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The proposed Euro 7 emission standard foresees that the emission behaviour of Euro 7 vehicles is monitored via an on-board monitoring (OBM) system. In Euro 7 vehicles, OBM systems will monitor the emissions of nitrogen oxides (NOX), ammonia (NH3) and particulate matter (PM) for every trip through a combination of measured and modelled data. Sensors employed to support on-board diagnostics (OBD) in current vehicles may be used to support OBM. According to the Euro 7 OBM concept presented in this paper, OBM will serve a dual purpose: the first is to warn the user of a vehicle about the need to perform repairs on the engine or the pollution control systems when these are needed. If these repairs are not performed in a timely manner, the OBM system will be able to ultimately prevent engine restart, akin to the existing low-reagent driver warning system in some compression ignition vehicles. The second purpose of OBM is to monitor the compliance of vehicle types with the emission limits.

The implementation of emission standards has brought significant reductions in vehicle emissions in the EU, but road transport is still a major source of air pollution. Future emission standards will aim at making road vehicles as clean as possible under a wide range of driving conditions and throughout their complete lifetime. The current paper presents the methodology followed by the Consortium for ultra LOw Vehicle Emissions (CLOVE) to support the preparation of the Euro 7 proposal. As a first step, the emission performance of the latest-technology vehicles under various driving conditions was evaluated. Towards this direction, an emissions database was developed, containing data from a wide range of tests, both within and beyond the current RDE boundaries.

Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great interest for the transport sector, since they combine high fuel efficiency with last mile full-electric driving. We focus on a specific use case: PHEV operation through future Zero Emission (ZE) zones of cities. As a first step towards self-learning control, we introduce a novel, adaptive supervisory controller that combines modular energy and emission management (MEEM) and deals with varying constraints and system uncertainty. This optimal control strategy is based on Pontryagin’s Minimum Principle and maximizes overall energy efficiency.

A model-based control approach is proposed to give proper reference for the feed-forward combustion control of Partially Pre-mixed Combustion (PPC) engines. The current study presents a simplified first principal model, which has been developed to provide a base estimation of the ignition properties. This model is used to describe the behavior of a single-cylinder heavy-duty diesel engine fueled with a mix of bio-butanol and n-heptane (80vol% bio-butanol and 20 vol% n-heptane). The model has been validated at 8 bar gross Indicated Mean Effective Pressure (gIMEP) in PPC mode. Inlet temperature and pressure have been varied to test the model capabilities. First the experiments were conducted to generate reference points with BH80 under PPC conditions. And then CFD simulations were conducted to give initial parameter set up, e.g. fuel distribution, zone dividing, for the multi-zone model.

CO2 regulations on heavy-duty transport are introduced in essentially all markets within the next decade, in most cases in several phases of increasing stringency. To cope with these mandates, developers of engines and related equipment are aiming to break new ground in the fields of combustion, fuel and hardware technologies. In this work, a novel diesel fuel injector, Delphi’s DFI7, is utilized to experimentally investigate and compare the performance of ramped injection rates versus traditional square fueling profiles. The aim is specifically to shift the efficiency and NOx tradeoff to a more favorable position. The design of experiments methodology is used in the tests, along with statistical techniques to analyze the data. Results show that ramped and square rates - after optimization of fueling parameters - produce comparable gross indicated efficiencies.

With the adoption of the Worldwide harmonized Light Vehicles Test Procedure (WLTP) and the Real Driving Emissions (RDE) regulations for testing and monitoring the vehicle pollutant emissions, as well as CO2 and fuel consumption, the gap between real world and type approval performances is expected to decrease to a large extent. With respect to CO2, however, WLTP is not expected to fully eliminate the reported 40% discrepancy between real world and type approval values. This is mainly attributed to the fact that laboratory tests take place under average controlled conditions that do not fully replicate the environmental and traffic conditions experienced over daily driving across Europe. In addition, any uncertainties of a pre-defined test protocol and the vehicle operation can be optimized to lower the CO2 emissions of the type approval test. Such issues can be minimized in principle with the adoption of a real-world test for fuel consumption.

The Reactivity Controlled Compression Ignition concept for dual-fuel engines has multiple challenges of which some can be overcome using Variable Valve Actuation approaches. For various fuel combinations, the engine research community has shown that running dual-fuel engines in RCCI mode, improves thermal efficiency and results in ultra-low engine-out nitrous oxides and soot. However, stable RCCI combustion is limited to a certain load range, depending on available hardware. At low loads, the combustion efficiency can drop significantly, whereas at high loads, the maximum in-cylinder pressure can easily exceed the engine design limit. In this paper, three VVA measures to increase load range, improve combustion efficiency, and perform thermal management are presented. Simulation results are used to demonstrate the potential of these VVA measures for a heavy-duty engine running on natural gas and diesel.

