A Parallel Prediction Method for Battery Capacity Based on the Multiscale and Temporal-Spatial Feature Fusion

Wanzhen Wang; Ngu Sze Song; Qian Wang; Man Qiu; Xiaomei Ni; Miaomiao Xin; Xiao Wang; Na Li; Shengze Ma

doi:10.46604/ijeti.2026.15949

Authors

Wanzhen Wang School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China/ Faculty o Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Ngu Sze Song Faculty o Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Qian Wang School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China
Man Qiu School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China
Xiaomei Ni School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China/ Faculty o Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Miaomiao Xin School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China/ Faculty o Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
Xiao Wang School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China
Na Li School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China
Shengze Ma School of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, China

DOI:

https://doi.org/10.46604/ijeti.2026.15949

Keywords:

battery, multi-scale, spatial features, temporal features, channel attention

Abstract

This study proposes a parallel feature fusion-based method to accurately predict battery capacity for battery health management (BHM). Existing data-driven approaches suffer from ineffective extraction of multi-scale features and feature redundancy induced by sequential strategies. To address these gaps, a hybrid deep learning framework is proposed. Specifically, discharge voltage data are first standardized to unify sample dimensions. Then, parallel multi-scale branches are constructed to simultaneously capture the spatial and temporal features of battery discharge signals. A channel attention module is subsequently employed to adaptively filter redundant features and enhance the weight of degradation-related feature representations. Finally, a fully connected network maps the refined features to battery capacity values. The experimental results validate that the proposed method outperforms single-model and sequential baselines, with 0.00369 to 0.01331 RMSE on NASA battery, 0.0498 on CALCE batteries, and 1.8196 on Oxford batteries.

References

R. R. Kumar, C. Bharatiraja, K. Udhayakumar, S. Devakirubakaran, K. S. Sekar, and L. Mihet-Popa, “Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications,” IEEE Access, vol. 11, pp. 105761-105809, 2023.

S. Ji, J. Zhu, Y. Yang, G. Dos Reis, and Z. Zhang, “Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects,” Small Methods, vol. 8, no. 7, article no. 2301021, 2024.

Y. He, W. Bai, L. Wang, H. Wu, and M. Ding, “SOH Estimation for Lithium-Ion Batteries: An Improved GPR Optimization Method Based on the Developed Feature Extraction,” Journal of Energy Storage, vol. 83, article no. 110678, 2024.

Y. Zhi, H. Wang, and L. Wang, “A State of Health Estimation Method for Electric Vehicle Li-Ion Batteries Using GA-PSO-SVR,” Complex & Intelligent Systems, vol. 8, no. 3, pp. 2167–2182, 2022.

X. Wang, B. Hu, X. Su, L. Xu, and D. Zhu, “State of Health Estimation for Lithium-Ion Batteries Using Random Forest and Gated Recurrent Unit,” Journal of Energy Storage, vol. 76, article no. 109796, 2024.

H. Feng and H. Yan, “State of Health Estimation of Large-Cycle Lithium-Ion Batteries Based on Error Compensation of Autoregressive Model,” Journal of Energy Storage, vol. 52, Part B, article no. 104869, 2022.

B. Ma, W. Guo, and Z. S. Li, “Prediction of Lithium-Ion Battery Capacity Based on the ARMA Method,” Proceedings of IEEE Conference on Global Reliability and Prognostics and Health Management (PHM-Shanghai), pp. 1-7, 2020.

S. Kim, P. Y. Lee, M. Lee, J. Kim, and W. Na, “Improved State-of-Health Prediction Based on Auto-Regressive Integrated Moving Average with Exogenous Variables Model in Overcoming Battery Degradation-Dependent Internal Parameter Variation,” Journal of Energy Storage, vol. 46, article no. 103888, 2022.

J. Wu, J. Wang, M. Lin, and J. Meng, “Retired Battery Capacity Screening Based on Deep Learning with Embedded Feature Smoothing Under Massive Imbalanced Data,” Energy, vol. 318, article no. 134761, 2025.

N. He, Q. Wang, Z. Lu, Y. Chai, and F. Yang, “Early Prediction of Battery Lifetime Based on Graphical Features and Convolutional Neural Networks,” Applied Energy, vol. 353, Part A, article no. 122048, 2024.

C. Li, H. Zhang, P. Ding, S. Yang, and Y. Bai, “Deep Feature Extraction in Lifetime Prognostics of Lithium-Ion Batteries: Advances, Challenges and Perspectives,” Renewable and Sustainable Energy Reviews, vol. 184, article no. 113576, 2023.

