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A trip-based energy management strategy for electric vehicles (EVs) is proposed. It can use deterministic routing information obtained from, nowadays, available navigation systems and determines stochastic descriptions of process uncertainties such as stop events as unpredictable disturbances. A dynamic programming algorithm is used to calculate the optimal control trajectories required to reach the target destination safely and to suggest the driver an optimal driving style to maximize the battery range. The algorithm is implemented on a rapid prototyping platform using MATLAB/Simulink. Simulations and experimental results obtained from an EV prototype car are presented.

In this paper optimal control problems for hybrid powertrain vehicles with different drive-modes are considered and solved using numerical techniques. This leads to the formulation of hybrid optimal control problems. The aim is to find optimal controls and optimal switchings between the drive-modes to minimize a cost function resembling fuel consumption. The problem is nonlinear and subject to constraints concerning both controls and state. The techniques include indirect methods as well as direct optimization methods. Efficiency and accuracy are evaluated for all methods using simulation studies. An experimental test on a near mass-production vehicle confirms the usability of the direct optimization approach.