Transformer-based prediction of wide-frequency battery impedance spectra from low-rate time-domain data
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OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Electric Vehicles and Infrastructure
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References (56)
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1. Designing ionic liquid electrolytes for a rigid and Li+-conductive solid electrolyte interface in high performance lithium metal batteries10.1016/j.cplett.2025.141959 ↗
2. Controllable preparation of low-cost coal gangue-based SAPO-5 molecular sieve and its adsorption performance for heavy metal ions10.3390/nano15050366 ↗
3. A review of modeling, acquisition, and application of lithium-ion battery impedance for onboard battery management10.1016/j.etran.2020.100093 ↗
4. Revolutionizing battery safety: real-time insights with dynamic electrochemical impedance spectroscopy10.1021/acsenergylett.5c00484 ↗
5. Reflections on the history of electrochemical impedance spectroscopy10.1016/j.electacta.2005.02.107 ↗
6. A deep learning approach for enhanced degradation diagnostics of NMC lithium-ion batteries via impedance spectra10.1016/j.jechem.2025.05.014 ↗
7. Identification and quantification of ageing mechanisms in li-ion batteries by electrochemical impedance spectroscopy10.1016/j.electacta.2022.140801 ↗
8. A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery Management systems10.1016/j.jpowsour.2017.03.042 ↗
9. Battery SOC estimation from EIS data based on machine learning and equivalent circuit model10.1016/j.energy.2023.128461 ↗
10. State of charge prediction of EV li-ion batteries using EIS: a machine learning approach10.1016/j.energy.2021.120116 ↗
11. A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries10.1016/j.apenergy.2022.119502 ↗
12. On-line state-of-health estimation of lithium-ion battery cells using frequency excitation10.1016/j.est.2020.101841 ↗
13. Rapid estimation of battery state of health using partial electrochemical impedance spectra and interpretable machine learning10.1016/j.jpowsour.2024.234413 ↗
14. A transformer-enhanced framework for lithium-ion battery capacity estimation using limited imaginary impedance feature10.1016/j.est.2025.116313 ↗
15. Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy10.1016/j.rser.2023.113807 ↗
16. Estimating battery state of health using electrochemical impedance spectroscopy and the relaxation effect10.1016/j.est.2021.103210 ↗
17. An improvement of equivalent circuit model for state of health estimation of lithium-ion batteries based on mid-frequency and low-frequency electrochemical impedance spectroscopy10.1016/j.measurement.2022.111795 ↗
18. Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques10.1016/j.est.2022.106295 ↗
19. Impedance-based forecasting of lithium-ion battery performance amid uneven usage10.1038/s41467-022-32422-w ↗
20. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning10.1038/s41467-020-15235-7 ↗
21. Practical on-board measurement of lithium ion battery impedance based on distributed voltage and current sampling10.3390/en11010064 ↗
22. A novel on-board electrochemical impedance spectroscopy system for real-time battery impedance estimation10.1109/TPEL.2021.3063506 ↗
23. Electrochemical impedance spectroscopy: a novel high-power measurement technique for onboard batteries using full-bridge conversion10.1109/TTE.2024.3362992 ↗
24. In-situ characterization of lithium-ion battery dynamic impedance spectrum for real-world driving applications
25. Passive impedance spectroscopy for monitoring lithium-ion battery cells during vehicle operation10.1016/j.jpowsour.2019.227297 ↗
26. Research on online passive electrochemical impedance spectroscopy and its outlook in battery management10.1016/j.apenergy.2024.123046 ↗
27. Localization of the complex spectrum: the S transform10.1109/78.492555 ↗
28. The wavelet transform, time-frequency localization and signal analysis10.1109/18.57199 ↗
29. Fast calculation of broadband battery impedance spectra based on S transform of step disturbance and response10.1109/TTE.2022.3153680 ↗
30. Advanced online broadband impedance spectrum acquisition of fuel cells by S-transform10.1109/TIE.2022.3177814 ↗
31. Deep neural network based battery impedance spectrum prediction using only impedance at characteristic frequencies10.1016/j.jpowsour.2023.233414 ↗
32. Fast EIS acquisition method based on SSA-DNN prediction model10.1016/j.energy.2023.129768 ↗
33. Fast acquisition method of battery electrochemical impedance spectra based on impedance fragments10.1016/j.est.2024.114269 ↗
34. Deep neural network battery impedance spectra prediction by only using constant-current curve10.1016/j.ensm.2021.05.047 ↗
35. Battery impedance spectrum prediction from partial charging voltage curve by machine learning10.1016/j.jechem.2023.01.004 ↗
36. Simultaneous prediction of impedance spectra and state for lithium-ion batteries from short-term pulses10.1016/j.electacta.2023.142218 ↗
37. Predicting battery impedance spectra from 10-second pulse tests under 10 hz sampling rate10.1016/j.isci.2023.106821 ↗
38. Constructing battery impedance spectroscopy using partial current in constant-voltage charging or partial relaxation voltage10.1016/j.apenergy.2023.122454 ↗
39. A trustworthy pipeline for data-driven estimation of lithium-ion battery electrochemical impedance spectroscopy using a physics-guided neural network10.1016/j.est.2025.116454 ↗
40. Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance10.1016/j.jpowsour.2019.227575 ↗
41. Investigation of capacity fade for 18650-type lithium-ion batteries cycled in different state of charge (SoC) ranges10.1016/j.jpowsour.2020.229422 ↗
42. Electrochemical impedance spectroscopy based state-of-health estimation for lithium-ion battery considering temperature and state-of-charge effect10.1109/TTE.2022.3160021 ↗
43. Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application10.1016/j.apenergy.2012.09.030 ↗
44. Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy10.1016/j.energy.2022.125972 ↗
45. The timescale identification decoupling complicated kinetic processes in lithium batteries10.1016/j.joule.2022.05.005 ↗
46. State of health estimation for lithium-ion batteries using impedance-based simplified timescale information10.1016/j.apenergy.2025.125272 ↗
47. Low-temperature separating lithium-ion battery interfacial polarization based on distribution of relaxation times (DRT) of impedance10.1109/TTE.2020.3028475 ↗
48. Impedance characterization of lithium-ion batteries aging under high-temperature cycling: importance of electrolyte-phase diffusion10.1016/j.jpowsour.2019.04.040 ↗
49. Distribution of relaxation times analysis of high-temperature PEM fuel cell impedance spectra10.1016/j.electacta.2017.02.011 ↗
50. The gaussian process hilbert transform (GP-HT): testing the consistency of electrochemical impedance spectroscopy data10.1149/1945-7111/aba937 ↗
51. Attention is all you need
52. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K.BERT: Pre-training of deep bidirectional transformers for language understanding. Preprint at https://doi.org/10.48550/arXiv.1810.04805 (2019).
53. Brown, T.B. et al. Language models are few-shot learners. Preprint at https://doi.org/10.48550/arXiv.2005.14165 (2020).
54. OpenAI. GPT-4 technical report. Preprint at https://doi.org/10.48550/arXiv.2303.08774 (2023).
55. Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. Preprint at https://doi.org/10.48550/arXiv.1910.10683 (2023).
56. Kingma, D.P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://doi.org/10.48550/arXiv.1412.6980 (2017).