Power, Control, and Data Processing Systems

SOC and SOH Estimation of Lithium Battery: Innovative Hybrid Octave and LSTM Network

Document Type : Original Research

Authors

Amirhossein Rahimian Zarif 1
Seyed Mohammadreza Mousavi 2
Javad Hamidzadeh 2

1 Faculty of Electrical and Biomedical Engineering, Sadjad University of Technology, Mashhad, Iran.

2 Faculty of computer engineering and information technology, Sadjad University, Mashhad, Iran.

https://doi.org/10.30511/pcdp.2026.2085637.1065
Abstract
Accurately estimating the state of charge (SOC) and state of health (SOH) of lithium-ion batteries remains a significant challenge due to complex electrochemical dynamics and varying operational conditions. This research aims to overcome these challenges by proposing a novel hybrid neural network that combines octave convolution (OctConv) and long short-term memory (LSTM). The methodology begins with preprocessing battery voltage, current, temperature, internal impedance, and capacity data through outlier removal and min-max normalization, followed by partitioning into training, validation, and test sets. The OctConv module then decomposes the input into high- and low-frequency feature maps, enhancing extraction efficiency by reducing spatial redundancy. These multi-scale features are subsequently processed by LSTM layers to model both short-term fluctuations and long-term degradation trends across battery cycles. The model was trained using the Adam optimizer with MSE loss and ReLU activations and evaluated against established deep learning approaches, including OCT, LSTM, and CNN-LSTM, on the NASA dataset. Experimental results demonstrate the superior performance of the proposed OCT-LSTM model. It achieved significant improvements in SOC and SOH estimation, reducing the mean squared error (MSE) by 84.53% and 82.44%, respectively, compared to the CNN-LSTM benchmark. Furthermore, the model attained a 6.21% reduction in simulation parameters, highlighting greater parameter efficiency. The proposed method outperforms state-of-the-art approaches across multiple metrics, including the number of parameters, MSE, RMSE, MAE, prediction accuracy, and computational speed, demonstrating its potential as a significant advancement in this domain.

