International Journal of Engine Research

The progression of emission legislation has intensified the efforts of the automotive industry to develop improved exhaust gas after-treatment systems. The requirement to fulfill Euro 6d-TEMP in real-world driving scenarios, the already significant calibration effort for Euro 6d and the Euro 7 emission standards in discussion have significantly increased the work load for calibration engineers and the requirements for testing resources. Many original equipment manufacturers are implementing taskforces in order not to have to discard the planned start of production for their products, and some are even already forced to reduce their product portfolio. This is due to the diverse testing matrix required to cover all possible real driving emissions test scenarios. One big challenge is the extension and possible variation of boundary conditions regarding ambient temperatures, traffic conditions, road gradients and other varying driving resistances. Moreover, the test duration can cause considerable differences in the measured emissions, even if the same route is driven repeatedly. Addressing these challenges makes the application of a dedicated, event-targeted emission calibration mandatory. Since only a few sequences of the time-consuming road tests are relevant for improving the emission calibration, the methodology presented in this article focuses on the exact reproduction of these emission events on an emission chassis dynamometer with the aim of implementing calibratable solutions for these events. This is done using a real driving emission-cycle-generator which creates real driving emission compliant severe test scenarios and which focuses on the statistical relevance related to the typical product specific operation. The underlying generation process accesses a large database with real driving emission measurement results focusing on vehicle- or vehicle-group-specific challenges, using statistical approaches. It will be demonstrated how this procedure reduces test time and how it helps to tackle the substantial real driving emission work-load, while providing a dependable base to achieve real driving emission legislation compliance.

A model-based methodology is presented, which allows the estimation of the characteristic phases of diesel combustion using a semi-physical model approach combined with state and parameter estimation through extended Kalman filtering. The physical relation between the fuel injection and the characteristic diesel combustion phases, such as premixed, diffusive combustion and burn-out, are modeled separately by linear dynamic transfer functions formulated in crank angle frequency domain and transformed into state space representation. The resulting state variables are the released burning energy and its derivatives of each combustion phase. Associated crank angle constants determine the dynamics of the combustion phases and represent the rate parameters to be estimated. By incorporating further physical assumptions regarding the fuel path and air–fuel-mixing dynamics, the combustion phase parameters are estimated online for each working cycle. Cylinder pressure signals and online combustion analysis are used to determine the burn rate of the diesel engine at the test bench. Investigations have shown that the estimated rate parameters depend on the current engine operation point. They are estimated during measurements and stored in lookup tables through an online-learning method based on a fast recursive least squares estimation algorithm.

Cycle-to-cycle feedback control is employed to achieve optimal combustion phasing while maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the exhaust gas recirculation valve position. The control development is based on a control-oriented model that captures the effects of throttle position, exhaust gas recirculation valve position, and spark timing on the combustion phasing. Under the assumption that in-cylinder pressure information is available, an adaptive extended Kalman filter approach is used to estimate the exhaust gas recirculation rate into the intake manifold based on combustion phasing measurements. The estimation algorithm is adaptive since the cycle-to-cycle combustion variability (output covariance) is not known a priori and changes with operating conditions. A linear quadratic regulator controller is designed to maintain optimal combustion phasing while maximizing exhaust gas recirculation levels during load transients coming from throttle tip-in and tip-out commands from the driver. During throttle tip-outs, however, a combination of a high exhaust gas recirculation rate and an overly advanced spark, product of the dynamic response of the system, generates a sequence of misfire events. In this work, an explicit reference governor is used as an add-on scheme to the closed-loop system in order to avoid the violation of the misfire limit. The reference governor is enhanced with model-free learning which enables it to avoid misfires after a learning phase. Experimental results are reported which illustrate the potential of the proposed control strategy for achieving an optimal combustion process during highly diluted conditions for improving fuel efficiency.

