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This research aims to develop an inverse control method capable of adaptively simulating dynamic models of car subsystems in the rig-test condition. Accurate simulation of the actual vibration conditions is one of the most crucial factors in realizing reliable rig-test platforms. However, most typical rig tests are conducted under simple random or harmonic sweep conditions. Moreover, the conventional test methods are hard to directly adapt to the actual vibration conditions when switching the dynamic characteristics of the subsystem in the rig test. In the present work, we developed an inverse controller to adaptively control the vibration exciter referring to the target vibration signal. An adaptive LMS filter, employed for the control algorithm, updated the filter weights in real time by referring to the target and the measured acceleration signals.

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 high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

This paper presents the novel active vibration control (AVC) system that controls vehicle body vibration to reduce the structural borne road noise. As a result of vehicle noise testing in an electric vehicle, the predominant frequency of vehicle body vibration that worsens interior noise is in the range of 150-250Hz. Such vibration in that frequency range, commonly masked in engine vibrations, are hard to neglect for electric vehicles. The vibration source of that frequency is the resonance of tire cavity mode. Resonator or absorption material has been applied inside the tire for the control of cavity noise as a passive method. They require an increment of weight and cost. Therefore, a novel method is necessary. The vibration amplified by resonance of cavity mode is transferred to the vehicle body throughout the suspension system. To reduce the vibration, AVC system is applied to the suspension mount.

It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study.

After the COVID-19 pandemic, leisure activities and cultures have undergone significant transformations. Particularly, there has been an increased demand for outdoor camping. Consequently, the need for capabilities that allow vehicles to navigate not only paved roads but also unpaved and rugged terrains has arisen. In this study, we aim to address this demand by utilizing AI to introduce a 'Stuck Probability Estimation Algorithm' for vehicles on off-road. To estimate the 'Stuck Probability' of a vehicle, a mathematical model representing vehicle behavior is essential. The behavior of off-road driving vehicles can be characterized in two main aspects: firstly, the harshness of the terrain (how uneven and rugged it is), and secondly, the extent of wheel slip affecting the vehicle's traction.

The steering system is a critical component for controlling a vehicle's direction. In the context of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, where drivers may not always be actively holding the steering wheel, early detection of precursor noise signals is essential to prevent serious accidents resulting from the loss of steering system functionality. It is therefore imperative to develop a device capable of early detection and notification of steering system malfunctions. Therefore, the current study aimed to quantify the noise levels generated within the Column-based Electric Power Steering (C-EPS) system of a D-segment sedan. To this end, we measured the uniaxial acceleration in nine noise-generating areas while simultaneously collecting data from three Controller Area Network (CAN) sources that are directly related to steering operation.

In the early stages of vehicle development, it is critical to establish performance goals for the major systems. The fundamental modes of body and chassis frames are typically assessed using FE models that are discretized using shell elements. However, the use of the shell-based FE method is problematic in terms of fast analysis and quick decision-making, especially during the concept phase of a vehicle design because it takes much time and effort for detailed modeling. To overcome this weakness, a one-dimensional (1D) method based on beam elements has been extensively studied over several decades, but it was not successful because of low accuracy for thin-walled beam structures. This investigation proposes a 1D method based on thin-walled beam theory with comparable accuracy to shell models. Most body pillars and chassis frame members are composed of thin-walled beam structures because of the high stiffness-to-mass ratio of thin-walled cross sections.

Recently, regulations on automobile emission have been significantly strengthened to address climate change. The automobile industry is responding to these regulations by developing electric vehicles that use batteries and fuel-cells. Automobile emissions are environmentally harmful, especially in the case of vehicles equipped with high-temperature and high-pressure diesel engines using compression-ignition, the proportion of nitrogen oxides (NOx) emissions reaches as high as 85%. Additionally, air pollution caused by particulate matter (PM) is six to ten times higher compared to gasoline engines. Therefore, the electrification of commercial vehicles using diesel engines could potentially yield even greater environmental benefits. For commercial vehicles battery electric vehicles (BEVs) require a large number of batteries to secure a long driving range, which reduces their maximum payload capacity.

The existing FCEV have been developed with only a few vehicle models. With the diversification of both passenger and commercial FCEV lineups, as well as the increasing demand for vehicle trailer towing, there is a growing need for high-capacity fuel cell stacks to be applied in vehicles. However, at the current level, there are limitations and issues that arise, such as insufficient power output and reduced driving speed. As a results, the importance of thermal energy management has been increasing along with the increase in required power. Traditional cooling performance enhancement methods have mainly focused on developing increased hardware specifications, but even this approach has reached its limitation due to package, cost and weight problem. Therefore, it is essential to develop a new cooling system to solve the increases in heat dissipation.

