{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:40:21Z","timestamp":1776922821648,"version":"3.51.2"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Science and Technology Support Program","award":["2014BAG06B02"],"award-info":[{"award-number":["2014BAG06B02"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2014HGCH0003"],"award-info":[{"award-number":["2014HGCH0003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s00521-019-04556-4","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T14:21:41Z","timestamp":1572013301000},"page":"14431-14445","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Energy management strategy for electric vehicles based on deep Q-learning using Bayesian optimization"],"prefix":"10.1007","volume":"32","author":[{"given":"Huifang","family":"Kong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0656-5283","authenticated-orcid":false,"given":"Jiapeng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"issue":"4","key":"4556_CR1","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/JPROC.2009.2012990","volume":"97","author":"CC Chan","year":"2009","unstructured":"Chan CC, Wong YS, Bouscayrol A, Chen K (2009) Powering sustainable mobility: roadmaps of electric, hybrid, and fuel cell vehicles [point of view]. Proc IEEE 97(4):603\u2013607","journal-title":"Proc IEEE"},{"key":"4556_CR2","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.apenergy.2016.05.030","volume":"194","author":"B Wang","year":"2017","unstructured":"Wang B, Xu J, Cao B, Ning B (2017) Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles. Appl Energy 194:596\u2013608","journal-title":"Appl Energy"},{"key":"4556_CR3","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.apenergy.2014.06.087","volume":"135","author":"Z Song","year":"2014","unstructured":"Song Z, Li J, Han X, Xu L, Lu L, Ouyang M et al (2014) Multi-objective optimization of a semi-active battery\/supercapacitor energy storage system for electric vehicles. Appl Energy 135:212\u2013224","journal-title":"Appl Energy"},{"issue":"1","key":"4556_CR4","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TPEL.2011.2151206","volume":"27","author":"J Cao","year":"2012","unstructured":"Cao J, Emadi A (2012) A new battery\/ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles. IEEE Trans Power Electron 27(1):122\u2013132","journal-title":"IEEE Trans Power Electron"},{"key":"4556_CR5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.jpowsour.2015.04.043","volume":"287","author":"L Zhang","year":"2015","unstructured":"Zhang L, Hu X, Wang Z, Sun F, Dorrell DG (2015) Experimental impedance investigation of an ultracapacitor at different conditions for electric vehicle applications. J Power Sources 287:129\u2013138","journal-title":"J Power Sources"},{"issue":"3","key":"4556_CR6","doi-asserted-by":"publisher","first-page":"849","DOI":"10.6113\/JPE.2015.15.3.849","volume":"15","author":"B Wang","year":"2015","unstructured":"Wang B, Xu J, Cao B, Qiyu L, Qingxia Y (2015) Compound-type hybrid energy storage system and its mode control strategy for electric vehicles. J Power Electron 15(3):849\u2013859","journal-title":"J Power Electron"},{"key":"4556_CR7","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.apenergy.2012.12.081","volume":"105","author":"JP Trov\u00e3o","year":"2013","unstructured":"Trov\u00e3o JP, Pereirinha PG, Jorge HM, Antunes CH (2013) A multi-level energy management system for multi-source electric vehicles\u2014an integrated rule-based meta-heuristic approach. Appl Energy 105:304\u2013318","journal-title":"Appl Energy"},{"issue":"12","key":"4556_CR8","doi-asserted-by":"publisher","first-page":"5940","DOI":"10.1109\/TPEL.2013.2255316","volume":"28","author":"JM Blanes","year":"2013","unstructured":"Blanes JM, Gutierrez R, Garrigos A, Lizan JL, Cuadrado JM (2013) Electric vehicle battery life extension using ultracapacitors and an FPGA controlled interleaved buck\u2013boost converter. IEEE Trans Power Electron 28(12):5940\u20135948","journal-title":"IEEE Trans Power Electron"},{"key":"4556_CR9","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1016\/j.apenergy.2014.05.013","volume":"137","author":"X Hu","year":"2015","unstructured":"Hu X, Johannesson L, Murgovski N, Egardt B (2015) Longevity-conscious dimensioning and power management of the hybrid energy storage system in a fuel cell hybrid electric bus. Appl Energy 137:913\u2013924","journal-title":"Appl Energy"},{"key":"4556_CR10","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.apenergy.2014.11.020","volume":"139","author":"Z Song","year":"2015","unstructured":"Song Z, Hofmann H, Li J, Han X, Ouyang M (2015) Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl Energy 139:151\u2013162","journal-title":"Appl Energy"},{"key":"4556_CR11","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.3233\/IFS-152054","volume":"30","author":"ST Sisakht","year":"2016","unstructured":"Sisakht ST, Barakati SM (2016) Energy management using fuzzy controller for hybrid electrical vehicles. J. Intell. Fuzzy Syst. 30:1411\u20131420","journal-title":"J. Intell. Fuzzy Syst."