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The three major challenges in the power electronics in hybrid and electric vehicles are: System cost, power density and reliability. High temperature power device and packaging technologies increases the power density and reliability while reducing system cost. Advanced Silicon devices with synthesized high-temperature packaging technologies can achieve junction temperature as high as 200C (compared to the present limitation of 150C) eliminating the need for a low-temperature radiator and therefore these devices reduces the system cost. The silicon area needed for a power inverter with high junction temperature capability can be reduced by more than 50 - 75% thereby significantly reducing the packaging space and power device and package cost. Smaller packaging space is highly desired since multiple vehicle platforms can share the same design and therefore reducing the cost further due to economies of scale.

Axiomatic design is utilized to identify the design characteristics of an On-Line Electric Vehicle and to manage the design information. The On-Line Electric Vehicle, which is being developed at the Korea Advanced Institute of Science and Technology, is a different concept of an electric vehicle from conventional electric vehicles which use the electric power of a charged battery(s). It is operated by an electric power supplied by the contactless power transmission technique between the roadway side and the vehicle. In other words, the power is transmitted based on the principle of an electric transformer. The On-Line Electric Vehicle can overcome the limitations of conventional electric vehicles such as the weight of the battery and driving distance problems. Because designers have little experience and knowledge about the On-Line Electric Vehicle in the developmental stage, an appropriate design guide is needed. The axiomatic approach is employed for the design process.

As world-wide container volume increases and very large container ships emerge as a dominant player in the maritime cargo transport market, functional capabilities of container ports need to be greatly enhanced. To address this problem, KAIST is undertaking a project to design a novel container transport system, namely Mobile Harbor. Mobile Harbor refers to a system that can go out to a large container ship anchoring in the open sea, load and unload containers between the container ship and the Mobile Harbor, and transport them to their destinations. Designing Mobile Harbor presents a number of challenges as with many other large-scale engineering projects, especially at the beginning stage of the project.

A variety of methodologies to use embedded multicore controllers efficiently has been discussed in the last years. Several assumptions are usually made in the automotive domain, such as static assignment of tasks to the cores. This paper shows an approach for efficient task allocation depending on different system modes. An engine management system (EMS) is used as application example, and the performance improvement compared to static allocation is assessed. The paper is structured as follows: First the control algorithms for the EMS will be classified according to operating modes. The classified algorithms will be allocated to the cores, depending on the operating mode. We identify mode transition points, allowing a reliable switch without neglecting timing requirements. As a next step, it will be shown that a load distribution by mode-dependent task allocation would be better balanced than a static task allocation.

The newly released Distributed System Interface 3 (DSI3) Bus Standard specification defines three modulation levels form which 16 valid symbols are coded. This complex structure is best decoded with symbol pattern recognition. This paper proposes a simplification of the correlation score calculation that sharply reduces the required number of operations. Additionally, the paper describes how the pattern recognition is achieved using correlation scores and a decoding algorithm. The performance of this method is demonstrated by mean of simulations with different load models between the master and the sensors and varying noise injection on the channel. We prove than the pattern recognition can decode symbols without any error for up to 24dBm.

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.

This paper discusses the RDC method utilizing delta-sigma analog-to-digital converter hardware module (DSADC) integrated in the Infineon's microcontroller family. With its higher resolution capability when compared to the regularly used ADC with successive-approximation (SAR), DSADC seems to have more potential. On the other hand, DSADC's inherent properties, such as asynchronous sampling rate and group delay, which when not handled properly, would have negative effects to the rotor positioning system. The solution to overcome those side-effects involves utilization of other internal microcontroller's resources such as timers and capture units, as well as additional software processing run inside CPU. The rotor positioning system is first modeled and simulated in high-level simulation language environment (Matlab and Simulink) in order to predict the transient- and steady state behaviors. The group delay itself is obtained by simulating the model of DSADC module implementation.

In-vehicle communication faces increasing bandwidth demands, which can no longer be met by today's MOST150, FlexRay or CAN networks. In recent years, Fast Ethernet has gained a lot of momentum in the automotive world, because it promises to bridge the bandwidth gap. A first step in this direction is the introduction of Ethernet as an On Board Diagnostic (OBD) interface for production vehicles. The next potential use cases include the use of Ethernet in Driver Assistance Systems and in the infotainment domain. However, for many of these use cases, the Fast Ethernet solution is too slow to move the huge amount of data between the Domain Controllers, ADAS Systems, Safety Computer and Chassis Controller in an adequate way. The result is the urgent need for a network technology beyond the Fast Ethernet solution. The question is: which innovation will provide enough bandwidth for domain controllers, fast flashing routines, video data, MOST-replacement and internal ECU buses?

