{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T04:12:12Z","timestamp":1759983132976,"version":"build-2065373602"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62201220","62201220","62201220","62201220","62201220"],"award-info":[{"award-number":["62201220","62201220","62201220","62201220","62201220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04720-5","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T10:47:40Z","timestamp":1757587660000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AutoDS: a de-stationary transformer method for lithium-ion battery remaining useful life prediction"],"prefix":"10.1007","volume":"19","author":[{"given":"Xiaoyuan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yushi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congcong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"4720_CR1","doi-asserted-by":"publisher","unstructured":"Zhang, J., Huang, H., Zhang, G., Dai, Z., Wen, Y., Jiang, L.: Cycle life studies of lithium-ion power batteries for electric vehicles: A review. Journal of Energy Storage 93, 112231 (2024) https:\/\/doi.org\/10.1016\/j.est.2024.112231","DOI":"10.1016\/j.est.2024.112231"},{"issue":"3","key":"4720_CR2","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1166\/mex.2012.1075","volume":"2","author":"J Wen","year":"2012","unstructured":"Wen, J., Yu, Y., Chen, C.: A review on lithium-ion batteries safety issues: existing problems and possible solutions. Mater. Express 2(3), 197\u2013212 (2012)","journal-title":"Mater. Express"},{"key":"4720_CR3","doi-asserted-by":"publisher","unstructured":"Wu, J., Zhang, C., Chen, Z.: An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy 173, 134\u2013140 (2016) https:\/\/doi.org\/10.1016\/j.apenergy.2016.04.057","DOI":"10.1016\/j.apenergy.2016.04.057"},{"issue":"1","key":"4720_CR4","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s11760-023-02816-4","volume":"18","author":"Q Sun","year":"2024","unstructured":"Sun, Q., Liu, Y., Li, S.: Weighted directed graph-based automatic seizure detection with effective brain connectivity for eeg signals. SIViP 18(1), 899\u2013909 (2024)","journal-title":"SIViP"},{"issue":"5","key":"4720_CR5","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.1109\/TII.2020.3008223","volume":"17","author":"L Ren","year":"2020","unstructured":"Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., Deen, M.J.: A data-driven auto-cnn-lstm prediction model for lithium-ion battery remaining useful life. IEEE Trans. Industr. Inf. 17(5), 3478\u20133487 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4720_CR6","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1016\/j.egyr.2023.05.121","volume":"9","author":"X Guo","year":"2023","unstructured":"Guo, X., Wang, K., Yao, S., Fu, G., Ning, Y.: Rul prediction of lithium ion battery based on ceemdan-cnn bilstm model. Energy Rep. 9, 1299\u20131306 (2023)","journal-title":"Energy Rep."},{"key":"4720_CR7","doi-asserted-by":"crossref","unstructured":"Liu, J., Saxena, A., Goebel, K., Saha, B., Wang, W.: An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. In: Annual Conference of the PHM Society, vol. 2 (2010)","DOI":"10.36001\/phmconf.2010.v2i1.1896"},{"issue":"7","key":"4720_CR8","doi-asserted-by":"publisher","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","volume":"67","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Xiong, R., He, H., Pecht, M.G.: Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans. Veh. Technol. 67(7), 5695\u20135705 (2018). https:\/\/doi.org\/10.1109\/TVT.2018.2805189","journal-title":"IEEE Trans. Veh. Technol."},{"key":"4720_CR9","doi-asserted-by":"publisher","unstructured":"Shi, Z., Chehade, A.: A dual-lstm framework combining change point detection and remaining useful life prediction. Reliability Engineering & System Safety 205, 107257 (2021) https:\/\/doi.org\/10.1016\/j.ress.2020.107257","DOI":"10.1016\/j.ress.2020.107257"},{"key":"4720_CR10","doi-asserted-by":"publisher","unstructured":"Xiao, B., Liu, Y., Xiao, B.: Accurate state-of-charge estimation approach for lithium-ion batteries by gated recurrent unit with ensemble optimizer. IEEE Access 7, 54192\u201354202 (2019) https:\/\/doi.org\/10.1109\/ACCESS.2019.2913078","DOI":"10.1109\/ACCESS.2019.2913078"},{"key":"4720_CR11","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"4720_CR12","doi-asserted-by":"publisher","unstructured":"Chen, D., Hong, W., Zhou, X.: Transformer network for remaining useful life prediction of lithium-ion batteries. IEEE Access 10, 19621\u201319628 (2022) https:\/\/doi.org\/10.1109\/ACCESS.2022.3151975","DOI":"10.1109\/ACCESS.2022.3151975"},{"issue":"17","key":"4720_CR13","doi-asserted-by":"publisher","first-page":"6328","DOI":"10.3390\/en16176328","volume":"16","author":"Y