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Modeling thermal systems in Battery Electric Vehicles (BEVs) is crucial for enhancing energy efficiency through predictive control strategies, thereby extending vehicle range. A major obstacle in this modeling is the often limited availability of detailed system information. This research introduces a methodology using neural networks for system identification, a powerful technique capable of approximating the physical behavior of thermal systems with minimal data requirements. By employing black-box models, this approach supports the creation of optimization-based operational strategies, such as Model Predictive Control (MPC) and Reinforcement Learning-based Control (RL). The system identification process is executed using MATLAB Simulink, with virtual training data produced by validated Simulink models to establish the method's feasibility. The neural networks utilized for system identification are implemented in MATLAB code.

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.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

Numerical analyses of the liquid fuel injection and subsequent fuel-air mixing for a high-tumble direct injection engine with an active pre-chamber ignition system at operation conditions of 2000 RPM are presented. The Navier-Stokes equations for compressible in-cylinder flow are solved numerically using a hierarchical Cartesian mesh based finite-volume method. To determine the fuel vapor before ignition large-eddy flow simulations are two-way coupled with the spray droplets in a Lagrangian Particle Tracking (LPT) formulation. The combined hierarchical Cartesian mesh ensures efficient usage of high performance computing systems through solution adaptive refinement and dynamic load balancing. Computational meshes with approximately 170 million cells and 1.0 million spray parcels are used for the simulations.

Vehicle OEM’s for MHD applications are facing significant challenges in meeting the stringent 2027 low-NOx and GHG emissions regulations. To meet such challenges, advanced engine and aftertreatment technologies along with powertrain electrification are being applied to achieve robust solutions. FEV has previously conducted model-based assessments to show the potential of 48V engine and aftertreatment technologies to simultaneously meet GHG and low NOx emission standards. This study focuses on evaluating the full potential of 48V electrification technology through addition of 48V P3 hybrid system to the previously developed 48V advanced engine and aftertreatment technology package. Previously, a model-based approach was utilized for selection and sizing of a 48V system-enabled engine and aftertreatment package for class 6-7 MHD application.

Fuel economy improvement of Class 8 long-haul trucks has been a constant topic of discussion in the commercial vehicle industry due to the significant potential it offers in reducing GHG emissions and operational costs. Among the different vehicle categories in on-road transportation, Class 8 long-haul trucks are a significant contributor to overall GHG emissions. Furthermore, with the upcoming 2027 GHG emission and low-NOx regulations, advanced powertrain technologies will be needed to meet these stringent standards. Connectivity-based powertrain optimization is one such technology that many fleets are adopting to achieve significant fuel savings at a relatively lower technology cost. With advancements in vehicle connectivity technologies for onboard computing and sensing, the full potential of connected vehicles in reducing fuel consumption can be realized through V2X (Vehicle-to-Everything) communication.

Prospective combustion engine applications require the highest possible energy conversion efficiencies for environmental and economic sustainability. For conventional Spark-Ignition (SI) engines, the quasi-hemispherical flame propagation combustion method can only be significantly optimized in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Pre-Chamber (PC) initiated jet ignition combustion systems significantly shift the flammability and flame stability limits towards higher dilution areas due to high levels of introduced turbulence and a significantly increased flame area in early combustion stages, leading to considerably increased combustion speeds and high efficiencies. By now, vehicle implementations of PC-initiated combustion systems remain niche applications, especially in combination with lean mixtures.

The increasingly stringent limits on pollutant emissions from internal combustion engine-powered vehicles require the optimization of advanced combustion systems by means of virtual development and simulation tools. Among the gaseous emissions from spark-ignition engines, the unburned hydrocarbon (HC) emissions are the most challenging species to simulate because of the complexity of the multiple physical and chemical mechanisms that contribute to their emission. These mechanisms are mainly three-dimensional (3D) resulting from multi-phase physics - e.g., fuel injection, oil-film layer, etc. - and are difficult to predict even in complex 3D computational fluid-dynamic (CFD) simulations. Phenomenological models describing the relationships between the physical-chemical phenomena are of great interest for the modeling and simplification of such complex mechanisms.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of electrified propulsion systems, including electrified and electric drive units (EDU), is expected to play a significant role in helping OEMs meet fleet CO2 reduction targets for 2025 and beyond. The change to vehicles propelled by electrified powertrains leads to a reduction in vehicle noise levels. Despite the overall noise levels being low, the NVH behavior of such vehicles can be objectionable due to the presence of tonal noise coming from electric machines and geartrain components. In order to ensure customer acceptance of electrically propelled vehicles, it is imperative that these NVH challenges are understood and solved. Specifically, this paper discusses the EDU NVH development process. This includes considerations for CAE/test-based development and validation processes to ensure optimal NVH development.

