René Zweigel

Iterative learning approach for diesel combustion control using injection rate shaping

State of the art in combustion control consists of discrete and clearly separated multiple injection events. Future available injector types establish highly flexible injection, allowing for continuously variable injection timings and therewith shaping of the fuel injection rate. This leads to a more direct influence on combustion, e.g. control of time-continuous pressure traces instead of mean value control. The overall advantage of rate-shaping approaches is higher engine efficiency and reduction of engine-out emissions at the same time. However, well investigated and established model-based control approaches are not applicable anymore. For that reason, this paper presents a simple and effective control method which uses iterative learning control (ILC) combined with variable injection rate. The presented control algorithm is validated for diesel combustion. To make ILC applicable to combustion processes, the underlying learning rule will be extended by model-based combustion knowledge. Static and dynamic simulation results are presented which emphasize the relevance of the approach.

Controlling GCAI (Gasoline Controlled Auto Ignition) in an Extended Operating Map

Abstract The gasoline controlled autoignition (GCAI) is a modern combustion method with which the fuel consumption and the pollutant emissions can be reduced. The major drawback of the combustion method is the limited operating map. In this contribution it is shown how the operating map can be extended towards lower loads by the use of a spark plug for a spark-assisted GCAI combustion. Compared to the GCAI combustion, the spark plug is used additionally and the controller has to be adapted, such that the spark-assisted GCAI combustion is also considered. As controller a model-based predictive controller (MPC) is developed. In this contribution a special focus is set on the investigation of the underlying model for the MPC.

Triple-injection strategy for model-based control of premixed charge compression ignition diesel engine combustion:

Low-temperature combustion concepts are of great interest due to their potential to reduce nitrogen oxides (NOx) and soot simultaneously. However, low-temperature combustion often leads to an increase in total unburnt hydrocarbons and carbon monoxide. Furthermore, combustion sound level becomes a challenge, especially at higher loads. Various studies show that these drawbacks can be compensated by advanced injection strategies, for example, split injections. In this study, a significantly modified triple-injection approach is proposed. First, the corresponding impact on engine performance is evaluated at stationary conditions, and second, its observed advantages are evaluated at transient operation. Stationary results show that NOx, soot, and combustion sound level are simultaneously reduced without losses in fuel efficiency and without any remarkable rise in total unburnt hydrocarbon as well as carbon monoxide emissions, satisfying Euro 6 emission regulations. Under transient conditions, model-based predictive control of the engine, which allows for reliable steady-state measurements and permits validation tests at transient operating points, is successfully demonstrated for single and triple injection. With both injection strategies, control of indicated mean effective pressure, combustion phasing (CA50 (crank angle (CA) when 50% fuel is consumed)), and NOx emissions is achieved. As a result of this work, the identified optimal triple-injection strategy leads to lower total unburnt hydrocarbon emissions and to significantly reduced combustion sound level at the same level for NOx emissions in comparison with the single-injection approach. Thus, the proposed triple-injection strategy combined with sophisticated model-based control is a promising concept for future engine emission control.

A 2-stage MPC Approach for the Cycle-To-Cycle Dynamics of GCAI (Gasoline Controlled Auto Ignition)

Abstract Modern combustion methods, like Gasoline Controlled Autoignition (GCAI), impose very high requirements on the process control. In the investigated set-up, fast reference tracking is needed, while still being able to reject disturbances and satisfy constraints. Model-based predictive controllers (MPC) have a great potential in terms of fulfilling these requirements. In this contribution, a 2-stage MPC controller is introduced. This controller can be used to handle the cycle-to-cycle dynamics of the GCAI process.

Advanced state estimation for navigation of automated vehicles

Abstract For the emerging topic of automated and autonomous vehicles in all major sectors, reliable and accurate state estimation for navigation of these vehicles becomes increasingly important. Inertial navigation, aided with measurements from global navigation satellite systems (GNSS), allows high-rate and low-cost estimation of position, velocity and orientation in real-time applications. As the available satellite constellations for navigation are modernized and their number is rising, usage of multi-constellation, dual-frequency and integration of correction data lead to increased accuracy, especially in areas with partial shadowing. Different coupling methods, e.g. tightly- and loosely-coupled integrations, were developed to combine inertial and GNSS measurements. Also different error estimation filters were applied to the navigation problem, and evaluated against each other. For the typical navigation task, the objective is to choose a suitable algorithm for the specific requirements of the target application, and deploy it using an appropriate implementation strategy. This contribution gives a short introduction into the field of aided inertial navigation techniques, provides useful hints for implementation, and evaluates their performance in experiments using two different railway vehicles, an autonomous maritime vessel, and an unmanned aerial quadrotor.

Model-Based Emission Control of a Compression Ignition Engine

XV

Hybrid multi-objective MPC for fuel-efficient PCCI engine control

In the present paper the closed-loop control of an innovative combustion engine is investigated. The investigated combustion engine is characterized by the ability of handling different combustion modes, such as Premixed Charge Compression Ignition (PCCI), which are applied depending on the operating point. In the paper the challenges related to the increased complexity of the control algorithm and the corresponding calibration effort are addressed. For this reason a tailored hybrid multi-objective MPC algorithm is developed based on a piecewise affine (PWA) model. The MPC algorithm consists of a 2-staged approach, including a stationary and a dynamic optimization. With this algorithm a nonlinear optimization is performed in every time step considering the task of tracking reference values and minimizing values i.e. fuel consumption, such that the optimal combustion mode is chosen automatically. The developed algorithm is finally validated in simulation.