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

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An improved Transformer incorporating fuzzy information entropy and average input strategy for SOC estimation of lithium-ion battery

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A novel reduced-order electrochemical model of lithium-ion batteries with both high fidelity and real-time applicability

Xie, Y., Xu, J., Jin, C., Jia, Z., & Mei, X. (2024) · Energy · doi:10.1016/j.energy.2024.132425 · Cited by 20 · 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

Edge-cloud collaborative method for state of charge estimation of lithium-ion batteries by combining Kalman filter and deep learning

Jin, Z., Fu, S., Fan, H., Tao, Y., Dong, Y., Wang, Y., & Sun, Y. (2025) · Energy · doi:10.1016/j.energy.2025.137234 · Cited by 19 · see citing works

Data-driven lithium-ion battery SOH prediction: A novel SHMM-transformer-BiGRU hybrid neural network method

Liu, Z., Liu, Y., Zhang, Y., Wu, C., Zhang, S., & Sun, C. (2025) · Measurement · doi:10.1016/j.measurement.2025.118579 · Cited by 17 · see citing works

Critical review on improved hybrid deep learning modeling strategies for online battery state of charge estimation in large-scale energy storage systems

Wang, S., Hu, B., Zhang, Q., Dang, Q., & Fernández, C. (2026) · Journal of Power Sources · doi:10.1016/j.jpowsour.2026.239714 · Cited by 15 · see citing works

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

Lu, W., Yuan, Z., Wang, T., Li, P., & Zhang, Y. (2024) · eTransportation · doi:10.1016/j.etran.2024.100372 · Cited by 14 · see citing works

Simplified numerical modeling and analysis of electrolyte behavior in multiple physical fields for lithium-ion batteries

Yu, H., Yang, K., Zhang, L., Wang, W., Zhang, Z., Shen, L., Chen, S., Yang, S., Li, J., & Liu, X. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.108536 · Cited by 14 · see citing works

An Improved AhI Method With Deep Learning Networks for State of Charge Estimation of Lithium-Ion Battery

Chang, W.-E., & Kung, C. (2024) · IEEE Access · doi:10.1109/access.2024.3389969 · Cited by 13 · see citing works

Data-physics-driven co-estimation of capacity and state of charge for lithium-ion batteries in electric vehicle

Zhou, D., Zhu, Z., Jiang, W., Li, C., Ma, Y., Wang, W., Tong, Q., & Lu, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2024.115188 · Cited by 12 · see citing works

An improved particle swarm optimization-adaptive square root Unscented Kalman filter algorithm for accurate state of charge estimation of lithium-ion batteries

Dang, Q., Shi, K., Wang, S., Hu, B., Gao, Z., & Fernández, C. (2026) · Energy · doi:10.1016/j.energy.2026.140316 · Cited by 12 · see citing works

Research on lithium battery state of charge estimation based on improved Adaptive Iterative eXogenous Kalman Filter and multidimensional estimation scheme

He, Z., Fu, X., Pan, C., Zhang, X., Ji, X., & Hu, S. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112763 · Cited by 12 · see citing works

Online state-of-charge and capacity co-estimation for lithium-ion batteries under aging and varying temperatures

Son, D., Song, Y., Park, S., Oh, J., & Kim, S. (2025) · Energy · doi:10.1016/j.energy.2025.134434 · Cited by 12 · see citing works

Deep learning based emulator for predicting voltage behaviour in lithium ion batteries

Oka, K., Tanibata, N., Takeda, H., Nakayama, M., Noguchi, S., Karasuyama, M., Fujiwara, Y., & Miyuki, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-80371-9 · Cited by 12 · see citing works

Small-Sample Battery Capacity Prediction Using a Multi-Feature Transfer Learning Framework

Lu, X., Yang, X., Wang, X., Shi, Y., Wang, J., Yao, Y., Gao, X., Xie, H., & Chen, S. (2025) · Batteries · doi:10.3390/batteries11020062 · Cited by 10 · see citing works

