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Coatings have the potential to improve bearing tribological performance. However, every coating application process and material combination may create different residual stresses and coating microstructures, and their effect on bearing fatigue and wear performance is unclear. The aim of this work is to investigate coating induced residual stress effects on bearing failure indicators using a microstructural contact mechanics (MSCM) finite element (FE) model. The MSCM FE model consists of a two-dimensional FE model of a coated bearing surface under sliding contact where individual grains are represented by FE domains. Interactions between FE domains are represented using contact element pairs. Unique to this layered rolling contact FE model is the use of polycrystalline material models to represent realistic bearing and coating microstructural behavior. The MSCM FE model was compared to a second non-microstructural contact mechanics (non-MSCM) model.

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.

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.

SCR on Filter (SCRoF) is an efficient and compact NOX and PM reduction technology already used in series production for light-duty applications. The technology is now finding its way into the medium duty and heavy duty market. One of the key challenges for successful application is the robustness to real world variations. The solution to this challenge can be found by using model-based control algorithms, utilizing state estimation by physics-based catalyst models. This paper focuses on the development, validation and real time implementation of a physics-based control oriented SCRoF model. An overview of the developed model will be presented, together with a brief description of the model parameter identification and validation process using engine test bench measurement data. The model parameters are identified following a streamlined approach, focusing on decoupling the effects of deNOx and soot phenomena.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

A challenge for the aerodynamic optimization of trucks is the limited availability of wind tunnels for testing full scale trucks. FAW wants to introduce a development process which is mainly based on CFD simulation in combination with some limited amount of wind tunnel testing. While maturity of CFD simulation for truck aerodynamics has been demonstrated in recent years, a complete validation is still required before committing to a particular process. A 70% scale model is built for testing in the Shanghai Automotive Wind Tunnel Center (SAWTC). Drag and surface pressures are measured for providing a good basis for comparison to the simulation results. The simulations are performed for the truck in the open road driving condition as well as in an initial digital model of the aerodynamic wind tunnel of SAWTC. A full size truck is also simulated in the open road driving condition to understand the scaling effect.

Research in any field of study requires analysis and comparisons or real-time predictions to extract useful information. To prove that the results have practical potential, various filtering techniques and methodologies should be designed and implemented. Filters being a class of signal processing helps innovate new technologies with various kinds of outcomes, using filters there are always various methods to solve a problem. Considering the current COVID-19 situation, researchers are working on sequencing the novel coronavirus and the genomes of people afflicted with COVID-19 using CPUs and GPUs along with various filtering techniques. In this paper we are using a method of filter implementation to collect raw heart rate data samples from fingertip and ear lobe and process those results on CPUs and GPUs. Our method of implementation to collect raw heart rate data is using a photoplethysmography method.

CO2 regulations on heavy-duty transport are introduced in essentially all markets within the next decade, in most cases in several phases of increasing stringency. To cope with these mandates, developers of engines and related equipment are aiming to break new ground in the fields of combustion, fuel and hardware technologies. In this work, a novel diesel fuel injector, Delphi’s DFI7, is utilized to experimentally investigate and compare the performance of ramped injection rates versus traditional square fueling profiles. The aim is specifically to shift the efficiency and NOx tradeoff to a more favorable position. The design of experiments methodology is used in the tests, along with statistical techniques to analyze the data. Results show that ramped and square rates - after optimization of fueling parameters - produce comparable gross indicated efficiencies.

This paper presents an optimal control co-design framework of a parallel electric-hydraulic hybrid powertrain specifically tailored for heavy-duty vehicles. A pure electric powertrain, comprising a rechargeable lithium-ion battery, a highly efficient electric motor, and a single or double-speed gearbox, has garnered significant attention in the automotive sector due to the increasing demand for clean and efficient mobility. However, the state-of-the-art has demonstrated limited capabilities and has struggled to meet the design requirements of heavy-duty vehicles with high power demands, such as a class 8 semi-trailer truck. This is especially evident in terms of a driving range on one battery charge, battery charging time, and load-carrying capacity. These challenges primarily stem from the low power density of lithium-ion batteries and the low energy conversion efficiency of electric motors at low speeds.