In recent years, with the advent of the Fourth Industrial Revolution and the COVID-19 pandemic, people's lives worldwide have undergone significant changes. Additionally, the emergence of a new generation of consumers known as the millennial generation has led to a high demand for multipurpose family cars. The perspective is shifting towards choosing premium products that enhance the quality of life and pursue their own happiness and comfort through technology, rather than simply selecting a midsize SUV based on the increase in family size. We aim to meet the needs of these global customers by conducting research and developing various new features that were not previously available in midsize SUVs. In this study, we defined the actual target users for midsize SUVs and established UX concepts by analyzing their characteristics. Based on this, we employed an optimal design approach by analyzing the evaluation results by country for the various features implemented within the vehicle.

Even if an optimal design is produced in the mid-to-late stages of development, when the maturity of development is increasing, it is already difficult to accept the proposal between the organizations and functions. In case the optimal proposal is made with a small amount of information in the preceding stage, it will be helpful for mutual decision-making. In addition, if all members have a system and development environment that enables access and utilization of necessary data in a timely manner, it is possible to produce quick results through collaboration. To implement such a system and development environment, “digital modeling" of tangible and intangible assets will be essential and to implement an "integrated IT environment" that can access and utilize digital models. Until now, Hyundai Motor Company has not yet fully established a digital development environment that all researchers can simultaneously utilize during the concept development stage.

Engine off control is conducted on parallel hybrid vehicles in order to reduce fuel consumption. It is efficient in terms of fuel economy, however, noise and vibration is generated on engine cranking and transferred through engine mount on every mode transition from EV to HEV. Engine crank position control has been studied in this paper in order to reduce vibration generated when next cranking starts. System modeling of an architecture composed of an engine, P1 and P2 motors has been conducted. According to the prior studies, there exists correlation between crank vibration level and the crank angle. Thus a method to locate pistons on a specific crank angle which results in a local minimum of vibration magnitude could be considered. The P1 motor facilitates this crank position control when engine turns off, for its location directly mounted on a crankshaft allows the system model to obtain more precise crank position estimation and improved linearity in torque control as well.

An architecture virtual driving performance development process and strategy were established using the concept model. Driving performance concept models for each level and performance, that can be utilized in the architecture stage, were developed. Advanced concept models such as smart driver and comfort models were developed for reliable emergency handling and comfort performance prediction. System characteristic DB(DataBase) structure was designed and formed to utilize the concept model for major vehicle platforms and models. System characteristics can be configured by automatically extracting system characteristics from ADAMS model or SPMD(Suspension Parameters Measuring Device) DB. In addition, when the concept model is completed by updating the weight, specifications and tire characteristic of the new vehicle platform, handling and ride comfort performance can be analyzed.

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.

This research addresses the pressing need for reducing vehicle aerodynamic resistance, with a specific focus on mitigating wheel and tire resistance, which constitutes approximately 25% of the overall vehicle drag. While the prevailing method for reducing resistance in mass production development involves wheel opening reduction, it inadvertently increases wheel weight and has adverse effects on brake cooling performance. To overcome these challenges, novel complementary resistance reduction methods that can be employed in conjunction with an appropriate degree of wheel opening reduction are imperative. In this study, we introduce symmetrical wheels with a fan-like shape as a solution. The fan configuration influences the surrounding flow by either drawing it in or pushing it out, depending on the direction of rotation. Application of these fan-type wheels to a vehicle's wheels results in the redirection of flow inwards or outwards during high-speed driving due to wheel rotation.

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.

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.

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.

In recent years, the use of high-power inverters has become increasingly prevalent in vehicles applications. With the increasing number of electric vehicle models comes the need for efficient and reliable testing methods to ensure the proper functioning of these inverters. One such method is the use of Hardware-in-the-Loop (HiL) environments, where the inverter is connected to a simulated environment to test its performance under various operating conditions. HiL testing allows for faster and more cost-effective testing than traditional methods and provides a safe environment to evaluate the inverter's response to different scenarios. Further, in such an environment, it is possible to specifically stimulate those system states in which conflicts between the lines arise regarding the ideal system parametrization. By combining HiL testing with design-of-experiments and modelling methods, the propulsion system can hence be optimized in a holistic manner.