journal article·2024-08

Specialized convolutional transformer networks for estimating battery health via transfer learning

Jingyuan Zhao, Zhenghong Wang

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OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Electric Vehicles and Infrastructure

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References (57)

Show source-provided references
1. Commercialization of lithium battery technologies for electric vehicles10.1002/aenm.201900161 ↗
2. Fast charging of energy-dense lithium-ion batteries10.1038/s41586-022-05281-0 ↗
3. Projections of the costs of light-duty battery-electric and fuel cell vehicles (2020–2040) and related economic issues10.1016/j.retrec.2024.101440 ↗
4. Rapidly declining costs of truck batteries and fuel cells enable large-scale road freight electrification
5. Projections of the costs of medium-and heavy-duty battery-electric and fuel cell vehicles (2020-2040) and related economic issues10.1016/j.esd.2023.101343 ↗
6. Vehicle choice modeling for emerging zero-emission medium- and heavy-duty vehicle markets in California
7. Burke · 2024 · e32823
8. Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects10.1039/D2EE03019E ↗
9. Battery lifetime prognostics10.1016/j.joule.2019.11.018 ↗
10. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: recent advances and perspectives10.1016/j.rser.2020.110048 ↗
11. Cycle life testing of lithium batteries: the effect of load-leveling10.20964/2018.02.37 ↗
12. Li-NMC battery internal resistance at wide range of temperature10.37934/arfmts.99.1.916 ↗
13. Microstructural evolution of battery electrodes during calendering10.1016/j.joule.2020.10.010 ↗
14. Insights and reviews on battery lifetime prediction from research to practice10.1016/j.jechem.2024.03.013 ↗
15. Battery health diagnostics: bridging the gap between academia and industry
16. Battery prognostics and health management for electric vehicles under industry 4.010.1016/j.jechem.2023.04.042 ↗
17. Cloud-based artificial intelligence framework for battery management system10.3390/en16114403 ↗
18. State-of-charge estimation for lithium-ion batteries using neural networks and EKF10.1109/TIE.2010.2043035 ↗
19. A review on the key issues for lithium-ion battery management in electric vehicles10.1016/j.jpowsour.2012.10.060 ↗
20. State-of-health estimation of lithium-ion batteries for electrified vehicles using a reduced-order electrochemical model10.1016/j.est.2022.104684 ↗
21. State of health estimation for lithium ion batteries based on charging curves10.1016/j.jpowsour.2013.10.114 ↗
22. Towards a smarter battery management system: a critical review on battery state of health monitoring methods10.1016/j.jpowsour.2018.10.019 ↗
23. A comparative study of equivalent circuit models for Li-ion batteries10.1016/j.jpowsour.2011.10.013 ↗
24. Deep learning10.1038/nature14539 ↗
25. Probabilistic machine learning and artificial intelligence10.1038/nature14541 ↗
26. Electric vehicle batteries: status and perspectives of data-driven diagnosis and prognosis10.3390/batteries8100142 ↗
27. Battery diagnosis: a lifelong learning framework for electric vehicles
28. Machine learning for battery research10.1016/j.jpowsour.2022.232125 ↗
29. Battery safety: fault diagnosis from laboratory to real world10.1016/j.jpowsour.2024.234111 ↗
30. Data-driven prediction of battery failure for electric vehicles10.1016/j.isci.2022.104172 ↗
31. Machine learning for continuous innovation in battery technologies10.1038/s41578-020-0216-y ↗
32. Machine learning for predicting battery capacity for electric vehicles10.1016/j.etran.2022.100214 ↗
33. Online state-of-health assessment for battery management systems10.1109/TIM.2011.2115630 ↗
34. Battery safety: machine learning-based prognostics10.1016/j.pecs.2023.101142 ↗
35. Specialized deep neural networks for battery health prognostics: opportunities and challenges10.1016/j.jechem.2023.08.047 ↗
36. Battery prognostics and health management from a machine learning perspective10.1016/j.jpowsour.2023.233474 ↗
37. Deep neural network battery charging curve prediction using 30 points collected in 10 min10.1016/j.joule.2021.05.012 ↗
38. Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks10.1016/j.apenergy.2023.121949 ↗
39. Spatial-temporal self-attention transformer networks for battery state of charge estimation
40. Multivariate prognosis of battery advanced state of health via transformers
41. Cloud-based deep learning for co-estimation of battery state of charge and state of health10.3390/en16093855 ↗
42. Li-ion battery capacity prediction using improved temporal fusion transformer model10.1016/j.energy.2024.131114 ↗
43. Battery health state estimation of combined transformer-GRU based on modal decomposition
44. A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model10.1016/j.energy.2022.125501 ↗
45. Convolutional transformer-based multi-view information perception framework for lithium-ion battery state-of-health estimation10.1109/TIM.2023.3300451 ↗
46. Informer: beyond efficient transformer for long sequence time-series forecasting
47. Full-process electric vehicles battery state of health estimation based on Informer novel model10.1016/j.est.2023.108626 ↗
48. Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries10.1016/j.apenergy.2019.114296 ↗
49. A vision transformer-based deep neural network for state of health estimation of lithium-ion batteries10.1016/j.ijepes.2023.109233 ↗
50. Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries10.1109/TIE.2019.2946551 ↗
51. Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning10.1039/D2EE01676A ↗
52. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation10.1038/s41467-022-29837-w ↗
53. Machine learning pipeline for battery state-of-health estimation10.1038/s42256-021-00312-3 ↗
54. Attention is all you need
55. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
56. State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer10.1016/j.energy.2023.129401 ↗
57. Hyper-parameter optimization of multi-attention recurrent neural network for battery state-of-charge forecasting