},{"key":"4556_CR12","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.jpowsour.2014.01.118","volume":"258","author":"A Santucci","year":"2014","unstructured":"Santucci A, Sorniotti A, Lekakou C (2014) Power split strategies for hybrid energy storage systems for vehicular applications. J Power Source 258:395\u2013407","journal-title":"J Power Source"},{"key":"4556_CR13","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.enconman.2013.07.065","volume":"76","author":"E Vinot","year":"2013","unstructured":"Vinot E, Trigui R (2013) Optimal energy management of HEVs with hybrid storage system. Energy Convers Manag 76:437\u2013452","journal-title":"Energy Convers Manag"},{"issue":"C","key":"4556_CR14","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.apenergy.2017.11.072","volume":"211","author":"R Xiong","year":"2018","unstructured":"Xiong R, Cao J, Yu Q (2018) Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl Energy 211(C):538\u2013548","journal-title":"Appl Energy"},{"key":"4556_CR15","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1016\/j.apenergy.2018.03.104","volume":"222","author":"J Wu","year":"2018","unstructured":"Wu J, He H, Peng J, Li Y, Li Z (2018) Continuous reinforcement learning of energy management with deep q network for a power split hybrid electric bus. Appl Energy 222:799\u2013811","journal-title":"Appl Energy"},{"key":"4556_CR16","doi-asserted-by":"publisher","first-page":"187","DOI":"10.3390\/app8020187","volume":"8","author":"Yue Hu","year":"2018","unstructured":"Hu Yue, Li Weimin, Xu Kun, Zahid Taimoor, Qin Feiyan, Li Chenming (2018) Energy management strategy for a hybrid electric vehicle based on deep reinforcement learning. Appl Sci 8:187. \nhttps:\/\/doi.org\/10.3390\/app8020187","journal-title":"Appl Sci"},{"key":"4556_CR17","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1109\/TPEL.2013.2262003","volume":"29","author":"B Hredzak","year":"2014","unstructured":"Hredzak B, Agelidis VG, Jang M (2014) Model predictive control system for a hybrid battery-ultracapacitor power source. IEEE Trans Power Electron 29:1469\u20131479","journal-title":"IEEE Trans Power Electron"},{"key":"4556_CR18","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D et al (2013) Playing Atari with deep reinforcement learning. Technical report. Deepmind Technologies, \narXiv:1312.5602\n\n [cs.LG]"},{"key":"4556_CR19","doi-asserted-by":"crossref","unstructured":"Neary P (2018) Automatic hyperparameter tuning in deep convolutional neural networks using asynchronous reinforcement learning. In: 2018 IEEE international conference on cognitive computing (ICCC), San Francisco, CA, pp 73\u201377","DOI":"10.1109\/ICCC.2018.00017"},{"key":"4556_CR20","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281\u2013305","journal-title":"J Mach Learn Res"},{"key":"4556_CR21","unstructured":"Barsce JC, Palombarini JA, Mart\u00ednez EC (2018) Towardsautonomous reinforcement learning: automatic setting of hyper-parameters using bayesian optimization. CoRR, vol. abs\/1805.04748. \nhttp:\/\/arxiv.org\/abs\/1805.04748"},{"issue":"4","key":"4556_CR22","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455\u2013492","journal-title":"J Glob Optim"},{"issue":"5","key":"4556_CR23","doi-asserted-by":"publisher","first-page":"3250","DOI":"10.1109\/TIT.2011.2182033","volume":"58","author":"N Srinivas","year":"2012","unstructured":"Srinivas N, Krause A, Kakade SM, Seeger M (2012) Information-theoretic regret bounds for gaussian process optimization in the bandit setting. IEEE Trans Inf Theory 58(5):3250\u20133265","journal-title":"IEEE Trans Inf Theory"},{"key":"4556_CR24","unstructured":"Contal E, Perchet V, Vayatis N (2014) Gaussian process optimization with mutual information. In: International conference on machine learning (ICML)"},{"key":"4556_CR25","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu A, Veness J, Bellemare G, Marc MG, Graves A, Riedmiller M, Fidjeland K, Andreas, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518:529\u2013533. \nhttps:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"key":"4556_CR26","unstructured":"Bergstra J, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Proceedings of neural information and processing systems"},{"key":"4556_CR27","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1012771025575","volume":"21","author":"DR Jones","year":"2001","unstructured":"Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21:345\u2013383","journal-title":"J Glob