A coordinate-aware Mamba2 framework for remaining useful life prediction of lithium-ion battery

Abstract

To address the limitations of existing methods in capturing long-term temporal dependencies, local capacity regeneration, and nonlinear degradation characteristics in lithium-ion battery remaining useful life prediction, this paper proposes a coordinate-aware Mamba2 framework based on state-space modeling. Mamba2 is adopted as the backbone to model long-range degradation evolution, while a coordinate feature attention network is introduced in the feature extraction stage to enhance the selection and representation of key degradation information. Additionally, a swiGLU-gated residual network is constructed for feature transformation and prediction, improving the modeling of complex dynamic relationships and endpoint stability. Through the collaborative design of these modules, the proposed framework effectively captures both global degradation trends and local fluctuation patterns. Experiments were conducted on the National Aeronautics and Space Administration, Tongji University, and Xi’an Jiaotong University datasets under single-variable input, multi-variable input, and cross-dataset generalization settings. The mean absolute error values are lower than 0.0098, 0.0016, and 0.0083, while the root mean square error values are lower than 0.0172, 0.0024, and 0.0111, respectively. Results demonstrate that coordinate-aware Mamba2 achieves superior accuracy, lower computational cost, faster training and inference, and stronger robustness to different prediction starting points.

Graphical abstract

Keywords

remaining lifespan prediction / lithium-ion battery / Mamba2 / coordinate attention / state-space model

References

[1] Li X J , Yu D , Søren Byg V , Daniel Ioan S . The development of machine learning-based remaining useful life prediction for lithium-ion batteries. Journal of Energy Chemistry, 2023, 82: 103–121

[2] Pepe S , Ciucci F . Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering. Applied Energy, 2023, 350: 121761

[3] Li R H , Kirkaldy N D , Oehler F F , Marinescu M , Offer G J , O’Kane S E J . The importance of degradation mode analysis in parameterising lifetime prediction models of lithium-ion battery degradation. Nature Communications, 2025, 16: 2776

[4] Fei Z C , Zhang Z J , Yang F F , Tsui K L . A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data. Journal of Energy Storage, 2023, 62: 106903

[5] Varini M , Campana P E , Lindbergh G . A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime. Journal of Energy Storage, 2019, 25: 100819

[6] Nejad S , Gladwin D T , Stone D A . A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources, 2016, 316: 183–196

[7] Meng J H , Cai L , Yang S X , Li J X , Zhou F F , Peng J C , Song Z X . An empirical-informed model for the early degradation trajectory prediction of lithium-ion battery. IEEE Transactions on Energy Conversion, 2024, 39(4): 2299–2311

[8] Khodadadi Sadabadi K , Jin X , Rizzoni G . Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health. Journal of Power Sources, 2021, 481: 228861

[9] Wang Z K , Zeng S K , Guo J B , Qin T C . State of health estimation of lithium-ion batteries based on the constant voltage charging curve. Energy, 2019, 167: 661–669

[10] Ma Y , Chen Y , Zhou X W , Chen H . Remaining useful life prediction of lithium-ion battery based on Gauss–Hermite particle filter. IEEE Transactions on Control Systems Technology, 2019, 27(4): 1788–1795

[11] Feng J Q , Cai F , Li H C , Huang K F , Yin H . A data-driven prediction model for the remaining useful life prediction of lithium-ion batteries. Process Safety and Environmental Protection, 2023, 180: 601–615

[12] Peng Q , Li W , Fowler M , Chen T , Jiang W , Liu K L . Battery calendar degradation trajectory prediction: data-driven implementation and knowledge inspiration. Energy, 2024, 294: 130849

[13] Ji S L , Zhu J X , Yang Y X , dos Reis G , Zhang Z S . Data-driven battery characterization and prognosis: recent progress, challenges, and prospects. Small Methods, 2024, 8(7): 2301021

[14] Patil M A , Tagade P , Hariharan K S , Kolake S M , Song T , Yeo T , Doo S . A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Applied Energy, 2015, 159: 285–297