Keywords

Keywords: state of charge (SOC) estimation
state of health (SOH) estimation
lithium-ion battery (LIB)
convolutional neural networks (CNNs)
octave convolution
long short-term memory (LSTM)
Subjects
[1]    E.O. Ofoegbu, State of charge (SOC) estimation in electric vehicle (EV) battery management systems using ensemble methods and neural networks, J. Energy Storage 114 (2025) 115833. https://doi.org/10.1016/j.est.2025.115833.
[2]    G. Chianese, L. Iannucci, O. Veneri, C. Capasso, Real-time estimation of battery SoC through neural networks trained with model-based datasets: Experimental implementation and performance comparison, Appl. Energy 389 (2025) 125783. https://doi.org/10.1016/j.apenergy.2025.125783.
[3]    X. Xia, Y. Peng, J. Hu, J. Shen, Z. Chen, State of Health Estimation for Lithium-Ion Batteries Based on Improved Support Vector Regression, 2024 IEEE Transp. Electrif. Conf. Expo, Asia-Pacific, ITEC Asia-Pacific 2024 326 (2024) 472–477. https://doi.org/10.1109/ITECAsia-Pacific63159.2024.10738640.
[4]    A. Dannier, G. Brando, M. Ribera, I. Spina, Li-Ion Batteries for Electric Vehicle Applications: An Overview of Accurate State of Charge/State of Health Estimation Methods, Energies 18 (2025) 786. https://doi.org/10.3390/en18040786.
[5]    V. Mahadevan, B.P. Vikraman, LSTM based battery management systems and FOPID based cooling system strategy for electric vehicles application, J. Energy Storage 116 (2025) 115937. https://doi.org/10.1016/j.est.2025.115937.
[6]    A.R. Zarif, S.A.G. Baygi, Y. Mafinejad, Hybrid Lithium-Ion Portable Renewable Energy Storage System (ESS), in: 2025 6th Int. Conf. Optim. Electr. Energy Consum. OEEC 2025, IEEE, 2025: pp. 1–6. https://doi.org/10.1109/OEEC66525.2025.11100136.
[7]    H. Huang, C. Bian, M. Wu, D. An, S. Yang, A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries, Energy 288 (2024) 129801. https://doi.org/10.1016/j.energy.2023.129801.
[8]    L. Zhang, S. Wang, S. Wang, B. Zhong, Z. Li, L. Wang, K. Wang, Battery health state prediction based on lightweight neural networks: A review, Ionics (Kiel). 30 (2024) 7781–7807. https://doi.org/10.1007/s11581-024-05857-y.
[9]    A.R. Zarif, A. Kazemi, Y. Mafinejad, A Novel Li-Ion Battery Charge Management System for Applying in Renewable Energies Based on Pulse Charge Method, in: 2024 9th Int. Conf. Technol. Energy Manag., IEEE, 2024: pp. 1–6. https://doi.org/10.1109/ICTEM60690.2024.10631901.
[10]    P. Dini, D. Paolini, Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review, Batteries 11 (2025) 107. https://doi.org/10.3390/batteries11030107.
[11]    P. Li, Z. Zhang, R. Grosu, Z. Deng, J. Hou, Y. Rong, R. Wu, An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries, Renew. Sustain. Energy Rev. 156 (2022) 111843. https://doi.org/10.1016/j.rser.2021.111843.
[12]    S.M. Miraftabzadeh, M. Longo, A. Di Martino, A. Saldarini, R.S. Faranda, Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles, Electron. 13 (2024) 1973. https://doi.org/10.3390/electronics13101973.
[13]    A.R. Zarif, A. Kazemi, Y. Mafinejad, Reconfigurable Pulse Charge BMS with Pre-Charge Capability for Li-Ion Energy Storage Systems (ESSs), in: 2025 6th Int. Conf. Optim. Electr. Energy Consum. OEEC 2025, IEEE, 2025: pp. 1–6. https://doi.org/10.1109/OEEC66525.2025.11100258.
[14]    I. Guedaouria, N. Doghmane, M.F. Harkat, A novel hybrid particle filter optimized by an unscented Kalman filter and genetic algorithm for joint SOC/SOH estimation of li-ion batteries, Ionics (Kiel). 31 (2025) 10719–10746. https://doi.org/10.1007/s11581-025-06663-w.
