{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T10:41:14Z","timestamp":1764672074309,"version":"3.46.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032119759","type":"print"},{"value":"9783032119766","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"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\/978-3-032-11976-6_9","type":"book-chapter","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T10:38:08Z","timestamp":1764671888000},"page":"127-141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BATT2GRAPH: A Hybrid CNN-LSTM and\u00a0Temporal Graph-Based Approach for\u00a0Lithium-Ion Battery SOH Prediction and\u00a0Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Hajer","family":"Akid","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Wadhah","family":"Mabrouk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Slimane","family":"Arbaoui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Samet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boudour","family":"Ammar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Xu, J., et al.: High-energy lithium-ion batteries: recent progress and a promising future in applications. J. Energy Environ. Mater., 1\u201326 (2023). https:\/\/doi.org\/10.1002\/eem2.12450","DOI":"10.1002\/eem2.12450"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Jorge, I., Mesbahi, T., Samet, A., Bon\u00e9, R.: Time series feature extraction for lithium-ion batteries state-of-health prediction. J. Energy Storage, 1\u20139 (2023). https:\/\/doi.org\/10.1016\/j.est.2022.106436","DOI":"10.1016\/j.est.2022.106436"},{"key":"9_CR3","unstructured":"International Energy Agency. https:\/\/www.iea.org\/energy-system\/transport\/electric-vehicles. Accessed 12 July 2025"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Liu, K., Liu, Y., Lin, D., Pei, A., Cui, Y.: Materials for lithium-ion battery safety. Sci. Adv., 1\u201311 (2018). https:\/\/doi.org\/10.1126\/sciadv.aas9820","DOI":"10.1126\/sciadv.aas9820"},{"key":"9_CR5","doi-asserted-by":"publisher","unstructured":"Wang, Q., Ping, P., Zhao, X., Chu, G., Sun, J., Chen, C.: Thermal runaway caused fire and explosion of lithium ion battery. J. Power Sour., 210\u2013224 (2012). https:\/\/doi.org\/10.1016\/j.jpowsour.2012.02.038","DOI":"10.1016\/j.jpowsour.2012.02.038"},{"key":"9_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2024.111532","author":"T Shan","year":"2024","unstructured":"Shan, T., Zhang, P., Wang, Z., Zhu, X.: Insights into extreme thermal runaway scenarios of lithium-ion batteries fire and explosion: a critical review. J. Energy Storage (2024). https:\/\/doi.org\/10.1016\/j.est.2024.111532","journal-title":"J. Energy Storage"},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Yang, S., Zhang, C., Jiang, J., Zhang, W., Zhang, L., Wang, Y.: Review on state-of-health of lithium-ion batteries: characterizations, estimations and applications. J. Clean. Prod., 1\u201323 (2021). https:\/\/doi.org\/10.1016\/j.jclepro.2021.128015","DOI":"10.1016\/j.jclepro.2021.128015"},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"123542","DOI":"10.1016\/j.apenergy.2024.123542","volume":"369","author":"S Vignesh","year":"2024","unstructured":"Vignesh, S., et al.: State of Health (SOH) estimation methods for second life lithium-ion battery-review and challenges. Appl. Energy 369, 123542 (2024). https:\/\/doi.org\/10.1016\/j.apenergy.2024.123542","journal-title":"Appl. Energy"},{"key":"9_CR9","doi-asserted-by":"publisher","unstructured":"Karmawijaya, M.I., Haq, I.N., Leksono, E., Widyotriatmo, A.: Development of big data analytics platform for electric vehicle battery management system. In: 2019 6th International Conference on Electric Vehicular Technology (ICEVT), pp. 151\u2013155. IEEE (2019). https:\/\/doi.org\/10.1109\/ICEVT48285.2019.8994013","DOI":"10.1109\/ICEVT48285.2019.8994013"},{"key":"9_CR10","doi-asserted-by":"publisher","unstructured":"Rodpongpun, S., Niennattrakul, V., Ratanamahatana, C. A.: Selective subsequence time series clustering. Knowl.-Based Syst., 361\u2013368 (2012). https:\/\/doi.org\/10.1016\/j.knosys.2012.04.022","DOI":"10.1016\/j.knosys.2012.04.022"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Shumway, R.H., Stoffer, D.S., Stoffer, D.S.: Time Series Analysis and Its Applications. Springer, New York (2000). https:\/\/doi.org\/10.1007\/978-3-319-52452-8","DOI":"10.1007\/978-3-319-52452-8"},{"key":"9_CR12","unstructured":"Andriamampianina, L., Ravat, F., Song, J., Vall\u00e8s-Parlangeau, N.: A generic modelling to capture the temporal evolution in graphs. In: 16e journ\u00e9es EDA: Business Intelligence & Big Data. EDA, pp. 19\u201332 (2020). https:\/\/hal.science\/hal-03109670"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Kutner, D.C., Larios-Jones, L.: Temporal reachability dominating sets: contagion in temporal graphs. In: International Symposium on Algorithmics of Wireless Networks, pp. 101\u2013116 (2023). https:\/\/doi.org\/10.1007\/978-3-031-48882-5_8","DOI":"10.1007\/978-3-031-48882-5_8"},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"107322","DOI":"10.1016\/j.est.2023.107322","volume":"65","author":"W Duan","year":"2023","unstructured":"Duan, W., Song, S., Xiao, F., Chen, Y., Peng, S., Song, C.: Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter. J. Energy Storage 65, 107322 (2023). https:\/\/doi.org\/10.1016\/j.est.2023.107322","journal-title":"J. Energy Storage"},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"129597","DOI":"10.1016\/j.energy.2023.129597","volume":"286","author":"L Chen","year":"2024","unstructured":"Chen, L., et al.: A new SOH estimation method for Lithium-ion batteries based on model-data-fusion. Energy 286, 129597 (2024). https:\/\/doi.org\/10.1016\/j.energy.2023.129597","journal-title":"Energy"},{"key":"9_CR16","doi-asserted-by":"publisher","first-page":"120235","DOI":"10.1016\/j.energy.2021.120235","volume":"225","author":"L Vichard","year":"2021","unstructured":"Vichard, L., Ravey, A., Venet, P., Harel, F., Pelissier, S., Hissel, D.: A method to estimate battery SOH indicators based on vehicle operating data only. Energy 225, 120235 (2021). https:\/\/doi.org\/10.1016\/j.energy.2021.120235","journal-title":"Energy"},{"issue":"2","key":"9_CR17","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.joule.2019.11.018","volume":"4","author":"X Hu","year":"2020","unstructured":"Hu, X., Xu, L., Lin, X., Pecht, M.: Battery lifetime prognostics. Joule 4(2), 310\u2013346 (2020). https:\/\/doi.org\/10.1016\/j.joule.2019.11.018","journal-title":"Joule"},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"118348","DOI":"10.1016\/j.apenergy.2021.118348","volume":"308","author":"S Khaleghi","year":"2022","unstructured":"Khaleghi, S., et al.: Developing an online data-driven approach for prognostics and health management of lithium-ion batteries. Appl. Energy 308, 118348 (2022). https:\/\/doi.org\/10.1016\/j.apenergy.2021.118348","journal-title":"Appl. Energy"},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Audin, P., Jorge, I., Mesbahi, T., Samet, A., De Beuvron, F.D.B., Bon\u00e9, R.: Auto-encoder LSTM for Li-ion SOH prediction: a comparative study on various benchmark datasets. In Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1529\u20131536. IEEE (2021).https:\/\/doi.org\/10.1109\/ICMLA52953.2021.00246","DOI":"10.1109\/ICMLA52953.2021.00246"},{"key":"9_CR20","doi-asserted-by":"publisher","first-page":"100413","DOI":"10.1016\/j.egyai.2024.100413","volume":"17","author":"S Arbaoui","year":"2024","unstructured":"Arbaoui, S., Samet, A., Ayadi, A., Mesbahi, T., Bon\u00e9, R.: Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries. Energy AI 17, 100413 (2024). https:\/\/doi.org\/10.1016\/j.egyai.2024.100413","journal-title":"Energy AI"},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Zhang, H., Sun, H., Kang, L., Zhang, Y., Wang, L., Wang, K.: Prediction of health level of multiform lithium sulfur batteries based on incremental capacity analysis and an improved LSTM. Prot. Control Mod. Power Syst. 9(2), 21\u201331 (2024). https:\/\/doi.org\/10.23919\/PCMP.2023.000280","DOI":"10.23919\/PCMP.2023.000280"},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"127585","DOI":"10.1016\/j.energy.2023.127585","volume":"276","author":"H Xu","year":"2023","unstructured":"Xu, H., Wu, L., Xiong, S., Li, W., Garg, A., Gao, L.: An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries. Energy 276, 127585 (2023). https:\/\/doi.org\/10.1016\/j.energy.2023.127585","journal-title":"Energy"},{"issue":"3","key":"9_CR23","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.3390\/su13031583","volume":"13","author":"TE Kalayc\u0131","year":"2021","unstructured":"Kalayc\u0131, T.E., Bricelj, B., Lah, M., Pichler, F., Scharrer, M.K., Rube\u0161a-Zrim, J.: A knowledge graph-based data integration framework applied to battery data management. Sustainability 13(3), 1583 (2021). https:\/\/doi.org\/10.3390\/su13031583","journal-title":"Sustainability"}],"container-title":["Lecture Notes in Computer Science","Information Integration and Web Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11976-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T10:38:13Z","timestamp":1764671893000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11976-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"ISBN":["9783032119759","9783032119766"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11976-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,3]]},"assertion":[{"value":"3 December 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"iiWAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Integration and Web Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Matsue","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"8 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iiwas2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iiwas.org\/conferences\/iiwas2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}