{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:07:39Z","timestamp":1764198459655,"version":"3.46.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819549627"},{"type":"electronic","value":"9789819549634"}],"license":[{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-4963-4_26","type":"book-chapter","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:06:50Z","timestamp":1763744810000},"page":"315-326","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["State-of-Health Estimation for\u00a0Lithium-Ion Batteries Using Lightweight Features from\u00a0the\u00a0Constant Current Charging Phase"],"prefix":"10.1007","author":[{"given":"Giada","family":"Pietrocola","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8937-3668","authenticated-orcid":false,"given":"Francesco","family":"Porpora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6144-0654","authenticated-orcid":false,"given":"Mario","family":"Molinara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6825-3317","authenticated-orcid":false,"given":"Luca","family":"Gerevini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3523-9375","authenticated-orcid":false,"given":"Michele","family":"Vitelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-7350","authenticated-orcid":false,"given":"Claudio","family":"Marrocco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2895-6544","authenticated-orcid":false,"given":"Alessandro","family":"Bria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, K., Xie, Y., Liu, J., Fu, C., Liu, B.: Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current\u2013constant voltage charging strategy, Renewable Energy, vol. 146, pp. 2688\u20132699, (2020). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960148119312649","DOI":"10.1016\/j.renene.2019.08.077"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Han, X., et al.: A review on the key issues of the lithium ion battery degradation among the whole life cycle, eTransportation, vol.\u00a01, p. 100005, (2019). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2590116819300050","DOI":"10.1016\/j.etran.2019.100005"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review, Renewable and Sustainable Energy Reviews, vol. 113, p. 109254, (2019). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S136403211930454X","DOI":"10.1016\/j.rser.2019.109254"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Lipu, M.H., et al.: A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: challenges and recommendations, J. Clean. Prod. vol. 205, pp. 115\u2013133, (2018). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0959652618327793","DOI":"10.1016\/j.jclepro.2018.09.065"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Berecibar, M., et al.: Critical review of state of health estimation methods of li-ion batteries for real applications, Renewable and Sustainable Energy Reviews, vol.\u00a056, pp. 572\u2013587, (2016). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364032115013076","DOI":"10.1016\/j.rser.2015.11.042"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Liu, K., et al.: Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries, Control Engineering Practice, vol. 124, p. 105176, (2022). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0967066122000715","DOI":"10.1016\/j.conengprac.2022.105176"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Ling, L., Wei, Y.: State-of-charge and state-of-health estimation for lithium-ion batteries based on dual fractional-order extended kalman filter and online parameter identification, IEEE Access, vol.\u00a09, pp. 47\u00a0588\u201347\u00a0602 (2021)","DOI":"10.1109\/ACCESS.2021.3068813"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Ren, B., Xie, C., Sun, X., Zhang,., Yan, D.: Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm, IET Power Electronics, vol.\u00a013, no.\u00a012, pp. 2531\u20132537, (2020). Available: https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/abs\/10.1049\/iet-pel.2019.1589","DOI":"10.1049\/iet-pel.2019.1589"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Martino, G., Porpora, F., Di\u00a0Monaco, M., Tomasso, G.: Evaluation of lithium-ion cell characterization procedures and model calibration issues, In: 2024 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), pp. 1\u20136 (2024)","DOI":"10.1109\/ESARS-ITEC60450.2024.10819934"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Wang, G., Cui, N., Li, C., Cui, Z., Yuan, H.: A state-of-health estimation method based on incremental capacity analysis for li-ion battery considering charging\/discharging rate, J. Energy Storage, vol.\u00a073, p. 109010, (2023). Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352152X23024088","DOI":"10.1016\/j.est.2023.109010"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Huang, C.