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Cylinder pressure-based combustion control is widely introduced for passenger cars. Benefits include enhanced emission robustness to fuel quality variation, reduced fuel consumption due to more accurate (multi-pulse) fuel injection, and minimized after treatment size. In addition, it enables the introduction of advanced, high-efficient combustion concepts. The application in truck engines is foreseen, but challenges need to be overcome related to durability, increased system costs, and impact on the cylinder head. In this paper, a new single cylinder pressure sensor concept for heavy-duty Diesel engines is presented. Compared to previous studies, this work focuses on heavy-duty Diesel powertrains, which are characterized by a relatively flexible crank shaft in contrast to the existing passenger car applications.

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.

Complying to both the increasingly stringent pollutant emissions as well as (future) GHG emission legislation - with increased focus on in-use real-world emissions - puts a great challenge to the engine/aftertreatment control development process. Control system complexity, calibration and validation effort has increased dramatically over the past decade. A trend that is likely to continue considering the next steps in emission and GHG emission legislation. Control-oriented engine models are valuable tools for efficient development of engine monitoring and control systems. Furthermore, these (predictive) engine models are more and more used as part of control algorithms to ensure legislation compliant and optimized performance over the system lifetime. For these engine models, it is essential that simulation and prediction of system variables during non-nominal engine operation, such as non-standard ambient conditions, is well captured.

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.

The operation of a dual fuel combustion engine using combustion mode-switching offers the benefit of higher thermal efficiency compared to single-mode operation. For various fuel combinations, the engine research community has shown that running dual fuel engines in Reactivity Controlled Compression Ignition (RCCI) mode, is a feasible way to further improve thermal efficiency compared to Conventional Dual Fuel (CDF) operation of the same engine. In RCCI combustion, also ultra-low engine-out NOx and soot emissions have been reported. Depending on available hardware, however, stable RCCI combustion is limited to a certain load range and operating conditions. Therefore, mode-switching is a promising way to implement RCCI in practice on short term. In this paper, a model-based development approach for a dual fuel mode-switching controller is presented. Simulation results demonstrate the potential of this controller for a heavy-duty engine running on natural gas and diesel.

For natural gas (NG)-diesel RCCI, a multi-zonal, detailed chemistry modeling approach is presented. This dual fuel combustion process requires further understanding of the ignition and combustion processes to maximize thermal efficiency and minimize (partially) unburned fuel emissions. The introduction of two fuels with different physical and chemical properties makes the combustion process complicated and challenging to model. In this study, a multi-zone approach is applied to NG-diesel RCCI combustion in a heavy-duty engine. Auto-ignition chemistry is believed to be the key process in RCCI. Starting from a multi-zone model that can describe auto-ignition dominated processes, such as HCCI and PCCI, this model is adapted by including reaction mechanisms for natural gas and NOx and by improving the in-cylinder pressure prediction. The model is validated using NG-diesel RCCI measurements that are performed on a 6 cylinder heavy-duty engine.

A new generation of BMW child seat and occupant detection system SBE2 for a smart airbag system is described. The SBE2 system consists of two subsystems: OC (occupant classification) and FDS (field detection system). The OC system is a force-sensitive sensor array that measures a pressure profile. The FDS system detects child seat and occupant according to the change of electrical field generated by four capacitive plates. Combining the signals from both subsystems, the BMW SBE2 system can distinguish fully automatically between a child seat and a person.

The SI-engine has a disadvantage in fuel economy compared with a DI-Diesel engine. One of the major effects is the throttle-driven load control with its pumping losses. The main target is to reduce these losses in the thermodynamic process with a throttle-free load control. BMW has developed fully variable valve trains as a possible technical solution to realise a load control by regulating the valve lift and the closing time of the inlet valve. The essential variability can be achieved by fully variable mechanical valve trains or mechatronic systems both showing a robust running behavior in emissions and cyclic fluctuations. The camshaft driven mechanical system is based on the technology of the BMW Double-VANOS system. An additional variability makes it possible to shift the valve lift continuously in order to control the valve closing. The highest variability is given by a system with each valve being controlled seperatly.

While hybrid-electric powertrain features such as regenerative braking and electric driving can improve the fuel economy of a vehicle significantly, these features may also have a considerable impact on driving dynamics. That is why extra effort is necessary to ensure safety and comfort that customers usually expect from a conventional vehicle. The purpose of this paper is to initiate a discussion regarding different drivetrain concepts, necessary changes in chassis systems, and the impact on vehicle dynamics. To provide input to this essential discussion, braking and steering systems, as well as suspension design, are analyzed regarding their fit with hybrid systems. It is shown how an integration of hybrid technology and chassis systems benefits vehicle dynamics and why “by-wire” technology is a key enabler for safe and comfortable hybrid-electric vehicles.

