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NVH analysis based on numerical simulations before actual test vehicle is available becomes common process in the automotive industry. Furthermore, the latest work scope is extending even to conceptual study in the very early design stage, beyond traditional numerical simulations simply using 3-D CAD data. In case when reasonable information is provided at this very early vehicle development stage, a better decision on the design concept would be possible, and subsequent design process can be carried out in more efficient manner. The core of this trend is that it allows us to predict vehicle performance at the conceptual design stage without 3-D CAD data, and then, with this prediction, to suggest meaningful design directions for next stage. From this point of view, FRF-Based Substructuring (FBS) methodology has potential to be used as an appropriate tool for this purpose.

Today, many automakers are using LED lamp sources in exterior lamps to establish brand awareness and introduce specialized lamp designs. These eye-catching LED lamp source solutions require many control functions as the lamp functions are diversified and advanced, and accordingly the requirements for standardization and optimization of controllers are increasing. In particular, our LED rear combination lamps have a variety of LED loads according to the design of the lamp model, the installation position, and the diagnostic regulations, so that the design complexity and the number of specifications of the controller are increased [4]. In recent years, more and more aesthetic designs and new technologies are used by various automakers to optimize their controllers in cooperation with global partners to optimize costs [1].

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.

Shudder vibration of a hydraulic power steering system during parking maneuver was studied with numerical and experimental methods. To quantify vibration performance of the system and recognize important stimuli for drivers, a shudder metric was derived by correlation between objective measurements and subjective ratings. A CAE model for steering wheel vibration analysis was developed and compared with measured data. In order to describe steering input dependency of shudder, a new dynamic friction modeling method, in which the magnitude of effective damping is determined by average velocity, was proposed. The developed model was validated using the measured steering wheel acceleration and the pressure change at inlet of the steering gear box. It was shown that the developed model successfully describes major modes by comparing the calculated FRF of the hydraulic system with measured one from the hydraulic excitation test.

Suspension is a system which operates dynamically according to road condition unlike other system statically mounted to the body. Especially this is more remarkable in high performance vehicle because there are more high inputs from road to suspension than normal vehicle. For this reason, the tightening torque of suspension system of high performance vehicle is more important than other systems and normal vehicle. To support the clamping between parts against force from road when cornering, optimized tightening torque is required to maximize R&H performance. For this optimization, it should be conducted first to comprehend how much performance effects on vehicle by tightening torque. This paper presents relationship between tightening torque of suspension parts hardware and R&H performance.

The driver’s seat in heavy trucks is designed for an upright driving posture with narrow back and cushion angles; thus, the seatback offers very little support. This makes the sitting posture prone to shifting during long trips, leading to loss of comfort and increase in fatigue. Sitting posture stability allows initial posture to be maintained during long drives, and the lack of stability causes fatigue and body pain during the drive. This study confirmed that enhancement of sitting posture stability of the driver’s seat in heavy trucks requires appropriate support from the cushion. The study also analyzed the support characteristics of each part of the cushion, and presented development guidelines of new cushion. Although subjective assessments of sitting posture stability have been performed, this study presented a method for quantitative and efficient assessment of sitting posture stability using the PAM-COMFORT simulation tool and virtual testing.

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.

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.

Bad weather conditions such as torrential rain, heavy snow, and thick fog frequently occur worldwide. Vehicle accidents in such bad weather conditions account for a significant portion of all vehicle accidents, and the level of damage is relatively severe compared to other accidents that occur in clear weather. This paper analyzes the driver's driving stability in bad weather conditions, which has such a significant meaning, in various ways through experiments on the inconvenience experienced by the driver. In this study, three levels of bad weather conditions were implemented in a driving simulator environment to evaluate driver inconvenience for six activities. Through driving experiment, quantitative bio-signals and vehicle signals were analyzed in each weather condition. The SD survey was used to assess the driver's inconvenience level for activities performed while driving and analyze the ranking of inconvenience.

In this study it will show, big data analysis and user survey of driving records were conducted to investigate frequency of use and ease of operation of the regen paddle to control one-pedal driving system in electric vehicle. According to 3.8 million driving record big data analysis result, it was found that the driver manipulates 3.31 times on average during a single trip, mainly during the early stages of driving. According to user observation research result in 41.8% of participants did not used or used less than 5 time of regen paddle during one single trip. Also 336 participants, which occupy 83%, responded that the regen paddle manipulation for one-pedal driving was inconvenient. In conclusion, because of the use frequency of the regen paddle is low and the operation of regen paddle is inconvenient. It seems necessary to change the design of the regen paddle.

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.

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.

Professional bus drivers are highly exposed to physical fatigue and work-related injuries because driving task includes complicated actions that require a variety of ability and cause extreme concentration or strain. For this reason, there has always been some sense of concern regarding driver fatigue, especially for drivers of commercial vehicles. In this study, we have tried to analyze quantitative fatigue degree of urban bus drivers by measuring their physiological signals. The investigation is made up of the following approaches: a traditional questionnaire survey and video-ethnographic method with 4-way cameras. The close-circuit cameras are installed to observe the upper and lower body of real drivers when they are in driving or even resting. This approach can help to understand urban bus drivers' behaviors and fatigue-related issues. Based on the video-ethnographic investigation results above, we have got certain patterns of drivers.

