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Predictive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles

The performance of equivalent consumption minimization strategy (ECMS) strongly relies on the value of equivalent factor (EF).The EF is related to the future driving cycles. To this end, this paper proposes a predictive ECMS for hybrid electric vehicle utilizing the predicted energy, which is calculated by the predicted velocity from BP neural network. The dynamic EF adaptation law is devised according to the future driving information. The aim is to improve the adaptability to the varying driving cycles and further optimize the fuel efficiency. Simulations for two typical driving cycles are conducted to illustrate the effectiveness of the proposed method. The results indicate that the performance of predictive ECMS is more robust than one without preview.