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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.

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.

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.

Despite the widespread adoption of lithium-ion batteries in various applications such as energy storage, concerns related to thermal management have been persisting, primarily due to the heat generated during their operation and the associated adverse effects on its efficiency, safety, and lifetime. Hence, the thermal characterization of lithium-ion batteries is essential for optimizing the layout of the battery cells for a pack design and the corresponding thermal management system. This study focuses on an experimental investigation of heat generation of Li-ion batteries under different operating conditions, including charge-discharge rates, ambient temperatures, states of charge, and compressive pressure. The experiments were conducted using a custom-designed multifunctional calorimeter, enabling precise measurement of the heat generation rate of the battery and the entropy coefficient. The measured results have shown a good match with the calculated heat generation rate.

In recent years, the automotive industry has been making efforts to develop vehicles that satisfy customers’ emotions rather than malfunctions by improving the durability of vehicles. The durability and reliability of vehicles sold in the U.S. can be determined through the VDS (Vehicle Dependability Study) published by JD Power. The VDS is index which is the number of complaints per 100 units released by J.D. POWER in every year. It investigates customers who have used it for 3 years after purchasing a new car and consists of 177 specific problems grouped into 8 categories such as PT, ACEN, FCD, Exterior. The VDS-4 has been strengthened since the introduction of the new evaluation system VDS-5 in 2015. In order to improve the VDS index, it is important to gather various customer complaints such as internet data, warranty data, Enprecis data and clarify the problem and cause. Enprecis data is survey of customer complaints by on-line in terms of VDS.

Monitoring driver thermal stress is an integral step for developing an automated climate control function. In this experimental study, various physiological measures for driver’s thermal stress were tracked while intentionally by altering thermal conditions of the seat with a seat air conditioning system (ACS) in summer and a seat heating system (HS) in winter. It was aimed to determine reliable physiological measures for identifying the changes in thermal status induced by the two seat climate control systems. In the first experiment, twenty experienced drivers drove a comfortable sedan for 60 minutes on a real highway while varying the intensity of the seat ACS every 10 minutes to incur ‘hot’ – ‘cool’ – ‘hot’ – ‘cool’ thermal stress. In the second experiment, a new group of eighteen drivers drove the same highway for 30 minutes while increasing the intensity of seat HS to incur ‘cold’ to ‘warm’ thermal stress.

In the path following problem, a commercial vehicle has a delay of a hydraulic steering actuator and slow steering response accordingly. In addition, there are disturbances due to the harsh driving conditions of commercial vehicles. These disturbances may include uncertainties about actuator dynamic delay, modeling error and steering angle sensor offset. Designing a lateral controller with good performance that can overcome this problem is the key to successfully carrying out autonomous driving of commercial vehicles. Usually, it is difficult to consider disturbances with uncertainties in the geometric based control methods. Therefore, this paper proposed a lateral controller using feedback augmented disturbance observer for the commercial vehicle. First, a dynamics was modeled which can describe delay of the hydraulic actuator of the commercial vehicle. After that, a lateral controller was designed based on this dynamics model.

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.

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.

In recent years, the automobile industry has been making efforts to develop vehicles that satisfy customers' emotions rather than malfunctions. The Vehicle Dependability Study(VDS) has been strengthened emotion items since the introduction of the new evaluation system VDS3 from 2015. The ratio of emotion items increased from 11% to 25%. In order to clarify the problem and cause of emotion items, we analyzed verbatim which is the customers' complaint data provided by J.D power every year, but it was difficult to extract customers' intention because the number of verbatim is small and expressed in terms of customer’s term rather than engineer’s term. To solve the problem, we are additionally colleting big data such as internet, warranty, online survey. Since the amount of data is very large, we developed textmining techniques such as dictionary, topic, Support Vector Machine(SVM), n-gram to improve process.

This paper presents the research related to the self-driving system that has been actively carried out recently. Previous studies have been limited to ensure the path following performance in linear and steady state-alike handling region with small lateral acceleration. However, in the high speed driving, the vehicle cornering response is extended to nonlinear region where tire grips are saturated. This requires a technology to create the driving path for minimum time maneuvering while grasping the tire grip limits of the vehicle in real time. The entire controller consists of three stages-hierarchy: The target motion is determined in the supervisor phase, and the target force to follow the target behavior is calculated in the upper stage controller. Finally, the lower stage controller calculates the actuator phase control input corresponding to the target force.

This paper proposes a speed controller using a disturbance observer to regulate the speed of a commercial vehicles, and presents vehicle test results to evaluate the performance of the proposed controller. Most ADAS (Advanced Driver Assistance System) and automated driving systems need to reliably regulate the vehicle's speed under any circumstances. A conventional PID controller is commonly used to control the vehicle speed, but performance of it varies depending on changes in external conditions. Commercial vehicles are even more susceptible to these changes than passenger cars and more difficult to obtain an accurate plant model. Considering these features, a speed controller using a disturbance observer is designed for commercial vehicles. The proposed controller treats changes in external conditions as disturbances. The modeling uncertainty is also treated as a disturbance.

