The DI Diesel engine has gained an increasing market share in the recent 25 years and has converted from a niche application to an established, highly appreciated propulsion system in the Light-Duty vehicle segment, covering passenger car as well as light commercial applications. In vehicle classes with high market penetration this low CO2 concept offers a substantial contribution to minimize Greenhouse gas (GHG) emissions from the transportation sector.

The upcoming steps in the emissions legislation in Europe consider a stringent tightening of the procedure to homologate vehicles. The WLTP will be introduced as the type-approval cycle from 2017, substituting the less severe NEDC. On the other hand, real driving emissions (RDE) cycles will also be run under realistic driving conditions: the vehicle must fulfill severe NOx emission targets. At the same time, the minimization of the CO2 emissions is also pursued by the governments. The current work first compares the different emission procedures, later presents the FEV SimEx simulation tool applied to the longitudinal powertrain and aftertreatment simulation of Diesel vehicles, and finally analyses the specific case of the powertrain design of a C-Segment vehicle to meet upcoming RDE and WLTP requirements.

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.

En los algoritmos de control y diagnóstico de los motores diesel la precisión en la estimación de las variables resulta crítica. En el caso de las emisiones de óxidos de nitrógeno (NOx) recientemente se han desarrollado sensores con una buena precisión de medida estacionaria pero que, debido a su lentitud y a la existencia de un retraso significativo, presentan unas características dinámicas insuficientes para el control. Por otro lado, existen diferentes tipos de modelos capaces de reproducir con mayor o menor precisión la respuesta dinámica de los NOx; sin embargo, ninguno de ellos está exento de deriva asociada al envejecimiento del motor y de los diferentes sensores que suministran las entradas al modelo. La combinación de un modelo de emisiones con un sensor de NOx permite proporcionar una estimación que combina las características dinámicas del modelo con la precisión del sensor. En este trabajo se combina la información a través de un modelo en espacio de estados que permite la observación y corrección de la deriva del modelo de NOx. El vector de estado que describe la salida objetivo se aumenta con un estado extra que define la deriva o error estacionario entre el modelo derivado y la referencia de medida del sensor. El vector de estado es observado mediante un filtro de Kalman. Dicho modelo es lineal invariante en el tiempo y las covarianzas de los ruidos que afectan a los estados son consideradas como constantes. Bajo estas hipótesis, el filtro es estacionario, es decir, la ecuación de Riccati que estima la ganancia del filtro converge tras un número determinado de iteraciones. El presente artículo resuelve la ecuación iterativa de Riccati para dichas condiciones y deriva la solución analítica del filtro. Asimismo, dicho algoritmo es usado para la estimación de NOx en un motor diesel y en el nuevo ciclo Europeo de conducción (NEDC).

The implantation of nitrogen oxide sensors in diesel engines is necessary in order to track emissions at the engine exhaust line for diagnosis and control of the after-treatment devices. However, the use of models is still necessary since the sensor outputs are delayed and filtered. The present paper deals with the problem of the nitrogen oxide estimation in two parts; Part 1 deals with a control-oriented model for the nitrogen oxide estimation, while Part 2 presents data fusion of the model and the sensor to improve the estimation, which is presented in the following. The use of models for the nitrogen oxide estimation is an alternative but the drift and the ageing are still issues. In order to overcome this problem, the fusion of different signals can be carried out in a smart way by means of a Kalman filter. There exist different ways of presenting this fusion, from directly tracking the bias to updating the model parameters. For this, different algorithms are proposed in this paper with the aim of correcting the model output. Furthermore, the estimation of the actual nitrogen oxide concentration, by preventing sensor delay and filtering, is also integrated in the algorithm, which is a suitable strategy for combining nitrogen oxide sensors and models on an onboard basis.

This chapter is divided into two parts. The first part, which comprises Sects. 3.2 and 3.3, is devoted to the description of the experimental configuration and tests used for this work. Section 3.2 describes the experimental set-up used in the present work, including the relevant characteristics of the engine, sensors and test cell equipment, while the Sect. 3.3 presents the steady-state and dynamic tests performed to tune and validate the methods. In the second part of the chapter, the use of onlinemethods for characterising $$\mathrm {NO_{x}}$$ and $$\lambda $$ -1 output from exhaust gas concentration sensors is emphasised. $$\mathrm {NO_{x}}$$ output is characterised by a novel method based on SOI steps (Galindo et al. 2011), while $$\lambda $$ -1 output is characterised by performing injection steps (Guardiola et al. 2013).

The present dissertation covers the topic of the online dynamic estimation of \(\lambda ^{-1}\) and NO\(_\text {x}\) in diesel engines. In this chapter, the main contributions and conclusions are presented and organised according to the thematic.

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.

The methods presented in the Chap. 5 can be applied to different engine variables. Chap. 6 applies them to the fast estimation of $$\lambda ^{-1}$$ and NO $$_\text {x}$$ . In order to do that, different algorithms based on the dynamic estimation methods of Chap. 5 are proposed.

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.

Chapter 2 emphasises the need of information for a proper control of the diesel engine and reviews sensors and models for estimating gas concentrations in diesel engines, while also presents different methods for fast estimation of variables. These methods are focused on the drift correction and updating algorithms for look-up tables.

The dynamic problems of the sensors and the drift on models motivate the design of adaptive on-board strategies. Concretely, two kind of models and algorithms are proposed in order to solve these issues, which are: 1. Augmented models for drift correction and dynamic estimation of variables, and 2. Learning algorithms for updating look-up tables and parameters on the basis of COMs, which can be used for calibration and/or online adaptation.

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.