{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T06:50:05Z","timestamp":1769755805167,"version":"3.49.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031441455","type":"print"},{"value":"9783031441462","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44146-2_6","type":"book-chapter","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T09:02:22Z","timestamp":1695373342000},"page":"55-66","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Predictive Battery Maintenance Through the\u00a0Use of\u00a0Explainable Boosting Machine"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9308-1062","authenticated-orcid":false,"given":"Sadiqa","family":"Jafari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Vutetakis, D.G., Viswanathan, V.V.: Determining the state-of-health of maintenance-free aircraft batteries. In: Proceedings of the Tenth Annual Battery Conference on Applications and Advances, vol. 2, pp. 13\u201318 (1995)","DOI":"10.1109\/BCAA.1995.398516"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Xiongzi, C., Jinsong, Y., Diyin, T., Yingxun, W.: Remaining useful life prognostic estimation for aircraft subsystems or components: a review. In: IEEE 2011 10th International Conference on Electronic Measurement & Instruments, vol. 2, pp. 94\u201398 (2011)","DOI":"10.1109\/ICEMI.2011.6037773"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Asmai, S.A., Hussin, B., Yusof, M.M.: A framework of an intelligent maintenance prognosis tool, pp. 241\u2013245. IEEE (2010)","DOI":"10.1109\/ICCRD.2010.69"},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ress.2014.09.014","volume":"133","author":"D An","year":"2015","unstructured":"An, D., Kim, N.H., Choi, J.H.: Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliab. Eng. Syst. Saf. 133, 223\u2013236 (2015)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"An, D., Kim, N.H., Choi, J.-H.: Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. J. Energy Storage 26, 100951 (2019)","DOI":"10.1016\/j.est.2019.100951"},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"50587","DOI":"10.1109\/ACCESS.2018.2858856","volume":"6","author":"L Ren","year":"2018","unstructured":"Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., Zhang, L.: Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access 6, 50587\u201350598 (2018)","journal-title":"IEEE Access"},{"issue":"1","key":"6_CR7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/batteries9010013","volume":"9","author":"P Sharma","year":"2023","unstructured":"Sharma, P., Bora, B.J.: A review of modern machine learning techniques in the prediction of remaining useful life of lithium-ion batteries. Batteries 9(1), 13 (2023)","journal-title":"Batteries"},{"key":"6_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2022.132194","volume":"360","author":"Z Said","year":"2022","unstructured":"Said, Z., et al.: Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale organic rankine cycle using hybrid nanofluid. J. Clean. Prod. 360, 132194 (2022)","journal-title":"J. Clean. Prod."},{"key":"6_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.127270","volume":"330","author":"E Saleh","year":"2022","unstructured":"Saleh, E., Tarawneh, A., Naser, M.Z., Abedi, M., Almasabha, G.: You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete. Constr. Build. Mater. 330, 127270 (2022)","journal-title":"Constr. Build. Mater."},{"key":"6_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120813","volume":"261","author":"H Tian","year":"2020","unstructured":"Tian, H., Qin, P., Li, K., Zhao, Z.: A review of the state of health for lithium-ion batteries: research status and suggestions. J. Clean. Prod. 261, 120813 (2020)","journal-title":"J. Clean. Prod."},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"87178","DOI":"10.1109\/ACCESS.2019.2925468","volume":"7","author":"J Qu","year":"2019","unstructured":"Qu, J., Liu, F., Ma, Y., Fan, J.: A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery. IEEE Access 7, 87178\u201387191 (2019)","journal-title":"IEEE Access"},{"issue":"7","key":"6_CR12","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)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.egyr.2022.10.298","volume":"8","author":"J Wu","year":"2022","unstructured":"Wu, J., Kong, L., Cheng, Z., Yang, Y., Zuo, H.: RUL prediction for lithium batteries using a novel ensemble learning method. Energy Rep. 8, 313\u2013326 (2022)","journal-title":"Energy Rep."},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1016\/j.egyr.2021.05.019","volume":"7","author":"M Wei","year":"2021","unstructured":"Wei, M., Gu, H., Ye, M., Wang, Q., Xu, X., Wu, C.: Remaining useful life prediction of lithium-ion batteries based on Monte Carlo Dropout and gated recurrent unit. Energy Rep. 7, 2862\u20132871 (2021)","journal-title":"Energy Rep."},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.ress.2018.02.022","volume":"174","author":"J Yu","year":"2018","unstructured":"Yu, J.: State of health prediction of lithium-ion batteries: multiscale logic regression and Gaussian process regression ensemble. Reliab. Eng. Syst. Saf. 174, 82\u201395 (2018)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2020.228069","volume":"459","author":"P Li","year":"2020","unstructured":"Li, P., et al.: State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. J. Power Sources 459, 228069 (2020)","journal-title":"J. Power Sources"},{"issue":"10","key":"6_CR17","doi-asserted-by":"publisher","first-page":"9543","DOI":"10.1109\/TVT.2019.2932605","volume":"68","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Ni, Y., Lu, S., Wang, J., Zhang, X.: Remaining useful life prediction of lithium-ion batteries using support vector regression optimized by artificial bee colony. IEEE Trans. Veh. Technol. 68(10), 9543\u20139553 (2019)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"2","key":"6_CR18","doi-asserted-by":"publisher","first-page":"375","DOI":"10.3390\/en13020375","volume":"13","author":"J Jia","year":"2020","unstructured":"Jia, J., Liang, J., Shi, Y., Wen, J., Pang, X., Zeng, J.: SOH and RUL prediction of lithium-ion batteries based on Gaussian process regression with indirect health indicators. Energies 13(2), 375 (2020)","journal-title":"Energies"},{"issue":"398","key":"6_CR19","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1080\/01621459.1987.10478440","volume":"82","author":"T Hastie","year":"1987","unstructured":"Hastie, T., Tibshirani, R.: Generalized additive models: some applications. J. Am. Stat. Assoc. 82(398), 371\u2013386 (1987)","journal-title":"J. Am. Stat. Assoc."},{"key":"6_CR20","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.est.2018.12.011","volume":"21","author":"X Li","year":"2019","unstructured":"Li, X., Zhang, L., Wang, Z., Dong, P.: Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. J. Energy Storage 21, 510\u2013518 (2019)","journal-title":"J. Energy Storage"},{"key":"6_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123622","volume":"248","author":"F Yao","year":"2022","unstructured":"Yao, F., He, W., Wu, Y., Ding, F., Meng, D.: Remaining useful life prediction of lithium-ion batteries using a hybrid model. Energy 248, 123622 (2022)","journal-title":"Energy"}],"container-title":["Lecture Notes in Networks and Systems","Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44146-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T18:38:47Z","timestamp":1730140727000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44146-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441455","9783031441462"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44146-2_6","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"23 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NiDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Novel & Intelligent Digital Systems Conferences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nids2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iis-international.org\/nids2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}