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In electronic vehicles (EVs) or hybrid electronic vehicles (HEVs), an inverter system has a direct-current-link capacitor (DC-link capacitor) which provides reactive power, attenuates ripple current, reduces the emission of electromagnetic interference, and suppresses voltage spikes. A film capacitor has been used as the DC-link capacitor in high level power system, but the film capacitor's performance has deteriorated over operating time. The decreasing performance of the film capacitor may cause a problem when supplying and delivering energy from the battery to the vehicle's power system. Therefore, the lifetime prediction of the film capacitor could be one of critical factors in the EVs and HEVs. For this reason, the lifetime and reliability of the film capacitor are key factors to show the stability of the vehicle inverter system. There are a lot of methods to predict the lifetime of the film capacitor.

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.

With fuel efficiency becoming an increasingly critical aspect of internal combustion engine (ICE) vehicles, the necessity for research on efficient generation of electric energy has been growing. An energy management (EM) system controls the generation of electric energy using an alternator. This paper presents a strategy for the EM using a control mode switch (CMS) of the alternator for the (ICE) vehicles. This EM recovers the vehicle’s residual kinetic energy to improve the fuel efficiency. The residual kinetic energy occurs when a driver manipulates a vehicle to decelerate. The residual energy is commonly wasted as heat energy of the brake. In such circumstances, the wasted energy can be converted to electric energy by operating an alternator. This conversion can reduce additional fuel consumption. For extended application of the energy conversion, the future duration time of the residual power is exploited. The duration time is derived from the vehicle’s future speed profile.

A Light Detection And Ranging (LiDAR) is now becoming an essential sensor for an autonomous vehicle. The LiDAR provides the surrounding environment information of the vehicle in the form of a point cloud. A decision-making system of the autonomous car is able to determine a safe and comfort maneuver by utilizing the detected LiDAR point cloud. The LiDAR points on the cloud are classified as dynamic or static class depending on the movement of the object being detected. If the movement class (dynamic or static) of detected points can be provided by LiDAR, the decision-making system is able to plan the appropriate motion of the autonomous vehicle according to the movement of the object. This paper proposes a real-time process to segment the motion states of LiDAR points. The basic principle of the classification algorithm is to classify the point-wise movement of a target point cloud through the other point clouds and sensor poses.

An efficient development environments such as Software-in-the-Loop Simulation (SILS) and Rapid Control Prototyping (RCP) have been widely used to reduce the development time and cost of real-time ECUs. However, conventional SILS does not consider temporal behaviors caused by computation time, task scheduling, network-induced delays, and so on. As a result, the control performance of ECU is likely to be degraded after implementation. To overcome this problem, SILS/RCP which considers the temporal behaviors was suggested in the previous research. In this study, we validated the proposed SILS/RCP environments which are used to design an Electronic Stability Control (ESC) system which is one of the hard real-time control systems. The proposed SILS/RCP environments make it possible to realize ECUs in the early design phase by considering temporal behaviors.

FlexRay is a deterministic and fault-tolerant in-vehicle network(IVN) protocol. It is expected to become a practical standard for automotive communication systems. According to the FlexRay protocol specifications, there are about 60 configurable parameters which should be determined in the design phases. The parameters increase the complexities of FlexRay-based control system development. In this study, we are suggesting a formal design process for FlexRay-based control systems, which is focused on network parameter optimization. We introduce design phases from functional system models to implementations. These phases present formal ways for task allocation, node assignment, network configuration, and implementations. In the network configuration phase, two FlexRay core parameters are selected to optimize network design. Optimal methods of the core parameters provide concise guide lines for optimal communication cycle length and optimal static slot length.

