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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.

The societies around the world remain far from meeting the agreed primary goal outlined under the 2015 Paris Agreement on climate change: reducing greenhouse gas (GHG) emissions to keep global average temperature rise to well below 20°C by 2100 and making every effort to stay underneath of a 1.5°C elevation. Current emissions are rebounding from a brief decline during the economic downturn related to the Covid-19 pandemic. To get back on track to support the realization of the goal of the Paris Agreement, research suggests that GHG emissions should be roughly halved by 2030 on a trajectory to reach net zero by around mid-century.2 Although these are averaged global targets, every sector and country or market can and must contribute, especially higher-income and more developed countries bear the greater capacity to act. In 2020 direct tailpipe emissions from transport represented around 8 GtC02e, or nearly 15% of total emissions.

Electric vehicles offer cleaner transportation with lower emissions, thus their increased popularity. Although, electric powertrains contribute to quieter vehicles, the shift from internal combustion engines to electric powertrains presents new Noise, Vibration, and Harshness challenges. Unlike traditional engines, electric powertrains produce distinctive tonal noise, notably from motor whistles and gear whine. These tonal components have frequency content, sometimes above 10 kHz. Furthermore, the housing of the powertrain is the interface between the excitation from the driveline via the bearings and the radiated noise (NVH). Acoustic features of the radiated noise can be predicted by utilising the transmitted forces from the bearings. Due to tonal components at higher frequencies and dense modal content, full flexible multibody dynamics simulations are computationally expensive.

The European Commission is going to publish the new Euro7 standard shortly, with the target of reducing the impact on pollutant emissions due to transportation systems. Besides forcing internal combustion engines to operate cleaner in a wider range of operating conditions, the incoming regulation will point out the role of On-Board Monitoring (OBM) as a key enabler to ensure limited emissions over the whole vehicle lifetime, necessarily taking into account the natural aging of involved systems and possible electronic/mechanical faults and malfunctions. In this scenario, this work aims to study the potential of data-driven approaches in detecting emission-relevant engine faults, supporting standard On-Board Diagnostics (OBD) in pinpointing faulty components, which is part of the main challenges introduced by Euro7 OBM requirements.

The advent of digitalization opens up new avenues for advances in large internal combustion engine technology. Key engine components are becoming "intelligent" through advanced instrumentation and data analytics. By generating value-added data, they provide deeper insight into processes related to the components. An intelligent common rail diesel fuel injection valve for large engine applications in combination with machine learning allows reliable prediction of key combustion parameters such as maximum cylinder pressure, combustion phasing and indicated mean effective pressure. However, fault-related changes to the injection valve also have to be considered. Based on experiments on a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 liter, this study investigates the extent to which the intelligent injection valve can improve the reliability of combustion parameter predictions in the presence of injection valve faults.

The parameterization of the electrochemical pseudo-two-dimensional (P2D) model plays an important role as it determines the acceptance and application range of subsequent simulation studies. Electrochemical impedance spectroscopy (EIS) is commonly applied to characterize batteries and to obtain the exchange current density and the solid diffusion coefficient of a given electrode material. EIS measurements performed with frequencies ranging from 1 MHz down to 10 mHz typically do not cover clearly isolated solid state diffusion processes of lithium ions in positive or negative electrode materials. To extend the frequency range down to 10 μHz, the distribution function of relaxation times (DRT) is a promising analysis method. It can be applied to time-domain measurements where the battery is excited by a current pulse and relaxed for a certain period.

Upcoming, increasingly stringent greenhouse gas (GHG) as well as emission limits demand for powertrain electrification throughout all vehicle applications. Increasing complexity of electrified powertrain architectures require an overall system approach combining modular component technology with integration and industrialization requirements when heading for further significant efficiency optimization. At the same time focus on reduced development time, product cost and minimized additional investment demand reuse of current production, machining, and assembly facilities as far as possible. Up to date additive manufacturing (AM) is an established prototype component, as well as tooling technology in the powertrain development process, accelerating procurement time and cost, as well as allowing to validate a significantly increased number of variants. The production applications of optimized, dedicated AM-based component design however are still limited.

