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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.

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.

Two-mode hybrid architectures with three planetary gear sets and four clutches will bring both flexibility and complexity to energy management of powertrains. In order to take full advantage of the increased degrees of freedom, highly delicate operation strategies are needed. We develop transmission efficiency models for power-split modes, and present a mode shift strategy assuming no battery power. When battery load leveling is additionally considered, the respective optimal operation set for each mode can be obtained and compared to yield a mode shift algorithm for general hybrid operation situations. The investigation of the strategies shows how frequently each mode is used, and verifies the effectiveness of fixed gear operations. We check the validity of the strategies by applying the algorithm to dynamic optimization and by predicting how it works during an actual driving simulation.

This paper describes a driving control algorithm based on skid steering for a Robotic Vehicle with Articulated Suspension (RVAS). The driving control algorithm consists of four parts; speed controller for tracking of the desired speeds, yaw rate controller which computes a yaw moment input to track desired yaw rates, longitudinal tire force distribution which determines an optimal desired longitudinal tire force and wheel torque controller which determines a wheel torque command at each wheel to keep slip ratio at each wheel below a limit value as well as track the desired tire force. Longitudinal and vertical tire force estimators are designed for optimal tire force distribution and wheel slip control. The dynamic model of RVAS for simulation study is validated using vehicle test data.

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.

As the number of user selectable electrical modules increases for passenger car, the number of cars with different combinations of option can easily exceed 100,000 cars. It results to a situation where we can not manually verify all the logical connection by making wiring combinations for each car. In this paper, we propose an algorithm that can reduce verification time for all possible wiring with available option combinations. The algorithm separates the whole wiring circuits into independent circuits and verifies the logical connections for each independent circuit with all possible options. The algorithm is time effective so the required time to verify the connections increases logarithmically as the number of possible car increases. The algorithm was implemented as software verification tool and its effectiveness was proved to be feasible.

This paper proposes a new screening attenuation evaluation method (PHSA) for hybrid power cables. Hybrid power cables connect battery, inverter and motor. As the noise and shield characteristics of these cables are different from general communication shield wires, new method for evaluating screening attenuation is needed. We considered the radiation direction, noise current path and various load terminations to evaluate the screening attenuation which is different from standard screening attenuation measurement. Feasibility and effectiveness of the proposed method were verified with real experiment results.

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.

This paper presents a Unified Chassis Control (UCC) strategy to improve vehicle stability and maneuverability by integrating Electronic Stability Control (ESC) and Active Front Steering (AFS). The UCC architecture consists of two parts: an estimator and a controller. The estimator is designed to estimate longitudinal and lateral tire forces and the controller is designed in two stages, namely, an upper level controller and a lower level controller. The upper level controller, provides the desired yaw moment for vehicle lateral stability by adopting a sliding control method. The lower level controller, provides the integration method of the AFS and ESC strategies for the desired yaw moment and is designed by optimal tire force coordination.

This paper presents a conditional misfire monitoring method to reduce the computing burden of the motoring. In this conditional monitoring method, the ECU performs misfire detection only when there is high probability of misfire events. The condition for performing the misfire detection is determined by the pre-index which is defined as the deviation of the segment durations of the crankshaft in this paper. The quantity of the code of calculating the pre-index is 7 times less than that of a conventional monitoring method so that the computing burden can be reduced with the conditional monitoring method. The experimental results shown that the pre-index and the conditional monitoring method are valid.

A series of experiments and computational analysis were carried out on the flow around a bluff body. Some non-streamlined ground vehicles, buildings and pipelines near to the ground could encounter very dangerous situations because of the unsteady wind loading caused by the periodic vortex shedding behind the bluff body. A two-dimensional bluff body model was used to simulate flow in the wake region. Spectral analysis of the velocity profiles in the underbody region was also used to examine the influence of the underbody flow in the wake region. By using a flow visualization technique, the critical gap height and the separation line on the ground were investigated for various gap heights and boundary layer thicknesses. Additionally, the 2-D Incompressible Navier-Stokes equation with an ε - SST (Strain Shear Stress Transport) turbulence model was used for comparison with experimental results.

