Benjamín Pla

A methodology to identify the intake charge cylinder-to-cylinder distribution in turbocharged direct injection Diesel engines

Exhaust gas recirculation (EGR) is currently the most important NOx emission control system. During the last few years the EGR rate has increased progressively as pollutant emission regulations have become more restrictive. High EGR rate levels have given the effect of the unsuitable EGR and air distribution between cylinders away, which causes undesirable engine behavior. In this sense, the study of the EGR distribution between cylinders achieves high importance. However, despite the fact that the EGR is continuously under study, not many studies have been undertaken to approach its distribution between cylinders. In concordance with the aspects outlined before, the aim of this paper is to propose a methodology that permits us to identify the EGR cylinder-to-cylinder dispersion in a commercial engine. In order to achieve this objective, experimental tests have been combined with both one-dimensional and three-dimensional fluid dynamic models.

A bias correction method for fast fuel-to-air ratio estimation in diesel engines

λ probes in turbocharged diesel engines are usually located downstream of the turbine, exhibiting a good dynamic response but a significant delay because of the exhaust line transport and the hardware itself. With the introduction of after-treatment systems, new sensors that can measure the exhaust concentrations are required for optimal control and diagnosis. Zirconia-based potentiometric sensors permit the measurement of nitrogen oxides and oxygen with the same hardware. However, their dynamic response is slower and more filtered than that of traditional λ probes and, in addition, the sensor location downstream of the after-treatment systems increases this problem. The paper uses a Kalman filter for online dynamic estimation of the relative fuel-to-air ratio λ−1 in a turbocharged diesel engine. The combination of a fast drifted fuel-to-air ratio model with a slow but accurate zirconia sensor permits the model bias to be corrected. This bias is modelled with a look-up table depending on the engine operating point and is integrated online on the basis of the Kalman filter output. The calculation burden is alleviated by using the converged gain of the steady-state Kalman filter, precalculated offline. Finally, robustness conditions for stopping the bias updating are included in order to account for the sensor and model uncertainties. The proposed algorithm and sensor layout are successfully proved in a turbocharged diesel engine. Experimental and simulation results are included to support validation of the algorithm.

A learning algorithm concept for updating look-up tables for automotive applications

Abstract Look-up tables are commonly used in the automotive field for handling operating point variations. However, constant maps cannot cope with systems variations and ageing. Methods, such as Kalman filter or Extended Kalman filter for non-linear cases, can be used for table adaptation providing an optimal solution to the problem. But these methods are computationally intensive, making difficult to implement them on commercial engine control units. The current paper proposes a learning method for online updating of look-up tables or maps. This algorithm uses precalculated membership functions based on a standard Kalman filter observer for weighting the adaptation. The main contribution of the method is the derivation of a steady-state Kalman filter observer that lowers the calculation burden and simplifies the implementation against the standard Kalman filter implementation that requires higher computational cost. As far as table is updated online while engine runs, this allows correcting drift errors and the unit-to-unit dispersion. The method is illustrated for mapping engine variables such as λ − 1 and N O x in a Diesel engine by using an adaptive look-up table, and its characteristics make it suitable for implementing in commercial engine electronic control units for online purposes.

On the combination of high-pressure and low-pressure exhaust gas recirculation loops for improved fuel economy and reduced emissions in high-speed direct-injection engines

In this paper, an experimental study of the combination of low-pressure and high-pressure exhaust gas recirculation architectures has been carried out. In the first part of the paper, the effects of both high-pressure and low-pressure exhaust gas recirculation architectures on engine behaviour and performance are analysed by means of a series of steady tests. In the second part, the effects of the combination of both architectures are addressed. The results show that the low-pressure configuration improves high-pressure exhaust gas recirculation results in brake-specific fuel consumption, nitrogen oxides and exhaust gas opacity; nevertheless, hydrocarbon emissions are increased, especially during the engine warm up. In addition, the exhaust gas recirculation rate achieved with low-pressure systems is limited by the pressure difference between diesel particulate matter outlet and compressor inlet; therefore, the high-pressure system can be used to achieve the required exhaust gas recirculation levels without increasing pumping losses. In this sense, the combination of both exhaust gas recirculation layouts offers significant advantages to reduce emissions and fuel consumption to meet future emission requirements.