Thermal Runaway Propagation Characteristics of Lithium-Ion Batteries with Different Cathode Materials: A Comparative Study

Li, Y., Wang, H., Wang, S., Xu, L., Li, Y., Sun, J., & Gao, Y. (2025) · Fire Technology · doi:10.1007/s10694-025-01703-3 · Cited by 10 · see citing works

Capacity prediction of lithium-ion batteries based on ensemble empirical mode decomposition and hybrid machine learning

Gao, K., Sun, J., Huang, Z., & Liu, C. (2024) · Ionics · doi:10.1007/s11581-024-05768-y · Cited by 10 · see citing works

Enhanced accuracy in state-of-charge estimation for lithium-ion batteries in electric vehicles using augmented adaptive extended Kalman filter

yadav, R., Manas, M., & Dubey, R. K. (2024) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2024.100868 · Cited by 10 · see citing works

Optimizing State of Charge Estimation in Lithium–Ion Batteries via Wavelet Denoising and Regression-Based Machine Learning Approaches

Al-Hiyali, M. I., Kannan, R., & Shutari, H. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16060291 · Cited by 9 · see citing works

State of charge estimation for lithium-ion battery using a multi-feature Mamba network and UKF under mixed operating conditions

Li, P., Ye, J., Hou, J., Deng, Z., & Xiang, S. (2025) · Energy · doi:10.1016/j.energy.2025.137539 · Cited by 9 · see citing works

Battery Passport and Online Diagnostics for Lithium-Ion Batteries: A Technical Review of Materials–Diagnostics Interactions and Online EIS

Tahir, M. U., Ibrahim, T., & Kerekes, T. (2025) · Batteries · doi:10.3390/batteries11120442 · Cited by 9 · see citing works

Rethinking Pooling for Multi-Granularity Features in Aerial-View Geo-Localization

Wang, T., Yang, Z., Chen, Q., Sun, Y., & Yan, C. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3484330 · Cited by 8 · see citing works

Enhanced SOC estimation method for lithium-ion batteries using Bayesian-optimized TCN–LSTM neural networks

Hu, T., Zhu, X., & Tian, M. (2025) · AIP Advances · doi:10.1063/5.0243811 · Cited by 8 · see citing works

State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States

Ozer, O., & Arabacı, H. (2025) · IEEE Access · doi:10.1109/access.2025.3591970 · Cited by 8 · see citing works

SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance

Hu, L., Tang, J., Xu, F., & Liang, X. (2025) · Energy · doi:10.1016/j.energy.2025.135935 · Cited by 7 · see citing works

Intelligent Battery Systems: System‐Level Integration of Data‐Driven Learning, Optimisation and Predictive Management

Ametefe, D. S., Sukor, N. S. A., John, D., Ametefe, G. D., Aliu, A. A., Owen, M. M., & Salaudeen, T. O. (2026) · Battery energy · doi:10.1002/bte2.70099 · Cited by 7 · see citing works

An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries

Tao, J., Wang, S., Cao, W., Zhang, M., & Bobobee, E. D. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.112412 · Cited by 7 · see citing works

AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model

Zhou, T., Gui, P., Hu, C., Xiao, W., Liu, D., & Wang, Z. (2025) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2025.120039 · Cited by 6 · see citing works

A review of attention-enhanced deep-learning models for state-of-charge estimation in lithium-ion batteries: Current progress and future directions

Shah, S. A. A., Mahrukh, Shah, S. M. A., Wang, S., Niazi, S. G., Xu, Z., & Wu, M. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121207 · Cited by 7 · see citing works

Augmented characteristic prediction model of lithium-ion battery under multi-temperature conditions: Reconstructed open circuit voltage and thermal effect jointly driven approach

Wei, X., Ma, T., Sun, J. F., Li, M., Wu, L., Liu, C., Wei, Z., Lyu, Z., & Li, H. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123406 · Cited by 6 · see citing works