-G., Li, H., Feng, G., Peng, W.: Electrochemical impedance spectroscopy based state-of-health estimation for lithium-ion battery considering temperature and state-of-charge effect. IEEE Trans. Transp. Electr. 8(4), 4633\u20134645 (2022)","DOI":"10.1109\/TTE.2022.3160021"},{"key":"26_CR12","unstructured":"Yang, W., Zhang, L., Liu, H.: State-of-health estimation based on constant current\u2013constant voltage features using gaussian process regression. J. Power Sources 523, 231117 (2022)"},{"key":"26_CR13","unstructured":"Liao, C., Huang, Z., Wang, K.: Lightweight soh estimation using cv phase features and data-driven models. Electrochim. Acta 478, 142740 (2024)"},{"key":"26_CR14","unstructured":"Cao, J., Wu, J., Chen, Y.: Feature selection and regression for battery soh estimation based on cc and cv characteristics. Appl. Energy 343, 120900 (2023)"},{"key":"26_CR15","unstructured":"Lanubile, A., Romano, P., De Angelis, G.: Interpretable soh estimation for EV batteries from real usage data. Commun. Eng. 2(1), 34 (2024)"},{"key":"26_CR16","unstructured":"Sahoo, S., Banerjee, S., Ray, A.: Transferable soh estimation using voltage rise-based features. Sci. Rep. 12(1), 10145 (2022)"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Roman, M., Nascimento, T., Sauer, D.U.: Comprehensive machine learning pipeline for soh estimation of lithium-ion batteries. Nat. Mach. Intell. 3(12), 1051\u20131060 (2021)","DOI":"10.1038\/s42256-021-00312-3"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Lu, Y., Zhou, J., Sun, L.: Domain adaptation for battery soh estimation without degradation data. Nat. Commun. 14(1), 2354 (2023)","DOI":"10.1038\/s41467-023-38458-w"},{"key":"26_CR19","unstructured":"Lan, Y., Xu, H., Li, Z.: Semi-supervised soh estimation using GRU-gaussian process hybrid model. Commun. Eng. 2(1), 15 (2024)"},{"key":"26_CR20","unstructured":"Zhao, B., Sun, R., Zhang, Q.: Histogram-based deep learning for soh estimation in real-world ev charging. Appl. Energy 345, 121003 (2023)"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xu, P., Wang, Y.: Kan-LSTM hybrid architecture for lithium-ion battery soh estimation. Batteries 10(2), 142 (2024)","DOI":"10.3390\/batteries10120433"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Di, G., Capua, M., Molinara, A., Maffucci, F., Porpora, N., Femia, Oliva, N.: Machine learning and genetic programming-based behavioral modeling approaches of li-ion batteries, In: 2025 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20135 (2025)","DOI":"10.1109\/ISCAS56072.2025.11043719"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Capua, G.D., Porpora, F., Milano, F., Oliva, N., Maffucci, A.: Behavioral models for lithium batteries based on genetic programming, IEEE Access, vol.\u00a012, pp. 108\u00a0275\u2013108\u00a0290 (2024)","DOI":"10.1109\/ACCESS.2024.3434716"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Geslin, A., Xu, L., Ganapathi, D., Moy, R., Chueh, W., Onori, S.: Dataset - dynamic cycling enhances battery lifetime. https:\/\/purl.stanford.edu\/td676xr4322. Accessed 2025\/07\/9","DOI":"10.26434\/chemrxiv-2024-8fxl9"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhai, Z., Zhao, Z., Di, Y., Chen, X.: Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat. Commun. 15, 4332 (2024)","DOI":"10.1038\/s41467-024-48779-z"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Smola, A., Sch\u00f6lkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199\u2013222 (2004)","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","DOI":"10.1023\/A:1010933404324"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958)","DOI":"10.1037\/h0042519"},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification, IEEE Trans. Inf. Theor. IT-13(1), pp. 21\u201327 (1967)","DOI":"10.1109\/TIT.1967.1053964"},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Draper, N.R., Smith, H.: Applied Regression Analysis. Wiley series in probability and statistics (1998)","DOI":"10.1002\/9781118625590"}],"container-title":["Lecture Notes in Computer Science","Multi-disciplinary Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4963-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:03:10Z","timestamp":1764198190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4963-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,22]]},"ISBN":["9789819549627","9789819549634"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4963-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,22]]},"assertion":[{"value":"22 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multi-disciplinary Trends in Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miwai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miwai25.miwai.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}