[15]    B.D. Soyoye, I. Bhattacharya, M.V. Anthony Dhason, T. Banik, State of Charge and State of Health Estimation in Electric Vehicles: Challenges, Approaches and Future Directions, Batteries 11 (2025) 32. https://doi.org/10.3390/batteries11010032.
[16]    L. Wu, X. Wei, C. Lin, Z. Huang, Y. Fan, C. Liu, S. Fang, Battery SOC estimation with physics-constrained BiLSTM under different external pressures and temperatures, J. Energy Storage 117 (2025) 116205. https://doi.org/10.1016/j.est.2025.116205.
[17]    H. Li, S. Wang, L. Yang, Z. Zhou, Y. Meng, W. Zhang, Z. Shuan, Y. Li, F. Lv, SOH estimation method for lithium-ion battery packs under real-world operating conditions based on a new attenuated model without additional experiments, Energy 330 (2025) 136802. https://doi.org/10.1016/j.energy.2025.136802.
[18]    S. Rout, S.K. Samal, D.J. Gelmecha, S. Mishra, Estimation of state of health for lithium-ion batteries using advanced data-driven techniques, Sci. Rep. 15 (2025) 30438. https://doi.org/10.1038/s41598-025-93775-y.
[19]    B.P. Saoji, S.D. Wankhade, J.K. Deshmukh, A.M. Gund, J.B. Mandhare, S.M. Satre, M. K. Bhole, Battery Management System in Electric Vehicle for Energy Storage System Using Extended Kalman Filter and Coulomb Counting Methods, Energy Storage 7 (2025) e70139. https://doi.org/10.1002/est2.70139.
[20]    M. Dong, X. Li, Z. Yang, Y. Chang, W. Liu, Y. Luo, W. Lei, M. Ren, C. Zhang, State of health (SOH) assessment for LIBs based on characteristic electrochemical impedance, J. Power Sources 603 (2024) 234386. https://doi.org/10.1016/j.jpowsour.2024.234386.
[21]    W. Guo, Q. Wang, G. Li, S. Xie, Dual-time scale collaborative estimation of SOC and SOH for lithium-ion batteries based on FOMIRUKF-EKF, Comput. Electr. Eng. 123 (2025) 110048. https://doi.org/10.1016/j.compeleceng.2024.110048.
[22]    Y. Liu, A. Lei, C. Yu, T. Huang, Y. Yu, An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles, Energies  17 (2024) 3287. https://doi.org/10.3390/en17133287.
[23]    P. Aher, R. Deshmukh, C. Chavan, S. Patil, M. Khare, A. Mandhana, A comprehensive equivalent circuit model of Li-ion batteries for SOC estimation in electric vehicles based on parametric sensitivity analysis, Ionics (Kiel). 31 (2025) 287–303. https://doi.org/10.1007/s11581-024-05950-2.
[24]    Y. Zhang, P. Gu, B. Duan, C. Zhang, A hybrid data-driven method optimized by physical rules for online state collaborative estimation of lithium-ion batteries, Energy 301 (2024) 131710. https://doi.org/10.1016/j.energy.2024.131710.
[25]    E.I. El-Sayed, S.K. ElSayed, M. Alsharef, Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques, Sustain. 16 (2024) 9301. https://doi.org/10.3390/su16219301.
[26]    S. Das, S. Samanta, S. Gupta, Online state-of-charge estimation by modified Coulomb counting method based on the estimated parameters of lithium-ion battery, Int. J. Circuit Theory Appl. 52 (2024) 749–768. https://doi.org/10.1002/cta.3806.
[27]    C.S. Huang, A Lithium-Ion Batteries Fault Diagnosis Method for Accurate Coulomb Counting State-of-Charge Estimation, J. Electr. Eng. Technol. 19 (2024) 433–442. https://doi.org/10.1007/s42835-023-01533-9.
[28]    Y. Fan, Z. Huang, H. Li, M. Bin Kaleem, R. Zhang, W. Liu, State of charge estimation for lithium-ion batteries with enhanced open-circuit voltage model, Meas. J. Int. Meas. Confed. 251 (2025) 117124. https://doi.org/10.1016/j.measurement.2025.117124.
[29]    L. Wang, J. Duan, S. Fan, K. Zhao, An estimated value compensation method for state of charge estimation of lithium battery based on open circuit voltage change rate, Energy 313 (2024) 134119. https://doi.org/10.1016/j.energy.2024.134119.
