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Advanced SI engines for passenger cars often use the cylinder deactivation technology for dethrottling and thus achieving a reduction of fuel consumption. The gas exchange valves of the deactivated cylinders are closed permanently by a zero lift of the cams. The solutions for cylinder deactivation can vary in the kind of gas composition included in the deactivated cylinders: charge air, exhaust gas or vacuum. All these strategies have in common the frequent loss of captured charge mass from cycle to cycle. Their two-stroke compression-expansion cycle additionally intensifies this phenomenon. Thus, a significant decrease of the minimum cylinder pressure can cause an undesired entry of lubricant into the combustion chamber. The idea was to ventilate the generally deactivated cylinders frequently to compensate the loss of captured cylinder charge mass. The task was to keep the minimum cylinder pressure above a certain limit to prevent the piston rings from a failure.

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.

The precision of direct fuel injection systems of combustion engines is crucial for the further reduction of emissions and fuel consumption. It is influenced by the dynamic behavior of the fuel system, in particular the injection valves and the common rail pressure. As model based control strategies for the fuel system could substantially improve the dynamic behavior, an accurate model of the common rail injection system for gasoline engines - consisting of the main components high-pressure pump, common rail and injection valves - that could be used for control design is highly desirable. Approaches for developing such a model are presented in this paper. For each key component, two models are derived, which differ in temporal resolution and number of degrees of freedom. Experimental data is used to validate and compare the models. The data was generated on a test bench specifically designed and built for this purpose.

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.

In this paper, a hybrid optimal control problem (HOCP) for the optimal heating of the three-way catalytic converter is solved. We propose a model for a hybrid vehicle that beneath State of Charge and fuel consumption includes thermal system states like engine cooling water temperature and catalytic converter temperature. Since models for noxious emissions with appropriate computational demand are not yet available for optimization purposes, an artificial state that resembles the emissions produced is introduced. A hybrid optimal control problem is then formulated for the beginning of the FTP-75 drive cycle whose target is to minimize the energy loss during the catalytic converter and engine cooling water heating phase. The corresponding input values to be optimized are continuous variables as ignition angle and cylinder charge as well as discrete decisions such as different injection schemes. As additional constraint, an upper limit is imposed on the artificial emissions state.

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.

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.

The objective of this investigation was to evaluate the effects of a variable intake and exhaust valve timing in terms of opening, closing, opening duration, lift curve and number of active valves per pair on a four cylinder direct-injecting SI engine for the catalyst heating idling phase at the beginning of an NEDC emission test procedure. The first step evaluated the engine behavior at a reference point of operation. Its parameters in valve timing were adjusted to match the valve timing of the base production engine. The second step investigated the effects of an earlier exhaust valve opening while the exhaust valve closing time was kept and the exhaust valve opening duration was extended. The third step was to answer the question for the optimum number of exhaust valves in order to minimize the wall heat losses inside the cylinder head. The optimum 3V exhaust valve timing has been defined as the basis for exhaust valve timing for steps four and five.

This paper proposes a current limits distribution control strategy for a parallel hybrid electric vehicle (parallel HEV) which includes an advanced powertrain concept with two electrical driving axles. One of the difficulties of an HEV powertrain with two electrical driving axles is the ability to distribute the electrical current of one high voltage battery appropriately to the two independent electrical motors. Depending on the vehicle driving condition (i.e., car maneuver) or the maximization of the entire efficiency chain of the system, a suitable control strategy is necessary. We propose an input-output feedback linearization strategy to cope with the nonlinear system subject to input constraints. This approach needs an external, state dependent saturation element, which translates the state dependent control input saturation to the new feedback linearizing input and therefore preserves the properties of the differential geometric framework.

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.