{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T16:26:56Z","timestamp":1769876816836,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031637858","type":"print"},{"value":"9783031637834","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-63783-4_14","type":"book-chapter","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T10:02:35Z","timestamp":1719568955000},"page":"177-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SOCXAI: Leveraging CNN and\u00a0SHAP Analysis for\u00a0Battery SOC Estimation and\u00a0Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Amel","family":"Hidouri","sequence":"first","affiliation":[]},{"given":"Slimane","family":"Arbaoui","sequence":"additional","affiliation":[]},{"given":"Ahmed","family":"Samet","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Ayadi","sequence":"additional","affiliation":[]},{"given":"Tedjani","family":"Mesbahi","sequence":"additional","affiliation":[]},{"given":"Romuald","family":"Bon\u00e9","sequence":"additional","affiliation":[]},{"given":"Fran\u00e7ois de Bertrand","family":"de Beuvron","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Boniol, P., Linardi, M., Roncallo, F., Palpanas, T., Meftah, M., Remy, E.: Unsupervised and scalable subsequence anomaly detection in large data series. VLDB J. 1\u201323 (2021)","DOI":"10.1007\/s00778-021-00655-8"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Chemali, E., Kollmeyer, P.J., Preindl, M., Ahmed, R., Emadi, A.: Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Trans. Ind. Electron. 6730\u20136739 (2018)","DOI":"10.1109\/TIE.2017.2787586"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Doyle, M., Fuller, T.F., Newman, J.: Modeling of galvanostatic charge and discharge of the lithium\/polymer\/insertion cell. J. Electrochem. Soc. 1526 (1993)","DOI":"10.1149\/1.2221597"},{"key":"14_CR4","unstructured":"El Khansa, H., Gervet, C., Brouillet, A.: Application of matrix profile techniques to detect insightful discords in climate data. Int. J. Soft Comput. Artif. Intell. Appl. (IJSCAI) (2022)"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Fuller, T.F., Doyle, M., Newman, J.: Simulation and optimization of the dual lithium ion insertion cell. J. Electrochem. Soc. 1 (1994)","DOI":"10.1149\/1.2054684"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"He, W., Williard, N., Chen, C., Pecht, M.: State of charge estimation for li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation. Int. J. Electr. Power Energy Syst. 783\u2013791 (2014)","DOI":"10.1016\/j.ijepes.2014.04.059"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Heitzmann, T., Samet, A., Mesbahi, T., Soufi, C., Jorge, I., Bon\u00e9, R.: Sochap: a new data driven explainable prediction of battery state of charge. In: Computational Science \u2013 ICCS 2023, pp. 463\u2013475 (2023)","DOI":"10.1007\/978-3-031-36030-5_37"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Huria, T., Ludovici, G., Lutzemberger, G.: State of charge estimation of high power lithium iron phosphate cells. J. Power Sources, 92\u2013102 (2014)","DOI":"10.1016\/j.jpowsour.2013.10.079"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Johnson, V.: Battery performance models in advisor. J. Power Sources, 321\u2013329 (2002)","DOI":"10.1016\/S0378-7753(02)00194-5"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Kashpruk, N., Piskor-Ignatowicz, C., Baranowski, J.: Time series prediction in industry 4.0: a comprehensive review and prospects for future advancements. Appl. Sci. (2023)","DOI":"10.3390\/app132212374"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Lee, J., Sun, H., Liu, Y., Li, X.: A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks. Energy AI, 100319 (2024)","DOI":"10.1016\/j.egyai.2023.100319"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Li, G., Jung, J.J.: Deep learning for anomaly detection in multivariate time series: approaches, applications, and challenges. Inf. Fusion, 93\u2013102 (2023)","DOI":"10.1016\/j.inffus.2022.10.008"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Linardi, M., Zhu, Y., Palpanas, T., Keogh, E.: Matrix profile x: Valmod-scalable discovery of variable-length motifs in data series. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1053\u20131066 (2018)","DOI":"10.1145\/3183713.3183744"},{"key":"14_CR14","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (2017)"},{"key":"14_CR15","unstructured":"Marques-Silva, J., Huang, X.: Explainability is not a game. arXiv preprint arXiv:2307.07514 (2023)"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Nakamura, T., Imamura, M., Mercer, R., Keogh, E.: Merlin: parameter-free discovery of arbitrary length anomalies in massive time series archives. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1190\u20131195 (2020)","DOI":"10.1109\/ICDM50108.2020.00147"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Plett, G.L.: Extended Kalman filtering for battery management systems of lipb-based HEV battery packs: Part 3. State and parameter estimation. J. Power Sources, 277\u2013292 (2004)","DOI":"10.1016\/j.jpowsour.2004.02.033"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Severson, K.A., et al.: Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy, 383\u2013391 (2019)","DOI":"10.1038\/s41560-019-0356-8"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Stefanopoulou, A., Kim, Y.: System-level management of rechargeable lithium-ion batteries. Rechargeable Lithium Batteries, 281\u2013302 (2015)","DOI":"10.1016\/B978-1-78242-090-3.00010-9"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Tafazoli, S., Keogh, E.: Matrix profile xxviii: discovering multi-dimensional time series anomalies with k of n anomaly detection. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), pp. 685\u2013693 (2023)","DOI":"10.1137\/1.9781611977653.ch77"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Tian, J., Chen, C., Shen, W., Sun, F., Xiong, R.: Deep learning framework for lithium-ion battery state of charge estimation: Recent advances and future perspectives. Energy Storage Mater. 102883 (2023)","DOI":"10.1016\/j.ensm.2023.102883"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Yan, Q.: SOC prediction of power battery based on SVM. In: 2020 Chinese Control And Decision Conference (CCDC), pp. 2425\u20132429 (2020)","DOI":"10.1109\/CCDC49329.2020.9164245"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Yeh, C.C.M., et al.: Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1317\u20131322 (2016)","DOI":"10.1109\/ICDM.2016.0179"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Zhu, Y., et al.: Matrix profile ii: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 739\u2013748 (2016)","DOI":"10.1109\/ICDM.2016.0085"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63783-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T10:05:31Z","timestamp":1719569131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63783-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031637858","9783031637834"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63783-4_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}