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Virtual sensing, i.e., the method of estimating quantities of interest indirectly via measurements of other quantities, has received a lot of attention in various fields: Virtual sensors have successfully been deployed in intelligent building systems, the process industry, water quality control, and combustion process monitoring. In most of these scenarios, measuring the quantities of interest is either impossible or difficult, or requires extensive modifications of the equipment under consideration – which in turn is associated with additional costs. At the same time, comprehensive data about equipment operation is collected by ever increasing deployment of inexpensive sensors that measure easily accessible quantities. Using this data to infer values of quantities which themselves are impossible to measure – i.e., virtual sensing – enables monitoring and control applications that would not be possible otherwise.

The three-way-catalyst (TWC) is an essential part of the exhaust aftertreatment system in spark-ignited powertrains, converting nearly all toxic emissions to harmless gasses. The TWC’s conversion efficiency is significantly temperature-dependent, and cold-starts can be the dominating source of emissions for vehicles with frequent start/stops (e.g. hybrid vehicles). In this paper we develop a thermal TWC model and calibrate it with experimental data. Due to the few number of state variables the model is well suited for fast offline simulation as well as subsequent on-line control, for instance using non-linear state-feedback or explicit MPC. Using the model could allow an on-line controller to more optimally adjust the engine ignition timing, the power in an electric catalyst pre-heater, and/or the power split ratio in a hybrid vehicle when the catalyst is not completely hot.

Due to stricter emission regulations and more environmental awareness, the powertrain systems are moving toward higher fuel efficiency and lower emissions. In response to these pressing needs, new technologies have been designed and implemented by manufacturers. As a result of increasing complexity of the powertrain systems, their control and optimization become more and more challenging. Virtual powertrain calibration, also known as model-based calibration, has been introduced to transfer a part of test bench testing into a virtual environment, and hence considerably reduce time and cost of product development process while increasing the product quality. Nevertheless, virtual calibration has not yet reached its full potential in industrial applications. Volvo Penta has recently developed a virtual test cell named VIRTEC, which is used in an ongoing pilot project to meet the Stage V emission standards.

The present paper aims at developing an innovative procedure to create a one-dimensional (1D) real-time capable simulation model for a heavy-duty diesel engine. The novelty of this approach is the use of the top-level engine configuration, test cell measurement data, and manufacturer maps as opposite to common practice of utilizing a detailed 1D engine model. The objective is to facilitate effective model adjustments and hence further increase the application of Hardware-in-the-Loop (HiL) simulations in powertrain development. This work describes the development of Fast Running Model (FRM) in GT-SUITE simulation software. The cylinder and gas-path modeling and calibration are described in detail. The results for engine performance and exhaust emissions produced satisfactory agreement with both steady-state and transient experimental data.

In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals.

In engine research and development there are often different engine parameters that produce similar effects on the end-point results. When calibrating modern engines, a huge number of parameters needs to be set, which also includes compensation parameters for model imperfections. In this context, simpler, more robust, and physically based models should be beneficial both for calibration work load and powertrain performance. In this study, we present an experimental methodology that uses intermediate (“intrinsic”) variables instead of engine parameters. By using simple thermodynamic models, the engine parameters EGR, IVC, and PBoost could be translated into oxygen concentration, temperature and gas density at the start of injection. The reason for this transformation of data is to “move” the Design of Experiment (DoE) closer to the situation of interest (i.e. the combustion) and to be able to construct simpler and more physically based models.

On page 1103, the published paper omitted important text before and after figure 6.

The increasing demand for improved efficiency of diesel engines requires more advanced combustion solutions. These solutions include the use of variable valve timings in combination with more traditional methods such as EGR, turbocharging and advanced injection systems. By modifying the characteristics of the charge air, further hardware optimization becomes possible. In the current investigation, the effect of late intake valve closing (LIVC) was investigated together with the effect of (external) exhaust gas recirculation (EGR) in a single cylinder heavy duty diesel engine. Different injection timings and injection pressures were investigated. The mass flow of oxygen was kept constant in order to show how the density and temperature of the reactant mixture affect the combustion and emission characteristics.

Parameter tuning was performed against data from a full scale engine rig with a Diesel Oxidation Catalysts (DOC). Several different catalyst configurations were used with varying Pt loading, washcoat thickness and volume. To illustrate the interplay between kinetics and mass transport, engine operating points were chosen with a wide variation in variables (inlet conditions) and both transient and stationary operation was used. A catalyst model was developed where the catalyst washcoat was discretized as tanks in series both radially and axially. Three different model configurations were used for parameter tuning, evaluating three different approaches to modeling of internal transport resistance. It was concluded that for a catalyst model with internal transport resistance the best fit could be achieved if some parameters affecting the internal mass transport were tuned in addition to the kinetic parameters.

When performing catalyst modeling and parameter tuning it is desirable that the experimental data contain both transient and stationary points and can be generated over a short period of time. Here a method of creating such concentration transients for a full scale engine rig system is presented. The paper describes a valuable approach for changing the composition of engine exhaust gas going to a DOC (or potentially any other device) by conditioning the exhaust gas with an additional upstream DOC and/or SCR. By controlling the urea injection and the DOC bypass a wide range of exhaust compositions, not possible by only controlling the engine, could be achieved. This will improve the possibilities for parameter estimation for the modeling of the DOC.