{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T15:40:36Z","timestamp":1792683636303,"version":"4.5.0"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115420","type":"journal-article","created":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T21:05:25Z","timestamp":1782075925000},"page":"115420","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"P2","title":["A linear-complexity deep learning framework for efficient battery State of Health estimation in resource-constrained systems"],"prefix":"10.1016","volume":"181","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5160-2241","authenticated-orcid":false,"given":"Huajiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxing","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanglin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8064-1374","authenticated-orcid":false,"given":"Chao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115420_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2023.107965","article-title":"A feature reuse based multi-model fusion method for state of health estimation of lithium-ion batteries","volume":"70","author":"Bai","year":"2023","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b2","first-page":"1","article-title":"Convolutional transformer -based multiview information perception framework for lithium-ion battery state-of-health estimation","volume":"72","author":"Bai","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115420_b3","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.jpowsour.2016.12.011","article-title":"Degradation diagnostics for lithium ion cells","volume":"341","author":"Birkl","year":"2017","journal-title":"J. Power Sources"},{"key":"10.1016\/j.engappai.2026.115420_b4","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.apenergy.2018.10.095","article-title":"State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters","volume":"235","author":"Cadini","year":"2019","journal-title":"Appl. Energy"},{"issue":"3","key":"10.1016\/j.engappai.2026.115420_b5","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/MCS.2010.936293","article-title":"Algorithms for advanced battery-management systems","volume":"30","author":"Chaturvedi","year":"2010","journal-title":"IEEE Control Syst. Mag."},{"key":"10.1016\/j.engappai.2026.115420_b6","series-title":"Attention is not all you need anymore","author":"Chen","year":"2023"},{"key":"10.1016\/j.engappai.2026.115420_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2023.109233","article-title":"A vision transformer-based deep neural network for state of health estimation of lithium-ion batteries","volume":"152","author":"Chen","year":"2023","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.engappai.2026.115420_b8","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R -squared is more informative than SMAPE, MAE, MAPE,MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.engappai.2026.115420_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.111896","article-title":"A heterogeneous learner fusion method with supplementary feature for lithium-ion batteries state of health estimation","volume":"92","author":"Feng","year":"2024","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b10","series-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"Vol. 9","author":"Glorot","year":"2010"},{"key":"10.1016\/j.engappai.2026.115420_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.131114","article-title":"Li-ion battery capacity prediction using improved temporal fusion transformer model","volume":"296","author":"Gomez","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125501","article-title":"A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model","volume":"262","author":"Gu","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b13","series-title":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"5938","article-title":"Flatten transformer: vision transformer using focused linear attention","author":"Han","year":"2023"},{"key":"10.1016\/j.engappai.2026.115420_b14","series-title":"Agent attention : on the integration of softmax and linear attention","author":"Han","year":"2024"},{"issue":"1","key":"10.1016\/j.engappai.2026.115420_b15","doi-asserted-by":"crossref","first-page":"19541","DOI":"10.1038\/s41598-021-98915-8","article-title":"Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model","volume":"11","author":"Hannan","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.115420_b16","series-title":"Deep residual learning for image recognition","author":"He","year":"2015"},{"key":"10.1016\/j.engappai.2026.115420_b17","series-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","author":"He","year":"2015"},{"key":"10.1016\/j.engappai.2026.115420_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.111444","article-title":"Interface synergistic stabilization of zinc anodes via polyacrylic acid doped polyvinyl alcohol ultra-thin coating","volume":"87","author":"Hu","year":"2024","journal-title":"J. Energy Storage"},{"issue":"11","key":"10.1016\/j.engappai.2026.115420_b19","doi-asserted-by":"crossref","first-page":"11811","DOI":"10.1007\/s11581-025-06657-8","article-title":"State of health estimation method for lithium-ion batteries based on multi-feature fusion and swin transformer model","volume":"31","author":"Huang","year":"2025","journal-title":"Ionics"},{"key":"10.1016\/j.engappai.2026.115420_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.128739","article-title":"State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining","volume":"282","author":"Huang","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129401","article-title":"State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer","volume":"285","author":"Jia","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125503","article-title":"State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression","volume":"262","author":"Jin","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.120114","article-title":"Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries","volume":"223","author":"z. Kong","year":"2021","journal-title":"Energy"},{"issue":"4","key":"10.1016\/j.engappai.2026.115420_b24","doi-asserted-by":"crossref","DOI":"10.1149\/1945-7111\/abf05f","article-title":"Performance of commercial Li-ion cells for future NASA missions and aerospace applications","volume":"168","author":"Krause","year":"2021","journal-title":"J. Electrochem. Soc."