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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.

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.

Two major trends can be identified for powertrain control in the next decade. The legislation will more and more focus on in-use emissions. Together with the global trend to reduce the CO₂ emissions, this will lead to an integral drive train approach. To develop and validate this integral drive train approach, the need for a new chapter in powertrain testing arises. The climatic-altitude chamber, suited for heavy vehicles, serves a wide variety of testing needs. Ambient temperature can be controlled between -45°C and +55°C and ambient pressure can be reduced up to a level found at an altitude to 4000 meters. The chamber's dynamometers enable transient testing of heavy-duty engines and vehicles and the chamber is equipped with a comprehensive array of emission measurement capabilities, working under extreme conditions.

In the past decade, SCR deNOx technology with urea injection has grown to maturity. European OEMs will apply SCR deNOx to meet future heavy-duty emissions legislation starting with EURO-4 (2005/2006). Numerous research programs in Europe and the US have shown a variety of system layouts and control strategies. The main differences are formed by: the engine-out NOx calibration the application of an NO to NO2 catalyst open-loop or closed-loop urea dosage control. This paper gives an overview of possible SCR system configurations that are required for different stages of future emission legislation. Engine-out NOx emission is strongly influenced by ambient conditions. Projections in this study show that a combination of cold climate and a wintergrade fuel is the most severe: it may lead to 30% lower engine-out NOx emission with respect to laboratory conditions.

The importance of automotive comfort is increasing, both socially and economically. Especially professional drivers often have comfort-related physical complaints, such as lower back pain. In addition, car manufacturers can use comfort to distinguish their cars from their competitors. However, the development and design of a new, more comfortable car seat is very time consuming and costly. The use of computer models of human and seat could facilitate this process. MADYMO human and seat models offer the possibility to predict comfort. This paper describes the application of the MADYMO multi-body 50th percentile human model for determination of human-seat interaction in vertical vibrations. The validation of the human model is based on volunteer tests with both a rigid seat and a standard car seat. The human model shows a good correlation with the volunteers.

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.

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.

The impact of comfort is becoming increasingly important. On one hand, manufacturers use comfort to distinguish their products from their competitors. On the other hand, more cars than ever are used professionally. The prolonged sitting in automotive conditions of professional drivers introduced new physical complaints, resulting in high social costs. However, the cause of these complaints is not well understood. The use of virtual testing tools can contribute to both speeding up and reducing the costs of the development process of new more comfortable cars and the research in the causes of the new complaints. Vibration loading has often been identified as a source of discomfort. In literature, several human models developed for prediction of human resonance behaviour in vibrations were described. In most of these human body models, the muscles are represented in a simplified way.

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.