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The reliable prediction of pollutant emissions generated by IC engine powertrains during the WLTP driving cycle is a key aspect to test and optimize different configurations, in order to respect the stringent emission limits. This work describes the application of an integrated modeling tool in a co-simulation environment, coupling a 1D fluid dynamic code for engine simulation with a specific numerical code for aftertreatment modelling by means of a robust numerical approach, to achieve a complete methodology for detailed simulations of driving cycles. The main goal is to allow an accurate 1D simulation of the unsteady flows along the intake and exhaust systems and to apply advanced thermodynamic combustion models for the calculation of cylinder-out emissions.

Due to increasingly stringent regulations, reduction of pollutant emissions and consumption are currently two major goals of the car industry. One way to reach these objectives is to enhance the management of the engine in order to optimize the whole combustion process. This requires the development of complex control strategies for the air and the fuel paths, and for the combustion process. In this context, engine 0D modelling emerges as a pertinent tool for investigating and validating such strategies. Indeed, it represents a useful complement to test bench campaigns, on the condition that these 0D models are accurate enough and manage to run quite fast, eventually in real time. This paper presents the different steps of the design of a high frequency 0D simulator of a downsized turbocharged Port Fuel Injector (PFI) engine, compatible with real time constraints.

The aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features.

ELEVATE (European Low Emission V4 Automotive Two-stroke Engine) was a research project part funded by the European Commission to design and develop a compact and efficient gasoline two-stroke automotive engine. Five partners were involved in the project, IFP (Institut Français Du Pétrole) who were the project leaders, Lotus, Opcon (Autorotor and SEM), Politecnico di Milano and Queen's University Belfast. The general project targets were to achieve Euro 3 emissions compliance without DeNOx catalisation, and a power output of 120 kW at 5000 rev/min with maximum torque of 250 Nm at 2000 rev/min. Specific targets were a 15% reduction in fuel consumption compared to its four-stroke counterpart and a size and weight advantage over the four-stroke diesel with significant reduction in particulate and NOx emissions. This paper describes the design philosophy of the engine as well as the application of the various partner technologies used.

A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

Nowadays, a continually growing system complexity due to the development of an increasing number of vehicle concepts in a steadily decreasing development time forces the engineering departments in the automotive industry to a deepened system understanding. The virtual design and validation of individual components from subsystems up to full vehicles becomes an even more significant role. As an answer to the challenge of reducing complete hardware prototypes, the virtual competence in NVH, among other methods, has been improved significantly in the last years. At first, the virtual design and validation of objectified phenomena in analogy to hardware tests via standardized test rigs, e.g. four poster test rig, have been conceived and validated with the so called MBS (Multi Body Systems).

This paper presents an after-treatment architecture combining a close coupled NOx trap and an under floor NOx trap. Instead of simply increasing the volume of the catalyst, we propose to broaden the active temperature window by splitting the LNT along the exhaust line. In order to design this architecture, a complete 1D model of NOx trap has been developed. Validated with respect to experimental data, this model has been useful to define the two volumes of LNT, making significant savings on the test bench exploitation. However, one of the main difficulties to operate the proposed architecture is the NOx purge and sulfur poisoning management. In order to optimize the NOx and sulfur purge launches, we have developed a control strategy based on an embedded reduced LNT model. These strategies have been validated on different driving cycles, by the means of simulation and of vehicle tests using rapid prototyping tools.

Handling characteristics, ride comfort and active safety are customer relevant attributes of modern premium vehicles. Electronic control units offer new possibilities to optimize vehicle performance with respect to these goals. The integration of multiple control systems, each with its own focus, leads to a high complexity. BMW and ITK Engineering have created a tool to tackle this challenge. A simulation environment to cover all development stages has been developed. Various levels of complexity are addressed by a scalable simulation model and functionality, which grows step-by-step with increasing requirements. The simulation environment ensures the coherence of the vehicle data and simulation method for development of the electronic systems. The article describes both the process of the electronic control unit (ECU) development and positive impact of an integrated tool on the entire vehicle development process.

The Particulate Measurement Programme (PMP) works on the identification of a method to replace or complete the existing particle mass (PM) measurement method. The French PMP subgroup, composed by IFP, PSA Peugeot-Citroën, Renault and UTAC, proposes an improved gravimetric method for the measurement of emitted particles, and conducted an inter-laboratory test to evaluate its performances. The technical programme is based on tests carried out on a Euro3 Diesel passenger car (PC), tested on the New European Driving Cycle (NEDC). To achieve low particulate matter (PM) emissions, the EGR is disconnected and a paraffinic fuel is used. The regulated pollutants are also measured. It is shown that the multiple filter weighing and a 0.1 μg balance instead of a 1 μg one are not necessary, as the first weighing and the 1 μg balance performances are satisfactory for type-approval purposes.

By the action on the steering wheel, the driver has the capability to control the trajectory of its vehicle. Nevertheless, the steering wheel has also the role of information provider to the driver. In particular, the torque level at the steering wheel informs the driver about the interaction between the vehicle and the road. This information flow is natural due to the mechanical chain between the road and the steering wheel. Many studies have shown that steering wheel torque feedback is crucial to ensure the control of the vehicle. In the context of uncoupled steering (steer-by-wire vehicle or driving simulators), the torque rendering on the steering wheel is a major challenge. In addition, of the trajectory control, the quality of this torque is a key for the immersion of drivers in virtual environment such as in driving simulators. The torque-rendering loop is composed of different steps.

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.

The rising environmental awareness has led to a growing interest in electric and lightweight vehicles. Four-wheeled Ultra-Efficient Lightweight Vehicles (UELVs) have the potential to improve the quality of urban life, reduce environmental impact and make efficient use of land. However, the safety of these vehicles in terms of dynamic behaviour needs to be better understood. This paper aims to provide a quantitative assessment of the handling behaviour of UELVs. An analytical single-track model and a numerical simulation by VI-CarRealTime are analysed to evaluate the dynamic performance of a UELV compared to a city car. This analysis shows that the lightweight vehicle has a higher readiness (i.e. lower reaction time to yaw rate) for step steering and lower steering effort (i.e. higher steady-state value). Experimental analysis through real-time driving sessions on the Dynamic Driving Simulator assesses vehicle responses and subjective perception for different manoeuvres.

Roundabouts are intersections at which automated cars seem currently not performing sufficiently well. Actually, sometimes, they get stuck and the traffic flow is seriously reduced. To overcome this problem a V2N-N2V (vehicle-to-network-network-to-vehicle) communication scheme is proposed. Cars communicate via 5G with an edge computer. A cooperative machine-learning algorithm orchestrates the traffic. Automated cars are instructed to accelerate or decelerate with the triple aim of improving the traffic flow into the roundabout, keeping safety constraints, and providing comfort for passengers on board of automated vehicles. In the roundabout, both automated cars and human-driven cars run. The roundabout scenario has been simulated by SUMO. Additionally, the scenario has been reconstructed into a dynamic driving simulator, with a real human driver in a virtual reality environment. The aim was to check the human perception of traffic flow, driving safety and driving comfort.