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The equivalent consumption minimization strategy is widely used in real-time energy optimization for fuel cell electric vehicles due to its approximate optimality and good real-time performance. However, equivalent factors have always been difficult to select in the research of this algorithm, and the existing methods have insufficient adaptability and robustness. In this paper, an equivalent factor predictor based on a neural network is proposed, which can predict the equivalent factor in real time by considering various operating conditions and vehicle states. In addition, we improve the real-time performance of the equivalent consumption minimum strategy. A new method to quickly solve the continuous optimal solution of the algorithm by transforming the objective function into the quadratic function of the control variable is studied. Simulation and experimental results show that the designed equivalent consumption minimization strategy can ensure state of charge of the battery in a better range, and the energy saving effect is approximately 3% more than that of dynamic programming and 8% to 10% less than that of the traditional rule algorithm. Finally, the proposed method saves 2 to 3 orders of magnitude in computation time compared with the traditional equivalent consumption minimum strategy.

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