{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T05:00:21Z","timestamp":1769922021598,"version":"3.49.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603337"],"award-info":[{"award-number":["61603337"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province Natural Science Fund","award":["LY19F030009"],"award-info":[{"award-number":["LY19F030009"]}]},{"name":"Ningbo Science and technology innovation major project","award":["2019B10109, 2019B10116"],"award-info":[{"award-number":["2019B10109, 2019B10116"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Transactions of the Institute of Measurement and Control"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p> A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition\u2013based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H<jats:sub>2<\/jats:sub> consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range. <\/jats:p>","DOI":"10.1177\/01423312211069488","type":"journal-article","created":{"date-parts":[[2022,1,13]],"date-time":"2022-01-13T05:25:27Z","timestamp":1642051527000},"page":"1939-1949","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["Q-Learning-based fuzzy energy management for fuel cell\/supercapacitor HEV"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7095-8968","authenticated-orcid":false,"given":"Jili","family":"Tao","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, NingboTech University, Ningbo, China"}]},{"given":"Ridong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong 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