{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:03:13Z","timestamp":1775768593630,"version":"3.50.1"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004725","name":"Ministry of Economic Affairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,4]]},"DOI":"10.1109\/iv55152.2023.10186787","type":"proceedings-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T17:20:05Z","timestamp":1690478405000},"page":"1-7","source":"Crossref","is-referenced-by-count":7,"title":["Towards Optimal Energy Management Strategy for Hybrid Electric Vehicle with Reinforcement Learning"],"prefix":"10.1109","author":[{"given":"Xinyang","family":"Wu","sequence":"first","affiliation":[{"name":"Fraunhofer IPA,Department Cyber Cognitive Intelligence (CCI)"}]},{"given":"Elisabeth","family":"Wedernikow","sequence":"additional","affiliation":[{"name":"Fraunhofer IPA,Department Cyber Cognitive Intelligence (CCI)"}]},{"given":"Christof","family":"Nitsche","sequence":"additional","affiliation":[{"name":"Fraunhofer IPA,Department Cyber Cognitive Intelligence (CCI)"}]},{"given":"Marco F.","family":"Huber","sequence":"additional","affiliation":[{"name":"Fraunhofer IPA,Department Cyber Cognitive Intelligence (CCI)"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1126\/science.153.3731.34"},{"key":"ref35","article-title":"Understanding dropout","volume":"26","author":"baldi","year":"2013","journal-title":"Advances in neural information processing systems"},{"key":"ref12","author":"camacho","year":"2013","journal-title":"Model Predictive Control"},{"key":"ref34","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/160510"},{"key":"ref37","first-page":"207","article-title":"Deep gaussian processes","author":"damianou","year":"2013","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.12.031"},{"key":"ref36","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"International Conference on Machine Learning"},{"key":"ref31","author":"lillicrap","year":"2015","journal-title":"Continuous control with deep reinforcement learning"},{"key":"ref30","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume":"12","author":"sutton","year":"1999","journal-title":"Advances in neural information processing systems"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.4271\/2008-01-1315"},{"key":"ref33","author":"hinton","year":"2012","journal-title":"Improving Neural Networks by Preventing Co-adaptation of Feature Detectors"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2011.2165571"},{"key":"ref32","author":"schaul","year":"2015","journal-title":"Prioritized experience replay"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2013.08.097"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2009.11.001"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.03.083"},{"key":"ref39","article-title":"Epa dynamometer drive schedules","year":"0"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1115\/DSCC2010-4211"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.2172\/1462683"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.04.021"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2019.05.038"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.03.104"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.03.082"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.pecs.2019.04.002"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.12.018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2475419"},{"key":"ref22","author":"sutton","year":"2018","journal-title":"Reinforcement Learning An Introduction"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3055136"},{"key":"ref28","volume":"37","author":"rummery","year":"1994","journal-title":"On-line Q-learning using connectionist systems"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"ref29","article-title":"Actor-critic algorithms","volume":"12","author":"konda","year":"1999","journal-title":"Advances in neural information processing systems"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.4271\/2015-01-0973"},{"key":"ref7","article-title":"Anteil ausgew&#x00E4;hlter kraftstoffarten an den neuzulassungen von personenkraftwagen in deutschland von 2012 bis 2022","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1504\/IJEHV.2007.014448"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.03.093"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/en11030476"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117297"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2016.2582721"},{"key":"ref40","volume":"50","author":"tsiakmakis","year":"2017","journal-title":"From nedc to wltp effect on the type-approval co2 emissions of light-duty vehicles"}],"event":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","location":"Anchorage, AK, USA","start":{"date-parts":[[2023,6,4]]},"end":{"date-parts":[[2023,6,7]]}},"container-title":["2023 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10186382\/10186383\/10186787.pdf?arnumber=10186787","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T20:50:49Z","timestamp":1705351849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10186787\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/iv55152.2023.10186787","relation":{},"subject":[],"published":{"date-parts":[[2023,6,4]]}}}