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A physics-based model for SCR (Selective Catalytic Reduction) was developed based on five independent SGB (Synthetic Gas Bench) tests. There are NH3 adsorption & desorption test, NO oxidation test, NH3 oxidation test, SCR reaction (NOx & NH3) test and SV (Space Velocity) test. To validate the accuracy of SCR model’s prediction, transient reactor tests were conducted at four different input conditions. A newly developed SCR model showed more than 90% prediction accuracy in transient test conditions in view of cumulative NOx. Validation of SCR model was conducted on 1.6L light duty diesel vehicle in the WLTC (Worldwide Harmonized Light vehicles Test Cycle). Based upon this SCR model, vehicle level SCR calibrations used for urea dosing control were made and validated in the emission test cycles like WLTC.

Electric Power Steering (EPS) needs to meet both functional and stability requirements, it plays significant role in controlling vehicle motion. In the meantime, customers emphasizes natural steering feel which can reflect vehicle motion and road surface information while isolate unwanted external disturbances. In general, conventional EPS control algorithms exert assist torque according to driver torque measured from torque sensor, while maintaining stability using stabilizing compensator. However, there exist significant trade-off between steering feel and stability, because the performances of assist torque control and stabilizing compensator are strongly coupled. In this paper a torque feedback control algorithm for EPS system is proposed in order to overcome the trade-off, and to achieve more natural, robust steering feel.

This paper describes a reference steering feel tracking algorithm for Electric-Power-Steering (EPS) system. Development of the EPS system with intended steering feel has been time-consuming procedure, because the feedforward map-based method has been applied to the conventional EPS system. However, in this study, a three-dimensional reference steering feel surface, which is determined from current vehicle states, is proposed. In order to track the proposed reference steering feel surface, sliding mode approach is applied to second-order steering dynamics model considering a coulomb friction model. An adaptive technique is utilized for robustness against uncertainties. In order to validate the proposed EPS control algorithm, hardware-in-the-loop simulation (HILS) has been conducted with respect to a typical steering test. It is shown that the reference steering feel is realized well by the proposed EPS control algorithm.

Road friction estimation algorithms had been studied for many years because it is very important factor for safety control and fuel efficiency of vehicle. But traditional solutions are hard to adapt in automotive industry because their performance is not sufficient enough and expensive to implement. Therefore, this paper proposes a road friction estimation algorithm based on a trained artificial neural-network which is low cost and robust. The suggested method doesn’t need expensive additional sensors such as optical or lidar sensor, also it shows better performance in real car environment compared to other algorithms based on vehicle dynamics. In this paper, we would describe this algorithm in detail and analyze the test results evaluated in real road conditions.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

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 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.

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.

Reductions in powertrain noise have led to an increased proportion of road noise, prompting various studies aimed at mitigating it. Road excitation primarily traverses through the vehicle suspension system, necessitating careful optimization of the characteristics of bushings at connection points. However, optimizing at the vehicle assembly stage is both time-consuming and costly. Therefore, it is essential to proceed with optimization at the subsystem level using appropriate objective functions. In this study, the blocked force and energy-based index derived from complex power were used to optimize the NVH performance. Calculating the complex power in each bushing enables computing the power flow, thereby providing a basis for evaluating the NVH performance. Through stiffness injection, the frequency response functions (FRF) of the system can be predicted according to arbitrary changes in the bushing stiffness.

In this study, a co-operative regenerative braking control algorithm was developed for an electric vehicle (EV) equipped with an electronic wedge brake (EWB) for its front wheels and an electronic mechanical brake (EMB) for its rear wheels. The co-operative regenerative braking control algorithm was designed considering the road friction characteristic to increase the recuperation energy while avoiding wheel lock. A powertrain model of an EV composed of a motor, and batteries and a MATLAB model of the control algorithm were also developed. They were linked to the CarSim model of the vehicle under study to develop an EV simulator. The EMB and EWB were modeled with an actuator, screw, and wedge to develop an EMB and EWB simulator. A co-simulator for an EV equipped with an EWB for the front wheels and an EMB for the rear wheels was fabricated, composed of the EV and the EMB and EWB simulator.

This paper proposes a torque tracking algorithm via steer by wire to achieve the target steering feel and proposed a modified friction model to obtain return-ability. A three dimensional reference steering wheel torque map is designed using the measurement data of the steering characteristics of the target vehicle at a transition test and a weave test. In order to track the reference steering wheel torque, a sliding mode control is used in the tracking algorithm. In addition, to achieve return-ability, the modified friction model for steer by wire is used instead of the friction model defined in the reference steering wheel torque map. The modified friction model is composed of various models according to the angular velocity. The angular velocity and the angular acceleration used in the control algorithm are estimated using a kalman filter.

The fuel economy of a vehicle can be improved by recuperating the kinetic energy when the vehicle is decelerated. However, if there is no electrical traction component, the recuperated energy can be used only by the other electrical systems of the vehicle. Thus, the fuel economy improvement can be maximized by balancing the recuperated energy and the consumed energy. Also, suitable alternator and battery management is required to maximize the fuel economy. This paper describes a design optimization process of the alternator and battery system equipped with recuperation control algorithms for a mid-sized sedan based on the fuel economy and system cost. A vehicle model using AVL Cruise is developed for cycle simulations and validated with experimental data. The validated model is used for the parametric study and design optimization of the alternator and battery systems with single and dual energy storage.