Han","year":"2023","unstructured":"Han, Y., Li, C., Zheng, L., Lei, G., Li, L.: Remaining useful life prediction of lithium-ion batteries by using a denoising transformer-based neural network. Energies 16(17), 6328 (2023)","journal-title":"Energies"},{"key":"4720_CR14","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1016\/j.procs.2022.12.383","volume":"217","author":"W Song","year":"2023","unstructured":"Song, W., Wu, D., Shen, W., Boulet, B.: A remaining useful life prediction method for lithium-ion battery based on temporal transformer network. Procedia Computer Science 217, 1830\u20131838 (2023)","journal-title":"Procedia Computer Science"},{"key":"4720_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.131114","volume":"296","author":"W Gomez","year":"2024","unstructured":"Gomez, W., Wang, F.-K., Chou, J.-H.: Li-ion battery capacity prediction using improved temporal fusion transformer model. Energy 296, 131114 (2024)","journal-title":"Energy"},{"issue":"16","key":"4720_CR16","doi-asserted-by":"publisher","first-page":"3976","DOI":"10.3390\/en17163976","volume":"17","author":"U Saleem","year":"2024","unstructured":"Saleem, U., Liu, W., Riaz, S., Li, W., Hussain, G.A., Rashid, Z., Arfeen, Z.A.: Transrul: A transformer-based multihead attention model for enhanced prediction of battery remaining useful life. Energies 17(16), 3976 (2024)","journal-title":"Energies"},{"issue":"5","key":"4720_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-023-3277-4","volume":"18","author":"L Zhao","year":"2024","unstructured":"Zhao, L., Song, S., Wang, P., Wang, C., Wang, J., Guo, M.: A mlp-mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries. Front. Comp. Sci. 18(5), 185329 (2024)","journal-title":"Front. Comp. Sci."},{"key":"4720_CR18","doi-asserted-by":"publisher","first-page":"20786","DOI":"10.1109\/ACCESS.2020.2968939","volume":"8","author":"K Park","year":"2020","unstructured":"Park, K., Choi, Y., Choi, W.J., Ryu, H.-Y., Kim, H.: Lstm-based battery remaining useful life prediction with multi-channel charging profiles. Ieee Access 8, 20786\u201320798 (2020)","journal-title":"Ieee Access"},{"key":"4720_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129401","volume":"285","author":"C Jia","year":"2023","unstructured":"Jia, C., Tian, Y., Shi, Y., Jia, J., Wen, J., Zeng, J.: State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer. Energy 285, 129401 (2023)","journal-title":"Energy"},{"key":"4720_CR20","unstructured":"Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arxiv. arXiv preprint arXiv:1803.01271 10 (2018)"},{"key":"4720_CR21","doi-asserted-by":"publisher","unstructured":"Audin, P., Jorge, I., Mesbahi, T., Samet, A., De\u00a0Bertrand De\u00a0Beuvron, F., Bon\u00e9, R.: Auto-encoder lstm for li-ion soh prediction: a comparative study on various benchmark datasets. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1529\u20131536 (2021). https:\/\/doi.org\/10.1109\/ICMLA52953.2021.00246","DOI":"10.1109\/ICMLA52953.2021.00246"},{"issue":"5","key":"4720_CR22","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.1109\/TII.2020.3008223","volume":"17","author":"L Ren","year":"2020","unstructured":"Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., Deen, M.J.: A data-driven auto-cnn-lstm prediction model for lithium-ion battery remaining useful life. IEEE Trans. Industr. Inf. 17(5), 3478\u20133487 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4720_CR23","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4720_CR24","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: Temporal 2d-variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186 (2022)"},{"key":"4720_CR25","unstructured":"Wang, S., Wu, H., Shi, X., Hu, T., Luo, H., Ma, L., Zhang, J.Y., Zhou, J.: Timemixer: Decomposable multiscale mixing for time series forecasting. arXiv preprint arXiv:2405.14616 (2024)"},{"key":"4720_CR26","doi-asserted-by":"crossref","unstructured":"Miller, I.: Probability, random variables, and stochastic processes. Taylor & Francis (1966)","DOI":"10.2307\/1266379"},{"key":"4720_CR27","unstructured":"Wang, Y., Wu, H., Dong, J., Qin, G., Zhang, H., Liu, Y., Qiu, Y., Wang, J., Long, M.: Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072 (2024)"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04720-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04720-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04720-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T03:29:35Z","timestamp":1759980575000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04720-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":27,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4720"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04720-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"13 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1110"}}