[30]    P.R. Dhabe, S.R. Paraskar, S.S. Jadhao, A Comprehensive Analysis of the Open-Circuit Voltage (OCV) Method for the Estimation of State of Charge, Trans. Indian Natl. Acad. Eng. (2025) 1–8. https://doi.org/10.1007/s41403-025-00517-7.
[31]    M.A. Pisani Orta, D. García Elvira, H. Valderrama Blaví, Review of State-of-Charge Estimation Methods for Electric Vehicle Applications, World Electr. Veh. J. 16 (2025) 87. https://doi.org/10.3390/wevj16020087.
[32]    X. Shu, Y. Li, K. Wei, W. Yang, B. Yang, M. Zhang, Research on the output characteristics and SOC estimation method of lithium-ion batteries over a wide range of operating temperature conditions, Energy 317 (2025) 134726. https://doi.org/10.1016/j.energy.2025.134726.
[33]    F. Guo, L.D. Couto, G. Mulder, K. Trad, G. Hu, O. Capron, K. Haghverdi, A systematic review of electrochemical model-based lithium-ion battery state estimation in battery management systems, J. Energy Storage 101 (2024) 113850. https://doi.org/10.1016/j.est.2024.113850.
[34]    Y. Xu, X. Ge, R. Guo, W. Shen, Recent advances in model-based fault diagnosis for lithium-ion batteries: A comprehensive review, Renew. Sustain. Energy Rev. 207 (2025) 114922. https://doi.org/10.1016/j.rser.2024.114922.
[35]    M.T. Talluri, S. Murugesan, V. Karthikeyan, S. Pragaspathy, The capacity estimation of Li–Ion battery using ML-based hybrid model, Electr. Eng. 107 (2025) 1695–1705. https://doi.org/10.1007/s00202-024-02608-0.
[36]    C. Ji, J. Dai, C. Zhai, J. Wang, Y. Tian, W. Sun, A Review on Lithium-Ion Battery Modeling from Mechanism-Based and Data-Driven Perspectives, Processes 12 (2024) 1871. https://doi.org/10.3390/pr12091871.
[37]    X. Wu, J. Chen, D. Wang, K. Xu, M. Shao, Y. Wang, Y. Long, Filter-Based Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries, IEEE Trans. Ind. Electron. (2025).
[38]    P. Takyi-Aninakwa, S. Wang, G. Liu, A.N. Bage, E.D. Bobobee, E. Appiah, Q. Huang, Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data, Appl. Energy 363 (2024) 123056. https://doi.org/10.1016/j.apenergy.2024.123056.
[39]    L. Breglio, A. Fiordellisi, G. Gasperini, G. Iodice, D. Palermo, M. Tufo, F. Ursumando, A. Mele, A Model-Based Strategy for Active Balancing and SoC and SoH Estimations of an Automotive Battery Management System, Modelling 5 (2024) 911–935. https://doi.org/10.3390/modelling5030048.
[40]    L. Kong, Y. Luo, S. Fang, T. Niu, G. Chen, L. Yang, R. Liao, Electrochemical model based SoC-SoH estimation of lithium-ion batteries under shipboard swaying conditions, J. Energy Storage 124 (2025) 116827. https://doi.org/10.1016/j.est.2025.116827.
[41]    T. Tang, X. Yang, M. Li, X. Li, H. Huang, C. Guan, J. Huang, Y. Wang, C. Zhou, Deep learning model-based real-time state-of-health estimation of lithium-ion batteries under dynamic operating conditions, Energy 317 (2025) 134697. https://doi.org/10.1016/j.energy.2025.134697.
[42]    S. Sun, H. Zhang, J. Ge, L. Che, State-of-health estimation for lithium-ion battery using model-based feature optimization and deep extreme learning machine, J. Energy Storage 72 (2023) 108732. https://doi.org/10.1016/j.est.2023.108732.
[43]    Z. Ye, Z.H. Deng, Y.H. Xu, L.F. Lai, R.Q. Zhang, L. Tong, H.G. Zhang, Y.Y. Li, M.H. Gong, G.J. Liu, M.X. Yan, Joint estimation of SOC and SOH for lithium-ion batteries based on FOAMIUHF-UKF model, Sci. Rep. 15 (2025) 1290. https://doi.org/10.1038/s41598-025-24571-x.
[44]    Y. Ni, K. Song, L. Pei, X. Li, T. Wang, H. Zhang, C. Zhu, J. Xu, State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model, Appl. Energy 385 (2025) 125539. https://doi.org/10.1016/j.apenergy.2025.125539.