In this study, a novel selective matching logic for a wheel/tire is proposed, to decrease the vehicle driving vibration caused by wheel/tire non-uniformity. The new logic was validated through matching simulation/in-line matching evaluation. A theoretical radial force variation model was established by considering the theoretical model of the existing references and the wheel/tire assembly mechanism. The model was validated with ZF’s high-speed uniformity equipment, which is standard in the tire industry. The validity of the new matching logic was verified through matching simulation and mass production in-line evaluation. In conclusion, the novel logic presented herein was demonstrated to effectively decrease the radial force variation caused by the wheel/tire.

The Rollover CAE model is developed for Rollover sensing algorithm in this paper. By using suggested CAE model, it is possible to make sensing data of rollover test matrix and these data can be used for calibration of rollover sensing algorithm. Developed vehicle model consists of three parts: a vehicle parts, an occupant parts and a ground boundary conditions. The vehicle parts include detailed suspension model and FE structure model. The occupant parts include ATD (anthropomorphic test device) male dummy and restraint systems: Curtain Airbag and Seat-Belt. We find analytical value of the suspension model through correlation with vehicle drop test, simulate this model under the conditions of untripped (Embankment, Corkscrew) and tripped (Curb-Trip, Soil-Trip) rollover scenarios. Comparison of the simulation and experimental data shows that the simulation results of suggested CAE model can be substituted for the experimental ones in calibration of rollover sensing algorithm.

The digital evaluation process of vehicle-seat dimensions is an efficient and cost-effective way to achieve better seating comfort and proper fit. The present study is intended to quantify the statistical relationships between seat dimensions (e.g., insert width and bolster height defined at SAE J2732) and subjective seat fit (e.g., too tight, right fit, or too wide). Subjective fit evaluations for 45 different vehicle seats and the corresponding vehicle seat dimensions at various cross-sectional planes were collected by seat engineers (experts). The best subset logistic regression analyses were applied to quantify the relationships between the collected expert evaluations and seat dimensions at each cross-sectional plane. As a result, significant seat dimensions on the seat fit were identified and their statistical relationships were quantified as regression coefficients.

Tire tread pattern noise is a major source of road noise generated by motor vehicles. Recently, noise control technology has been developing, and low-noise motor vehicles, such as electric vehicles and hybrid vehicles, have been commercialized. The importance of low-noise tires has increased since regulations R117 for tire noise and R51.03 for motor vehicle noise have been strengthened. To evaluate the tire noise in the development stage of motor vehicles, finished products of tires are required; hence, financial and time costs should be invested. Therefore, it is highly useful to predict tire noise levels in the early stages. Recently, a technology to predict the tire pattern noise using a supervised training method of artificial neural network (ANN) has been developed. The tire tread depth is estimated using the shading of the full image of the actual tire, and the leading edge of the contact patch is calculated using tire contact patch images.

The development of autonomous vehicle requires the state-of-the-art technologies in perception, planning, control, and system integration. This paper presents an overview of the system architecture and software architecture of autonomous vehicles for system integration. Network based system architecture in this paper provides a distributed computing system for autonomous driving. Further, a real-time path planning and a target speed generation are described based on the curvilinear coordinate system. The design of a path in the curvilinear coordinate system stretches the design space as like the Cartesian coordinate system to simplify the generation of the path. In determination of target speed, curvatures and risk of a generated path were utilized for safe autonomous driving.

Numerical simulations offer a wide range of benefits. Therefore, they are widely used in research and development. One of the biggest benefits is the possibility of automated parameter variation. This allows testing different scenarios very quickly. Nevertheless, physical experiments in the laboratory or on a test rig are still, and will remain, necessary. Physical experiments offer benefits, e.g., for very complex and/or nonlinear systems and are required for the validation of numerical models. To enhance the quality of experimental NVH investigations and to make use of the benefits of numerical simulation during experimental investigations at the same time, numerical models can be integrated into physical test rigs using the mechanical hardware-in-the-loop (mHIL) method (also referred to as real-time dynamic substructuring, hybrid testing or active control of impedance).

In the meantime, clarity and naturalness has been used as a standard for the TTS speech synthesis evaluation. However, the standard for speech synthesis evaluation should now be the human-voice and comprehension scale for the further application. This paper introduces subject on quality improvement of the composite sound explaining dB type and it’s gathering procedure, PS-RELP algorithm ending with explanation of various cases how the improved TTS can be applied in vehicle.

In order to perform the Optimal Route Planning avoiding traffic congestion, the structural elements (Rode type, Link type, Facilities type, Lane number, Turning type) in digital map and real-time traffic information are required. However, subjectively tuned cost weights of these elements, non theoretical relationship, and partially supported real-time traffic information that are mostly used for this implementation are not enough to satisfy. Therefore, in this research, by analyzing the relationship between the previously acquired traffic information history for some period of time and elements in digital map, we introduce the reasonable traffic information model that makes to estimate the speed information. Including the estimated speed, all the important factors of map database and the driver's preference, finally we made the cost model.