{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:36:54Z","timestamp":1772120214113,"version":"3.50.1"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Hebei Province Youth Science Fund","award":["Project No.CJ2024010S"],"award-info":[{"award-number":["Project No.CJ2024010S"]}]},{"name":"The on-campus project of Cangzhou Jiaotong College","award":["Project No.CJ2024010S"],"award-info":[{"award-number":["Project No.CJ2024010S"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>To meet the rising demand for electric vehicles (EVs), effective and dependable fast-charging reservation systems are required. Conventional charging reservation systems frequently lack coordination between user preferences, real-time station status, and environmental factors, leading to poor user experiences and station ineffectiveness. Existing methods to EV charging reservation systems fail to account for dynamic real-world conditions such as changing traffic patterns, station uptime, and IoT sensor inputs, resulting in suboptimal station allocation and failed reservations. This study fills a gap by proposing an IoT-Coordinated EV Fast Charging Reservation Approach (IoT-CFCRA), which uses real-time data to predict reservation success and suggest the best charging stations under different conditions. The IoT-CFCRA uses the IoT-Enhanced EV Charging Reservation Dataset, which contains user attributes, vehicle data, and IoT-enhanced station data like vehicle type, battery level, distance to station, sensor status, and real-time traffic. Data preprocessing entails normalization, encoding, and feature selection to find important features. A Support Vector Machine (SVM) model is trained to predict reservation success through hyperparameter tuning and 80\u221220 data splitting. The algorithm also includes a station scoring method that considers IoT uptime, distance, traffic conditions, and membership status to provide ranked station suggestions. Users receive real-time notifications to help them adapt to traffic conditions and reservation results. The experimental results show that the proposed IoT-CFCRA approach outperforms other methods. It achieved an accuracy of 87%, outperforming the best baseline (Gradient Boosting) by 4% and improving on Logistic Regression by 10%. The AUC score of 0.92 indicates excellent discriminative capability, a 5-point improvement over Random Forest. The F1-Score of 0.84 demonstrates a strong balance of precision and recall, outperforming SVM by 8%. Furthermore, RMSE was reduced to 0.25, indicating a 19.4% decrease in prediction error when compared to KNN (RMSE\u2009=\u20090.36). The cross-validation score of 88% confirms the model\u2019s robustness, outperforming the next-best performing model by 4%. These metrics highlight the resilience of the IoT-CFCRA in creating precise reservations and suggesting optimum charging stations. The IoT-CFCRA seamlessly combines IoT capacities with machine learning to tackle dynamic factors that influence EV charging reservations. The proposed approach encourages user-centered decision-making and effective resource allocation, laying the groundwork for future advances in IoT-driven EV infrastructure.<\/jats:p>","DOI":"10.1007\/s43926-025-00212-7","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T12:29:41Z","timestamp":1762345781000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["New energy vehicle fast charging reservation algorithm based on internet of things coordination"],"prefix":"10.1007","volume":"5","author":[{"given":"Weinan","family":"Han","sequence":"first","affiliation":[]},{"given":"Dongzhen","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"issue":"9","key":"212_CR1","doi-asserted-by":"publisher","first-page":"3847","DOI":"10.3390\/app11093847","volume":"11","author":"G Alkawsi","year":"2021","unstructured":"Alkawsi G, Baashar Y, Abbas U, Alkahtani D, Tiong SK. Review of renewable energy-based charging infrastructure for electric vehicles. Appl Sci. 2021;11(9):3847.","journal-title":"Appl Sci"},{"issue":"22","key":"212_CR2","doi-asserted-by":"publisher","first-page":"7566","DOI":"10.3390\/en14227566","volume":"14","author":"N Deb","year":"2021","unstructured":"Deb N, Singh R, Brooks RR, Bai K. A review of extremely fast charging stations for electric vehicles. Energies. 2021;14(22):7566.","journal-title":"Energies"},{"key":"212_CR3","doi-asserted-by":"crossref","unstructured":"Laghari AA, Wu K, Laghari RA, Ali M, Khan AA. A review and state of the Art of the internet of things (IoT). Arch Comput Methods Eng. 2021; 1\u201319.","DOI":"10.1007\/s11831-021-09622-6"},{"issue":"10","key":"212_CR4","doi-asserted-by":"publisher","first-page":"4248","DOI":"10.3390\/en16104248","volume":"16","author":"NV Emodi","year":"2023","unstructured":"Emodi NV, Akuru UB, Dioha MO, Adoba P, Kuhudzai RJ, Bamisile O. The role of the internet of things on electric vehicle charging infrastructure and consumer experience. Energies. 2023;16(10):4248.","journal-title":"Energies"},{"issue":"5","key":"212_CR5","doi-asserted-by":"publisher","first-page":"1908","DOI":"10.3390\/en15051908","volume":"15","author":"BP Rimal","year":"2022","unstructured":"Rimal BP, Kong C, Poudel B, Wang Y, Shahi P. Smart electric vehicle charging in the era of the internet of vehicles, emerging trends, and open issues. Energies. 2022;15(5):1908.","journal-title":"Energies"},{"key":"212_CR6","doi-asserted-by":"publisher","first-page":"111576","DOI":"10.1109\/ACCESS.2021.3103119","volume":"9","author":"S Shahriar","year":"2021","unstructured":"Shahriar S, Al-Ali AR, Osman AH, Dhou S, Nijim M. Prediction of EV charging behavior using machine learning. IEEE Access. 2021;9:111576\u201386.","journal-title":"IEEE Access"},{"issue":"8","key":"212_CR7","doi-asserted-by":"publisher","DOI":"10.3390\/s22082834","volume":"22","author":"R Flocea","year":"2022","unstructured":"Flocea R, H\u00eencu A, Robu A, Senocico S, Traciu A, Remus BM, et al. Electric vehicle smart charging reservation algorithm. Sensors. 2022;22(8):2834.","journal-title":"Sensors"},{"key":"212_CR8","unstructured":"Gopalakrishnan R, Biswas A, Lightwala A, Vasudevan S, Dutta P, Tripathi A. (2016). Demand prediction and placement optimization for electric vehicle charging stations. ArXiv Preprint arXiv:160405472."},{"key":"212_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00575-8","author":"X Bai","year":"2022","unstructured":"Bai X, Wang Z, Zou L, Liu H, Sun Q, Alsaadi FE. Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm. Complex Intell Syst. 2022(2). https:\/\/doi.org\/10.1007\/s40747-021-00575-8.","journal-title":"Complex Intell Syst"},{"issue":"1","key":"212_CR10","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s11831-019-09374-4","volume":"28","author":"S Deb","year":"2021","unstructured":"Deb S, Gao XZ, Tammi K, Kalita K, Mahanta P. Nature-inspired optimization algorithms applied for solving charging station placement problem: overview and comparison. Arch Comput Methods Eng. 2021;28(1):91\u2013106.","journal-title":"Arch Comput Methods Eng"},{"issue":"7","key":"212_CR11","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1177\/0037549717743807","volume":"94","author":"Z Tian","year":"2018","unstructured":"Tian Z, Hou W, Gu X, Gu F, Yao B. The location optimization of electric vehicle charging stations considering charging behavior. Simulation. 2018;94(7):625\u201336.","journal-title":"Simulation"},{"key":"212_CR12","first-page":"5","volume":"4","author":"S Dasi","year":"2024","unstructured":"Dasi S, Bondalapati SR, Subbaraju MP, Nimma D, Jangir P, Reddy RVK, Zareena N. IoT-Based intelligent energy management for EV charging stations. Power. 2024;4:5.","journal-title":"Power"},{"issue":"4","key":"212_CR13","first-page":"622","volume":"71","author":"PPM Prasad","year":"2022","unstructured":"Prasad PPM, Kanagasabai N, Kumar PS. Design and implementation of IOT-based innovative charging method for E-vehicles. Math Stat Eng Appl. 2022;71(4):622\u201332.","journal-title":"Math Stat Eng Appl"},{"key":"212_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2020.228504","volume":"476","author":"Y Wu","year":"2020","unstructured":"Wu Y, Zhang J, Ravey A, Chrenko D, Miraoui A. Real-time energy management of photovoltaic-assisted electric vehicle charging station by Markov decision process. J Power Sources. 2020;476:228504.","journal-title":"J Power Sources"},{"issue":"17","key":"212_CR15","doi-asserted-by":"publisher","first-page":"4842","DOI":"10.3390\/s20174842","volume":"20","author":"M Jawad","year":"2020","unstructured":"Jawad M, Qureshi MB, Ali SM, Shabbir N, Khan MUS, Aloraini A, Nawaz R. A cost-effective electric vehicle intelligent charge scheduling method for commercial smart parking lots using a simplified convex relaxation technique. Sensors. 2020;20(17):4842.","journal-title":"Sensors"},{"key":"212_CR16","doi-asserted-by":"crossref","unstructured":"Mohammed SY, Aljanabi M. Human-Centric IoT for Health Monitoring in the Healthcare 5.0 FrameworkDescriptive Analysis and Directions for Future Research. EDRAAK, 2023;21\u201326.","DOI":"10.70470\/EDRAAK\/2023\/005"},{"key":"212_CR17","doi-asserted-by":"crossref","unstructured":"Barazanchi II A, Hashim W. Enhancing IoT Device Security through Blockchain Technology: A Decentralized Approach. SHIFRA. 2023;10\u201316.","DOI":"10.70470\/SHIFRA\/2023\/002"},{"issue":"2","key":"212_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s10458-024-09671-8","volume":"38","author":"E Shafipour","year":"2024","unstructured":"Shafipour E, Stein S, Ahipasaoglu S. Personalised electric vehicle charging stop planning through online estimators. Auton Agent Multi-Agent Syst. 2024;38(2):45.","journal-title":"Auton Agent Multi-Agent Syst"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-025-00212-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-025-00212-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-025-00212-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T12:29:43Z","timestamp":1762345783000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-025-00212-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":18,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["212"],"URL":"https:\/\/doi.org\/10.1007\/s43926-025-00212-7","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5916841\/v1","asserted-by":"object"}]},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]},"assertion":[{"value":"28 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"124"}}