State of Charge Estimation of Lithium-Ion Battery for Underwater Vehicles Using MM-UKF Under Hierarchical Temperature Compensation

Yang, G., Fan, X., Li, R., & Zhang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3425950 · Cited by 6 · see citing works

Hyperparameter-optimized machine learning model for real-time estimation of lithium-ion battery state-of-charge

Touzout, W., Chikh, M. A. A., Gougam, F., Afia, A., Bouchikh, M. E.-A., & Tedjani, M. R. (2025) · Electrical Engineering · doi:10.1007/s00202-025-02997-w · Cited by 6 · see citing works

A Dual-Driven SOC Estimation Framework: Fusion of Multiscale Temporal Encoding Network and EKF Based on Feature Dimensionality Reduction

Wan, X., Chen, Z., Zhang, C., Hu, W., Wu, T., & Zhang, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3587361 · Cited by 6 · see citing works

Non-destructive and rapid parameter identification of a simplified electrochemical model for lithium-ion batteries via multi-step and physical-informed methods

Yu, H., Zhang, Z., Zhang, H., & Yang, S. (2025) · Energy storage materials · doi:10.1016/j.ensm.2025.104346 · Cited by 5 · see citing works

Accurate Co-Estimation of SOC and SOH for Lithium-Ion Batteries Across Lifespan and Wide Temperature Ranges

Shen, J., Liu, X., Zhou, C., Shu, X., Liu, Y., Chen, Z., & Wei, F. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3635907 · Cited by 5 · see citing works

Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions

Dwivedi, S., Akula, A., & PECHT, M. G. (2024) · Energy · doi:10.1016/j.energy.2024.133052 · Cited by 5 · see citing works

Transfer learning for state of charge estimation across batteries and chemistries: a lightweight, physics-guided LSTM with regime-aware temporal attention and staged adaptation

Özarslan, E. B., & Kursun, S. (2025) · Ionics · doi:10.1007/s11581-025-06846-5 · Cited by 4 · see citing works

Joint Model Parameter Identification and Extended Kalman Filter Algorithm for the State of Charge Estimation of Lithium Iron Phosphate Battery

Liang, G., Lin, S., Hu, W., Zhang, X., & Yang, J. (2024) · Journal of Electrochemical Energy Conversion and Storage · doi:10.1115/1.4066637 · Cited by 3 · see citing works

A pseudo-linear autoregressive battery model for joint estimation of state of charge and state of power

Cheng, C., Zeng, X., Xia, X., Chen, L., Xiang, L., & Liu, X. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118103 · Cited by 2 · see citing works

A Data-Driven Method with Fusing Mechanism Information for Li-Ion Battery State of Charge Estimation

Xiao, Z., Rao, J., Ji, C., Ma, F., Wang, J., & Sun, W. H. (2025) · Processes · doi:10.3390/pr13113597 · Cited by 2 · see citing works

Mathematical modelling of ion diffusion and state of charge prediction in sodium ion batteries with time series analysis

Shivaranjini, S., Srivastava, N., & Hristov, J. Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-37522-x · Cited by 2 · see citing works

FedGNN-SFCP: Federated graph neural networks for ship fuel consumption prediction with heterogeneous data fusion and missing data imputation

Lu, H., Pan, X., Lim, W. Y. B., & Yan, R. (2026) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2026.105016 · Cited by 2 · see citing works

Real‐Time State of Charge Estimation for Tri‐Electrode Rechargeable Zinc–Air Flow Batteries via Pulse Response

Lao-atiman, W., Bumroongsri, P., Olaru, S., & Kheawhom, S. (2025) · International Journal of Energy Research · doi:10.1155/er/9928721 · Cited by 2 · see citing works

Online State of Charge Estimation for Battery Based on the Improved Double Fraction‐Order Multi‐Innovation Unscented Kalman Filter With Full Parameters Identification and Self‐Rectification of Covariance Matrices

Xing, L., Ren, H., & Zhang, Z. (2024) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.4404 · Cited by 1 · see citing works