[15] Chen Z W , Shi N , Ji Y F , Niu M , Wang Y R . Lithium-ion batteries remaining useful life prediction based on BLS-RVM. Energy, 2021, 234: 121269

[16] Zhao J H , Zhu Y , Zhang B , Liu M Y , Wang J X , Liu C H , Zhang Y Y . Method of predicting SOH and RUL of lithium-ion battery based on the combination of LSTM and GPR. Sustainability, 2022, 14(19): 11865

[17] Qu X D , Shi D P , Zhao J Y , Tran M K , Wang Z H , Fowler M , Lian Y B , Burke A F . Insights and reviews on battery lifetime prediction from research to practice. Journal of Energy Chemistry, 2024, 94: 716–739

[18] Lv X , Cui S H , Wang Y , Lu J Y , Yu P M , Wang K . Patch time series transformer-based short-term photovoltaic power prediction enhanced by artificial fish. Energies, 2026, 19(1): 284

[19] Chen Y J , Lu J Y , Tang Z , Zhu J H , Wang S , Dong H J , Wang K . Recent progress in the physics-constrained state of health estimation for lithium-ion batteries. Energies, 2026, 19(8): 1920

[20] Jha M K . Machine learning applications for roadway pavement deterioration modeling. Journal of Computational and Cognitive Engineering, 2025, 4(1): 47–55

[21] Zhang X G , Wang Z , Gong Q S , Wang Y . State of health estimation of lithium-ion batteries based on hybrid neural networks with residual connections. Journal of the Electrochemical Society, 2025, 172(2): 020503

[22] Jiang W , Deng Y Y , Li W T , Song J L , Che S T , Wang K . Research progress of non-invasive magnetic resonance imaging in lithium-ion battery detection. Coatings, 2026, 16(4): 453

[23] Wang G F , Jiang B , Liu Y C , Wang L C , Zhang Y B , Yan J , Wang K . Source-load coordinated optimization framework for distributed energy systems using quasi-potential game method. Protection and Control of Modern Power Systems, 2025, 10(5): 103–122

[24] Vaiyapuri T , Elashmawi W H , Shridevi S , Asiedu W . VAE-CNN: deep learning on small sample dataset improves hydrogen yield prediction in co-gasification. Journal of Computational and Cognitive Engineering, 2025, 4(3): 332–342

[25] Lu J H , Xiong R , Tian J P , Wang C X , Hsu C W , Tsou N T , Sun F C , Li J . Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning. Energy Storage Materials, 2022, 50: 139–151

[26] Liu Z Y , Liu Y N , Zhang Y , Wu C Q , Zhang S F , Sun C . Data-driven lithium-ion battery SOH prediction: a novel SHMM-transformer-BiGRU hybrid neural network method. Measurement, 2026, 257: 118579

[27] Song K , Hu D , Tong Y , Yue X G . Remaining life prediction of lithium-ion batteries based on health management: a review. Journal of Energy Storage, 2023, 57: 106193

[28] Lin M Q , You Y Q , Meng J H , Wang W , Wu J , Stroe D I . Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning. Journal of Energy Chemistry, 2023, 85: 534–546

[29] Chen Z Q , Chen J G , Zhu Z C , Chen J , Lv T L , Qiao D D , Zheng Y J . Cloud-based estimation of lithium-ion battery life for electric vehicles using equivalent circuit model and recurrent neural network. Journal of Energy Storage, 2025, 114: 115718

[30] Wang Z Q , Liu N , Chen C L , Guo Y M . Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries. Information Sciences, 2023, 635: 398–413

[31] Hong J C , Liang F W , Yang H X , Zhang C , Zhang X Y , Zhang H Q , Wang W , Li K R , Yang J S . Multi- forword-step state of charge prediction for real-world electric vehicles battery systems using a novel LSTM-GRU hybrid neural network. ETransportation, 2024, 20: 100322

[32] Guo Y , Yang D F , Zhao K , Wang K . State of health estimation for lithium-ion battery based on bi-directional long short-term memory neural network and attention mechanism. Energy Reports, 2022, 8: 208–215