[45]    H. Thomas, M.H. Weatherspoon, Capacity and State-of-Health Prediction of Lithium-Ion Batteries Using Reduced Equivalent Circuit Models, Batteries 11 (2025) 162. https://doi.org/10.3390/batteries11040162.
[46]    L. Jin, F.P. Bereck, J. Granwehr, C. Scheurer, Extending Equivalent Circuit Models for State of Charge and Lifetime Estimation, Electrochem. Sci. Adv. 5 (2025) e202400024. https://doi.org/10.1002/elsa.202400024.
[47]    J. Wu, J. Meng, M. Lin, W. Wang, J. Wu, D.I. Stroe, Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy, Reliab. Eng. Syst. Saf. 252 (2024) 110450. https://doi.org/10.1016/j.ress.2024.110450.
[48]    X. Zhao, S. Liu, E. Li, Z. Wang, F. Gu, A.D. Ball, A hybrid intelligent model using the distribution of relaxation time analysis of electrochemical impedance spectroscopy for lithium-ion battery state of health estimation, J. Energy Storage 84 (2024) 110814. https://doi.org/10.1016/j.est.2024.110814.
[49]    W. Zhang, R. Ahmed, S. Habibi, State-of-health estimation for lithium-ion batteries based on electrochemical impedance spectroscopy measurements combined with unscented Kalman filter, J. Power Sources 625 (2025) 235450. https://doi.org/10.1016/j.jpowsour.2024.235450.
[50]    T. Pham, H. Bui, M. Nguyen, Q. Pham, V. Vu, T. Le, T. Quan, A Comprehensive Review on Data‐Driven Methods of Lithium‐Ion Batteries State‐of‐Health Forecasting, Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 15 (2025) e70009.
[51]    S.M. Mousavi, A.R. Zarif, J. Hamidzadeh, Lithium Battery SOH Estimation: A Novel Octave and LSTM Hybrid Neural Network Approach, in: 2025 12th Iran. Conf. Renew. Energies Distrib. Gener. ICREDG 2025, IEEE, 2025: pp. 1–6. https://doi.org/10.1109/ICREDG66184.2025.10966104.
[52]    J. Gong, B. Xu, F. Chen, G. Zhou, Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review, Energies 18 (2025) 337. https://doi.org/10.3390/en18020337.
[53]    S. Li, Z. Jiang, Z. Zhu, W. Jiang, Y. Ma, X. Sang, S. Yang, A framework of joint SOC and SOH estimation for lithium-ion batteries: Using BiLSTM as a battery model, J. Power Sources 635 (2025) 124624. https://doi.org/10.1016/j.jpowsour.2025.236342.
[54]    S. Li, Z. Jiang, Z. Zhu, W. Jiang, Y. Ma, X. Sang, S. Yang, A framework of joint SOC and SOH estimation for lithium-ion batteries: Using BiLSTM as a battery model, J. Power Sources 635 (2025) 236342. https://doi.org/10.1016/j.jpowsour.2025.236342.
[55]    M. Ye, G. Lian, W. Li, B. Xia, B. Zhang, Y. Li, Q. Wang, M. Wei, Data-optimization based SOC-SOH estimation for lithium-ion batteries with current bias compensation, Energy 321 (2025) 135490. https://doi.org/10.1016/j.energy.2025.135490.
[56]    A. Petrella, M. Marzolla, F. Mercuri, Fast estimation of lithium-ion battery state of health using time series classification, J. Energy Storage 138 (2025) 118747. https://doi.org/10.1016/j.est.2025.118747.
[57]    L. Chen, S. Xie, A.M. Lopes, H. Li, X. Bao, C. Zhang, P. Li, A new SOH estimation method for Lithium-ion batteries based on model-data-fusion, Energy 286 (2024) 129597. https://doi.org/10.1016/j.energy.2023.129597.
[58]    S.G. Padder, J. Ambulkar, A. Banotra, S. Modem, S. Maheshwari, K. Jayaramulu, C. Kundu, Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review, IEEE Access 13 (2025) 35048–35067. https://doi.org/10.1109/ACCESS.2025.3539528.
[59]    X. Fang, M. Xu, Y. Fan, SOC-SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter, Energies 17 (2024) 639. https://doi.org/10.3390/en17030639.