Innovative air path concepts for turbocharged spark-ignition engines with exhaust gas recirculation impose high demands on the control due to nonlinearities and cross-couplings. This contribution investigates the control of the air and exhaust gas recirculation paths of a two-stage turbocharged spark-ignition engine with low pressure exhaust gas recirculation. Using exhaust gas recirculation at high loads, the in-cylinder temperature can be lowered, reducing the knock tendency, while at the same time preventing the need for the enrichment of the air/fuel ratio. Air and exhaust gas recirculation paths are cross-coupled and show different delay times. To tackle these challenges, a data-based two-stage model predictive controller is proposed: The target selector accounts for the overactuated system structure, while the dynamic controller adjusts the charging pressure and exhaust gas recirculation rate. The prediction model setup is based on a small amount of dyno-run measurement data. To ensure real-time capability, the model is kept as simple as possible. This allows for fast turnaround times of the algorithm, while maintaining the necessary accuracy in steady-state and transient operation. This study focuses on a two-stage control concept based on a target selector for optimal stationary control inputs and the dynamic controller considering the dynamic behavior of the air and exhaust gas recirculation paths. Subsequently, the control concept for the two-stage turbocharged spark-ignition engine with low pressure exhaust gas recirculation is validated via experimental tests under real-driving conditions on an automotive test track, using a prototype test vehicle. Results show that boost pressure as well as exhaust gas recirculation rate setpoints are met without overshoot and control deviation with settling times being close to the boundaries set by the hardware.

When compared to traditional engines, homogeneous charge compression ignition has the potential to significantly reduce NO _x raw emissions, while maintaining a high fuel efficiency. Homogeneous charge compression ignition is characterized by compression-induced autoignition of a lean homogeneous air–fuel mixture. Since homogeneous charge compression ignition does not utilize direct ignition control, it is strongly dependent on the state of the cylinder charge and can suffer from high cyclic variability. With spark-assisted compression ignition, it has been demonstrated that misfires can be reduced, while preserving the high thermal efficiency of homogeneous charge compression ignition as a result of the more favorable physical mixture properties due to dilution. However, spark-assisted compression ignition reduces the NO _x benefits of homogeneous charge compression ignition, as it increases the local combustion temperatures. To merge the advantages of the homogeneous charge compression ignition and the spark-assisted compression ignition combustion processes, a field-programmable gate array for detailed simulation of the physical gas exchange is combined with a rapid spark system. The low latency and computational speed of the field-programmable gate array allows the simulation process to be implemented in real time. When combined with the rapid reaction time of the high-frequency current-based rapid ignition system, a feedforward controller based on the cylinder pressure or heat release is realized. The developed model-based controller determines if a spark is required to assist the homogeneous charge compression ignition combustion process. The use of the field-programmable gate array and rapid ignition system allows for the calculation of combustion properties and controller output within 0.1 °CA. This article presents the development and experimental validation of the developed controller on a single-cylinder research engine. The combustion stability has been significantly improved as reflected in an improved standard deviation of the indicated mean effective pressure and a reduction of the combustion phasing variations. Furthermore, compared to a traditional homogeneous charge compression ignition system, the hydrocarbon emissions can be reduced, while maintaining low NO _x emissions.

This work presents the assessment of direct water injection in spark-ignition engines using single cylinder experiments and tabulated chemistry-based simulations. In addition, direct water injection is compared with cooled low-pressure exhaust gas recirculation at full load operation. The analysis of the two knock suppressing and exhaust gas cooling methods is performed using the quasi-dimensional stochastic reactor model with a novel dual fuel tabulated chemistry model. To evaluate the characteristics of the autoignition in the end gas, the detonation diagram developed by Bradley and co-workers is applied. The single cylinder experiments with direct water injection outline the decreasing carbon monoxide emissions with increasing water content, while the nitrogen oxide emissions indicate only a minor decrease. The simulation results show that the engine can be operated at λ = 1 at full load using water–fuel ratios of up to 60% or cooled low-pressure exhaust gas recirculation rates of up to 30%. Both technologies enable the reduction of the knock probability and the decrease in the catalyst inlet temperature to protect the aftertreatment system components. The strongest exhaust temperature reduction is found with cooled low-pressure exhaust gas recirculation. With stoichiometric air–fuel ratio and water injection, the indicated efficiency is improved to 40% and the carbon monoxide emissions are reduced. The nitrogen oxide concentrations are increased compared to the fuel-rich base operating conditions and the nitrogen oxide emissions decrease with higher water content. With stoichiometric air–fuel ratio and exhaust gas recirculation, the indicated efficiency is improved to 43% and the carbon monoxide emissions are decreased. Increasing the exhaust gas recirculation rate to 30% drops the nitrogen oxide emissions below the concentrations of the fuel-rich base operating conditions.