Comparative Analysis of State of Charge Estimation Techniques for Electric Vehicle Batteries using various Artificial Neural Networks

Makkar, P., & Singh, S. B. (2025) · International Conference on Energy, Power and Environment (ICEPE) · doi:10.1109/icepe65965.2025.11139794 · Cited by 1 · see citing works

One 2 : An Intrusion Detection System for Both Internal and External Vehicular Network From Weak Labeled Data

Lu, H., Ren, Y., Liu, Y., Zhao, Y., Guo, K., Yang, Y., & Du, H. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3629862 · Cited by 1 · see citing works

Enhanced state of charge estimation and surface strain evolution of lithium-ion batteries via quasi-distributed optical sensing

Sheng, W., Yuan, R., & Peng, G. (2025) · Measurement · doi:10.1016/j.measurement.2025.119726 · Cited by 1 · see citing works

Accurate state of charge estimation of lithium-ion batteries using XGBoost optimized with driving training-based optimization

Karthikeyan, M., & Sugirtha, T. (2026) · Ionics · doi:10.1007/s11581-026-07116-8 · Cited by 1 · see citing works

State monitoring and intelligent management of lithium-ion batteries based on advanced sensing technology: From principles to application

Shi, Q., Xu, M., Li, J. J., Zhang, N., Liu, S., Wang, R., Xu, C., He, X., Li, H., Zhou, M., et al. (2026) · Energy storage materials · doi:10.1016/j.ensm.2026.105124 · Cited by 1 · see citing works

Research on SOC fusion estimation of lithium iron phosphate batteries over a wide temperature range

Kou, F., Xing, F., Tianxiang, Y., & Xuwei, D. (2026) · Ionics · doi:10.1007/s11581-026-07082-1 · Cited by 1 · see citing works

Prompt-guided large language model for estimating the state of charge of containerized lithium-ion battery energy storage systems

Liu, Y., Mao, Z., & Chen, Z. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123069 · Cited by 1 · see citing works

Early predicting state of health of lithium-ion battery based on dynamic dataset partitioning and optimized Gaussian process regression

Wang, C., Hui, Z., Shi, Z., Liao, H., Hou, Q., & Sun, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123209 · Cited by 1 · see citing works

Hybrid energy management system and topology of fuel cell-based electric vehicles: Key problems, progress, and future prospects

Waseem, M., Ahmad, M., & Lakshmi, G. S. (2026) · Next Energy · doi:10.1016/j.nxener.2026.100759 · Cited by 1 · see citing works

A blockchain‐based trusted vehicle service recommendation scheme in intelligent transport systems

Chen, J., Lu, H., Xu, W., Yang, Y., Amoon, M., & Kumari, S. (2024) · Security and Privacy · doi:10.1002/spy2.452 · Cited by 1 · see citing works

Fused Physical Information and Deep Learning for Estimating Lithium-Ion Battery State of Health in Multi-Dynamic OperationsPreprint

Tang, A., Xu, Y., Hu, Y., Tian, J., Nie, Y., Yan, F., Tan, Y., & Yu, Q. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4648736

A Lightweight Channel Correlation Invertible Network for Image Denoising

Sui, F., Wang, H., & Zhang, F. (2025) · IET Image Processing · doi:10.1049/ipr2.70119

Battery Degradation Trajectory Early Prediction with Degradation Recognition and Physics-Guided Under Different Charging StrategiesPreprint

Lin, Y., Wan, F., Yang, D., Li, S., Liu, R., Wenwei, Y., Mu, J., & Chen, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5372989

An enhanced bilayer long short-term memory method for energy consumption estimation of electric buses with real-time passenger load

Shan, X., Li, Q., Wan, C., Ouyang, M., Peng, H., Wu, G., & Barth, M. (2025) · Energy · doi:10.1016/j.energy.2025.138726

A tanks-in-series model for sodium-ion batteries

Nilugal, M. L., Subramanian, V. R., & Ramadesigan, V. (2025) · Ionics · doi:10.1007/s11581-025-06857-2