D. Chung, J. Ko, and K. Yoon, “State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method,” Journal of Electrical Engineering & Technology, vol. 17, no. 3, pp. 1931–1945, 2022.

J. Guo, M. Liu, P. Luo, X. Chen, H. Yu, and X. Wei, “Attention-Based BiLSTM for the Degradation Trend Prediction of Lithium Battery,” Energy Reports, vol. 9, pp. 655-664, 2023.

X. Wang, J. Wang, S. Zhang, Z. Zhang, Y. Li, and X. Liu, “BiLSTM-Akef Hybrid Driven Lithium-Ion Battery SOH Prediction Model Based on CEEMD with Different Distributions Noises,” Journal of Energy Storage, vol. 111, article no. 115437, 2025.

X. Wang, K. Dai, M. Hu, and N. Ni, “Lithium-Ion Battery Health State and Remaining Useful Life Prediction Based on Hybrid Model MFE-GRU-TCA,” Journal of Energy Storage, vol. 95, article no. 112442, 2024.

R. Sun, J. Chen, B. Li, and C. Piao, “State of Health Estimation for Lithium-Ion Batteries Based on Novel Feature Extraction and BiGRU-Attention Model,” Energy, vol. 319, article no. 134756, 2025.

Z. Lu, Z. Fei, B. Wang, and F. Yang, “A Feature Fusion-Based Convolutional Neural Network for Battery State-of-Health Estimation with Mining of Partial Voltage Curve,” Energy, vol. 288, article no. 129690, 2024.

P. J. Weddle, S. Kim, B. Chen, Z. Yi, P. Gasper, et al., “Battery State-of-Health Diagnostics During Fast Cycling Using Physics-Informed Deep-Learning,” Journal of Power Sources, vol. 585, article no. 233582, 2023.

L. Yao, C. Zhao, Y. Xiao, H. Dai, Z. Fei, and L. Zhang, “Battery Health Prediction Using Two-Dimensional Multi-Channel Ensemble Models,” Journal of Energy Storage, vol. 86, article no. 111304, 2024.

X. Li, C. Gwan, S. Zhao, X. Gao, and Y. Zhu, “Multimodal Temperature Prediction for Lithium-Ion Battery Thermal Runaway Using Multi-Scale Gated Fusion and Bidirectional Cross-Attention Mechanisms,” Journal of Energy Storage, vol. 116, article no. 116098, 2025.

Y. Jiang, Y. Chen, F. Yang, and W. Peng, “State of Health Estimation of Lithium-Ion Battery with Automatic Feature Extraction and Self-Attention Learning Mechanism,” Journal of Power Sources, vol. 556, article no. 232466, 2023.

J. Park, G. H. Lee, J. Kim, Y. Yoo, and I. Lee, “Detailed Architectural Design of a Multi-Head Self-Attention Model for Lithium-Ion Battery Capacity Forecasting,” IEEE Access, vol. 13, pp. 48212-48225, 2025.

Z. He, X. Ni, C. Pan, S. Hu, and S. Han, “Full-Process Electric Vehicles Battery State of Health Estimation Based on Informer Novel Model,” Journal of Energy Storage, vol. 72, article no. 108626, 2023.

J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132–7141, 2018.

B. Sahaand and K. Goebel, “Battery Data Set.” NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA.

X. Guo, K. Wang, S. Yao, G. Fu, and Y. Ning, “RUL Prediction of Lithium Ion Battery Based on CEEMDAN-CNN BiLSTM Model,” Energy Reports, vol. 9, pp. 1299-1306, 2023.

L. Liu, W. Sun, C. Yue, Y. Zhu, and W. Xia, “Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models,” Energies, vol. 17, no. 19, article no. 4932, 2024.

L. Hu, W. Wang, and G. Ding, “RUL Prediction for Lithium-Ion Batteries Based on Variational Mode Decomposition and Hybrid Network Model,” Signal, Image and Video Processing, vol. 17, no. 6, pp. 3109-3117, 2023.

W. He, N. Williard, M. Osterman, and M. Pecht, “Prognostics of Lithium-Ion Batteries Based on Dempster–Shafer Theory and the Bayesian Monte Carlo Method,” Journal of Power Sources, vol. 196, no. 23, pp. 10314–10321, 2011.

[C. Birkl, “Diagnosis and Prognosis of Degradation in Lithium-Ion Batteries,” Doctor of Philosophy, University of Oxford, 2017.