After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions. A heuristically Equivalent Consumption Minimization Strategy (ECMS) is presented which obtains the optimal settings of control signals to determine optimum engine efficiency and emissions behavior in addition to the desired exhaust gas temperature. In a second approach, a model-based predictive controller (MPC) concept based on a linear battery model and static operating maps of the vehicle and engine is applied. The benefit of this concept is the consideration of future information such as slope of the track. In this investigation, a long-haul hybrid truck has been chosen exemplarily to demonstrate the energy flow manager for commercial vehicles. The controller parameters are calibrated and the potential of the coordinator is assessed using a virtual development platform based on the longitudinal dynamics vehicle simulation environment VeLoDyn for ComApps.