Validation of powertrain systems is nowadays performed with specific durability relevant load cycles, which represent the lifetime requirement of individual powertrain components. The definition of such durability relevant load cycles, which are used for vehicle testing should ideally be based on the actual vehicle's usage. Recording driving cycles within a vehicle is one of the most typical ways of collecting vehicle usage and relevant end customer behavior, but the generation of such measured vehicle data can be time consuming. In addition, this method of capturing on-road measurements has limitations in the variation of vehicle loadings (e.g., number of passengers, luggage, trailer usage etc.). Especially for new applications, entering new target markets, these kinds of in-vehicle measurements are not possible in early development stages, as the required vehicle or powertrain configuration is not available in hardware or incapable of measurements.

This paper presents calculations of external car aerodynamics by using the Partial-Averaged Navier-Stokes (PANS) variable resolution model in conjunction with the Finite Volume (FV) immersed-boundary method. The work presented here is the continuation of the study reported in Basara et al. [1, 2]. In that work, it was shown that the same accuracy of predicted aerodynamic forces could be achieved for both types of computational meshes, the standard body-fitted mesh and the immersed boundary (IB) Cartesian mesh, by using the Reynolds-Averaged Navier-Stokes (RANS) k-ζ-f model as well as by using the Partially-Averaged Navier-Stokes (PANS) method. Based on the accuracy achieved, Basara et al. [2] concluded that further work could focus on evaluating the turbulence modelling on the immersed boundary meshes only.

India is the market with the highest sales of agricultural tractors and the market with the highest number of agricultural tractor park, as well. Even though taking into account the lower average power of Indian agricultural tractors compared to regions with considerably larger field sizes, their cumulated diesel fuel consumption reaches a significant size. The possible use of alternative powertrains like battery-electric, especially considering the lower power of the Indian tractor market, seems feasible, but might be struggling with challenges in terms of charging infrastructure and the possibly resulting lower productivity due to required charging times. Therefore AVL proposes to investigate the use of alternative fuels for internal combustion engines, a topic which is also being discussed by other global tractor OEMs. In that context the focus is typically on higher tractor powers due to current storage limitations of battery-electric systems and other alternatives.

With the increasing focus on reducing CO2 emissions to combat global warming and climate change, the automotive industry is exploring near zero-emission alternative fuels to replace traditional fossil-based fuels like diesel, gasoline, and CNG. Methanol is a promising alternative fuel that is being evaluated in India due to its easy transportation and storage, as well as its production scalability and availability potential. This study focuses on the retro-fitment solution of M100 (pure methanol) SI port-fuel injection (PFI) mode of combustion. A heavy duty single-cylinder engine test setup was used to assess methanol SI combustion characteristic. Lean operation strategy has been investigated. At lean mixture conditions a significant drop in NOX and CO emissions was achieved. The fuel injection techniques and the impact of exhaust gas recirculation (EGR) on the conventional stoichiometric combustion process is highlighted.

India striving for carbon neutrality influences futures powertrain architecture of commercial vehicles. The use of CO2-free drives as battery electric have been demonstrated for various applications. The productivity still is a challenge due to missing high power charging infrastructure or limited range. This draws the attention to the use of sustainable fuels due to lower refueling times. The hydrogen engine got highest attention in the last couple of years. For markets as the EU the driver for hydrogen is the CO2 emission reduction, whereas for markets as India hydrogen offers the additional opportunity for more independence from fossil imports. Different OEMs all over the world have converted diesel engines to hydrogen operation with strong focus on performance and emission demonstration, so far with limited technology readiness of different key components.

Many Indian cities are amongst the most polluted cities in the world. Transport sector is identified as one of the major contributors to air pollution. Following the global trend, Government of India is also promoting near zero emission fuels with zero CO2 emissions as a way forward to solve the emission problems. With its policies like Green Hydrogen Mission, government of India plans to accelerate the adoption of Hydrogen as a fuel in the country. These initiatives have created a breakthrough in development of Hydrogen ICEs by the Indian OEM’s. Hydrogen ICE have only NOx emissions as the most prominent engine out emissions. NOx emission in Hydrogen engines is very sensitive to operating lambda, where in, after a certain threshold lambda the emissions rise significantly. Therefore, the air management system plays a very important role in the hydrogen engine performance & NOx emissions. This study evaluates various air management system options for a heavy-duty Hydrogen engine.

