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In this paper, the influence of prediction horizon on prediction error, fuel consumption, and real time was studied in detail. The framework of energy management strategy was proposed in terms of the model prediction control theory. The radial basis function neural network was presented as the predictor to obtain the short\u2010term velocity in the future. A dynamic programming algorithm was applied to obtain optimized control laws in the prediction horizon. Considering the onboard controller\u2019s real\u2010time performance, we established a simple fuel cell vehicle mathematical model for simulation. Different prediction horizons were adopted on UDDS and HWFET to test the influence on prediction and energy management strategy. Simulation results showed the strategy performed well in fuel economy and real\u2010time performance, and the prediction horizon of around 20\u2009s was appropriate for this strategy.<\/jats:p>","DOI":"10.1155\/2021\/9985063","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T03:20:06Z","timestamp":1621912806000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Energy Management of Fuel Cell Vehicles Based on Model Prediction Control Using Radial Basis Functions"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7738-1479","authenticated-orcid":false,"given":"Weiwei","family":"Xin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1153-808X","authenticated-orcid":false,"given":"Weiguang","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Jirong","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Shangjun","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Chunyu","family":"Ji","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2012.2206064"},{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","unstructured":"BuntinD. 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