New equivalent consumption minimization strategy (ECMS) tools have been developed and applied to identify the optimal control strategy of a dual-mode parallel hybrid electric vehicle equipped with a compression-ignition engine. In this architecture, the electric machine is coupled to the engine through either a single-speed gearbox (torque-coupling) or a planetary gear set (speed-coupling). One of the main novelties of the present study concerns the definition of the instantaneous equivalent consumption (EC) function, which takes into account not only fuel consumption (FC) and the energy flow through the electric components, but also NO. emissions, battery aging, and the battery SOC. The EC function has been trained using a cross-validation machine-learning technique, based on a genetic algorithm, where the training data set has been selected in order to maximize performances over a testing data set. The adoption of this technique, in conjunction with the new definition of EC, have led to the identification of very robust controllers, which provide an accurate control for different driving scenarios, even when the EC function is not specifically trained on the same missions over which it is tested. To this aim, a data set of fifty driving cycles and six user-defined missions, which cover a total distance of 70-100 km, has been considered as a training driving set. The ECMS controllers can be implemented in a vehicle control unit, and their performance has resulted to be close to that of a dynamic programming tool, which has here been used as benchmark, over a large set of different missions, without need for feedback control on the battery SOC or driving pattern prediction. (C) 2016 Elsevier Ltd. All rights reserved.