},{"key":"10.1016\/j.engappai.2026.115420_b25","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"2796","article-title":"Accurate uncertainties for deep learning using calibrated regression","volume":"Vol. 80","author":"Kuleshov","year":"2018"},{"key":"10.1016\/j.engappai.2026.115420_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.110004","article-title":"A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability","volume":"188","author":"Lee","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.115420_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.136844","article-title":"Lithium-ion battery soh prediction based on multi-dimensional features and multi-model feature selector","volume":"331","author":"Li","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.134030","article-title":"Bmsformer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early soc data with strong correlated single health indicator","volume":"313","author":"Li","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108666","article-title":"Health prognosis via feature optimization and convolutional neural network for lithium-ion batteries","volume":"133","author":"Lin","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115420_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2021.230774","article-title":"A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries","volume":"518","author":"Lin","year":"2022","journal-title":"J. Power Sources"},{"issue":"8","key":"10.1016\/j.engappai.2026.115420_b31","doi-asserted-by":"crossref","first-page":"3654","DOI":"10.3390\/en6083654","article-title":"Satellite lithium -ion battery remaining cycle life prediction with novel indirect health indicator extraction","volume":"6","author":"Liu","year":"2013","journal-title":"Energies"},{"issue":"1","key":"10.1016\/j.engappai.2026.115420_b32","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.1038\/s41467-023-38458-w","article-title":"Deep learning to estimate lithium-ion battery state of health without additional degradation experiments","volume":"14","author":"Lu","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.engappai.2026.115420_b33","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jechem.2022.06.049","article-title":"A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries","volume":"74","author":"Luo","year":"2022","journal-title":"J. Energy Chem."},{"key":"10.1016\/j.engappai.2026.115420_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2019.113626","article-title":"Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network","volume":"253","author":"Ma","year":"2019","journal-title":"Applied Energy"},{"issue":"3","key":"10.1016\/j.engappai.2026.115420_b35","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1109\/TII.2023.3306575","article-title":"An enhanced protective relaying scheme for tcsc compensated line connecting dfig-based wind farm","volume":"20","author":"Mohanty","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.engappai.2026.115420_b36","article-title":"Decision tree approach for fault detection in a tcsc compensated line during power swing","volume":"146","author":"Mohanty","year":"2023","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.engappai.2026.115420_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.124435","article-title":"State of charge estimation for LiFePO4 batteries joint by PID observer and improved EKF in various OCV ranges","volume":"377","author":"Peng","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.engappai.2026.115420_b38","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.est.2019.01.005","article-title":"Empirical Li -ion aging model derived from single particle model","volume":"21","author":"Rechkemmer","year":"2019","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b39","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.jpowsour.2017.05.004","article-title":"Gaussian process regression for forecasting battery state of health","volume":"357","author":"Richardson","year":"2017","journal-title":"J. Power Sources"},{"issue":"5","key":"10.1016\/j.engappai.2026.115420_b40","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1038\/s42256-021-00312-3","article-title":"Machine learning pipeline for battery state-of-health estimation","volume":"3","author":"Roman","year":"2021","journal-title":"Nat. Mach. Intell."},{"issue":"6","key":"10.1016\/j.engappai.2026.115420_b41","doi-asserted-by":"crossref","first-page":"4505","DOI":"10.1109\/TSG.2022.3188598","article-title":"A review of modeling, management, and applications of grid-connected li-ion battery storage systems","volume":"13","author":"Rouholamini","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.engappai.2026.115420_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.121712","article-title":"Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features","volume":"238","author":"Son","year":"2022","journal-title":"Energy"},{"issue":"10","key":"10.1016\/j.engappai.2026.115420_b43","doi-asserted-by":"crossref","first-page":"8723","DOI":"10.1109\/TIE.2019.2946551","article-title":"Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries","volume":"67","author":"Tan","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"3","key":"10.1016\/j.engappai.2026.115420_b44","doi-asserted-by":"crossref","first-page":"2250","DOI":"10.1109\/TSG.2022.3202811","article-title":"Security-ensured state of charge estimation of lithium-ion batteries subject to malicious attacks","volume":"14","author":"Tian","year":"2023","journal-title":"IEEE Trans. Smart Grid"},{"issue":"10","key":"10.1016\/j.engappai.2026.115420_b45","doi-asserted-by":"crossref","first-page":"10363","DOI":"10.1109\/TPEL.2020.2978493","article-title":"State-of-health estimation based on differential temperature for lithium ion batteries","volume":"35","author":"Tian","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"key":"10.1016\/j.engappai.2026.115420_b46","first-page":"4271","article-title":"Attention is all you need","volume":"Vol. 30","author":"Vaswani","year":"2017"},{"issue":"5","key":"10.1016\/j.engappai.2026.115420_b47","doi-asserted-by":"crossref","first-page":"103","DOI":"10.23919\/PCMP.2024.000161","article-title":"Source-load coordinated optimization framework for distributed energy systems using quasi-potential game method","volume":"10","author":"Wang","year":"2025","journal-title":"Prot. Control. Mod. Power Syst."