prediction of Lithium-Ion Batteries Based on Terminal Voltage Prediction Model

Cao, Y., Wei, X., Dai, H., & Fang, Q. (2017) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc.2017.8330859 · Cited by 3

Ant Colony Particle Filter Based State of Energy Estimation Method for Lithium-ion Batteries

Liu, X., Zheng, C., Wu, J., He, Y., & Liu, X. (2020) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm49381.2020.9195359 · Cited by 3

Electrochemical-Model-Based Estimation of Li-ion Battery SoC: Performance and Limitations of EKF Method in an Electromobility Use Case

Ali, S., Bratcu, A. I., Thivel, P., & Munteanu, I. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.08.122 · Cited by 2

Scientometric Analysis and Systematic Review of Battery State of Health Estimation Methods for Wheelchair Applications

Farh, H. M. H., & Al-Shamma’a, A. A. (2025) · Journal of Disability Research · doi:10.57197/jdr-2025-0576 · Cited by 2

Physics-informed dual-stream networks for battery health estimation

Yang, Z., Nan, J., Ge, Z., Cai, M., & Zhao, J. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.109973 · Cited by 3

Indirect prediction of remaining discharge energy of lithium-ion batteries: Looking into future complex non-constant temperature operating conditions

Zhu, G., Hu, M., Liu, C., Tang, G., & Xia, Y. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108817 · Cited by 2

Remaining Service Life Prediction of Lithium-Ion Batteries Based on Randomly Perturbed Traceless Particle Filtering

Liu, Y., Chen, J., Yong, J., Yang, C., Yan, L., & Zheng, Y. (2024) · Energies · doi:10.3390/en17215482 · Cited by 2

Gaussian Process Model of Uncertainty in Safety-Critical Autonomous Driving

Kolaric, P., Jing, J., Huang, C., Qu, S., Liu, J., Zhao, Y., & Wang, L. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422367 · Cited by 2

New State of Energy estimator for a lead-acid battery

Pellegrino, L., Alimonti, G., & Redaelli, M. (2018) · AEIT International Annual Conference · doi:10.23919/aeit.2018.8577397 · Cited by 1

State of Charge Estimation Based on Thermal Modeling Compensation Considering Capacity Variation by Internal Temperature Effects of LiFePO4 Battery

Lim, J. H., Heo, G. W., Lim, J.-Y., Kim, D., Jun, B.-S., & Lee, B. (2024) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec48139.2024.10509486 · Cited by 1

Development of supervising system for battery energy storage system based on the two-level architecture

Lin, S., Song, W., Lv, J., Chen, Y., Chen, M., & Feng, Z. (2016) · International Conference on Smart Grid and Clean Energy Technologies · doi:10.1109/icsgce.2016.7876025

New paradigm for battery life management in data centers

Haider, S. N., Zhao, Q., & Yang, W. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865532

Cooperative Estimation of SOC and SOH for Lithium-Ion Battery Based on a Thermo-Electro-Aging ModelPreprint

Liu, Q., Li, Z., Xu, X., Chi, B., & Jiang, J. (2020) · doi:10.46855/energy-proceedings-4174

Electrochemical Terminology | State-of-Charge (SoC)

Wenzl, H., Benger, R., & Hauer, I. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-96022-9.00094-3

Comparative Analysis of Multiple Methods of SOC Estimation in Electric Vehicle

Vanlalhriatpuia, F., Lalhmangaihzuala, F., Matsa, A., & Singh, K. R. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0154-4_9

State of energy estimation during electric vehicle charging based on hybrid machine learning models

Ju, S., & Li, P. (2025) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070251380306

Estimating the health status of lithium-ion batteries using features extracted from relaxation voltage

Wang, D., Wang, B., Ruan, D., & Hong, X. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae19cf

A hybrid multi-head attention and Harris Hawks optimized Long Short-Term Memory model for high-precision lithium-ion battery remaining useful life prediction

Raju, V. A. G., Nayak, J., Dash, P. B., & Mishra, M. (2026) · Applications in Energy and Combustion Science · doi:10.1016/j.jaecs.2026.100527 · Cited by 1

Li-Ion Battery State of Charge Estimation for Electric Vehicles: An Electrochemical Model-Based EKF Validated Using Real Data

Nawaz, U., Bratcu, A. I., & Besançon, G. (2026) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med70602.2026.11598186

State of Charge and State of Energy Estimation for Semi-Solid Lithium-Ion Batteries Based on Multi-Scale Temporal Convolutional Network

Wang, L., Yang, G., Zhao, N., Wang, Z., Li, R., & Zhang, X. (2026) · Batteries · doi:10.3390/batteries12100428

Battery Fundamentals

Williamson, S. S. (2026) · doi:10.1002/9781394334124.ch02

Evaluation of the model-based state-of-charge estimation methods for lithium-ion batteries

Zhang, Y., Xiong, R., & He, H. (2016) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2016.7520184 · Cited by 11

A Data-Driven Based State of Energy Estimator of Lithium-ion Batteries Used to Supply Electric Vehicles

Zhang, Y. Z., He, H. W., & Xiong, R. (2015) · Energy Procedia · doi:10.1016/j.egypro.2015.07.228 · Cited by 20

Uncertainty-Aware State of Energy Estimation for Lithium-Ion Batteries via Hybrid Kernel Sparse Gaussian Process

Xiao, C., Shi, H., Chen, L., Cai, Z., Zou, Y., & Yu, C. (2026) · Batteries · doi:10.3390/batteries12080312 · Cited by 1

A novel lithium-ion battery model for state of charge estimation under dynamic currents

Wu, J., Zhang, C., & Chen, Z. (2015) · International Conference on Electric Power and Energy Conversion Systems · doi:10.1109/epecs.2015.7368513 · Cited by 2

Battery state-of-charge estimation using data-driven Gaussian process Kalman filters

Kwang-Jae, L., Lee, W.-H., & Kim, K. K. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108392 · Cited by 40