Controlled Auto-Ignition (CAI) combustion is a new combustion concept. Unlike the conventional internal combustion engine, CAI combustion takes place homogeneously throughout the fuel/air mixture with self ignition, and the mixture is burned without flame propagation. The start of combustion (SOC) is a critical factor in the combustion because SOC affects exhaust gas emissions, engine power, fuel economy and combustion characteristics. This paper presents a control oriented SOC detection method using a 10 bar of difference pressure, and proposes 50 percent normalized difference pressure for SOC detection parameter. Difference pressure is defined as the difference between the in-cylinder firing pressure and the in-cylinder motoring pressure. These methods were determined by CAI combustion experiments. Managing the difference pressure is a fast and precise method for SOC detection.

In CRDI diesel engines, the piezo injector is gradually replacing the solenoid injector due to the quick response of the actuator. Operating performance of the injectors in the CRDI diesel engine has an influence on engine emissions. Therefore, accurate injector control is one of the most important parts of the CRDI engine control. The objective of this paper is the development of a piezo injector driver for CRDI diesel engines. Electrical characteristics of the piezo injector were analyzed. A control strategy for charging and discharging the actuator are proposed. The developed injector driver is verified by experiments under various fuel pressures, injection durations and driving circuit voltages.

The successful monitoring of the combustion process depends on the accurate measurement of in-cylinder pressure. Piezoelectric transducers are normally used for in-cylinder pressure measurement. However, rapid changes in the temperature of the transducer housing and the quartz sensing element can change the transducer offset voltage. Therefore, piezoelectric transducers require referencing the output to the absolute pressure (pegging). This study reviews several pegging methods and proposes a modified method based on a variable polytropic coefficient. The feasibility of the proposed method was validated using both the simulated and the experimental pressure data from a common-rail direct injection (CRDI) diesel engine.

A fault detection method with parity equations is proposed in this paper. Due to low cost implementation, the velocity of a motor is not measurable in EPB systems. Therefore, residuals are not reliable with a low resolution encoder to estimate the motor velocity. In this paper, we propose a fault detection method with sensorless estimation using current ripples. The method estimates position and velocity of the motor by detecting periodical oscillations of the armature current caused by rotor slots. This method could estimate position and velocity of the motor with less computational effort than a state observer. Moreover, the method is less sensitive to motor parameters than model-based estimation methods. The effectiveness of this method is validated with experimental data. The simulation results show that various faults have their own residual patterns. Therefore, we could detect the fault by monitoring the residual signals.

This paper presents a model based sensor fault detection and isolation algorithm for the vertical acceleration sensors of the Continuous Damping Control (CDC) system, installed on the sprung mass. Since sensor faults of CDC system have a critical influence on the ride performance as well as the vehicle stability, the sensor fault detection algorithm must be implemented into the overall CDC algorithm. In this paper, each vertical acceleration sensor installed on the sprung mass (two in the front corners and one in the rear) separately estimates the vertical acceleration of the center of gravity of the sprung mass. Then, the sensor fault is detected by cross-checking all three vertical acceleration estimates independently obtained by the each vertical acceleration sensor.

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.

Electromechanical relays that are coupled with fuses have been used for controlling electrical loads in vehicles. In the past decade, semiconductor power switches have been developed for overcoming the physical limits of relays and fuses. Semiconductor power switches can not only replace relays and fuses but can also improve a system's reliability and efficiency. In this study, we introduce the Smart Automotive Switch (SAS), which is a smart high side power switch of Fairchild Korea semiconductor. Functional capabilities, such as power switching, protection and self-diagnosis of SASs are presented in case studies involving, for example, headlights, glow plugs, and fuel pump control systems. Through these experimental studies, the suitability of SASs is validated for designing improved automotive electronic control systems.

Lane keeping assistant system (LKAS) is expected to reduce the driver workload with assisting the driver during driving and is regarded as a promising active safety system. For the proposed LKAS which requires cooperative driving between driver and the assistance system, a Model Based Predictive Controller (MBPC) is proposed to minimize the effect of system overshoot caused by the time delay from the vision-based lane detection system. In order to validate the proposed LKAS controller, a HIL (Hardware In the Loop) simulator is built using steering mechanism, single camera, torque motor, sensors, etc. The performance of the proposed system is demonstrated in various roadways.