Most of the vehicles with internal combustion engines worldwide use fossil fuels. The widely used fuels available on the market are gasoline, diesel, and CNG. These fuels are getting costlier every year while at the same time generating pollutants through exhaust gases. Hence in the market, electric vehicles are effectively providing pollution-free solutions in the passenger car and lightweight carrier vehicle segments. However, the off-road, heavy-duty, and stationary applications with high load factors, are in general less favorable for battery electric scenarios since frequent charging will be mandatory and time-consuming. Hence, for these applications, the replacement of an internal combustion engine is quite difficult. There are various renewable fuels like ammonia, methanol, and biodiesel under research tests and study. As these are renewable fuels, the cost of these fuels can be lowered during mass production.

Climate change and global warming are one of the major challenges faced by the world today. A significant number of Indian cities rank among the most polluted globally, with vehicular emissions being the primary contributor. To address this issue, the Government of India is actively advocating for the adoption of zero-emission vehicles such as electric vehicles through policies and initiatives like FAME II [1], PMP and the National Mission for Transformative Mobility and Storage. The acceptance of electric vehicles is growing in the Indian market seeing more than 200% increase in sales in the year 2022 compared to 2021 with a large share of 2-wheelers, 3-wheelers and compact cars getting electrified. Further adoption of electrification on a much larger scale currently faces the major challenge of high overall vehicle cost compared to conventional vehicles, with the major contribution coming from the HV battery which is the costliest system on the electric vehicles.

Non-exhaust emissions are clearly one of the focal points for the upcoming Euro 7 legislation. The new United Nations Global Technical Regulation (UN GTR) defining the framework for brake emission measurements is about to be officially published. The first amendment to this text is already on the way through the United Nations Economic Commission for Europe (UNECE) hierarchy for decision making. In real life, the final emission factor as the ultimate result of a test is influenced by inaccuracies of numerous parts of the measurement system as well as additional contributing factors like the performance of the particulate filter handling process, which might not be primarily related to equipment specifications.

The internal combustion engine contains several actuators to control engine performance and emissions. These are controlled within the engine ECU and follow a specific operating strategy to achieve objectives such as NOx reduction and fuel economy. However, these two goals are conflicting and a compromise is required. The operating state depends on system constraints such as engine speed, load, temperature levels, and aftertreatment system efficiency. This results in constantly changing target values to stay within the defined limits, especially the legal emission limits. The conventional approach is to use multiple operating modes. Each mode represents a specific compromise and is activated accordingly. Multiple modes are required to meet emissions regulations under all required conditions, which increases the calibration effort. This new control approach uses an artificial neural network to replace the conventional multiple mode approach.

Pre-ignition in a boosted spark-ignition engine can be triggered by several mechanisms, including oil-fuel droplets, deposits, overheated engine components and gas-phase autoignition of the fuel-air mixture. A high pre-ignition resistance of the fuel used mitigates the risk of engine damage, since pre-ignition can evolve into super-knock. This paper presents the pre-ignition propensities of 11 RON 89-100+ gasoline fuel blends in a single-cylinder research engine. Albeit the addition of two high-octane components (methanol and reformate) to a toluene primary reference fuel improved the pre-ignition resistance, one high-RON fuel experienced runaway pre-ignition at relatively low boost pressure levels. A comparison of RON 96 blends showed that the fuel composition can affect pre-ignition resistance at constant RON.

Hydrogen promises to provide some highly desired features for clean and efficient combustion, but harvesting efficiency and emission potentials as well as meeting engine durability requirements needs careful adaption of both, combustion system components and engine operation strategies. Key points for H2-ICE combustion are some specific and unique features of H2/air mixtures, among which – to name only a few – excellent dilutability, lean burn capability, low ignition energy and high molecular diffusivity and their consequences on ICE operation do play prominent roles. H2 admission via port or direct injection, compression ratio selection and injection timing provide a set of parameters to control combustion features.

Within gear pair development, the simulation of loaded transmission error, line of action and summarized mesh point are crucial information in design optimization as well as reliability, NVH and efficiency prediction. These properties and variables are difficult to evaluate and are usually only assessed through proxy-variables such as unloaded transmission error or contact pattern assessment. Alternatively, large design loops can be generated when prototypes are produced to directly assess the results of reliability, NVH and efficiency and simulation models updated to the results, but not directly calibrated. This work will showcase an advanced test facility with the unique capabilities to evaluate all gear contact types (including hypoid, beveloid, cylindrical and spiral) under loaded conditions while assessing position and force data that can be used to validate simulation models directly and enhance design development.