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.

Recently, vehicle networks have increased complexity due to the demand for autonomous driving or connected devices. This increasing complexity requires high bandwidth. As a result, vehicle manufacturers have begun using Ethernet-based communication for high-speed links. In order to deal with the heterogeneity of such networks where legacy automotive buses have to coexist with high-speed Ethernet links vehicle manufacturers introduced a vehicle gateway system. The system uses Ethernet as a backbone between domain controllers and CAN buses for communication between internal controllers. As a central point in the vehicle, the gateway is constantly exchanging vehicle data in a heterogeneous communication environment between the existing CAN and Ethernet networks. In an in-vehicle network context where the communications are strictly time-constrained, it is necessary to measure the delay for such routing task.

A main challenge when developing next generation architectures for automated driving ECUs is to guarantee reliable functionality. Today’s fail safe systems will not be able to handle electronic failures due to the missing “mechanical” fallback or the intervening driver. This means, fail operational based on redundancy is an essential part for improving the functional safety, especially in safety-related braking and steering systems. The 2-out-of-2 Diagnostic Fail Safe (2oo2DFS) system is a promising approach to realize redundancy with manageable costs. In this contribution, we evaluate the reliability of this concept for a symmetric and an asymmetric Electronic Power Steering (EPS) ECU. For this, we use a Markov chain model as a typical method for analyzing the reliability and Mean Time To Failure (MTTF) in majority redundancy approaches. As a basis, the failure rates of the used components and the microcontroller are considered.

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.

In this paper, we propose a vision based lateral control scheme for autonomous lane change system on highways. Three main techniques are proposed, to improve the lane keeping/lane change performance, and to reduce the ripple in the yaw rate on highways. First, we propose a model based lane prediction method to cope with the momentary failure of lane detection. Second, we innovate an approach to steering wheel angle control based on torque overlay for the EPS of the lateral control. Finally, the multi-rate lane-keeping control scheme is proposed to improve the lateral control performance and to reduce the ripple in the yaw rate. The performance of the proposed method was experimentally evaluated via test vehicle

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.

Intelligent driving assistant systems have been studied meticulously for autonomous driving. When the systems have the responsibility for driving itself, such as in an autonomous driving system, it should be aware of its’ surroundings including moving vehicles and must be able to evaluate collision risk for the ego vehicle's planned motion. However, when recognizing surrounding vehicles using a sensor, the measured information has uncertainty because of many reasons, such as noise and resolution. Many previous studies evaluated the collision risk based on the probabilistic theorem which the noise is modeled as a probability density function. However, the previous probabilistic solutions could not assess the collision risk and predict the motion of surrounding vehicles at the same time even though the motion is possible to be changed by the estimated collision risk.

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.

In addition to basic switching functionality, smart power switches mainly provide diagnostic and protection functions, e.g. for short circuits to the load, which makes it all the more surprising that short circuit protected smart switches have been used for years in automotive applications without there being a precise definition of a short circuit. This article describes what Infineon has done to fill this gap. It was first necessary to define the kind of short circuits likely to occur in automotive applications and to specify the use and operating points of the smart switches. The next logical step was the standardization of the test circuit and application conditions in the AEC (Automotive Electronics Council) to allow an industry-wide comparison of the test results.

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.

The recent adoption of the ISO26262 Functional Safety Standard has lead to the need for a greater degree of rigor in the technical, organizational and process aspects of electronic ECU engineering. One new facet of this standard also covers (in part 9.7) the analysis of dependent failures at manufacturing time, not only the microcontroller, but also for the plethora of connected system ASICs, input circuits, output drivers and communication devices in the PCB of the ECU. This paper will describe the CAN based end of line ECU self test system that was implemented at a major tier 1 supplier to address the issues of efficiently gaining a high degree of diagnostic coverage of single point faults and latent faults in highly integrated automotive ECUs.

More than ever, microcontroller performance in cars has a direct impact on the driving experience, on compliance with improved safety, ever-stricter emissions regulations, and on fuel economy. The simple microcontrollers formerly used in automobiles are now being replaced by powerful number-crunchers with incredible levels of peripheral integration. As a result, performance can no longer be measured in MIPS (Millions of Instructions Per Second). A microcontroller's effectiveness is based on coherent partitioning between analog and digital, hardware and software, tools and methodology. To make an informed choice among the available devices, the designer needs benchmarks that are specific to automotive applications, and which provide a realistic representation of how the device will perform in the automotive environment.