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The most challenging part of the engine combustion development is the reduction of pollutants (e.g. CO, THC, NOx, soot, etc.) and CO2 emissions. In order to achieve this goal, new combustion techniques are required, which enable a clean and efficient combustion. For compression ignition engines, combustion rate shaping, which manipulates the injected fuel mass to control the in-cylinder pressure trace and the combustion rate itself, turned out to be a promising opportunity. One possibility to enable this technology is the usage of specially developed rate shaping injectors, which can control the injection rate continuously. A feasible solution with series injectors is the usage of multiple injections to control the injection rate and, therefore, the combustion rate. For the control of the combustion profile, a detailed injector model is required for predicting the amount of injected fuel. Simplified 0D models can easily predict single injection rates with low deviation.

The complexity of automobile powertrains grows continuously. At the same time, development time and budget are limited. Shifting development tasks to earlier phases (frontloading) increases the efficiency by utilizing test benches instead of prototype vehicles (road-to-rig approach). Early system verification of powertrain components requires a closed-loop coupling to real-time simulation models, comparable to hardware-in-the-loop testing (HiL). The international research project Advanced Co-Simulation Open System Architecture (ACOSAR) has the goal to develop a non-proprietary communication architecture between real-time and non-real-time systems in order to speed up the commissioning process and to decrease the monetary effort for testing and validation. One major outcome will be a generic interface for coupling different simulation tools and real-time systems (e.g. HiL simulators or test benches).

In order to reduce development cost and time, frontloading is an established methodology for automotive development programs. With this approach, particular development tasks are shifted to earlier program phases. One prerequisite for this approach is the application of Hardware-in-the-Loop test setups. Hardware-in-the-Loop methodologies have already successfully been applied to conventional as well as electrified powertrains considering various driving scenarios. Regarding driving performance and energy demand, electrified powertrains are highly dependent on the dc-link voltage. However, there is a particular shortage of studies focusing on the verification of variable dc-link voltage controls by Hardware-in-the-Loop setups. This article is intended to be a first step towards closing this gap. Thereto, a Hardware-in-the-Loop setup of a battery electric vehicle is developed.

The use of state-of-the-art model-based calibration tools generate only limited benefits for seamless validation in powertrain calibration due to the often neglected system-level simulation of a closed-loop vehicle environment. This study presents a Hardware-in-the-Loop (HiL)-based virtual calibration approach to establish an accurate virtual calibration platform using physical plant models. It is based on a customisable real-time HiL simulation environment. The use of physical models to predict the behaviour of a complete powertrain makes the HiL test bench particularly suited for Engine Control Unit (ECU) calibration. With the virtual test rig approach, the calibration for the critical extended driving and ambient conditions of the new Real Driving Emissions (RDE) requirements can efficiently be optimised. This technique offers a clear advantage in terms of reducing calibration time and costs.

In order to thoroughly investigate and improve the path from biofuel production to combustion, the Cluster of Excellence “Tailor-Made Fuels from Biomass” was installed at RWTH Aachen University in 2007. Since then, a variety of fuel candidates have been investigated. In particular, 2-methyl tetrahydrofurane (2-MTHF) has shown excellent performance w.r.t. the particulate (PM) / NOx trade-off [1]. Unfortunately, the long ignition delay results in increased HC-, CO- and noise emissions. To overcome this problem, the addition of di-n-butylether (DNBE, CN ∼ 100) to 2-MTHF was analyzed. By blending these two in different volumetric shares, the effects of the different mixture formation and combustion characteristics, especially on the HC-, CO- and noise emissions, have been carefully analyzed. In addition, the overall emission performance has been compared to EN590 diesel.

Modern vehicles become increasingly software intensive. Software development therefore is critical to the success of the manufacturer to develop state of the art technology. Standards like ISO 26262 recommend requirement-based verification and test cases that are derived from requirements analysis. Agile development uses continuous integration tests which rely on test automation and evaluation. All these drove the development of a new model-based software verification environment. Various aspects had to be taken into account: the test case specification needs to be easily comprehensible and flexible in order to allow testing of different functional variants. The test environment should support different use cases like open-loop or closed-loop testing and has to provide corresponding evaluation methods for continuously changing as well as for discrete signals.