The interaction of fuel sprays and in-cylinder flow in direct-injection engines is expected to alter kinetic energy and integral length scales at least during some portions of the engine cycle. High-speed particle image velocimetry was implemented in an optical four-valve, pent-roof spark-ignition direct-injection single-cylinder engine to quantify this effect. Non-firing motored engine tests were performed at 1300 RPM with and without fuel injection. Two fuel injection timings were investigated: injection in early intake stroke represents quasi-homogenous engine condition; and injection in mid compression stroke mimics the stratified combustion strategy. Two-dimensional crank angle resolved velocity fields were measured to examine the kinetic energy and integral length scale through critical portions of the engine cycle. Reynolds decomposition was applied on the obtained engine flow fields to extract the fluctuations as an indicator for the turbulent flow.

To meet future emission targets, it becomes increasingly important to optimize the synergy between engine and aftertreatment system. By using an integrated control approach minimal fluid (fuel and DEF) consumption is targeted within the constraints of emission legislation during real-world operation. In such concept, the on-line availability of engine-out NOx emission is crucial. Here, the use of a Virtual NOx sensor can be of great added-value. Virtual sensing enables more direct and robust emission control allowing, for example, engine-out NOx determination during conditions in which the hardware sensor is not available, such as cold start conditions. Furthermore, with use of the virtual sensor, the engine control strategy can be directly based on NOx emission data, resulting in reduced response time and improved transient emission control. This paper presents the development and on-line implementation of a Virtual NOx sensor, using in-cylinder pressure as main input.

This article describes a NOx sensor based urea dosing control strategy for heavy-duty diesel aftertreatment systems using Selective Catalytic Reduction. The dosing control strategy comprises of a fast-response, model-based ammonia storage control system in combination with a long-timescale tailpipe-feedback module that adjusts the dosing quantity according to current aftertreatment conditions. This results in a control system that is robust to system disturbances such as biased NOx sensors and variations in AdBlue concentrations. The cross-sensitivity of the tailpipe NOx sensor to ammonia is handled by a novel, smart signal filter that can reliably identify the contributions of NOx and NH3 in the tailpipe sensor signal, without requiring an artificial perturbation of the dosing signal.

SCR on Filter (SCRoF) is an efficient and compact NOX and PM reduction technology already used in series production for light-duty applications. The technology is now finding its way into the medium duty and heavy duty market. One of the key challenges for successful application is the robustness to real world variations. The solution to this challenge can be found by using model-based control algorithms, utilizing state estimation by physics-based catalyst models. This paper focuses on the development, validation and real time implementation of a physics-based control oriented SCRoF model. An overview of the developed model will be presented, together with a brief description of the model parameter identification and validation process using engine test bench measurement data. The model parameters are identified following a streamlined approach, focusing on decoupling the effects of deNOx and soot phenomena.

Heavy-duty diesel engines are used in a wide range of applications. For varying operating environments, the engine and aftertreatment system must comply with the real-world emission legislation limits. Simultaneously, minimal fuel consumption and good drivability are crucial for economic competitiveness and usability. Meeting these requirements takes substantial development and calibration effort, and complying with regulations results in a trade-off between emissions and fuel consumption. TNO's Integrated Emission Management (IEM) strategy finds online, the cost-optimal point in this trade-off and is able to deal with variations in operating conditions, while complying with legislation limits. Based on the actual state of the engine and aftertreatment system, an optimal engine operating point is computed using a model-based optimal-control algorithm.

Heavy-duty diesel engines are used in different application areas, like long-haul, city distribution, dump truck and building and construction industry. For these wide variety of areas, the engine performance needs to comply with the real-world legislation limits and should simultaneously have a low fuel consumption and good drivability. Meeting these requirements takes substantial development and calibration effort, where an optimal fuel consumption for each application is not always met in practice. TNO's Integrated Emission Management (IEM) strategy, is able to deal with these variations in operating conditions, while meeting legislation limits and obtaining on-line cost optimization. Based on the actual state of the engine and aftertreatment, optimal air-path setpoints are computed, which balances EGR and SCR usage.