Strain variability at precise measuring point on 18650 Li-ion cylindrical cells and its use in degradation prediction under lithium plating condition

Gutierrez, R., Herrán, A., Arcelus, O., Gucciardi, E., Granados, L., Rikarte, J., & Arteta, E. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.120125

Advanced gradient descent optimization via a gradient-boosting regressor model for accurate state-of-charge estimation of lithium-ion batteries

Okemakinde, F. E., Adejare, A. A., Yun, S.-T., & Kim, J. (2026) · International Journal of Green Energy · doi:10.1080/15435075.2026.2628959

A SOC estimation method for flow batteries based on EIS and LSTM-TCN neural network

Wang, M., Zhao, Y., & Zheng, M. (2025) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc66000.2025.11392936

AI‐Organized Multiscale Battery Modeling: Linking Structure and Property from Quantum to Device Scales

Jin, S., WANG, Z., Li, M., & Deng, Z. (2026) · Small Structures · doi:10.1002/sstr.70446

A Physics-Informed iTransformer and Kolmogorov–Arnold Network Hybrid Architecture for Lithium-Ion Battery State-of-Health Estimation

孙光秀, Zhang, Q., & Zhou, Z. (2026) · Energy & Fuels · doi:10.1021/acs.energyfuels.6c01985

State of charge estimation for lithium-ion batteries under dynamic noisy conditions using a physics-constrained extended long short-term memory–sparse transformer

Xiao, Y., Wang, F., Liu, W., & Wan, F. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.123570

An error compensation strategy for state-of-charge estimation based on ESPM and AOO-BPNN

Li, C., Ma, Z., & Zhou, Z. (2026) · Ionics · doi:10.1007/s11581-026-07273-w

A Review of State-of-Charge Estimation Methods for Lithium-Ion Batteries Based on Improved Particle Filtering Algorithms

Wang, S., Zhang, L., Fall, M., Bobobee, E. D., & Fernández, C. (2026) · Journal of Energy Engineering · doi:10.1061/jleed9.eyeng-6743

Critical pretreatment technologies for recycling lithium-ion batteries: A review

Zhu, Y., Li, W., Yang, J., Gu, Y., Luo, W., & Xiao, J. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124485

A Two-State Core–Surface Enhanced Single Particle Model for Real-Time Thermal Gradient Estimation of Lithium-Ion Batteries in Electric Vehicles

Abah, D. E., & Kannan, R. (2026) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2026.3739568

Numerical Analysis of Two-State Core-Surface Enhanced Single Particle Model for Capturing Thermal Gradients in Lithium-Ion Batteries

Abah, D. E., Kannan, R., & Jamahori, H. F. (2026) · IEEE Industrial Electronics and Applications Conference (IEACon) · doi:10.1109/ieacon68870.2026.11734516

A Mechanism-aid Deep Learning Method for Li-ion Battery State-of-charge Estimation

Xiao, Z., Ji, C., Ma, F., Wang, J., Sun, W., & Zhai, C. (2024) · Chemical engineering transactions · doi:10.3303/cet24114070

Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning

Tian, J., Xiong, R., Lu, J., Chen, C., & Shen, W. (2022) · Energy storage materials · doi:10.1016/j.ensm.2022.06.007 · Cited by 280 · see citing works

Lithium-ion Battery SOC Estimation by Integrating Electrochemical Model with Deep Learning

Wang, F., Pang, H., Liu, J., Chen, K., & Nan, W. (2023) · New Energy and Energy Storage System Control Summit Forum (NEESSC) · doi:10.1109/neessc59976.2023.10349281 · Cited by 8 · see citing works

SOC Estimation of Li-ion Batteries With Learning Rate-Optimized Deep Fully Convolutional Network

Hannan, M. A., How, D. N. T., Lipu, M. S. H., Ker, P. J., Dong, Z. Y., Mansur, M., & Blaabjerg, F. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3041876 · Cited by 233 · 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