Stochastic Preignition (SPI) is an abnormal combustion event that occurs in a turbocharged engine and can lead to the loss in fuel economy and engine hardware damage, and in turn result in customer dissatisfaction. It is a significant limiting factor on the use and continued downsizing of turbocharged spark ignited direct injection (SIDI) gasoline engines. Understanding and mitigating all the factors that cause and influence the rate and severity of SPI occurrence are of critical importance to the engine’s continued use and fuel economy improvements for future designs. Previous studies have shown that the heavy molecular weight components of the fuel formulations are one factor that influences the rate of SPI from a turbocharged SIDI gasoline engine. All the previous studies have involved analyzing the fuel’s petroleum hydrocarbon chemistry, but not specifically the additives that are put in the fuel to protect and clean the internal components over the life of the engine.

In the field of gasoline powertrain calibration, the challenges are growing due to ever shorter time-to-market requirements and a simultaneous increase in powertrain complexity. In addition, the great variety of vehicle variants requires an increasing number of prototypes for calibration and validation tasks within the framework of the current Real Driving Emissions (RDE) regulations and the expected Post Euro 6 emission standards. Hardware-in-the-Loop (HiL) simulations have been introduced successfully to support the calibration tasks in parallel to the conventional vehicle development activities. The HiL approach enables a more reliable compliance with emission limits and improves the quality of calibrations, while reducing the number of prototype vehicles, test resources and thus overall development costs.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

The growing number of passenger car variants and derivatives in all global markets, their high degree of software differentiability caused by regionally different legislative regulations, as well as pronounced market-specific customer expectations require a continuous optimization of the entire vehicle development process. In addition, ever stricter emission standards lead to a considerable increase in powertrain hardware and control complexity. Also, efforts to achieve market and brand specific multistep adjustable drivability characteristics as unique selling proposition, rapidly extend the scope for calibration and testing tasks during the development of powertrain control units. The resulting extent of interdependencies between the drivability calibration and other development and calibration tasks requires frontloading of development tasks.

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.

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.

Gasoline particulate filters (GPF) recently entered the market, and are already regarded a state-of-the-art solution for gasoline exhaust aftertreatment systems to enable EU6d-TEMP fulfilment and beyond. Especially for coated GPF applications, the prognosis of the emission conversion performance over lifetime poses an ambitious challenge, which significantly influences future catalyst diagnosis calibrations. The paper presents key-findings for the different GPF application variants. In the first part, experimental GPF ash loading results are presented. Ash accumulates as thin wall layers and short plugs, but does not penetrate into the wall. However, it suppresses deep bed filtration of soot, initially decreasing the soot-loaded backpressure. For the emission calibration, the non-linear backpressure development complicates the soot load monitoring, eventually leading to compromises between high safety against soot overloading and a low number of active regenerations.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of electrified propulsion systems is expected to play an increasingly important role in helping OEM’s meet fleet CO2 reduction targets for 2025 and beyond. This will also include a strong growth in the global demand for electric drive units (EDUs). The change from conventional vehicles to vehicles propelled by EDUs leads to a reduction in overall vehicle exterior and interior noise levels, especially during low-speed vehicle operation. Despite the overall noise levels being low, the NVH behavior of such vehicles can be objectionable due to the presence of tonal noise coming from electric machines and geartrain components as well as relatively high shares of road/wind noise. In order to ensure customer acceptance of electrically propelled vehicles, it is imperative that these NVH challenges are understood and solved.

The occurrence of abnormal combustion events leading to high peak pressures and severe knock can be considered to be one of the main challenges for modern turbocharged, direct-injected gasoline engines. These abnormal combustion events have been referred to as Stochastic Pre-Ignition (SPI) or Low-Speed Pre-Ignition (LSPI). The events are characterized by an undesired, early start of combustion of the cylinder charge which occurs before or in parallel to the intended flame kernel development from the spark plug. Early SPI events can subsequently lead to violent auto-ignitions that are often referred to as Mega- or Super-Knock. These heavy knock events lead to strong pressure oscillations which can destroy production engines within a few occurrences. SPI occurs mainly at low engine speed and high engine load, thus limiting the engine operating area that is in particular important to achieve good drivability in downsized engines.