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 describes design and evaluation of a driving mode decision and lane change control algorithm of automated vehicle in merge situations on highway intersection. For the development of a highly automated driving control algorithm in merge situation, driving mode change from lane keeping to lane change is necessary to merge appropriately. In a merge situation, the driving objective is slightly different to general driving situation. Unlike general situation, the lane change should be completed in a limited travel distance in a merge situation. Merge mode decision is determined based on surrounding vehicles states and remained distance of merge lane. In merge mode decision algorithm, merge availability and desired merge position are decided to change lane safely and quickly. Merge availability and desired merge position are based on the safety distance that considers relative velocity and relative position of subject and surrounding vehicles.

This paper proposes a reference steering wheel torque map and a torque tracking algorithm via steer-by-wire to achieve the targeted steering feel. The reference steering wheel torque map is designed using the measurement data of rack force and steering characteristic of a target performance of the vehicle at transition steering test. Since the target performance of the vehicle is only tested in nominal road condition, various road conditions such as disturbances and tire-road friction are not considered. Hence, the measurement data of the rack force that reflects the road conditions in the reference steering wheel torque map have been used. The rack force is the net force which consists of tire aligning moment, road friction force and normal force on the tire kingpin axis. A motor and a magnetorheological damper are used as actuators to generate the desired steering feel using the torque tracking algorithm.

This paper presents an integrated control of in-wheel motor (IWM) and electronic limited slip differential (eLSD) to enhance the vehicle lateral stability and maneuverability. The two actuators are utilized in the proposed controller to achieve separate purposes. The IWM controller is designed to modify the understeer gradient for enhanced handling characteristic and maneuverability. The eLSD controller is devised to improve the lateral stability to prevent oversteer in a severe maneuver. The proposed controller consists of a supervisor, upper-level controller and lower-level controller. The supervisor determines a target motion based on a target understeer gradient for IWM control and a yaw rate reference for eLSD control. The upper-level controller generates a desired yaw moment for the target motion. In the lower-level controller, the desired yaw moment is converted to the control inputs for IWMs in the two front wheels and eLSD at the rear axle.

A module based IPS (Intelligent Power Switch) evaluation system is proposed in this paper. As the IPS is gradually replacing the conventional relay and fuses, the stability and reliability of power system depends more on these IPS. The proposed IPS evaluation system outperforms the conventional manual evaluation in terms of speed and efficiency. This paper will introduce the structure of hardware and software of the IPS evaluation system. The system is placed between the module and cable connector to evaluate the module in an operating car without changing the cables. The control and signal processing is carried out by personal computer which is connected to the evaluation system by USB (Universal Serial Bus). The load resistance can be switch from actual load to arbitrary value using relay circuitry and DC electric load controlled by GPIB (General Purpose Interface Bus). CAN (Controller Area Network) circuits were added to control the IPS mounted inside the module.

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.

This paper describes the ground system model and algorithm for a ground level simulation tool. First, the modeling of an automotive ground system will be discussed and the algorithm for a simulation tool will be explained. We divided the model into a ground tree and a ground body. The ground tree model consists of resistance formed by the wires that connect the load to ground point with various structures and the ground body model consists of resistance between ground points in the car body. The wires with large current, such as engine ground cable, was modeled in detail by dividing the resistance into wire, bolt, and clamping resistance, in order to simulate the effect of increased contact resistance after durability test. The algorithm of the ground level simulation tool was designed to adjust the currents of the alternator, battery, and ground points in order to evaluate the various driving and load conditions.

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 paper presents a methodology of trajectory planning for the surrounding-aware lane change maneuver of autonomous vehicles based on a data-driven method. The lateral motion is planned by sampling candidate patterns which are defined based on quintic polynomial functions over time. Based on the cost evaluation among the sampled candidates, the optimal lateral motion pattern is selected as a reference and tracked by the controller. The longitudinal motion is planned and controlled using Model Predictive Control (MPC) which is an optimal control method designed considering the surrounding traffic information. To realize the lane change motion similar to the human driving behavior in the surrounding traffic situation, the human driving pattern is modeled in the form of motion parameters and considered in planning the lateral and longitudinal motion.