[60]    T. Yuan, N. Li, H. Sun, S. Tan, A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP, Comput. Mater. Contin. 80 (2024) 4497–4512. https://doi.org/10.32604/cmc.2024.056061.
[61]    C. Li, L. Yang, Q. Li, Q. Zhang, Z. Zhou, Y. Meng, X. Zhao, L. Wang, S. Zhang, Y. Li, F. Lv, SOH estimation method for lithium-ion batteries based on an improved equivalent circuit model via electrochemical impedance spectroscopy, J. Energy Storage 86 (2024) 111167. https://doi.org/10.1016/j.est.2024.111167.
[62]    Y. Song, J. Lu, H. Zhang, G. Liu, SOC and SOH Prediction of Lithium-Ion Batteries Based on LSTM–AUKF Joint Algorithm, Energy Sci. Eng. 13 (2025) 240–254. https://doi.org/10.1002/ese3.1992.
[63]    T. Çarkıt, M. Alçı, Investigation of Voc and SoH on Li-ion batteries with an electrical equivalent circuit model using optimization algorithms, Electr. Eng. 106 (2024) 1781–1792. https://doi.org/10.1007/s00202-021-01484-2.
[64]    E.H. Jamila, N. Taoufik, A Review of the Estimation of State of Charge (SOC) and State of Health (SOH) of Li-Ion Batteries in Electric Vehicles, World Sustain. Ser. Part F2854 (2024) 519–541. https://doi.org/10.1007/978-3-031-56292-1_42.
[65]    G. Chen, H. Zhou, Y. Xu, W. Zhu, L. Chen, Y. Wang, A novel SOC-OCV separation and extraction technology suitable for online joint estimation of SOC and SOH in lithium-ion batteries, Energy 326 (2025) 136246. https://doi.org/10.1016/j.energy.2025.136246.
[66]    D. Shen, J. Ding, S. Wang, Parallel neural network combined with adaptive Kalman filter for co-estimation of SOH and SOC of lithium-ion batteries, J. Energy Storage 120 (2025) 116455. https://doi.org/10.1016/j.est.2025.116455.
[67]    D. Seo, J. Shin, State-of-Health (SOH)–Based Diagnosis System for Lithium-Ion Batteries Using DNN With Residual Connection and Statistical Feature, Int. J. Energy Res. 2025 (2025) 4046189. https://doi.org/10.1155/er/4046189.
[68]    M. Haghighi, M.R. Masoudi, M. Rahimipour Behbahani, Smart Homes Energy Management System Integrated with Renewable Energy Sources and Demand Response Programs, Power, Control. Data Process. Syst. 2 (2025).
[69]    M.R. Masoudi, M. Haghighi, M. Rahimipour Behbahani, Optimal Operation of Solar Energy System integrated with Energy Storage Systems, Power, Control. Data Process. Syst. 1 (2024).
[70]    M. Monemi Bidgoli, R. Ghani, Optimal Energy Management of Water-Energy Nexus in Multi-Carrier Systems Integrated with Renewable Sources, Power, Control. Data Process. Syst. 1 (2024).
[71]    Z. Bao, J. Jiang, C. Zhu, M. Gao, A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery, Energies 15 (2022) 1–16. https://doi.org/10.3390/en15124399.
[72]    L. Zhang, T. Ji, S. Yu, G. Liu, Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method, Batteries 9 (2023). https://doi.org/10.3390/batteries9030177.
[73]    X. Zhang, J. Hou, Z. Wang, Y. Jiang, Joint SOH-SOC estimation model for lithium-ion batteries based on GWO-BP neural network, Energies 16 (2022) 132.
[74]    S. Amir, M. Gulzar, M.O. Tarar, I.H. Naqvi, N.A. Zaffar, Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries, IEEE Access 10 (2022) 18279–18288. https://doi.org/10.1109/ACCESS.2022.3148528.
[75]    M. Oza, R. Lamba, R. Kumar, Attention Based Mechanism for State-of-Health Estimation of Li-Ion Batteries, in: 2024 IEEE 4th Int. Conf. Sustain. Energy Futur. Electr. Transp. SEFET 2024, IEEE, 2024: pp. 1–6. https://doi.org/10.1109/SEFET61574.2024.10718141.
[76]    S. Bharti, V.K. Saini, A. Kumar Yadav, R. Kumar, A.S. Al-Sumaiti, Optimal Parameter Estimation of CAPN Model for Li-ion Battery, in: 2023 Int. Conf. Comput. Electron. Electr. Eng. Their Appl. IC2E3 2023, IEEE, 2023: pp. 1–6. https://doi.org/10.1109/IC2E357697.2023.10262727.
