Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles

Chao Sun, Fengchun Sun, Hongwen He*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

307 引用 (Scopus)

摘要

Energy management strategy is crucial in improving the fuel economy of hybrid electric vehicles (HEVs). This paper targets at evaluating the role of velocity forecast in the adaptive equivalent consumption minimization strategies (ECMS) for HEVs. A neural network based velocity predictor is constructed to forecast the short-term future driving behaviors by learning from history data. Then the velocity predictor is combined with adaptive-ECMS to provide temporary driving information for real-time equivalence factor (EF) adaptation. Compared with traditional adaptive-ECMS, which uses historical driving profile for EF estimation, the proposed strategy is able to foresee the change of the driving behaviors and adjust the EF more reasonably. Simulation results show that, compared with traditional adaptive-ECMS, the proposed improvement with velocity forecast incorporated is able to achieve better fuel economy and more stable battery state of charge (SOC) trajectory, with a fuel consumption reduction by over 3%.

源语言英语
页(从-至)1644-1653
页数10
期刊Applied Energy
185
DOI
出版状态已出版 - 1 1月 2017

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Sun, C., Sun, F., & He, H. (2017). Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Applied Energy, 185, 1644-1653. https://doi.org/10.1016/j.apenergy.2016.02.026