Specialized deep neural networks for battery health prognostics: Opportunities and challenges

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384

Physics-informed machine learning

Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436

Domain Adaptation via Transfer Component Analysis

Pan, S. J., Tsang, I. W.-H., Kwok, J. T.-Y., & Yang, Q. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2091281 · Cited by 4,809

Deep Reinforcement Learning: A Brief Survey

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733

Generative Adversarial Networks: An Overview

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540

Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.03.012 · Cited by 3,291

Data-driven prediction of battery cycle life before capacity degradation

Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228

Parallel deep learning with attention-gated fusion for robust battery health monitoring under dynamic operating conditions

Sun, X., Jiang, Y., Liu, B., Liu, C., Rong, C., & Lu, H. (2026) · ENGINEERING Energy · doi:10.1007/s11708-026-1046-4 · Cited by 1

Voltage Prediction in Lithium-Ion Batteries: Integrating Physics-based Model with Neural Networks for Enhanced Accuracy

Nagar, A., Garg, A., & Kim, J. (2026) · Journal of Industrial and Engineering Chemistry · doi:10.1016/j.jiec.2026.02.024 · Cited by 1

Few-Shot Battery State-of-Health Estimation via Meta-Learning and Cross-Domain Adaptation

Zhang, S., & Shen, W. (2026) · IEEE PES International Meeting (PES IM) · doi:10.1109/pesim67009.2026.11438910 · Cited by 1

Interpreting lithium-ion battery aging under fast-charging via data-driven physics and explainable machine learning

Zhang, X., Wang, H., Liu, Y., Mao, J., Liu, W., Cheng, H., & Lü, Y. (2026) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2026.07.006 · Cited by 1

Enhancing AI Efficiency: The Synergy of Transformer Models and FPGA Technology

Guo, X. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-0096-0_4 · Cited by 1

A Hybrid Method for Improving SOC Prediction in Lithium-Ion Batteries by Combining EDNN with WOA

Indumathi, V., R.Gopalakrishnan, Vijayakumar, C., Pavithran, A., M.Kamalesh, & M.Balaji (2025) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears64219.2025.10940352 · Cited by 1

State-of-Health Estimation for Lithium-Ion Batteries Based on Lightweight DimConv-GFNet

Huang, K., Kang, J., Wang, J. V., Wang, Q., & Wu, O. (2025) · Batteries · doi:10.3390/batteries11050174 · Cited by 1

A Reinforcement Learning-based Approach to in-Pool Memory Allocation for Distributed Heterogeneous Memory Pools

Guo, Q. Y., Guo, H., Guo, T., Lu, Y., Zhang, J., Huang, L., & Hu, B. (2024) · International Conference on Mechatronics and Computer Technology Engineering (MCTE) · doi:10.1109/mcte62870.2024.11118263 · Cited by 1

Practical State of Health Prediction for Lithium-Ion Batteries Under Complex Dynamic Discharge ScenariosPreprint

Sun, X., Wei, Z., Liu, B., Li, Y., Liu, C., & Lü, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5099012 · Cited by 1

Data-Driven Model for Mono and Custom-Built Bi-Facial PV Output Based on Deep Neural Networks

Mahim, T. M., Rahim, A. H. M. A., & Rahman, M. M. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441318

A Lithium Battery Fault Diagnosis Model Driven by Both Data and Models, Generated Based on Fault DataPreprint

Zhang, L., Wang, L., Zhang, J., Wu, Q., Jiang, L., Shi, Y., Lyu, L., & Cai, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4848357

Performance Analysis of CBAM-Based Capsule Net for Neural Network for Speech Emotion Recognition

Barsainyan, N., & Singh, D. K. (2024) · International Journal of Electrical and Electronics Engineering · doi:10.14445/23488379/ijeee-v11i12p122

An Interpretable Transformer Model for Battery Health Prediction Based on a Multitask LearningPreprint

Indumathi, V. M., & Alcaraz, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5214913

Application of capsule networks based on reparameterized heterogeneous convolution in multi-scale heterogeneous environment matrix in predictive modeling of interdisciplinary complex systems

Liu, S., & Zhang, X. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07198-5

Health Feature Extraction and Deep Learning for Lithium-Ion Battery Soh Estimation Under Dynamic Operating ConditionsPreprint

Lü, H., Sun, X., Wei, Z., Liu, B., Li, Y., & Liu, C. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5364199

A High-Performance Memory-Mapped Data Multi-Level Allocation Approach for Real-Time Performance Adaptation

Guo, Q., Guo, T., Guo, H., Lu, Y., Zhang, J., Huang, L., Zhang, J., & Luo, H. (2024) · International Conference on Mechatronics and Computer Technology Engineering (MCTE) · doi:10.1109/mcte62870.2024.11117958