Modern exhaust systems contain not only a piping network to transport hot gas from the engine to the atmosphere, but also functional components such as the catalytic converter and turbocharger. The turbocharger is common place in the automotive industry due to their capability to increase the specific power output of reciprocating engines. As the exhaust system is a main heat source for the under body of the vehicle and the turbocharger is located within the engine bay, it is imperative that accurate surface temperatures are achieved. A study by K. Haehndel [1] implemented a 1D fluid stream as a replacement to solving 3D fluid dynamics of the internal exhaust flow. To incorporate the 3D effects of internal fluid flow, augmented Nusselt correlations were used to produce heat transfer coefficients. It was found that the developed correlations for the exhaust system did not adequately represent the heat transfer of the turbocharger.

The thermal prediction of a vehicle under-body environment is of high importance in the design, optimization and management of vehicle power systems. Within the pre-development phase of a vehicle's production process, it is important to understand and determine regions of high thermally induced stress within critical under-body components. Therefore allowing engineers to modify the design or alter component material characteristics before the manufacture of hardware. As the exhaust system is one of the primary heat sources in a vehicle's under-body environment, it is vital to predict the thermal fluctuation of surface temperatures along corresponding exhaust components in order to achieve the correct thermal representation of the overall under-body heat transfer. This paper explores a new method for achieving higher accuracy exhaust surface temperature predictions.

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.

Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving. In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design.

Initiatives have been and will be taken to define very strict emission limitations for Heavy-Duty (HD) urban transport vehicles in Europe. This will probably led to a class of very clean, so-called, Enhanced Environmentally friendly Vehicles (EEV). This paper presents an overview of the current and the (expected) future emission legislation for this type of vehicle together with the different technology that can be used for the development of internal combustion engines for these vehicles. A number of engine concepts, based on the experience of developing these concepts, are described and compared both from a technological and economic standpoint. It can be concluded that EEV emission limits can be achieved with advanced diesel engine concepts as well as with gas engines. From the economical viewpoint it can be concluded that gas engines can compete with diesel engines.

Despite increasingly stringent crash requirements, the body structures of future mainstream production cars need to get lighter. Carbon fiber reinforced polymer (CFRP) composites with a density 1/5th of steel and very high specific energy absorption represent a material technology where substantial mass can be saved when compared to traditional steel applications. BMW have addressed the demanding challenges of producing several hundred composite Body-in-White (BIW) assemblies a day and are committed to significant adoption of composites in future vehicle platforms, as demonstrated in the upcoming i3 and i8 models. A next step to further integrate composites into passenger cars is for primary structural members, which also perform critical roles in passive safety by absorbing large amounts of energy during a crash event.

Delphi Automotive Systems and BMW have been jointly developing Solid Oxide Fuel Cell (SOFC) technology for application in the transportation industry primarily as an on-board Auxiliary Power Unit (APU). In the first application of this joint program, the APU will be used to power an electric air conditioning system without the need for operating the vehicle engine. The SOFC-based APU technology has the potential to provide a paradigm shift in the supply of electric power for passenger cars. Furthermore, supplementing the conventional fuel with reformate in the internal combustion engine, extremely low emissions and high system efficiencies are possible. This is consistent with the increasing power demands in automobiles in the new era of more comfort and safety along with environmental friendliness.

In a NOx adsorber programme the feasibility for applying this technology to heavy duty diesel engines was investigated. After modelling and simulations for realising best λ < 1 engine conditions a platform was build which was used to obtain good NOx adsorber regeneration settings in a number of steady state key points without violating pre-defined limits. With these results the NOx adsorber was evaluated and tested. Besides establishing NOx conversions and BSFC penalties the programme also looked at the adsorber capabilities of dealing with sulphur poisoning and how well the adsorber could be de-sulphurised. This programme showed clearly the stronger and weaker points in the NOx adsorber technology for heavy duty application. From NOx conversion - BSFC penalty trade off curves it became clear that at lower loads high conversions (> 90%) with small fuel penalties (< 2.5%) were possible. However at high load the conversions were reduced (< 70%) and the fuel penalties increased (> 6%).

Up to now, reduction of fuel consumption of vehicles equipped with CVT transmission has not been exploited to its full potential due to the reduced driveability when driving the optimum efficiency engine operating points. An ISG system with torque boost capabilities can be used to restore this driveability. This paper discusses the goals, the CAE simulation tool, the methodology used in the preparative study for evaluation and dimensioning of a CVT-ISG concept, as well as the simulation results. The conclusions, generated from numerous simulations, provide vital information for the component selection, and for the development of the powertrain management system.

A key criterion for launching autonomous vehicles on real roads is the knowledge of their capability to ensure traffic safety. In contrast to ADAS, deriving this measure of safety is difficult to achieve as the functional scope of an autonomous driving function exceeds by far the one of ADAS. As a consequence, real-world testing solely is not sufficient enough to cover the required test volume. This assessment problem imposes new requirements on a valid test concept for automated driving. A possible solution represents simulation by enabling it to generate reliable test kilometers. As a first step, we discuss in this paper the feasibility of simulation frameworks to re-simulate a real-world test in certain scenarios. We will demonstrate that even with ground truth information of the vehicle odometry and corresponding environment model an acceptable accordance of functional behavior is not guaranteed.

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.