A plug-in hybrid electric vehicle (PHEV) satisfies the driver's power demand with two types of energy potentials: fuel and electrical energy provided by a battery. Classically, the battery consumption is planned over a trip to minimize the expected fuel consumption. A cautious driver will save battery potential to cross restricted areas (with desired low or even zero fuel consumption) without the fuel engine. This paper proposes an approach to minimize energy consumption while controlling the risk of a PHEV falling short of battery potential when crossing a restricted area. We use a nonlinear Gaussian process, trained on real vehicle data, for predicting the vehicle consumption. We take into account prediction uncertainty by ensuring that the driver's highest power demand will be satisfied with a high probability. The interest of the approach is demonstrated by a simulated trip around Paris.