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Abstract: The paper presents a formulation of the energy management problem for Hybrid Electrical Vehicles and Plug-in Hybrid Electrical Vehicles alike, which permits to consider different cost indexes like fuel consumption, total and primary energy consumption, economic cost or CO2 footprint. In-depth analysis of the problem optimal solution is done by means of the application of the method, which also permits the optimal tuning of other implementable control strategies. Such an approach is used to understand the effect of the selected cost index, the regional energetic share, the driving conditions, and for deriving rules for battery sizing. [Copyright &y& Elsevier]

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance. [ABSTRACT FROM PUBLISHER]