Premixed combustion concepts like PCCI and RCCI have attracted much attention, since these concepts offer possibilities to reduce engine out emissions to a low level, while still achieving good efficiency. Most RCCI studies use a combination of a high-cetane fuel like diesel, and gasoline as low-cetane fuel. Limited results have been published using natural gas as low-cetane fuel; especially full scale engine results. This study presents results from an experimental study of diesel-CNG RCCI operation on a 6 cylinder, 8 l heavy duty engine with cooled EGR. This standard Tier4f diesel engine was equipped with a gas injection system, which used single point injection and mixed the gaseous fuel with air upstream of the intake manifold. For this engine configuration, RCCI operating limits have been explored. In the 1200-1800 rpm range, RCCI operation with Euro-VI engine out NOx and soot emissions was achieved between 2 and 9 bar BMEP without EGR.

This paper presents a model-based control and calibration design method for online cost-based optimization of engine-aftertreatment operation under all operating conditions. The so-called Integrated Emission Management (IEM) strategy online minimizes the fuel and AbBlue consumption. Based on the actual state of engine and aftertreatment systems, optimal air management settings are determined for EGR-SCR balancing. Following a model-based approach, the strategy allows for a systematic control design and calibration procedure for engine and aftertreatment systems. The potential of this time efficient method is demonstrated by experiments for a heavy-duty Euro-VI engine. The Integrated Emission Management strategy is developed and calibrated offline over a cold and hot World Harmonized Transient Cycle (WHTC) for the set emission targets. The total IEM development and calibration process takes approximately 20 weeks from model identification to the acceptance tests.

This paper presents the characterization of the random noise in driveline output shaft torque measurements that is commonly induced by road disturbances. To investigate the interaction between the shaft torque and road side excitation, torque signals are measured using a magnetoelastic torque sensor, as well as a conventional strain gauge sensor, under various types of road surfaces and conditions such as unevenness. A generalized de-trending method for producing a stationary random signal is first conducted. Statistical methods, in particular the probability density function and transform technique, are utilized to provide an evident signature for identifying the road excitation effect on the vehicle output shaft torque. Analysis results show how the road surface can act as a disturbance input to the vehicle shaft torque.

A new cost-based control strategy is presented that optimizes engine-aftertreatment performance under all operating conditions. This Integrated Emission Management strategy minimizes fuel consumption within the set emission limits by on-line adjustment of air management based on the actual state of the exhaust gas aftertreatment system. Following a model-based approach, Integrated Emission Management offers a framework for future control strategy development. This approach alleviates calibration complexity, since it allows to make optimal trade-offs in an operational cost sense. The potential of the presented cost-optimal control strategy is demonstrated for a modern heavy-duty Euro VI engine. The studied diesel engine is equipped with cooled EGR, Variable Geometry Turbocharger, and a DPF-SCR aftertreatment system.

The next generation off-road vehicles will see additional exhaust gas aftertreatment systems, ranging from DOC-SCR only to full DOC-DPF-SCR-AMOX systems. This will increase system complexity and development effort significantly. Emission requirements and the high number of vehicle configurations within the off-road industry will require a new process for development and validation. The introduced model-based approach using physical models of aftertreatment can reduce development effort and cost, improve performance robustness and help to identify performance issues early in the development process. A method to investigate and optimize a large matrix of variations by simulation is introduced. This can lead to a significant reduction in the number of required calibrations and can assist in the development of design specifications for the aftertreatment system. A case study for SCR calibration successfully demonstrates the potential of model-based development.

A virtual diesel aftertreatment exhaust line is presented comprising DOC, DPF, SCR models and a unique Ammonia Oxidation catalyst model. All models are one dimensional models based on first principles. These models offer an attractive compromise between speed, accuracy and complexity for a variety of model applications: off-line simulation, control strategy development, Hardware in the Loop applications and model-based calibration. The implemented models are fast and suitable for real-time applications. Use of these virtual exhaust line models in a product development process has the potential of saving time and resources. The aftertreatment models are fitted based on specifically designed engine dynamometer experiments, which can be performed in a limited time frame. The effective test time required on a validated test setup is estimated on the order of 12 days in total. Specifically developed software tools facilitate the model fit process.

This paper presents a cylinder pressure-based control (CPBC) system for conventional diesel combustion with high EGR levels. Besides the commonly applied heat release estimation, the CPBC system is extended with a new virtual NOx and PM sensor. Using available cylinder pressure information, these emissions are estimated using a physically based combustion model. This opens the route to advanced On-Board Diagnostics and to optimized fuel consumption and emissions during all operating conditions. The potential of closed-loop CA50 and IMEP control is demonstrated on a multi-cylinder heavy-duty EGR engine. For uncalibrated injectors and fuel variations, the combustion control system makes the engine performance robust for the applied variations and reduces the need for a time-consuming calibration process. Cylinder balancing is shown to enable auto-calibration of fuel injectors and to enhance fuel flexibility.