Scenario-oriented adaptive ECMS using speed prediction for fuel cell vehicles in real-world driving

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To exploit the energy-saving potential and optimize the battery state of charge (SOC) maintaining capability of energy management strategies for fuel cell hybrid vehicles in specific driving scenarios, this study proposes a scenario-oriented adaptive equivalent consumption minimization strategy (SA-ECMS) based on a Nanjing-oriented driving cycle (NODC) and future speeds predicted via a hybrid neural network model. The proposed strategy determines the initial value of the equivalent factor (EF) and the proportional coefficient of the adaptive increment based on the NODC. Then, it periodically adjusts the EF via local optimization process according to the predicted speed to enhance scenario-specific adaptability and energy efficiency performance. Simulation results show that the hybrid neural network model achieves an average calculation time of 0.0033 s with a root-mean-square error of 0.85 m/s for 10 s prediction horizon, outperforming existing speed prediction models. Compared with the existing SOC feedback-based ECMS, the proposed SA-ECMS effectively suppresses the battery SOC within a narrower fluctuation range of −0.12% to 0.33%, achieves a deviation of only 0.0026 from the SOC reference value, and reduces the equivalent hydrogen-fuel consumption by 2.49% to 7.06 g/km.