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. Through nine chapters of this work, which include the introduction and conclusion chapters, backward and forward PHEV simulation models are described, dynamic programming-based control variables optimization results are presented and analysed, optimal, adaptive, and model predictive power flow control strategies are proposed, and finally a novel method of synthesis of battery state-of-charge reference trajectory is presented for the blended operating regime and the general case of varying road grade and low emission zone presence. A brief overview of each chapter is given below. Chapter 1: Introduction. Outlines the motivation for the conducted research and gives a literature review of the relevant topics of the thesis, which include PHEV modelling and optimal power flow control, adaptive and model predictive control, and battery state-of-charge reference trajectory planning. Chapter 2: Backward-looking model of a Plug-in Hybrid Electric Vehicle. The powertrain of a PHEV given in P2 parallel configuration is described along with basic technical parameters. PHEV power flows and operating modes are briefly analysed. A basic backward-looking (BWD) vehicle model is described, which neglects powertrain dynamics effects including dynamic power losses. The required wheel torque and speed are determined from the driving cycle data and the vehicle longitudinal dynamics equation, and are used in a backward manner to calculate the propulsion machines' and battery variables. Chapter 3: Forward-looking and extended backward model. A more accurate forward-looking (FWD) PHEV model is presented, which takes into account the dominant powertrain dynamics effects, evaluated in the direction from propulsion machines to wheels. The corresponding simulation model is implemented in Simcenter's Amesim software environment, and it includes a low-level control strategy with actuator dynamics. The low-level control is aimed at achieving the operating points set by the high-level control strategy, which is done through appropriate coordination of the main clutch, propulsion machines, automated manual transmission, and mechanical brakes. Based on the energy balance analysis derived from the FWD model simulation results, an extended BWD (EXT-BWD) model is proposed, which includes the engine start-up and gear shift transient losses. These transient losses include power losses due to main clutch and synchronizers slippage, engine start-up losses, and electric machine-based dog clutch synchronization losses. The EXT-BWD model is validated by comparing its fuel consumption and battery state of charge (SoC) time responses with those obtained by the more accurate FWD model, where DP optimal control variables are used as inputs of both models. Chapter 4: Control variable optimisation. A PHEV powertrain control variables optimization method is proposed, which is based on the dynamic programming (DP) algorithm adjusted for the additional state variables introduced by the EXT-BWD model. With the aim of improving computing efficiency, the DP algorithm is implemented in the C++ programming language, while the obtained results are processed and analysed in the Matlab/Simulink environment. The DP algorithm cost function reflects the conflicting criteria of reducing fuel consumption throughout the driving cycle while maintaining the target value of SoC at the end of driving cycle. In addition to the hard constraints on control variables, soft constraints are implemented to limit the powertrain variables within defined physical limits. Optimization is carried out for the cases of charge sustaining (CS) and blended (BLND) operating modes. The DP optimal results obtained by using the BWD and EXT-BWD models are compared with each other, in order to further highlight the differences in fuel consumption predictions predicted by the two models. In addition, the optimal SoC trajectories over travelled distance were analysed for various driving cycles, including the special cases of varying road grade and low emission zone presence. Chapter 5: High-level control strategy. A high-level power flow control strategy of a parallel PHEV is proposed, which combines a rule-based (RB) controller with an equivalent consumption minimization strategy (ECMS). The RB controller regulates the battery SoC, calculates the transmission input power demand, and determines the engine on/off state. The ECMS performs instantaneous optimization (i.e., optimal allocation) of the control variables, which include the engine torque and the transmission gear ratio. Two versions of the control strategy are considered, depending on whether the BWD or EXT-BWD model is used within the ECMS strategy. With the aim of reducing the number of gear shifts in the case of BWD model-based strategy, a gear shift delay algorithm is introduced through expansion of the ECMS cost function. The two versions of control strategy are verified by simulation and compared by using the EXT-BWD and FWD powertrain models in the CS regime and for several driving cycles. Chapter 6: Adaptive control strategy. The insights obtained from the control variable optimisation results given in the fourth chapter are used to establish an adaptation mechanism of the control strategy proposed in the fifth chapter. The DP optimal results are collected on micro-cycles that are obtained by segmenting the certification cycles, in order to reflect the influence of specific local statistical features of the driving cycle (e.g., city driving, cruising, aggressive driving). The values of three high-level control parameters are identified from the DP optimal results, and quadratic, linear-in-parameters regression models are formed and fed by the micro-cycle features calculated on a moving history time horizon. The adaptive management strategy is compared with the non-adaptive strategy described in the fifth chapter by means of simulation verification using the EXT-BWD and FWD models. Chapter 7: Model predictive control. A synthesis of the high-level control strategy based on the MPC approach is proposed. The cost function of the proposed MPC strategy consists of the fuel consumption on the prediction horizon and the predicted optimal remaining fuel consumption from the end of prediction horizon to the end of driving cycle. The remaining fuel consumption is described by a regression model fed by the remaining trip distance, SoC value at the end of prediction horizon, and optionally the mean value and standard deviation of the vehicle speed profile on the remaining section of driving cycle. The regression model is established and parametrised on the basis of DP-optimal results for several driving cycles. In this way, MPC does not use a multi-criteria objective function, i.e. the one that would combine fuel consumption and SoC error penalty at the end of the prediction horizon. This avoids adjusting the weighting coefficients of the objective function, which would otherwise be sensitive to the characteristics of driving cycles. At the same time, the SoC at the end of the driving cycle is implicitly accounted for, instead of explicitly (and thus generally sub-optimally) via the SoC at the end of the prediction horizon. The control variable optimization on the moving prediction horizon is performed by the DP algorithm, and the proposed strategy is implemented and verified through simulation by using the EXT-BWD PHEV model. Chapter 8: Battery state-of-charge reference planning for blending operating regime. Two methods of SoC reference profile synthesis with respect to travelled distance are proposed based on the insights obtained from the DP optimal SoC responses analysis presented in the fourth chapter. Special emphasis is on the cases of varying road grade and low emission zone presence. The offline synthesis method sets the SoC reference profile before the trip based on the knowledge or a sufficiently precise estimation of the trip distance, the average vehicle speed or the trip duration, and the road grade profile along the trip. Therefore, the proposed method is suitable for vehicles operating on pre-known and repetitive routes, with the historical vehicle tracking data available (e.g., city buses and delivery vehicles). The online SoC reference synthesis method relies on the recorded driving data during the trip to determine the upcoming SoC reference profile, and it only requires knowledge or a sufficiently precise estimate of the driving cycle total distance. Validation of the proposed synthesis methods is performed for RB+ECMS and EXT-BWD model, and the results are compared with those obtained by using the linear SoC reference profile. Chapter 9: Conclusion. Concluding remarks are given and possible future work directions are discussed. The main contributions of the doctoral thesis are listed at the end of the chapter, and they include: (i) computationally efficient backward-looking PHEV model, which accounts for powertrain transient loss effects described by analytical models; (ii) optimal PHEV power flow control strategy, which takes into account the powertrain transient losses and, thus further reducing the fuel consumption, and improving the drivability and drive comfort; (iii) adaptive PHEV power flow control strategy, which adapts the optimal control strategy parameters with respect to driving cycle features that are identified on a moving horizon in the immediate past, (iv) model predictive PHEV power flow control strategy, which realizes the final battery SoC condition in a robust way involving a regression model of the remaining fuel consumption from the end of prediction horizon to the end of driving cycle; and (v) method for SoC reference trajectory synthesis in PHEV blended operating regime based on the available driving cycle features.