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Due to increasing amount of modules and customized options in commercial vehicles, it becomes more and more difficult to verify the circuit design. In this paper, a wire segment error locating algorithm is proposed to automate the exact wire segment error locating process. When a wrong connection is found by existing tool, guided by the exact description of wire segment error, this algorithm can locate exact wire segment error in the connection by searching for the one that has at least one neighboring segment from a correct connection.

The achievement of improved NVH performance with light weighted body and low cost is very important, but difficult job to be accomplished in vehicle development. One of the various methods for the accomplishment of this goal is the optimization of the stiffness attached to a vehicle body and chassis. It is known that sufficient stiffness at the body attachments improves the flexibility of bushing rate tuning. In this paper, the theoretical consideration and analysis tool to estimate local stiffness value quantitatively are introduced. Also, the local stiffness values at various attachment locations in trimmed body are measured. The operational forces at body attachments are estimated through the TPA (Transfer Path Analysis). The suitability of attachment stiffness is judged based on the required NVH target to attain the optimal attachment stiffness in vehicle body.

This paper introduces a systematic wiring guideline which includes coupling noise calculation, wire layout design, and wire type selection methodologies. The coupling theory between wires has been introduced long time ago but it was not successfully applied to real automotive wiring design due to the complexity in the theory such as large number of parameters and many different conditions in automotive wiring environment. In this paper, the complexity is reduced by separating physical parameters and electrical parameters and identifying controllable parameters and given parameters. This paper first introduces parameters which are used in the coupling equations and automotive wiring design, then the coupling noise calculation method which uses the coupling equations is introduced. The systematic automotive wiring guideline which prevents noise problem in various design stage such as system filter design, wire layout design, wire type selection is introduced.

In recent years, Large-Eddy Simulation (LES) is spotlighted as an engineering tool and severe research efforts are carried out on its applicability to Internal Combustion Engines (ICEs). However, there is a general lack of definitive conclusions on LES quality criteria for ICE. This paper focuses on the application of LES quality criteria to ICE and to their correlation, in order to draw a solid background on future LES quality assessments for ICE. In this paper, TCC-III single-cylinder optical engine from University of Michigan is investigated and the analysis is conducted under motored condition. LES quality is mainly affected by grid size and type, sub-grid scale (SGS) model, numeric schemes. In this study, the same grid size and type are used in order to focus on the effect on LES quality of SGS models and blending factors of numeric scheme only.

This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

This research aims presents the method classifying the noise source and evaluating the sound quality of the noise caused by operating of electric power steering wheel in an electric vehicle. The steering wheel has been operated by the motor drive by electric power and it called motor-driven electric power (MDPS) system. If the motor is attached to the steering column of the steering device, it is called C-MDPS system. The steering device of the C-MDPS system comprises of motor, bearings, steering column, steering wheel and worm shaft. Among these components the motor and bearings are main noise sources of C-MDPS system. When the steering wheel is operated in an electric vehicle, the operating noise of the steering device inside the vehicle is more annoying than that in a gasoline engine vehicle since the operating noise is not masked by engine noise. Defects in the C-MDPS system worsen the operating noise of the steering system.

Tire Pressure Monitoring System (TPMS) has become a popular system due to regulation in many countries. TPMS consists of sensors that measure air pressure and temperature in the tires. Each sensor transmits tire information to TPMS central unit for display purpose via RF. Commercial trailers having bodies longer than 7 m require RF repeaters to increase the data transmission range. Located near to rear wheels, RF repeater receives sensor signal in the rear wheels and transmits the signal to TPMS central unit. In this paper, we discuss RF repeater which transmits at multiple frequencies in order to increase signal reception. On TPMS central unit, RF receiver is able to tune to receive frequencies used in sensors and RF repeater. Other method for improving reception is to transmit same payload multiple times at same frequency as that of sensor. In the paper, other important specifications are discussed as RF repeater design is concerned. A user’s case of RF repeater is implemented.

The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.

Automakers have been pushing harder for the development of a safety-critical bus for the past years since there are many advantages of x-by-wire systems including steer-by-wire, brake-by-wire and throttle-by-wire. Such systems allow automakers to eliminate heavy hydraulic actuators and they offer the possibility of creating smarter and more efficient components that can be connected to a network bus. However, automakers and vendors know that a reliable, fault-tolerant bus is needed for such applications. Controller Area Network (CAN) buses, which are commonly used for powertrain and other automotive controls, are not considered reliable enough for drive-by-wire. The problem with CAN is that it is only event-based, so there is always a possibility that a message won't get through. For important applications, time-triggered architectures are needed because as time goes on, a slot for important messages is always assigned.

The paper presents a new offset compensation method of a yaw rate sensor and a lateral acceleration sensor. It is necessary to compensate the offsets of the analog sensors, such as the yaw rate sensor and the lateral acceleration sensor, to acquire accurate signals. This paper proposes two different offset compensation algorithms, the sequential compensation method and the model based compensation method. Both algorithms are combined with the algorithm map depending on the vehicle driving status. The proposed algorithm is verified by the computer simulations.

Shape optimization is one of methods to reduce the vehicle weight, however the method is not used widely because the definition method of shape basis vector is too difficult. In this study, shape optimization and newly shaped FE model generation with morphing technique is investigated. First the advantage and disadvantage have been compared between commercial software, Hyper Morph and DEP Morpher. Second, morphing technique is applied to several automotive components. The research results show that newly shaped FE model generation with morphing technique is successful and very efficient to reduce the time of product development.

As most of today's automatic transmissions adopt a electro-hydraulic control system, the role of electronically controlled solenoid valves occupies an important position. This paper presents a dynamic modelling technique of a proportional control solenoid valve(PCSV) for automatic transmissions in terms of the system identification theory, and analyzes the dynamic characteristics of the PCSV in frequency domain. Also we find that there are good matches between the nonlinear dynamic simulation results and the experimental data.

Validation of a vehicle simulation model of the Chevrolet Volt 2016 was conducted. The Chevrolet Volt 2016 is equipped with the new “Voltec” extended-range propulsion system introduced into the market in 2016. The second generation Volt powertrain system operates in five modes, including two electric vehicle modes and three extended-range modes. Model development and validation were conducted using the test data performed on the chassis dynamometer set in a thermal chamber of Argonne National Laboratory’s Advanced Powertrain Research Facility. First, the components of the vehicle, such as the engine, motor, battery, wheels, and chassis, were modeled, including thermal aspects based on the test data. For example, engine efficiency changes dependent on the coolant temperature, or chassis heating or air-conditioning operations according to the ambient and cabin temperature, were applied.

In contrast to highway, there are some sections not well maintained in urban roads. In these sections, there may be faint lane marks or static obstacles due to construction or some other reasons. Therefore, an automated vehicle following system such as traffic jam assistant should consider these sections to guarantee the safety of the system. In order to achieve this purpose, a model predictive control (MPC) scheme has been developed. The objectives of MPC are to compute the sequence of optimal steering input for vehicle following with obstacle avoidance. For this, the MPC uses the lead vehicle's state and obstacle's position obtained by lidars. For this purpose, a simplified nonlinear model of the vehicle was used to predict the future evolution of the system. Based on this prediction, performance index is optimized under operating constraints at each time step. A test vehicle equipped with two lidars on left and right corner of the front bumper has been developed.

Regulations regarding vehicles’ CO2 emissions are continuing to become stricter due to global warming. The CO2 regulations urge automobile manufacturers to develop gasoline engines with improved efficiency; however, the main obstacle to the improvement is the knock phenomenon in spark-ignition engines. If knock is predicted, the efficiency potential can be maximized in an engine by applying modest spark timing. Several research regarding knock prediction modeling have been conducted, and typically Livengood-Wu integral model is used to predict the knock occurrence. For the prediction, knock onset should be determined on a given pressure signal of given knock cycles for establishing the 0D ignition delay model. Several methodologies for knock onset determination have been developed because checking all the knock onset position by hand is impossible considering the breadth of data sets.

Large-eddy simulation (LES) applications for internal combustion engine (ICE) flows are constantly growing due to the increase of computing resources and the availability of suitable CFD codes, methods and practices. The LES superior capability for modeling spatial and temporal evolution of turbulent flow structures with reference to RANS makes it a promising tool for describing, and possibly motivating, ICE cycle-to-cycle variability (CCV) and cycle-resolved events such as knock and misfire. Despite the growing interest towards LES in the academic community, applications to ICE flows are still limited. One of the reasons for such discrepancy is the uncertainty in the estimation of the LES computational cost. This in turn is mainly dependent on grid density, the CFD domain extent, the time step size and the overall number of cycles to be run. Grid density is directly linked to the possibility of reducing modeling assumptions for sub-grid scales.

This paper presents motion planning and control algorithm for urban automated driving using high-definition(HD) map. Many automakers have developed and commercialized advanced driver assistance system(ADAS) based on vision-only lane extraction in motorway environments. Compared to the motorway environments where the lane is continuous and clearly visible, however, in urban roads, degradation of the lane quality such as lane occlusion and lane loss occurs frequently. This leads to the poor quality of the local guide path for the autonomous vehicles with vision-only lane extraction. Global HD map is used to provide the lane information continuously instead of vision-only lane extraction. With the existence of global position of host vehicle and the HD map, the proposed sequential algorithm performs the lane keeping and lane changing decision and control with safety margin in multi-vehicle situation.

Fatigue life of a V-belt pulley, which is commonly used in automotive powertrain to transfer power to other parts, is predicted based on damage analysis by finite element analysis (FEA). Load conditions on pulley are analyzed by considering interactions among the pulley, V belt, bracket and bolts. Both normal force and traction force on the contact surfaces between the pulley and V belt were calculated. Assembly load due to the tightening of the bolts as well as operation load was considered to describe the actual load conditions in durability test. Static analysis at initial position of the pulley after assembly was performed with given load conditions. As the pulley rotated every ten degrees, consecutive static analyses were followed to find out the stress history of the pulley during operation. Using stress history data calculated from FE analysis, damage over one rotation of pulley was calculated and fatigue life, number of rotation to failure, was estimated.

This paper presents an Interacting Multiple Model (IMM) based side slip angle estimation method to estimate side slip angle under various road conditions for vehicle stability control. Knowledge of the side slip angle is essential enhancing vehicle handling and stability. For the estimation of the side slip angles in previous researches, prior knowledge of tire parameters and road conditions have been employed, and sometimes additional sensors have been needed. These prior knowledge and additional sensors, however, necessitates many efforts and make an application of the estimation algorithm difficult. In this paper, side slip angle has been estimated using on-board vehicle sensors such as yaw rate and lateral acceleration sensors. The proposed estimation algorithm integrates the estimates from multiple Kalman filters based on the multiple models with different parameter set.

This paper describes a lateral disturbance estimator for an application to a Motor Driven Power Steering (MDPS)-based driving assistant system. A vehicle motion can be disturbed laterally by wind force or load from bank angle acting on the vehicle in the lateral direction. An MDPS-based driving assistant system can be used to reduce steering effort of a human driver in a driving situation with lateral disturbance. In designing the MDPS-based driving assistant system, the lateral wind disturbance should be estimated to determine an assistant torque. An estimator for the vehicle lateral disturbance estimation has been developed. The proposed estimator consists of two parts: a tire self-aligning torque estimator and the lateral disturbance estimator. The lateral disturbance estimator has been designed on the basis of a 2-DOF bicycle model with available sensor signals from the MDPS module. A numerical simulation has been conducted in order to evaluate the proposed estimator.