Optim"},{"key":"4556_CR28","doi-asserted-by":"crossref","unstructured":"Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the Knowledge discovery and data mining, pp 847\u2013855","DOI":"10.1145\/2487575.2487629"},{"key":"4556_CR29","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","volume":"78","author":"Y Xia","year":"2017","unstructured":"Xia Y, Liu C, Li YY, Liu N (2017) A boosted decision tree approach using bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225\u2013241","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4556_CR30","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1002\/asjc.1259","volume":"18","author":"RT Meyer","year":"2016","unstructured":"Meyer RT, DeCarlo RA, Pekarek S (2016) Hybrid model predictive power management of a battery-supercapacitor electric vehicle. Asian J Control 18(1):150\u2013165","journal-title":"Asian J Control"},{"key":"4556_CR31","doi-asserted-by":"crossref","unstructured":"Yue SA, Wang YA, Xie QA, Zhu DA, Pedram MA, Chang NB (2015) Model-free learning-based online management of hybrid electrical energy storage systems in electric vehicles. In: Conference of the IEEE Industrial Electronics Society. IEEE","DOI":"10.1109\/IECON.2014.7048959"},{"key":"4556_CR32","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1016\/j.apenergy.2015.12.035","volume":"185","author":"S Zhang","year":"2015","unstructured":"Zhang S, Xiong R, Sun F (2015) Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system \u2606. Appl Energy 185:1654\u20131662","journal-title":"Appl Energy"},{"key":"4556_CR33","doi-asserted-by":"crossref","unstructured":"Golchoubian P, Azad NL (2015) An optimal energy management system for electric vehicles hybridized with supercapacitor. In: ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, p V001T10A004","DOI":"10.1115\/DSCC2015-9888"},{"issue":"8","key":"4556_CR34","doi-asserted-by":"publisher","first-page":"3942","DOI":"10.1016\/j.jpowsour.2010.11.134","volume":"196","author":"J Wang","year":"2011","unstructured":"Wang J, Liu P, Hicks-Garner J, Sherman E, Soukiazian S, Verbrugge M et al (2011) Cycle-life model for graphite-lifepo4 cells. J Power Sources 196(8):3942\u20133948","journal-title":"J Power Sources"},{"key":"4556_CR35","doi-asserted-by":"publisher","first-page":"A713","DOI":"10.1149\/1.3374634","volume":"157","author":"M Safari","year":"2010","unstructured":"Safari M, te Morcret M, Teyssot A, Delacourt C (2010) Life-prediction methods for Lithium-ion batteries derived from a fatigue approach. J Electrochem Soc 157:A713\u2013A720","journal-title":"J Electrochem Soc"},{"key":"4556_CR36","volume-title":"Reinforcement learning: an introduction","author":"AG Barto","year":"1998","unstructured":"Barto AG, Sutton RS (1998) Reinforcement learning: an introduction. MIT Press, Cambridge"},{"key":"4556_CR37","unstructured":"Anschel O, Baram N, Shimkin N (2017) Averaged-dqn: variance reduction and stabilization for deep reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning, vol 70. JMLR. org, pp 176\u2013185"},{"key":"4556_CR38","doi-asserted-by":"publisher","first-page":"014008","DOI":"10.1088\/1749-4699\/8\/1\/014008","volume":"8","author":"J Bergstra","year":"2015","unstructured":"Bergstra J, Komer B, Eliasmith C, Yamins D, Cox DD (2015) Hyperopt: a python library for model selection and hyperparameter optimization. Comput Sci Discov 8:014008","journal-title":"Comput Sci Discov"},{"key":"4556_CR39","unstructured":"Levesque J-C, Durand A, Gagne C, Sabourin R (2017) Bayesian optimization for conditional hyperparameter spaces. In: Int. Joint Conf. Neural Networks, Anchorage, Alaska, USA, pp 286\u2013293"},{"issue":"C","key":"4556_CR40","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.apenergy.2014.08.035","volume":"134","author":"Z Song","year":"2014","unstructured":"Song Z, Hofmann H, Li J, Hou J, Han X, Ouyang M (2014) Energy management strategies comparison for electric vehicles with hybrid energy storage system. Appl Energy 134(C):321\u2013331","journal-title":"Appl Energy"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04556-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04556-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04556-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T23:28:51Z","timestamp":1603409331000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04556-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,24]]},"references-count":40,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["4556"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04556-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,24]]},"assertion":[{"value":"3 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no competing interests\u00a0in the present work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}