Prediction of Battery Internal Resistance Dynamics Over Lifespan Using Early-Cycle Data and Random Forest-Optimized Exponential Regression

Ashidqi, M. D., Apribowo, C. H. B., Dyartanti, E. R., Nizam, M., & Purwanto, A. (2025) · International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP) · doi:10.1109/ict-pep67281.2025.11231907

An innovation framework for investigating the performance of proton exchange membrane fuel cells: a perspective of the EANN model toward lower emission

Bekmyrza, K. Z., Kuterbekov, K. A., Kabyshev, A. M., Kubenova, M. M., Baratova, A. A., Aidarbekov, N. K., & Yadav, B. K. (2025) · International Journal of Low-Carbon Technologies · doi:10.1093/ijlct/ctaf144

Automatic Control Optimization of New Energy Vehicle Battery Management System Based on Neural Architecture Search

Wang, J. (2025) · International Conference on Computer Engineering and Intelligent Control (ICCEIC) · doi:10.1109/icceic67916.2025.11308904

State of Health Estimation for Lithium-Ion Batteries Based on Relaxation Time Distribution and Neural Networks

Ge, S., Chen, X., Han, X., Li, Q., Min, F., Lai, C., Xie, J., & Wang, H. (2025) · International Symposium on New Energy and Electrical Technology (ISNEET) · doi:10.1109/isneet68549.2025.11407063

Two-stage prediction-correction with residual compensation: A framework for robust state of charge estimation under wide temperaturesPreprint

Fan, C., Liu, Y., Lui, K., Li, M., Turgun, Y., & Peng, Q. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6462395

An interpretable transformer model for battery health prediction based on a multitask learning framework and cross-attention mechanism

Indumathi, V., & Gopalakrishnan, R. (2026) · AIP Advances · doi:10.1063/5.0326508 · Cited by 1

Surrogate-assisted global sensitivity analysis enabling hierarchical parameterization of physics-based battery aging models

Li, Y., Kemény, M., Svensson, A. M., & Ye, M. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.122175

A Lightweight State-Aware Residual Calibration Network for Lithium-Ion Battery SOH Estimation under Cross-Condition ScenariosPreprint

Dong, Y., Li, R., Zhang, M., & Lv, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7076241

Explainable state of health estimation of lithium-ion batteries with extremely minimal labels via weakly supervised learning

Qian, C., Guan, H., Xiao, Y., Wang, L., Ren, Y., Xia, Q., Yang, D., & Wang, Z. (2026) · eTransportation · doi:10.1016/j.etran.2026.100623

Research Progress on Thermal Runaway and Thermal Management of Lithium‐Ion Batteries

Li, S., Zhao, W., Zhang, Y., Wu, X., & Chen, Z. (2026) · Safety Science and Technology · doi:10.1002/sst3.70045

Towards interpretable and trustworthy lithium-ion battery prognostics and health management with physics-informed machine learning

Wang, H., Zhu, T., Cao, Z., & Wen, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124442

A physics-guided transformer for robust state of health estimation in real-world electric vehicles

Guo, L., Li, Y., & Zhang, W. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124747

Decoding Capacity Regeneration in Zinc‐Silver Batteries: Gas‐Guided Multi‐Physics Modeling and Hybrid AI Prediction

Zhang, B., Hu, C., Leng, M., Zhang, K., Liu, X., Kong, X., Chen, Y., Wang, L., & He, X. (2026) · Small · doi:10.1002/smll.76380

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi, M., Abu‐Rub, H., & Ghrayeb, A. (2024) · IEEE Open Journal of Industry Applications · doi:10.1109/ojia.2024.3354899 · Cited by 41

Prognostics and health management of Lithium-ion battery using deep learning methods: A review

Zhang, Y., & Li, Y. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112282 · Cited by 304

Investigation of Deep Learning Based Techniques for Prognostic and Health Management of Lithium-Ion Battery

Saleem, U., Li, W., Liu, W., Ahmad, I., Aslam, M. M., & Lateef, H. U. (2023) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai58194.2023.10194122 · Cited by 9

Deep Residual Net-Based Prognosis Method for Lithium-ion Batteries with Information Fusion from Different Scales

Zhu, Y., Li, X., & Zhang, W. (2022) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-yantai55411.2022.9941914 · Cited by 1

Real-Time State of Health Prediction of Lithium Ion Batteries Based on Deep Neural Networks

Hind, E. O., Hassani, I. E., & Masrour, T. (2025) · International Conference on Circuit, Systems and Communication (ICCSC) · doi:10.1109/iccsc66714.2025.11134871