Previously, an equivalent consumption minimization strategy (ECMS) was developed that provides near-optimal performance of hybrid vehicles based on an adaptation of equivalence factor from state of charge feedback. However, under real-world driving conditions with uncertainties, such as hilly roads, ECMS requires a predictive scheme utilizing future driving information in order to prevent a loss of optimality. In this paper, we synthesize predictive ECMS in a feedforward way to adjust the equivalence factor based on its theoretical connection with future driving statistics, in a systematic manner. First, a useful noncausal adaptation strategy is extracted from dynamic programming results. Then, the inverse problem is formulated and solved to derive an explicit representation of the constant optimal equivalence factor with justified assumptions. Finally, a causal, predictive adaptation strategy using this closed-form solution is synthesized to mimic the noncausal one, and its effectiveness is evaluated for fuel cell hybrid electric vehicles. Results show that if the predicted statistical information reflects well the future driving conditions, the proposed strategy accurately estimates the constant optimal equivalence factor, including the jump behavior, thereby yielding less than 1.5% loss of fuel optimality. Moreover, this approach is extendible to other configurations.