{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T07:10:38Z","timestamp":1771657838957,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032179142","type":"print"},{"value":"9783032179159","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-17915-9_8","type":"book-chapter","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:36:47Z","timestamp":1771655807000},"page":"142-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering Analysis to\u00a0Determine the\u00a0Optimizing Potentials in\u00a0Drivetrain Consumption with\u00a0SHAP Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5929-9567","authenticated-orcid":false,"given":"Sunilkumar","family":"Raghuraman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4270-3713","authenticated-orcid":false,"given":"Daniel","family":"Baumann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2638-4861","authenticated-orcid":false,"given":"Marc","family":"Schindewolf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2567-2340","authenticated-orcid":false,"given":"Eric","family":"Sax","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,22]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Beckers, C.J., Paroha, M., Besselink, I.J., Nijmeijer, H.: A microscopic energy consumption prediction tool for fully electric delivery vans (5) (2020). https:\/\/doi.org\/10.5281\/zenodo.4023302","DOI":"10.5281\/zenodo.4023302"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wu, G., Sun, R., Dubey, A., Laszka, A., Pugliese, P.: A review and outlook of energy consumption estimation models for electric vehicles (2021)","DOI":"10.4271\/13-02-01-0005"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"De\u00a0Cauwer, C., Verbeke, W., Coosemans, T., Faid, S., Van\u00a0Mierlo, J.: A data-driven method for energy consumption prediction and energy-efficient routing of electric vehicles in real-world conditions. Energies 10(5) (2017). https:\/\/doi.org\/10.3390\/en10050608, https:\/\/www.mdpi.com\/1996-1073\/10\/5\/608","DOI":"10.3390\/en10050608"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"He, Y., Li, J., Chen, Z., Wu, C., Ba, J., Li, Z.: Research on identification approach of risky driving behaviors for new energy vehicles based on internet of vehicles data. In: 2021 6th International Conference on Transportation Information and Safety (ICTIS), pp. 1320\u20131326 (2021). https:\/\/doi.org\/10.1109\/ICTIS54573.2021.9798518","DOI":"10.1109\/ICTIS54573.2021.9798518"},{"key":"8_CR5","unstructured":"IEA: Global EV outlook 2021: Scaling up the transition to electric mobility (2021). https:\/\/www.iea.org\/reports\/global-ev-outlook-2021\/trends-and-developments-in-electric-vehicle-markets"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021). https:\/\/doi.org\/10.1016\/j.ymssp.2020.107398, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0888327020307846","DOI":"10.1016\/j.ymssp.2020.107398"},{"issue":"12","key":"8_CR7","doi-asserted-by":"publisher","first-page":"11409","DOI":"10.1109\/TVT.2019.2936772","volume":"68","author":"K Kivek\u00e4s","year":"2019","unstructured":"Kivek\u00e4s, K., Lajunen, A., Baldi, F., Veps\u00e4l\u00e4inen, J., Tammi, K.: Reducing the energy consumption of electric buses with design choices and predictive driving. IEEE Trans. Veh. Technol. 68(12), 11409\u201311419 (2019). https:\/\/doi.org\/10.1109\/TVT.2019.2936772","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"9","key":"8_CR8","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464\u20131480 (1990)","journal-title":"Proc. IEEE"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Lu, S., Li, R.: DAC: deep autoencoder-based clustering, a general deep learning framework of representation learning (2021)","DOI":"10.1007\/978-3-030-82193-7_13"},{"key":"8_CR10","unstructured":"Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions (2017)"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Mahmoud, M., Garnett, R., Ferguson, M., Kanaroglou, P.: Electric buses: a review of alternative powertrains. Renew. Sustain. Energy Rev. 62(C), 673\u2013684 (2016). https:\/\/doi.org\/10.1016\/j.rser.2016.05.01, https:\/\/ideas.repec.org\/a\/eee\/rensus\/v62y2016icp673-684.html","DOI":"10.1016\/j.rser.2016.05.01"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Raghuraman, S., Baumann, D., Schindewolf, M., Sax, E.: Influential factors on drivetrain consumption in electric city buses and assessing the optimization potentials. In: Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA, pp. 330\u2013337. INSTICC, SciTePress (2024). https:\/\/doi.org\/10.5220\/0012758700003756","DOI":"10.5220\/0012758700003756"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"R\u00f6sch, T., Raghuraman, S., Sommer, M., Junk, C., Baumann, D., Sax, E.: Multi-layer approach for energy consumption optimization in electric buses. In: 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), pp.\u00a01\u20136 (2023). https:\/\/doi.org\/10.1109\/VTC2023-Spring57618.2023.10199518","DOI":"10.1109\/VTC2023-Spring57618.2023.10199518"},{"key":"8_CR14","unstructured":"SustainableBus: Electric bus public transport: main fleets and projects around the world (2023), https:\/\/www.sustainable-bus.com\/electric-bus\/electric -bus-public-transport-main-fleets-projects-around-world\/. Accessed 08 Dec 2023"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/OJVT.2021.3065529","volume":"2","author":"AT Thorgeirsson","year":"2021","unstructured":"Thorgeirsson, A.T., Scheubner, S., F\u00fcnfgeld, S., Gauterin, F.: Probabilistic prediction of energy demand and driving range for electric vehicles with federated learning. IEEE Open J. Veh. Technol. 2, 151\u2013161 (2021). https:\/\/doi.org\/10.1109\/OJVT.2021.3065529","journal-title":"IEEE Open J. Veh. Technol."},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Varga, B.O., Sagoian, A., Mariasiu, F.: Prediction of electric vehicle range: a comprehensive review of current issues and challenges. Energies 12(5) (2019). https:\/\/doi.org\/10.3390\/en12050946, https:\/\/www.mdpi.com\/1996-1073\/12\/5\/946","DOI":"10.3390\/en12050946"},{"key":"8_CR17","unstructured":"Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, vol.\u00a01, pp. 29\u201339. Manchester (2000)"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Zhang, J., Wang, Z., Liu, P., Zhang, Z.: Energy consumption analysis and prediction of electric vehicles based on real-world driving data. Appl. Energy 275, 115408 (2020). https:\/\/doi.org\/10.1016\/j.apenergy.2020.115408, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S030626192030920X","DOI":"10.1016\/j.apenergy.2020.115408"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Zhang, R., Yao, E.: Electric vehicles\u2019 energy consumption estimation with real driving condition data. Transp. Res. Part D: Transp. Environ. 41, 177\u2013187 (2015). https:\/\/doi.org\/10.1016\/j.trd.2015.10.010, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361920915001625","DOI":"10.1016\/j.trd.2015.10.010"},{"key":"8_CR20","doi-asserted-by":"publisher","first-page":"157693","DOI":"10.1109\/ACCESS.2019.2950390","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Yuan, W., Fu, R., Wang, C.: Design of an energy-saving driving strategy for electric buses. IEEE Access 7, 157693\u2013157706 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2950390","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Data Management Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-17915-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:36:49Z","timestamp":1771655809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17915-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032179142","9783032179159"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17915-9_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"22 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DATA 2024","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Management Technologies and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dijon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"9 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"data2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/data.scitevents.org\/?y=2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}