The lean-burn capability of the Diesel-ignited gas engine combined with its potential for high efficiency and low CO ${}_2$ emissions makes this engine concept one of the most promising alternative fuel converters for passenger cars. Instead of using a spark plug, the ignition relies on the compression-ignited Diesel fuel providing ignition centers for the homogeneous air-gas mixture. In this study the amount of Diesel is reduced to the minimum amount required for the desired ignition. The low-load operation of such an engine is known to be challenging, as hydrocarbon (HC) emissions rise. The objective of this study is to develop optimal low-load operation strategies for the input variables equivalence ratio and exhaust gas recirculation (EGR) rate. A physical engine model helps to investigate three important limitations, namely maximum acceptable HC emissions, minimal CO ${}_2$ reduction, and minimal exhaust gas temperature. An important finding is the fact that the high HC emissions under low-load and lean conditions are a consequence of the inability to raise the gas equivalence ratio resulting in a poor flame propagation. The simulations on the various low-load strategies reveal the conflicting demand of lean combustion with low CO ${}_2$ emissions and stoichiometric operation with low HC emissions, as well as the minimal feasible dual-fuel load of $3.2$ bar brake mean effective pressure.

This paper presents models and control algorithms for the energy management of the Formula 1 race car in qualifying scenarios, whereby the car's performance is solely determined by the lap time achieved over a single lap. Specifically, we first extend an existing convex model of the Formula 1 car to include the waste-gate action, and analytically derive its lap-time-optimal control policy. Second, we combine nonlinear programming techniques with convex optimization to numerically compute the optimal solution for an out-lap-flying-lap sequence. Third, we devise equivalent lap time minimization strategies, whereby three PI controllers adapt the optimal control policy implemented on the car in real-time, in order to track precomputed control strategies in a minimum-lap-time fashion. Finally, we benchmark the control algorithms presented against the optimal solution and validate their performance with high-fidelity simulations. The results obtained show the optimality and the robustness of the presented approach.

This article analyzes the influence of the ignition retardation on the fuel consumption, the cumulative tailpipe hydrocarbon emissions, and the temperature inside the three-way catalytic converter in a gasoline direct injection engine operated under idling conditions. A dedicated cylinder-individual, model-based, multivariable controller was used in experiments in order to isolate the effect of the ignition retardation on the hydrocarbon emissions as much as possible. An optimal control problem for a gasoline engine at a cold-start is formulated, which is used to interpret the experimental data obtained. The corresponding goal is to minimize the fuel consumption during an initial idling phase of a fixed duration while guaranteeing that the three-way catalytic converter reaches a sufficiently high final temperature and at the same time making sure that the cumulative hydrocarbon emissions stay below a given limit. The experimental data indicates that the engine should be operated with maximum ignition retardation in order to reach any temperature inside the three-way catalytic converter as quickly as possible concurrently with minimum tailpipe emissions and with the minimum possible fuel consumption.

This paper investigates the combustion phasing control of natural gas-diesel engines. In this study, the combustion phasing is influenced by manipulating the start and the duration of the diesel injection. Instead of using both degrees of freedom to control the center of combustion only, we propose a method that simultaneously controls the combustion phasing and minimizes the amount of diesel used. Minimizing the amount of diesel while keeping the center of combustion at a constant value is formulated as an optimization problem with an equality constraint. A combination of feedback control and extremum seeking is used to solve this optimization problem online. The necessity to separate the different time scales is discussed and a structure is proposed that facilitates this separation for this specific example. The proposed method is validated by experiments on a test bench.

Tractable safety-ensuring algorithms for cyber-physical systems are important in critical applications. Approaches based on Control Barrier Functions assume continuous enforcement, which is not possible in an online fashion. This letter presents two tractable algorithms to ensure forward invariance of discrete-time controlled cyber-physical systems. Both approaches are based on Control Barrier Functions to provide strict mathematical safety guarantees. The first algorithm exploits Lipschitz continuity and formulates the safety condition as a robust program which is subsequently relaxed to a set of affine conditions. The second algorithm is inspired by tube-NMPC and uses an affine Control Barrier Function formulation in conjunction with an auxiliary controller to guarantee safety of the system. We combine an approximate NMPC controller with the second algorithm to guarantee strict safety despite approximated constraints and show its effectiveness experimentally on a mini-Segway.