},{"issue":"7","key":"10.1016\/j.engappai.2026.115420_b48","doi-asserted-by":"crossref","first-page":"5634","DOI":"10.1109\/TIE.2017.2782224","article-title":"Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression","volume":"65","author":"Wei","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"9","key":"10.1016\/j.engappai.2026.115420_b49","doi-asserted-by":"crossref","first-page":"11763","DOI":"10.1016\/j.eswa.2011.03.063","article-title":"Intelligent prognostics for battery health monitoring based on sample entropy","volume":"38","author":"Widodo","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115420_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.117752","article-title":"Movable sio2 reinforced polymethyl methacrylate dual network coating for highly stable zinc anodes","volume":"132","author":"Wu","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2023.109191","article-title":"Data-driven state of health estimation for lithium-ion battery based on voltage variation curves","volume":"73","author":"Wu","year":"2023","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b52","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.jechem.2024.06.017","article-title":"A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms and future challenges","volume":"97","author":"Xie","year":"2024","journal-title":"J. Energy Chem."},{"issue":"5","key":"10.1016\/j.engappai.2026.115420_b53","doi-asserted-by":"crossref","first-page":"4110","DOI":"10.1109\/TVT.2018.2864688","article-title":"Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles","volume":"68","author":"Xiong","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.engappai.2026.115420_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112072","article-title":"State of health estimation for lithium-ion batteries based on incremental capacity analysis and transformer modeling","volume":"165","author":"Xu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.115420_b55","doi-asserted-by":"crossref","unstructured":"Xu, H., Peng, Y., Su, L., Health State Estimation Method of Lithium Ion Battery Based on NASA Experimental Data Set. In: IOP Conference Series: Materials Science and Engineering 452 (3) (2018) 032067, http:\/\/dx.doi.org\/10.1088\/1757-899X\/452\/3\/032067, publisher: IOP Publishing, TLDR: Two novel methods for estimating the health status of lithium-ion battery are proposed using Bootstrap methods and two machine learning models, regression tree and random forest, are compared.","DOI":"10.1088\/1757-899X\/452\/3\/032067"},{"key":"10.1016\/j.engappai.2026.115420_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.118550","article-title":"A novel hybrid neural network-based state of health estimation method with multi-feature extraction for lithium-ion batteries","volume":"137","author":"Yan","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2021.103857","article-title":"Robust state of health estimation of lithium-ion batteries using convolutional neural network and random forest","volume":"48","author":"Yang","year":"2022","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b58","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.energy.2017.10.043","article-title":"Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique","volume":"142","author":"Zhang","year":"2018","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.115420_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107698","article-title":"A novel health indicator by dominant invariant subspace on grassmann manifold for state of health assessment of lithium-ion battery","volume":"130","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115420_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2023.107063","article-title":"State of health estimation method for lithium-ion batteries using incremental capacity and long short-term memory network","volume":"64","author":"Zhang","year":"2023","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.115420_b61","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122417","article-title":"State-of-health estimation for lithium-ion battery via an evolutionary stacking ensemble learning paradigm of random vector functional link and active-state-tracking long\u2013short-term memory neural network","volume":"356","author":"Zhang","year":"2024","journal-title":"Appl. Energy"},{"key":"10.1016\/j.engappai.2026.115420_b62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.egyr.2022.12.093","article-title":"State of health estimation for lithium battery random charging process based on CNN -GRU method","volume":"9","author":"Zheng","year":"2023","journal-title":"Energy Rep."},{"key":"10.1016\/j.engappai.2026.115420_b63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jpowsour.2016.04.119","article-title":"A novel health indicator for on-line lithium-ion batteries remaining useful life prediction","volume":"321","author":"Zhou","year":"2016","journal-title":"J. Power Sources"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017045?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626017045?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T14:53:28Z","timestamp":1792680808000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626017045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":63,"alternative-id":["S0952197626017045"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115420","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A linear-complexity deep learning framework for efficient battery State of Health estimation in resource-constrained systems","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115420","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115420"}}