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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.

The quality of the powertrain design has a significant impact on the fuel consumption and emissions of hybrid vehicles. Lack of experience with these relatively new technologies, the enormous variety of hybrid powertrain configurations, and the multitude of components make this area an ideal application for computer-based modeling and optimizations. Global optimization techniques have the advantage to explore systematically the design space to find the optimal configuration space. In this paper, a systematic procedure for an optimal design of hybrid powertrain configurations using an evolutionary algorithm is proposed. It will be shown that the design steps for parallel and power-split configurations are quite similar. This results in a computing approach with high synergy effects and the ability to exchange components seamless to compare different ‘virtual’ configurations.

For compliance with future more stringent emission standards exhaust emissions must be reduced. One possibility is to improve air-fuel ratio control quality. The approach presented in this paper uses virtual sensors to get a rough picture of the spatial distribution of lambda and oxygen storage states across the catalyst. This additional process information is gathered by means of a novel model for three-way catalysts. A state-space controller is used to maintain oxygen storage states predicted by the model at desired levels. The proposed control strategy has been implemented on a turbocharged, direct injection engine and successfully validated by means of emission measurements. A comparison with a commonly used air-fuel ratio control strategy is presented.