The project deals with design of adaptive and predictive energy management control strategies for plug-in hybrid electric vehicles (PHEV), as a key transition technology towards an energy efficient, clean, quiet and sustainable transport of the future. The project is organised around three mutually interconnected research themes: (i) Markov chain-based synthesis of naturalistic driving cycles taking into account road grade and vehicle mass variability; (ii) optimised PHEV energy management control strategy providing minimal fuel consumption/emissions, favourable drivability and comfort, and modest battery degradation, for a wide range of operating modes and driving conditions; and (iii) adaptive and stochastic model predictive control strategies which account for on-line estimated and predicted statistical features of driving cycles. The proposed methodology is demonstrated through a case study of city buses (PHEV vs. conventional/Diesel bus), for which a rich set of recorded driving cycles is available to the project team. Although the research is focused on PHEVs of parallel (P2) configuration and city bus application including aspects of low-emission zones (LEZ), the developed methodology is applicable to other electric vehicle types (HEV, EREV, and also BEV), configurations (series and series-parallel) and categories (e.g. passenger vehicles and trucks). The proposed control strategies are systematically verified with respect to control trajectory optimisation benchmark provided, while quantifying improvements gained through adaptation and prediction mechanisms, and assessing transferability to other PHEV powertrain configurations. To accomplish the ambitious research goals, the project brings together an multidisciplinary research group including researchers from the areas of mechanical engineering, electrical engineering and computing, and traffic engineering.