Vehicle connectivity presents opportunities for reduction of energy consumption and pollutant emissions. Potential for efficiency enhancement through predictive measures has been demonstrated in research projects such as simTD and ECOMOVE. Powertrain systems exploiting information from vehicle connectivity have widened the system boundary resulting in additional degrees-of-freedom for predictive trajectory planning. Heuristic methods based on component characteristics are currently widely used for Energy Management (EM) functionality of hybridized powertrains. Despite its better usability, increased calibration effort and sensitivity to synthetic calibration scenarios are drawbacks of such control methods. Availability of predictive data, better computing power and challenges posed by varied scenarios in real driving have led to interest in online-optimizing EM functionality. Equivalent consumption based formulation has been studied for the development of optimal control based EM functions. Equivalent Consumption Minimization Strategy (ECMS) approaches based on Pontryagin Minimum principle have difficulty in handling inequality state constraints. Extensions of ECMS make use of modifications to the equivalence factor/co-state based on prediction of driving conditions. The proposed method uses limited time horizon predicted data to optimize engine on/off state and torque split among the energy converters using direct optimal control. Along with its ability to handle constraints on the system states directly, the proposed method does not require an explicit model of additional dynamics. Further, the developed EM functionality adapts online based on situation-aware prediction along with offering possibility to tune online the optimization process using heuristics on constraint-limits. These advantages along with this real-time capability and flexibility to handle change of control objectives as well as variation of control weighting reduces calibration effort due to the reduced number of functional parameters. Results of the functionality shall be compared with a developed predictive ECMS method. The functionalities developed along with their real-time capability will be demonstrated using the Combustion Engine Assist (CEA) concept.