In general, when a problem occurs in a component of powertrains, various phenomena appear, and abnormal noise is one of them. The service mechanics diagnose the noise through analysis by using their ears and equipment. However, depending on their experiences, analysis time and diagnostic accuracy vary greatly. To shorten the analysis time and improve the diagnostic accuracy, we have developed a technology to diagnose powertrain parts that cause abnormal noises. To create the best deep learning model for our diagnosis, we tried to collect many abnormal noises from various parts. The collected noise data was measured under idle and various operating conditions from our vehicles and test cells. This noise data is abnormal noises generated from engines, transmissions, drive system and PE (Power Electric) parts of eco-friendly vehicles. From the collected data, we distinguished good and bad data through detailed analysis in time and frequency domain.

The windshield is an integral part of almost every modern passenger car. Combined with current developments in the automotive industry such as electrification and the integration of lightweight material systems, the reduction of interior noise caused by stochastic and transient wind excitation is deemed to be an increasing challenge for future NVH measures. Active control systems have proven to be a viable alternative compared to traditional passive NVH measures in different areas. However, for windshield actuation there are neither comparative studies nor actually established actuation concepts available to the automotive industry. This paper illustrates a comparative conceptual study on windshield actuation for the active control of wind noise in a passenger car. Making use of an experimental modal analysis of the windshield installed in a medium-sized vehicle, a reduced order numerical simulation model is derived.

Current developments in the automotive industry such as electrification and consistent lightweight construction increasingly enable the application of active control systems for the further reduction of noise in vehicles. As different stochastic noise sources such as rolling and wind noise as well as noise radiated by the ventilation system are becoming more noticeable and as passive measures for NVH optimization tend to be heavy and construction-space intensive, current research activities focus on active reduction of noise caused by the latter mentioned sources. This paper illustrates the development, implementation and experimental investigation of an active noise control system integrated into the ventilation duct system of a passenger car.

High-voltage battery system plays a critical role in eco-friendly vehicles due to its effect on the cost and the electric driving range of eco-friendly vehicles. In order to secure the customer pool and the competitiveness of eco-vehicle technology, vehicle electrification requires lowering the battery cost and satisfying the customer needs when driving the vehicles in the real roads, for example, maximizing powers for fun drive, increasing battery capacities for achieving appropriate trip distances, etc. Because these vehicle specifications have a critical effect on the high-voltage battery specification, the key technology of the vehicle electrification is the appropriate decision on the specification of the high-voltage battery system, such as battery capacity and power. These factors affect the size of battery system and vehicle under floor design and also the profitability of the eco-friendly vehicles.

This paper investigates the sensitivity of stiffness of front and rear suspension systems on the structure-borne road noise inside a vehicle cabin. A flexible multi-body dynamics based approach is used to simulate the structural dynamics of suspension systems including rubber bushings, suspension arms, a subframe and a twist beam. This approach can accurately predict the force transfer to the trimmed body at each suspension mounting point up to a frequency range of 0 to 300 Hz, which is validated against a force measurement test using a suspension test rig. Predicted forces at each mounting point are converted to road noise inside the cabin by multiplying it with experimentally obtained noise transfer functions. All of the suspension components are modeled as flexible bodies using Craig-Bampton component mode synthesis method.

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 this paper, a reconfigurable object-oriented simulator is proposed to analyze the performance of Bluetooth Voice Communication Package (VCP) for telecom purposes like hands-free vehicular communication. It consists of a graphical user interface (GUI) for research or validation engineers to investigate system specific performance. For example, a research engineer can utilize this GUI to analyze a system performance using different noise reduction filtering techniques in vehicular hands-free applications. Also, a validation engineer can utilize this GUI to evaluate vehicular Bluetooth audio quality for different vehicles at different driving conditions (e.g. speeds, fan levels, etc.). The proposed Bluetooth VCP model consists of modules like Audio Equalization (EQ), Acoustic Echo Canceller (AEC), and Noise Suppression (NS). This dynamic GUI platform provides the scope to add and analyze new proposed filtering techniques.

When customers use a tailgate (or trunk), some systems such as power tailgate and smart tailgate have been introduced and implemented for improving convenience. However, they still have some problems in some use cases. Some people have to search for the outside button to open the tailgate, or they should take out the key and push a button. In some cases, they should move their leg or wait a few seconds which makes some people feel that it is a long time. In addition, they have to push the small button which is located on the inner trim in order to close the tailgate. This paper proposes a new tailgate control technology and systems based on acoustic patterns in order to solve some inconvenience. An acoustic user interaction (AUI) is a technology which responds to human’s rubbing and tapping on a specific part analyzing the acoustic patterns. The AUI has been recently spotlighted in the automotive industry as well as home appliances, mobile devices, musical instruments, etc.