This paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy - Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy - Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles.