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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.

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.

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.

As data science technologies are being widely applied on various industries, the importance of data itself increased. A typical manufacturer company has a vast data set of products as 2D&3D drawing formats, but a common problem was that building a database from the 2D&3D drawings costs much, and it is hard to update the database after it once built. Also, it is high-cost job when the new factor researched and necessary to investigate the new factors on previously fixed or uploaded drawings. As new products are developed with time, these problems are getting more difficult. In this paper, an automated database building method using CATIA introduced and future probabilities are suggested. An aluminum wheel part was used as an example. An automated logic used CATIA V5’s VBA functions and was handled by python programming language.

A technology was developed to recognize and predict the urgent degradation of the state of the rotating equipment installed in Hyundai-Kia factories. It is also being applied to activities to prevent equipment failures by establishing a monitoring system using this technology. Vibration data and artificial intelligence (AI) algorithms were used to predict conditions. It was developed and installed so that maintenance engineers could predict failures in advance. This is to improve preventive diagnosis of thousands of rotating equipment in the factory. And it has the advantage of allowing a small number of engineers to monitor exponentially increasing number of equipment. Vibration data including trends and alarms were collected along with the production schedule, and wavelet-based preprocessing DB9 (Daubechies 9) was performed to remove noise such as outliers. Two different AI algorithm models were developed to recognize and predict changes in equipment state.

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.

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.

For making metal touch feeling and lighting simultaneously, selective electroplating is widely applied in button, panel and etc. in interior/exterior parts of automotive. In this paper, new selective electroplating with printing are suggested as an alternative manufacturing process of two shot molding, PC (Polycarbonate) and ABS (Acrylonitrile-Butadiene-Styrene). Manufacturing process of selective electroplating with printing is as follows: For preventing to plate metal layer in area of letter or symbol, masking ink is printed on parts, button, panel, etc., with electroplatable PC+ABS. After conventional electroplating process, the part has electroplated metal layer except for the printed area. It had been studied the composition of ink and PC+ABS for obtaining skip plating and light transmittance on printed area.

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.

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.

The friction properties related to squeal noise was analyzed with the development histories and simplified computational method. Firstly, the development histories were investigated especially focusing on the case which the friction materials were modified to improve squeal noise occurrence. Based on the histories, the friction properties of selected friction materials were newly measured using dynamometer. The average friction coefficient levels, torque oscillations, the increment of friction coefficient during full-stop, and etc. were compared with the squeal noise occurrence, and the results showed that increase of friction properties cause production of squeal noise. The result suggested that the size of friction energy was important factors related to triggering the squeal noise. Also, the contact conditions between rotor disc and friction materials were significant factors deciding the noise occurrence.

In the automotive sector, the structure borne noise generated by the engine and road-tire interactions is a major source of noise inside the passenger cavity. In order to increase the global acoustic comfort, predictive simulation models must be available in the design phase. The acoustic trims have a major impact on the noise level inside the car cavity. Although several publications for this kind of simulations can be found, an extensive correlation study with measurement is needed, in order to validate the modeling approaches. In this article, a detailed correlation study for a complete car is performed. The acoustic trim package of the measured car includes all acoustic trims, such as carpet, headliner, seats and firewall covers. The simulation methodology relies on the influence of the acoustic trim package on the car structure and acoustic cavities. The challenge lies in the definition of an efficient and accurate framework for acoustic trimmed bodies.

Automotive companies are trying to enhance the customer’s impression by improving engine sound quality. The target of this sound quality is to create a brand sound that is preferred by their customers as well as quietness of interior noise. Over the past decade there have been many studies in the field of automotive sound quality. These have included the technologies such as tuning of intake orifice and exhaust orifice, tuning of structure-borne, intake feedback devices, active exhaust valves, ANC (Active Noise Cancellation) and ASD (Active Sound Design). The three elements of the sound that affect the feeling of the customer are known as engine order arrangement, frequency balance, and linearity. Here, the most important thing in sound quality development is the order arrangement.

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.

With the recent development of technologies for interpreting vibration and noise of vehicles, it has become possible for carmakers to reduce idle vibration and driving noise in the phase of preceding development. Thus, the issue of noise generation is drawing keen attention from production of prototype car through mass-production development. J. D. Power has surveyed the levels of customer satisfaction with all vehicles sold in the U.S. market and released the Initial Quality Study (IQS) index. As a growing number of emotional quality-related items are added to the IQS evaluation index, it is necessary to secure a sufficiently high quality level of low-frequency speaker sound against rattle noise. It is required to make a preceding review on the package tray panel, which is located at the bottom of the rear glass where the woofer speakers of a passenger sedan are installed, the door module panel in which the door speakers are built.

A new heat-resistant cast iron alloy has been developed for the exhaust manifolds of new passenger-car diesel engines. This development occurred because operating demands on exhaust manifolds have increased significantly over the past decade. These demands are due to higher exhaust gas temperatures resulting from tighter emission requirements, improved fuel efficiencies, and designs for higher specific engine power. These factors have led to much higher elevated temperature strength and oxidation resistance requirements on exhaust manifold alloys. Additionally, thermal fatigue that occurs directly as a result of thermal expansions and mechanical constraint has become an increasingly important issue. The research detailed in this paper focused on the optimization of the chemical composition of a Si-Mo ductile iron to improve the mechanical and physical properties for use in an engine exhaust manifold.