Premixed charged compression ignition (PCCI) is an advanced combustion strategy, which has the potential to achieve ultra-low nitrogen oxide and soot emissions at high thermal efficiencies. PCCI combustion is characterized by a complex nonlinear chemical-physical process, which indicates that a physical description involves significant development times and also high computation cost. This paper presents a method to use cylinder pressure data and engine operations parameters for prediction of PCCI engine emissions by unsupervised learning and nonlinear identification techniques. The proposed method first uses principal component analysis (PCA) to reduce the dimension of the cylinder-pressure data. Based on the PCA analysis, a multi-input multi-out model was developed for nitrogen oxide and soot emission prediction by multi-layer perceptron (MLP) neural network.

Hardware-in-the-Loop is a common and established testing method for automotive developments in order to study interactions between different vehicle components during early development phases. Hardware-in-the-Loop setups have successfully been utilized within several development programs for conventional and electrified powertrains already. However, there is a particular shortage of studies focusing on the development of inverter controls utilizing Hardware-in-the-Loop tests. This contribution shall provide a first step toward closing this gap. In this article, inverter controls with different pulse width modulations for varying modulation index are studied at a Hardware-in-the-Loop setup. Thereto, the inverter control for an interior permanent magnet synchronous machine is developed utilizing space vector pulse width modulation with overmodulation.

Advanced powertrains for modern vehicles require the optimization of conventional combustion engines in combination with tailored electrification and vehicle connectivity strategies. The resulting systems and their control devices feature many degrees of freedom with a large number of available adjustment parameters. This obviously presents major challenges to the development of the corresponding powertrain control logics. Hence, the identification of an optimal system calibration is a non-trivial task. To address this situation, physics-based control approaches are evolving and successively replacing conventional map-based control strategies in order to handle more complex powertrain topologies. Physics-based control approaches enable a significant reduction in calibration effort, and also improve the control robustness.

The efficient operation of powertrain test benches in research and development is strongly influenced by the state of “health” of the functional test object. Hence, the use of Early Damage Detection Systems (EDDS) with Unit Under Test (UUT) monitoring is becoming increasingly popular. An EDDS should primarily avoid total loss of the test object and ensure that damaged parts are not completely destroyed, and can still be inspected. Therefore, any abnormality from the standard test object behavior, such as an exceeding of predefined limits, must be recognized at an early testing time, and must lead to a shutdown of the test bench operation. With sensors mounted on the test object, it is possible to isolate the damage cause in the event of its detection. Advanced EDDS configurations also optimize the predefined limits by learning new shutdown values according to the test object behavior within a very short time.

To achieve global market and brand specific drivability characteristics as unique selling proposition for the increasing number of passenger car derivatives, an objectified evaluation approach for the drivability capabilities of the various cars is required. Thereto, it is necessary to evaluate the influence of different engine concepts in various complex and interlinked powertrain topologies during engine load change maneuvers based on physical criteria. Such an objectification approach enables frontloading of drivability related engineering tasks by the execution of drivability development and calibration work within vehicle subcomponent-specific closed-loop real-time co-simulation environments in early phases of a vehicle development program. So far, drivability functionalities could be developed and calibrated only towards the end of a vehicle development program, when test vehicles with a sufficient level of product maturity became available.

Electrification is a key enabler to reduce emissions levels and noise in commercial vehicles. With electrification, Batteries are being used in commercial hybrid vehicles like city buses and trucks for kinetic energy recovery, boosting and electric driving. A battery management system monitors and controls multiple components of a battery system like cells, relays, sensors, actuators and high voltage loads to optimize the performance of a battery system. This paper deals with the development of modular control architecture for battery management systems in commercial vehicles. The key technical challenges for software development in commercial vehicles are growing complexity, rising number of functional requirements, safety, variant diversity, software quality requirements and reduced development costs. Software architecture is critical to handle some of these challenges early in the development process.

Powertrain electrification is becoming increasingly common in the transportation sector to address the challenges of global warming and deteriorating air quality. This paper introduces a novel “Build Your Hybrid” approach to experience and test various hybrid powertrain concepts. This approach is applied to the light commercial vehicles (LCV) segment due to the attractive combination of a Diesel engine and a partly electrified powertrain. For this purpose, a demonstrator vehicle has been set up with a flexible P02 hybrid topology and a prototype Hybrid Control Unit (HCU). Based on user input, the HCU software modifies the control functions and simulation models to emulate different sub-topologies and levels of hybridization in the demonstrator vehicle. Three powertrain concepts are considered for LCVs: HV P2, 48V P2 and 48V P0 hybrid. Dedicated hybrid control strategies are developed to take full advantage of the synergies of the electrical system and reduce CO2 and NOx emissions.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.