[33] Chen J C , Chen T L , Liu W J , Cheng C C , Li M G . Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Advanced Engineering Informatics, 2021, 50: 101405

[34] Zhou Y X , Li Z J , Zhao M , Wu F M , Yang T Y . A transformer-based hybrid method with multi-feature for lithium battery remaining useful life prediction. Journal of Power Sources, 2025, 655: 237844

[35] Lei M F , Zhang M , Wang K . Research on bidding optimization strategy for virtual power plants with wind-solar-storage systems based on IGDT-DRO. Electrical Engineering, 2026, 108(3): 208

[36] Jiang W , Tan C C , Su E Q , Lu J Y , Shi H L , Wang Y , Song J L , Wang K . Advanced electronic materials for liquid thermal management of lithium-ion batteries: mechanisms, materials and future development directions. Coatings, 2026, 16(1): 59

[37] Ravishankar S , Battineni G . A survey on recent advancements in auto-machine learning with a focus on feature engineering. Journal of Computational and Cognitive Engineering, 2025, 4(1): 56–63

[38] Chen D Q , Hong W C , Zhou X Z . Transformer network for remaining useful life prediction of lithium-ion batteries. IEEE Access, 2022, 10: 19621–19628

[39] Han Y L , Li C H , Zheng L F , Lei G , Li L . Remaining useful life prediction of lithium-ion batteries by using a denoising transformer-based neural network. Energies, 2023, 16(17): 6328

[40] Jia C Y , Tian Y K , Shi Y H , Jia J F , Wen J , Zeng J C . State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer. Energy, 2023, 285: 129401

[41] Gu A , Dao T . Mamba: linear-time sequence modeling with selective state spaces. , 2023,

[42] Huang J H , Liu L , Zhao H W , Li T Q , Li B . RUL-Mamba: mamba-based remaining useful life prediction for lithium-ion batteries. Journal of Energy Storage, 2025, 120: 116376

[43] Liu X M. MambaCPU: enhanced correlation mining with state space models for CPU performance prediction. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). April 6–11, 2025, Hyderabad, India. IEEE, 2025: 1–5

[44] Wang Z H , Kong F H , Feng S , Wang M , Yang X C , Zhao H , Wang D L , Zhang Y F. . Is Mamba effective for time series forecasting?. Neurocomputing, 2025, 619: 129178

[45] Wang Y , Li Y H , Zhang Z Y , Yu P H , Li Y F , Liu B , Zou R M . Bidirectional Mamba network with multi-scale feature fusion and sparse-channel mixture of experts for battery state of charge estimation. Energy, 2025, 340: 139320

[46] Shi Z W . MambaLithium: selective state space model for remaining-useful-life, state-of-health, and state-of-charge estimation of lithium-ion batteries. , 2024,

[47] Lou C , Zhang J H , Mu X M , Zeng F P , Wang K . Innovative deep learning method for predicting the state of health of lithium-ion batteries based on electrochemical impedance spectroscopy and attention mechanisms. Frontiers of Chemical Science and Engineering, 2025, 19(6): 52

[48] Li H, Zhu Z Y, Chen X L, Fan Y S, Yan L S, Liu W R. Remaining useful life prediction of lithium-ion batteries using lag-llama model with auto-correlation analysis. 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). October 6–10, 2024, Kuching, Malaysia. IEEE, 2024: 437–442

[49] Chen P , Zhang Y Y , Cheng Y Y , Shu Y , Wang Y H , Wen Q S , Yang B , Guo C J . Pathformer: multi-scale transformers with adaptive pathways for time series forecasting. , 2024,

[50] Wang S Y , Wu H X , Shi X M , Hu T G , Luo H K , Ma L T , Zhang J Y , Zhou J . TimeMixer: decomposable multiscale mixing for time series forecasting. , 2024,

[51] Wang F J , Zhai Z , Zhao Z B , Di Y , Chen X F . Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nature Communications, 2024, 15: 4332

RIGHTS & PERMISSIONS

Higher Education Press