Electro-Mechanical Brake (EMB) systems can provide improved braking and stability functions such as ABS, EBD, TCS, ESC, BA, ACC, etc. For the implementation of the EMB systems, reliable and robust fault detection algorithm is required. In this study, a model-based fault detection algorithm is designed based on the analytical redundancy method in order to monitor possible faults in EMB systems. The performance of the proposed model-based fault detection algorithm is verified in simulations. The effectiveness of the proposed algorithm is demonstrated in various faulty cases.

A control method using an in-cylinder pressure sensor can directly and precisely control engine combustion, lowering harmful emissions and fuel consumption levels. However, this method cannot be applied to a conventional engine management system because of its inaccuracy and the high cost of the pressure sensor, as well as the high computational load. In this study, we propose a real-time IMEP estimation method for a common rail direct injection diesel engine using the difference pressure integral as a cylinder pressure variable. The proposed method requires less computational load, enabling the IMEP to be estimated in real-time. In addition, we validated the estimation algorithm through simulation and engine experiments. The IMEP was accurately estimated with a small root mean square error of below 0.2305 bar.

This paper presents a preliminary study of a sampling period decision for robust control of a distributed control system based on an in-vehicle network with three types of data (real-time synchronous data, real-time asynchronous data, and nonreal-time asynchronous data). The architecture of automotive systems is currently changing from a number of standalone electronic control units (ECUs) to a functionally integrated distributed system which is linked by a network. The control performance of the integrated networked control system can be changed by the characteristics of time delays among the application ECUs. A basic parameter for a scheduling method of the networked control systems, a maximum allowable delay bound is used, which guarantees stability of the networked control system, and it is derived from the characteristics of the given plant using presented theorems.

In this study, a Steer-by-Wire (SBW) controller was developed using the Hardware-In-the-Loop-Simulation (HILS) system. The mechanism of the HILS system consists of a hydraulic actuator for a lateral force on the front tires in a real vehicle. There are two motors in the SBW system controlled by one Electronic Control Unit (ECU). One motor in the steering wheel is to improve the driver's steering feel and the other motor in the steering linkage is to improve the vehicle maneuverability. The SBW controller's availability was verified through a number of simulations on the HILS system. The SBW fail-safe logic was tested through various simulations of the hazard environment on the HILS system. Consequently, the control logic of the SBW system was developed easily and safely in a laboratory.

In this work, experimental and numerical study was performed to investigate the macroscopic spray structure and the spray characteristics of common rail type high-pressure injector with multi-hole. The global spray structure and microscopic characteristics of high-pressure diesel injector were investigated at various injection and ambient pressures. Spray developing process and spray tip penetration were obtained by spray visualization system, and the quantitative spray characteristics such as local Sauter mean diameter were measured by using phase Doppler particle analyzer. The numerical study was conducted to analyze the atomization characteristics of common-rail type injector at the same conditions with the experiment. In order to improve the prediction accuracy, the numerical analysis was modified using the hybrid breakup model that is composed of primary breakup and secondary breakup.

Drivers’ physical and physiological states change with prolonged driving. Driving for extended periods of time can lead to an increased risk of low back pain and other musculoskeletal disorders, caused by the discomfort of the seats. Static and dynamic are the two main categories must be considered within the seating development. The posture and orientation of the occupant are the important factors on static comfort. Driving posture measurement is essential for the evaluation of a driver workspace and improved seat comfort design. This study evaluated the comfortable driving posture through physiological and ergonomics measurements of an automotive premium driver seat. The physiological evaluation includes electroencephalographic (EEG) for brain waves, Biopac’s AcqKnowledge program, and subjective measurements on 32 healthy individuals. JACK simulation was used for the ergonomics evaluation, i.e., the magnitude of the spinal loads about lumbar vertebrae was estimated.