A Hybrid WOA-DeepONet Framework for Data-Driven and Physics-Guided SOH/RUL Estimation in Lithium-Ion Batteries
DOI:
https://doi.org/10.4108/ew.9524Keywords:
Lithium-ion batteries, State of Health (SOH), Remaining useful Life (RUL), Whale Optimization Algorithm (WOA), Physics-informed DeepONet, Battery thermal modelingAbstract
INTRODUCTION: For energy storage systems to be safe, effective, and reliable, it is essential to accurately forecast the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries under complicated operating situations.
OBJECTIVES: This research suggests a hybrid modeling approach that combines a physics-informed Deep Operator Network (DeepONet) supplemented by encoders with a neural network optimized by the Whale Optimization Algorithm (WOA).
METHODS: The framework first utilizes WOA to improve the initialization of Backpropagation (BP) neural networks, thus enhancing convergence speed and avoiding local minima in early-stage training. Then, a multi-physics informed DeepONet model is constructed to learn the spatiotemporal evolution of battery thermal and electrochemical variables from virtual heating profiles.
RESULTS: By integrating boundary conditions, starting restrictions, and partial differential equation (PDE) residuals into the loss function, the model integrates physical supervision and guarantees forecast consistency with battery dynamics.
CONCLUSION: Both COMSOL-generated synthetic data and publicly available battery aging datasets are used in extensive research. The suggested approach outperforms conventional MLP, LSTM, and even standard DeepONet models, according to the results, with an R2 of 0.99 and an MSE of 0.0009.
Downloads
References
[1] Jeong J, Kwak E, Kim JH, Oh KY. Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data. ETransp. 2024;21:100337.
[2] Kim SW, Kwak E, Kim JH, Oh KY, Lee S. Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network. J Energy Storage. 2023;60:106654.
[3] Goswami BRD, Mastrogiorgio M, Ragone M, Jabbari V, Shahbazian-Yassar R, Mashayek F, Yurkiv V. A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries. J Power Sources. 2024;595:234065.
[4] Goswami BRD, Abdisobbouhi Y, Du H, Mashayek F, Kingston TA, Yurkiv V. Advancing battery safety: Integrating multiphysics and machine learning for thermal runaway prediction in lithium-ion battery module. J Power Sources. 2024;614:235015.
[5] Zafar MH, Bukhari SMS, Abou Houran M, Mansoor M, Khan NM, Sanfilippo F. DeepTimeNet: A novel architecture for precise surface temperature estimation of lithium-ion batteries across diverse ambient conditions. Case Stud Therm Eng. 2024;61:105002.
[6] Kim JH, Kwak E, Jeong J, Oh KY. Control-oriented multiphysics model of a lithium-ion battery for thermal runaway estimation under operational and abuse conditions. Appl Therm Eng. 2024;254:123895.
[7] Li K, Gao X, Peng S, Wang S, Zhang W, Liu P, et al. A comparative study on multidimensional signal evolution during thermal runaway of lithium-ion batteries with various cathode materials. Energy. 2024;300:131560.
[8] Wang G, Ping P, Kong D, Peng R, He X, Zhang Y, et al. Advances and challenges in thermal runaway modeling of lithium-ion batteries. Innov. 2024;5(4).
[9] Wang C, Chen Y, Luan W, Li S, Yao Y, Chen H. Interpretable deep learning for accelerated fading recognition of lithium-ion batteries. ETransp. 2023;18:100281.
[10] Jiang Y, Liu Z, Kabirzadeh P, Wu Y, Li Y, Miljkovic N, Wang P. Multi-fidelity physics-informed convolutional neural network for heat map prediction of battery packs. Reliab Eng Syst Saf. 2025;256:110752.
[11] Naguib M, Kollmeyer P, Emadi A. Application of deep neural networks for lithium-ion battery surface temperature estimation under driving and fast charge conditions. IEEE Trans Transp Electrific. 2022;9(1):1153-1165.
[12] Karmakar A, Zhou H, Vishnugopi BS, Mukherjee PP. Thermal runaway propagation analytics and crosstalk in lithium‐ion battery modules. Energy Technol. 2024;12(2):2300707.
[13] Zhang P, Chen H, Yang K, Lu Y, Huang Y. Accelerated computational strategies for multi-scale thermal runaway prediction models in Li-ion battery. Energy. 2024;305:132371.
[14] Shi C, Zhu D, Zhang L, Song S, Sheldon BW. Transfer learning prediction on lithium-ion battery heat release under thermal runaway conditions. Nano Res Energy. 2024;3(4).
[15] Cho G, Zhu D, Campbell JJ, Wang M. An LSTM-PINN hybrid method to estimate lithium-ion battery pack temperature. IEEE Access. 2022;10:100594-100604.
[16] Legala A, Li X. Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors. Energy AI. 2022;10:100194.
[17] Li H, et al. Immune multipath reliable transmission with fault tolerance in wireless sensor networks. In: Bio-inspired Computing–Theories and Applications: 11th International Conference, BIC-TA 2016, Xi’an, China, October 28–30, 2016, Revised Selected Papers, Part II. Singapore: Springer; 2016. p. 513–517.
[18] Zhao J, Han X, Ouyang M, Burke AF. Specialised deep neural networks for battery health prognostics: Opportunities and challenges. J Energy Chem. 2023;87:416–438.
[19] Grandhi, S. H., Al-Jawahry, H. M., Kumar, B. V., & Padhi, M. K. (2024, August). A quantum variational classifier for predictive maintenance and monitoring of battery health in electric vehicles. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-4). IEEE.
[20] Ouyang N, Zhang W, Yin X, Li X, Xie Y, He H, Long Z. A data-driven method for predicting thermal runaway propagation of battery modules considering uncertain conditions. Energy. 2023;273:127168.
[21] Grandhi, S. H., Alabdeli, H., Lakshmanan, M., Soujanya, T., & Soni, M. (2024, August). A hybrid optimized model for battery management system in E-vehicles. In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON) (pp. 1-5). IEEE.
[22] Ding S, Dong C, Zhao T, Koh L, Bai X, Luo J. A meta-learning based multimodal neural network for multistep ahead battery thermal runaway forecasting. IEEE Trans Ind Inform. 2020;17(7):4503–4511.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Qiang Yang, Zhiquan Qin, Haifeng Zhang, Jian Shi, Chao Lu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
