With the current concerns over vehicle emissions, oil availability and pricing there is a lot of interest in environmentally friendly vehicles such as electric and hybrid electric as ways of reducing costs and CO₂ emissions. In the case of a pure electric vehicle it is important to optimize the electric vehicle range so that it can travel as far as possible and ultimately does not leave the user of the vehicle stranded without enough charge in the battery to complete the journey. For hybrid electric vehicles there is a lot of scope to optimize its control so that the optimal use of electricity and internal combustion engine is maintained, for example, maximization of the use of plug-in charging opportunities. For both vehicle types, optimal battery management is important. There are a large number of potential intelligent power-saving strategies that can be used with the possibility to intelligently advise for the optimal use of plug-in charging - via renewable energy sources, vehicle to grid load leveling and allowing the engine to be run in its most efficient envelope of operation whilst maintaining the battery management within its optimal envelope. GPS navigation information in modern vehicles is common place and gives very good information on vehicle location and routes. If the precise origin of a journey is known and is combined with the time of day then for many users there is a good possibility of ascertaining a journey destination and characteristics. This paper firstly explores the possibilities for adopting high-level control strategies for reducing energy usage. A number of journey estimation methodologies are described and how these are used for different control strategies in terms of battery management. Secondly it is investigated whether it is possible to predict the requirements of a journey at its origin and secondly, if an incorrect prediction is made, how this is dealt with.