{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T07:23:08Z","timestamp":1769930588316,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031360299","type":"print"},{"value":"9783031360305","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\/978-3-031-36030-5_37","type":"book-chapter","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T17:02:13Z","timestamp":1687971733000},"page":"463-475","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["SocHAP: A New Data Driven Explainable Prediction of\u00a0Battery State of\u00a0Charge"],"prefix":"10.1007","author":[{"given":"Th\u00e9o","family":"Heitzmann","sequence":"first","affiliation":[]},{"given":"Ahmed","family":"Samet","sequence":"additional","affiliation":[]},{"given":"Tedjani","family":"Mesbahi","sequence":"additional","affiliation":[]},{"given":"Cyrine","family":"Soufi","sequence":"additional","affiliation":[]},{"given":"In\u00e8s","family":"Jorge","sequence":"additional","affiliation":[]},{"given":"Romuald","family":"Bon\u00e9","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1016\/j.cag.2021.09.002","volume":"102","author":"G Alicioglu","year":"2022","unstructured":"Alicioglu, G., Sun, B.: A survey of visual analytics for explainable artificial intelligence methods. Comput. Graph. 102, 502\u2013520 (2022)","journal-title":"Comput. Graph."},{"key":"37_CR2","unstructured":"Borovykh, A., Bohte, S., Oosterlee, C.W.: Conditional time series forecasting with convolutional neural networks (2018)"},{"issue":"8","key":"37_CR3","doi-asserted-by":"publisher","first-page":"6730","DOI":"10.1109\/TIE.2017.2787586","volume":"65","author":"E Chemali","year":"2018","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. Industr. Electron. 65(8), 6730\u20136739 (2018)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"37_CR4","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.jpowsour.2013.03.158","volume":"240","author":"Z Chen","year":"2013","unstructured":"Chen, Z., Mi, C.C., Fu, Y., Xu, J., Gong, X.: Online battery state of health estimation based on genetic algorithm for electric and hybrid vehicle applications. J. Power Sourc. 240, 184\u2013192 (2013)","journal-title":"J. Power Sourc."},{"key":"37_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2021.100081","volume":"5","author":"G dos Reis","year":"2021","unstructured":"dos Reis, G., Strange, C., Yadav, M., Li, S.: Lithium-ion battery data and where to find it. Energy AI 5, 100081 (2021)","journal-title":"Energy AI"},{"issue":"2","key":"37_CR6","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/S0378-7753(02)00190-8","volume":"110","author":"PM Gomadam","year":"2002","unstructured":"Gomadam, P.M., Weidner, J.W., Dougal, R.A., White, R.E.: Mathematical modeling of lithium-ion and nickel battery systems. J. Power Sour. 110(2), 267\u2013284 (2002)","journal-title":"J. Power Sour."},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"Gu, X., See, K., Wang, Y., Zhao, L., Pu, W.: The sliding window and shap theory-an improved system with a long short-term memory network model for state of charge prediction in electric vehicle application. Energies 14(12) (2021)","DOI":"10.3390\/en14123692"},{"key":"37_CR8","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1016\/j.rser.2017.05.001","volume":"78","author":"M Hannan","year":"2017","unstructured":"Hannan, M., Lipu, M., Hussain, A., Mohamed, A.: A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renew. Sustain. Energy Rev. 78, 834\u2013854 (2017)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"37_CR9","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1016\/j.ijepes.2014.04.059","volume":"62","author":"W He","year":"2014","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. Electrical Power Energy Syst. 62, 783\u2013791 (2014)","journal-title":"Int. J. Electrical Power Energy Syst."},{"key":"37_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"37_CR11","doi-asserted-by":"publisher","unstructured":"Koprinska, I., Wu, D., Wang, Z.: Convolutional neural networks for energy time series forecasting. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2018). https:\/\/doi.org\/10.1109\/IJCNN.2018.8489399","DOI":"10.1109\/IJCNN.2018.8489399"},{"key":"37_CR12","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.jclepro.2018.09.065","volume":"205","author":"MH Lipu","year":"2018","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. 205, 115\u2013133 (2018)","journal-title":"J. Clean. Prod."},{"key":"37_CR13","doi-asserted-by":"crossref","unstructured":"Lipu, M.H., Hussain, A., Saad, M., Ayob, A., Hannan, M.: Improved recurrent narx neural network model for state of charge estimation of lithium-ion battery using pso algorithm. In: 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 354\u2013359. IEEE (2018)","DOI":"10.1109\/ISCAIE.2018.8405498"},{"key":"37_CR14","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions (2017)"},{"issue":"6","key":"37_CR15","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.pnsc.2018.11.002","volume":"28","author":"S Ma","year":"2018","unstructured":"Ma, S., et al.: Temperature effect and thermal impact in lithium-ion batteries: A review. Prog. Nat. Sci. Mater. Internat. 28(6), 653\u2013666 (2018)","journal-title":"Prog. Nat. Sci. Mater. Internat."},{"issue":"9","key":"37_CR16","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1016\/j.apenergy.2008.11.021","volume":"86","author":"KS Ng","year":"2009","unstructured":"Ng, K.S., Moo, C.S., Chen, Y.P., Hsieh, Y.C.: Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy 86(9), 1506\u20131511 (2009)","journal-title":"Appl. Energy"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Perner, A., Vetter, J.: 8 - lithium-ion batteries for hybrid electric vehicles and battery electric vehicles. In: Scrosati, B., Garche, J., Tillmetz, W. (eds.) Advances in Battery Technologies for Electric Vehicles, Woodhead Publishing Series in Energy, pp. 173\u2013190. Woodhead Publishing (2015)","DOI":"10.1016\/B978-1-78242-377-5.00008-X"},{"key":"37_CR18","unstructured":"Pesaran, A., Santhanagopalan, S., Kim, G.H.: Addressing the impact of temperature extremes on large format li-ion batteries for vehicle applications (presentation) (May 2013)"},{"key":"37_CR19","unstructured":"Saha, B., Goebel, K.: Battery data set. NASA Ames Prognostics Data Repository (2007)"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Severson, K., Attia, P., Jin, N., et al.: Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4 (2019)","DOI":"10.1038\/s41560-019-0356-8"},{"key":"37_CR21","doi-asserted-by":"publisher","first-page":"35957","DOI":"10.1109\/ACCESS.2018.2850743","volume":"6","author":"W Wang","year":"2018","unstructured":"Wang, W., Wang, X., Xiang, C., Wei, C., Zhao, Y.: Unscented kalman filter-based battery soc estimation and peak power prediction method for power distribution of hybrid electric vehicles. Ieee Access 6, 35957\u201335965 (2018)","journal-title":"Ieee Access"},{"key":"37_CR22","doi-asserted-by":"publisher","unstructured":"Yoshio, M., Brodd, R.J., Noguchi, H.: Lithium-ion batteries: Science and Technologies. Springer (2009). https:\/\/doi.org\/10.1007\/978-0-387-34445-4","DOI":"10.1007\/978-0-387-34445-4"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36030-5_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T01:06:11Z","timestamp":1716512771000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36030-5_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360299","9783031360305"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36030-5_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","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":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"188","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}