The mass producible broad-band ANC system for road noise is developed with fully digital control system. For this configuration, installation packages are intensively considered by minimizing size of the controller, simplifying wiring system and implementing virtual microphone techniques. Virtual microphone technique enables error microphone to be installed in remote position of driver’s ear, and therefore, increases installation degree of freedom significantly. To enhance noise control performance with the minimum latency, filter design of FxLMS algorithm is optimized while additional audio compensation techniques are applied to maintain audio performance of amplifier. The present ANC system is equipped to HMC (Hyundai Motor Company) new release of hydrogen driven vehicle, which is introduced in the technology promotion event in Pyeongchang Olympic 2018.

In general, driving performance is developed to meet preference of average customers. But there is no single standardized guideline which can satisfy various driving tastes of all drivers whose gender, cultural background, and age are different. To resolve this issue, automotive companies have introduced drive mode buttons which drivers can manually select from Normal, Eco, and Sport driving modes. Although this multi-mode manual systems is more efficient than single-mode system, it is in a transient state where drivers need to go through troubles of frequently selecting their preferred drive mode in volatile driving situations It is also doubtful whether the three-categorized driving mode can meet complex needs of drivers.. In order to settle these matters, it is necessary to analyze individual driving style automatically and to provide customized driving performance service in real time.

Recently, automobile manufacturers are interested in the development of battery electric vehicle (BEV) having a longer mileage to satisfy customer needs. The BEV with high efficiency depends on the temperature of the electric components. Hence it is important to study the effect of the cooling system in electric vehicle in order to optimize efficiency and performance. In this study, we present a 1-D vehicle thermal management (VTM) simulation model. The individual vehicle subsystems were modeled including cooling, power electric (PE), mechanical, and control components. Each component was integrated into a single VTM model and it would be used to calculate energy transfer among electrical, thermal, and mechanical energy. As a result, this simulation model predicts a plenty of information including the state of each component such as temperature, energy consumption, and operating point about electric vehicle depending on driving cycles and environmental conditions.

Recently, increasing system complexity and various customer demands result in the need for highly efficient vehicle development processes. Once the brake torque is predicted accurately during the driving scenario in the earlier stage, it will be able to prevent the changing the vehicle or brake system design to satisfy the legal regulation and customer requirement. As brake torque performance target allocate brake pad friction coefficient level and characteristic, the accurate friction coefficient prediction should be preceded for accurate prediction for brake torque. Generally, the friction coefficient of the brake pad is known to vary nonlinearly depending on the physical properties of the disc and the pad, as well as the brake disc rotational speed, the disc temperature, and the hydraulic pressure. Furthermore, it varies depending on the driving scenario even when other conditions are the same. Therefore, it is necessary to apply new methods to solve these challenges.

In order to design the in-wheel motor (IWM) for Electric Vehicles (EV), it is necessary to analyze the desired (expected) duty cycle at a higher performance level in order that the IWM becomes commercially relevant. The duty cycle may be representative of different segments of the customer base. Or, the individual customer may wish to have a set of IWMs that uniquely meet his/her measured “demand” cycle for a balance of drivability and efficiency. Questions then arise: How to measure the demand cycle of an individual? What 2 or 3 standard duty cycles should be offered as customer choices for their vehicle? Should the IWM represent multiple speed domains to enhance efficiency and drivability? Can the vehicle be updated rapidly 2 to 3 years after purchase? Etc. In this paper, we lay the groundwork to answer these types of customer questions for an EV with four independent IWMs.

APAS refers to a low speed sound warning system of electric vehicles, which emits alerting sound only to target pedestrians by sound focusing techniques with array of speakers and object detective camera. In the present study, experimental and numerical investigations are conducted in designing speaker part and array of APAS with consideration of three main performance matrices; HEV/EV warning sound regulations in Europe and U.S., pedestrian awareness, and driver’s noise comfort. The present APAS speaker consists of back enclosure, wave guide and front grill. Each of these components plays an important role for characterizing frequency emphasis and sound directivity. The main impedance frequencies of the speaker are determined by considering warning sound regulations and also by analyzing acoustic frequency response at in/outside of a vehicle.

Drivers continually require steering performance improvement, particularly in the area of feedback from the road. In this study, we develop a new electrically-assisted steering logic by 1) analyzing existing steering systems to determine key factors, 2) modeling an ideal steering system from which to obtain a desirable driver torque, 3) developing a rack force observer to faithfully represent road information and 4) building a feedback compensator to track the tuned torque. In general, the estimator uses the driver torque, assist torque and other steering system signals. However, the friction of the steering system is difficult to estimate accurately. At high speed, where steering feeling is very important, greater friction results in increased error. In order to solve this problem, we design two estimators generated from a vehicle model and a steering system model. The observer that uses two estimators can reflect various operating conditions by using the strengths of each method.