Plug-In Hybrid Electric Vehicles (PHEV) represent a key transitional technology towards a fully electrified road transportation system. Due to the complexity of PHEV powertrains and strict control performance requirements, a relatively complex, optimal control strategy is typically used, whose design is usually based on quasi-static (so-called backward) vehicle model. The thesis proposes an extension of a parallel PHEV backward model with sub-models of dynamic losses occurring during the internal combustion engine start-up and automated manual transmission gear shifts. By introducing these sub-models, the accuracy of the backward model is found to approach that of the dynamic (so-called forward) model, while maintaining the high computational efficiency of the standard backward model. Next, PHEV control variables optimization is conducted by using a dynamic programming (DP) algorithm and the extended backward model. The optimization results are used to design and verify an optimal PHEV power flow control strategy, which takes into account the dynamic powertrain losses and is based on the equivalent consumption minimisation strategy (ECMS). The control strategy is extended by an algorithms that generates optimal reference trajectory of battery state of charge (SoC) for the PHEV blended operating regime, and which takes into account the effects of varying road slope and low emission zones. Furthermore, adaptive and modelpredictive power flow management strategies are designed. Adaptation of key control strategies parameters is based on a quadratic regression model inputted by characteristic driving cycle features, which are calculated on the moving horizon in the immediate past. The regression model is parameterized based on input/output data extracted from the DP optimization results obtained for characteristic driving micro-cycles. The model predictive control (MPC) strategy is based on the DP control variable optimization on a receding prediction horizon, an extended backward prediction model, and a regression model of the fuel consumption on the remaining part of driving cycle. Such a formulation does not include cost function weighting factors, which would otherwise be sensitive to the driving cycle features. The proposed control strategies are systematically verified by computer simulation, and compared with each other and the DP- benchmark, while quantifying improvements achieved by applying the adaptive and predictive control strategies.