{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T20:24:56Z","timestamp":1775852696056,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,16]],"date-time":"2019-01-16T00:00:00Z","timestamp":1547596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and\/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial\/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.<\/jats:p>","DOI":"10.3390\/en12020269","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T02:22:23Z","timestamp":1547778143000},"page":"269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions"],"prefix":"10.3390","volume":"12","author":[{"given":"Alexandre","family":"Lucas","sequence":"first","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), Directorate C\u2014Energy, Transport and Climate, Via E. Fermi 2749, I-21027 Ispra (VA), Italy"}]},{"given":"Ricardo","family":"Barranco","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), Directorate B\u2014Growth and Innovation, Via E. Fermi 2749, I-21027 Ispra (VA), Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5822-8150","authenticated-orcid":false,"given":"Nazir","family":"Refa","sequence":"additional","affiliation":[{"name":"ElaadNL, Utrechtseweg 310 (bld. 42B), 6812 AR Arnhem (GL), The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,16]]},"reference":[{"key":"ref_1","unstructured":"International Energy Agengy (IEA) (2018). Global EV Outlook 2018: Towards Corss-Modal Electrification."},{"key":"ref_2","unstructured":"International Energy Agengy (IEA) (2017). Digitalization & Energy."},{"key":"ref_3","unstructured":"European Commission (2014). Directive of the european parliament and of the council on the deployment of alternative fuels infrastructure. Off. J. Eur. Union, 12, 1\u201338."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.bdr.2016.04.003","article-title":"Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities","volume":"6","author":"Paffumi","year":"2016","journal-title":"Big Data Res."},{"key":"ref_5","unstructured":"Glombeka, M., Helmus, J.R., Lees, M., Hoed van den, R., and Quax, R. (2018, January 16\u201319). Vulnerability of Charging Infrastructure, a Novel Approach for Improving Charging Station Deployment. Proceedings of the 7th Transport Research Arena TRA 2018, Vienna, Austria."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Helmus, J., and Van Den Hoed, R. (2016). Key Performance Indicators of Charging infrastructure. World Electr. Veh. J., 8.","DOI":"10.3390\/wevj8040733"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lucas, A., Prettico, G., Flammini, M., Kotsakis, E., Fulli, G., and Masera, M. (2018). Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis. Energies, 11.","DOI":"10.3390\/en11071869"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Maase, S., Dilrosun, X., Kooi, M., and van den Hoed, R. (2018). Performance of Electric Vehicle Charging Infrastructure: Development of an Assessment Platform Based on Charging Data. World Electr. Veh. J., 9.","DOI":"10.3390\/wevj9020025"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3141\/2252-12","article-title":"Optimal Location of Charging Stations for Electric Vehicles in a Neighborhood in Lisbon, Portugal","volume":"2252","author":"Frade","year":"2011","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_10","unstructured":"(2018, October 30). Oplaadpunten. Available online: https:\/\/www.oplaadpunten.nl\/."},{"key":"ref_11","unstructured":"(2018, November 22). EAFO. Available online: http:\/\/www.eafo.eu\/electric-vehicle-charging-infrastructure."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.enpol.2018.08.030","article-title":"Fully charged: An empirical study into the factors that influence connection times at EV-charging stations","volume":"123","author":"Wolbertus","year":"2018","journal-title":"Energy Policy"},{"key":"ref_13","unstructured":"Wolbertus, R., and van den Hoed, R. (2016, January 19\u201322). Benchmarking Charging Infrastructure Utilization. Proceedings of the EVS29 Symposium Montr\u00e9al, Quebec, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.trd.2018.03.012","article-title":"Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments","volume":"62","author":"Wolbertus","year":"2018","journal-title":"Transp. Res. Part D"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_16","unstructured":"Breiman, L. (1997). Arcing the Edge, Statistics Department University of California. Technical Report 486."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_18","unstructured":"Llew, M., Baxter, J., Bartlett, P., and Frean, M. (December, January 29). Boosting Algorithms as Gradient Descent. Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS \u201999, Denver, CO, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_20","unstructured":"Llew, M., Baxter, J., Bartlett, P., and Frean, M. (1999). Boosting Algorithms as Gradient Descent in Function Space, Australian National University."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., and D\u017eeroski, S. (2017). Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-71249-9"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD16, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_23","unstructured":"(2018, November 06). Elaad. Available online: https:\/\/www.elaad.nl\/."},{"key":"ref_24","unstructured":"(2018, October 04). SKlearn. Available online: http:\/\/scikit-learn.org\/stable\/."},{"key":"ref_25","unstructured":"(2018, November 22). XGBoost. Available online: https:\/\/xgboost.readthedocs.io\/en\/latest\/."},{"key":"ref_26","unstructured":"Devore, J.L. (2011). Probability & Statistics for Engineers and the Sciences, Cengage Learning. [8th ed.]."},{"key":"ref_27","unstructured":"(1970, January 01). Scikit-Learn. Available online: http:\/\/scikit-learn.org\/stable\/modules\/model_evaluation.html."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control"}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/12\/2\/269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:26:28Z","timestamp":1760185588000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/12\/2\/269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,16]]},"references-count":29,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["en12020269"],"URL":"https:\/\/doi.org\/10.3390\/en12020269","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,16]]}}}