Cycle by Cycle Trapped Mass Estimation for Diagnosis and Control

The development of one cycle resolution control strategies and the research at HCCI engines demands an accurate estimation of the trapped mass. In contrast to current methods for determining the mass flow, which are only able to determine averaged values of the flow entering the cylinders, the present paper proposes a methodology based on the in-cylinder pressure resonance. The determination of such frequency allows inferring the cylinder mass with one cycle resolution. In addition, the method permits determining error metrics based on the mass conservation principle. Validation results for a reactivity controlled compression ignition (RCCI) engine equipped with electrohydraulic variable valve timing (VVT) are presented to illustrate the performance of the method.

Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.

Transient particle emission measurement with optical techniques

Particulate matter is responsible for some respiratory and cardiovascular diseases. In addition, it is one of the most important pollutants of high-speed direct injection (HSDI) passenger car engines. Current legislation requires particulate dilution tunnels for particulate matter measuring. However for development work, dilution tunnels are expensive and sometimes not useful since they are not able to quantify real-time particulate emissions during transient operation. In this study, the use of a continuous measurement opacimeter and a fast response HFID is proven to be a good alternative to obtain instantaneous particle mass emissions during transient operation (due to particulate matter consisting mainly of soot and SOF). Some methods and correlations available from literature, but developed for steady conditions, are evaluated during transient operation by comparing with mini-tunnel measurements during the entire MVEG-A transient cycle. A new correlation was also derived from this evaluation. Results for soot and SOF (obtained from the new correlation proposed) are compared with soot and SOF captured with particulate filters, which have been separated by means of an SOF extraction method. Finally, as an example of ECU design strategies using these sort of correlations, the EGR valve opening is optimized during transient operation. The optimization is performed while simultaneously taking into account instantaneous fuel consumption, particulate emissions (calculated with the proposed correlation) and other regulated engine pollutants.

ECU-oriented models for NOx prediction. Part 1: a mean value engine model for NOx prediction:

The implantation of nitrogen oxide sensors in diesel engines was proposed in order to track the emissions at the engine exhaust, with applications to the control and diagnosis of the after-treatment devices. However, the use of models is still necessary since the output from these sensors is delayed and filtered. The present paper deals with the problem of nitrogen oxide estimation in turbocharged diesel engines combining the information provided by both models and sensors. In Part 1 of this paper, a control-oriented nitrogen oxide model is designed. The model is based on the mapping of the nitrogen oxide output and a set of corrections which account for the variations in the intake and ambient conditions, and it is designed for implementation in commercial electronic control units. The model is sensitive to variations in the engine’s air path, which is solved through the engine volumetric efficiency and the first-principle equations but disregards the effect of variation in the injection settings. In order to consider the effect of the thermal transients on the in-cylinder temperature, the model introduces a dynamic factor. The model behaves well in both steady-state operation and transient operation, achieving a mean average error of 7% in the steady state and lower than 10% in an exigent sportive driving mountain profile cycle. The relatively low calibration effort and the model accuracy show the feasibility of the model for exhaust gas recirculation control as well as onboard diagnosis of the nitrogen oxide emissions.

Representation Limits of Mean Value Engine Models

Mean Value Engine Models (MVEMs) have been widely used for internal combustion engine modelling with main application areas on the design and development of engine control systems. However, modellers must be aware of the limitations of these MVEMs which are associated to the simplification of the geometry and the time scale, and the partial consideration of the physical phenomena involved. This chapter analyses through several real-life examples the effects of some of the most important simplifications done in MVEMs.

Adaptive calibration for reduced fuel consumption and emissions

This paper presents a model-based approach for continuously adapting an engine calibration to the traffic and changing pollutant emission limits. The proposed strategy does not need additional experimental tests beyond those required by the traditional calibration approach. The method utilises information currently available in the engine control unit to adapt the engine control to the particular driving patterns of a given driver. Additional information about the emissions limits should be provided by an external structure if an adaptation to the pollutant immission is required. The proposed strategy has been implemented in a light-duty diesel engine, and showed a good potential to keep NO x emissions around a defined limit.