[77]    A. Kumar Yadav, A. Kumar Saini, Dhiraj, Uncertainty aware State-of-Charge estimation for Li-ion Batteries using Deep Learning, in: 2023 2nd IEEE Int. Conf. Meas. Instrumentation, Control Autom. ICMICA 2023, IEEE, 2024: pp. 1–6. https://doi.org/10.1109/ICMICA61068.2024.10732706.
[78]    C. Zhang, T. Wang, B. Zhang, L. Qiao, G. Lian, M. Wei, Closed-Loop State of Charge Estimation of Lithium-Ion Batteries Based on Machine Learning and Kalman Filtering, Arab. J. Sci. Eng. (2025) 1–18. https://doi.org/10.1007/s13369-025-10244-4.
[79]    R. Sharma, A. Bala, A. Singh, M. Singh, Enhancing Battery Health Monitoring Using a Stacking Approach for Precise and Real-Time State-of-Health Estimation, Arab. J. Sci. Eng. (2025) 1–17. https://doi.org/10.1007/s13369-025-10453-x.
[80]    S. Arbaoui, A. Samet, A. Ayadi, T. Mesbahi, R. Boné, Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries, Energy AI 17 (2024) 100413. https://doi.org/10.1016/j.egyai.2024.100413.
[81]    R. Zheng, B. Yang, Y. Qian, H. Li, D. Gao, L. Jiang, Joint SOH and RUL estimation for lithium-ion batteries via optimal deep belief network with Bayesian algorithm, J. Energy Storage 114 (2025) 115891. https://doi.org/10.1016/j.est.2025.115891.
[82]    C.L. Sravanthi, J.N. Chandra Sekhar, N.C. Alluraiah, C. Dhanamjayulu, H. Kumar Pujari, B. Khan, An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data-Dependent Models, Int. Trans. Electr. Energy Syst. 2025 (2025) 2242749. https://doi.org/10.1155/etep/2242749.
[83]    J. Chen, P. Li, L. Wu, Joint prediction of SOH and RUL of lithium-ion batteries using single-cycle charging data, Energy 336 (2025) 138351. https://doi.org/10.1016/j.energy.2025.138351.
[84]    G. Patrizi, L. Martiri, A. Pievatolo, A. Magrini, G. Meccariello, L. Cristaldi, N.D. Nikiforova, A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries, Sensors 24 (2024) 3382. https://doi.org/10.3390/s24113382.
[85]    H. Cao, X. Lian, L. Chen, Z. Duan, A computationally efficient and lightweight model for high-accuracy OCT image classification, World Wide Web 28 (2025) 35. https://doi.org/10.1007/s11280-025-01348-w.
[86]    Z. Guo, Z. Zhao, Hybrid attention structure preserving network for reconstruction of under-sampled OCT images, Sci. Rep. 15 (2025) 7405. https://doi.org/10.1038/s41598-024-82812-x.
[87]    J. Hu, R. Liu, Z. Chen, D. Wang, Y. Zhang, B. Xie, Octave convolution-based vehicle detection using frame-difference as network input, Vis. Comput. 39 (2023) 1503–1515. https://doi.org/10.1007/s00371-022-02425-1.
[88]    J.A. Diaz-Madrid, G. Domenech-Asensi, R. Ruiz-Merino, J.F. Zapata-Perez, A real-time and energy-efficient SRAM with mixed-signal in-memory computing near CMOS sensors, J. Real-Time Image Process. 21 (2024) 143. https://doi.org/10.1007/s11554-024-01520-x.
[89]    B. Saha, K. Goebel, Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques, in: 2008 IEEE Aerosp. Conf., IEEE, 2008: pp. 1–8.
[90]    B. Bole, C. Kulkarni, M. Daigle, Randomized battery usage data set, NASA AMES Progn. Data Repos. 70 (2014).
[91]    H. Sun, J. Sun, K. Zhao, L. Wang, K. Wang, Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation, Math. Probl. Eng. 2022 (2022) 1–8. https://doi.org/10.1155/2022/9645892.
Power, Control, and Data Processing Systems
Volume 3, Issue 4
Autumn 2026
Pages 1-16

  • Receive Date 16 February 2026
  • Revise Date 19 June 2026
  • Accept Date 24 June 2026
  • First Publish Date 24 June 2026
  • Publish Date 01 December 2026

Home

AI Policy

Submit Manuscript

Contact Us