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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.

Due to the integration of many interacting subsystems like hybrid vehicle management, energy management, distance management, etc. into the VCU platform the design steps for function development and calibration become more and more complex. This makes an aid necessary to relieve the development. Therefore, the aim of the proposed simulation-based development and calibration design is to improve the time-and-cost consuming development stages of modern VCU platforms. A simulation-based development framework is shown on a complex function development and calibration case study using an advanced powertrain concept with a plug-in hybrid electric vehicle (PHEV) concept with two electrical axles.

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.

Most energy management systems for hybrid electric vehicles still use rule-based energy management systems that rely on information stored in lookup tables, to define the current mode of operation and set-points for the low-level control laws. Because of the high number of parameters, the calibration of such energy managements can be a cumbersome task for the engineers. Mathematical tools are therefore inalienable to the calibration process. In this paper, it will be demonstrated, how the theory of hybrid optimal control can be used to calculate an initial parameter set for the energy management of charge-sustaining hybrids. The calculation procedure includes the solution of a hybrid optimal control problem to determine the controls for the optimal operation of the vehicle over a given cycle.