Search Results

In the past decade, various Plug-in Hybrid Electric Vehicles have been demonstrated which offer the potential of a significant reduction in fuel consumption and emission. However, this capability strongly depends on the sizing of the components, driver's usage profile and the quality of the energy management. These challenges require new optimization procedures for a systematical exploration of the design space with the objective of an optimal powertrain configuration. A novel optimization strategy based on a multi-objective problem formulation is proposed. The optimization procedure consists of a multi-objective genetic algorithm for determining the best design parameters with respect to fuel consumption and driving performance. The approach is combined with an analytical optimal control problem to find the optimal continuous and discrete control trajectories for the energy management.

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.

In this paper an energy management is proposed which is optimal to certain driving scenarios which can be clustered into freeway, rural and urban situations. This strategy is non-predictive but uses information about the current driving situation provided by modern navigation systems to identify the current road type. Based on this information a set of simplified optimal control problems are solved offline via an indirect shooting algorithm. By relaxation of the problem formulation, the solutions of these optimal control problems can be stored into easily implementable maps. The energy management control is then determined from these maps during vehicle operation using the current road type, the vehicle speed and the required wheel-torque. The strategy is implemented in a dSPACE MicroAutoBox and validated on a near mass-production vehicle. The proposed methodology has shown fuel savings on a real world drive cycle.

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.