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A road test on semi-trailers is carried out, and accelerations of some characteristic points on the braking system,axles,and truck body is measured,also brake pressure and noise around the support frame is acquired.The measured data was analyzed to determine the causes of the brake noise, and the mechanism of the noise of the drum brake of semi-trailers during low-speed braking was investigated. The following conclusions are obtained: (1) Brake noise of the drum brake of the semi-trailer at low-frequency is generated from vibrations of the brake shoes, axle, and body, and the vibration frequency is close to 2nd natural frequency of the axle. (2) Brake noise is generated from stick-slip motion between the brake shoes and the brake drum, where the relative motion between the brake drum and the brake shoes is changed alternately with sliding and sticking, resulting in sudden changes in acceleration and shock vibration.

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.

Validation plays a crucial role in any Electronic Development process. This is true in the development of any automotive Electronic Control Unit (ECU) that utilizes the Automotive V process. From Research and Development (R&D) to End of Line (EOL), every automotive module goes through a plethora of Hardware (HW) and Software (SW) testing. This testing is tedious, time consuming, and inefficient. The purpose of this paper is to show a way to streamline validation in any part of the automotive V process using Python as a driving force to automate and control Hardware-in-the-loop (HIL) / Model-in-the-loop (MIL) / Software-in-the-loop (SIL) validation. The paper will propose and outline a framework to control test equipment, such as power supplies and oscilloscopes, load boxes, and external HW. The framework includes the ability to control CAN communication signals and messages.

This paper introduces reduced-order modeling techniques with Artificial Intelligence (AI) for Model-Based Development (MBD). In vehicle development, detailed physical models are replaced by reduced-order models (ROM) to expedite simulations. With recent advancements in AI-based reduced-order modeling, it is expected that modeling work will become more efficient, leading to reduced simulation times. However, the range of simulations (Model-in-the-Loop Simulation - MILS, Hardware-in-the-Loop Simulation - HILS, bench-system) compatible with ROM is limited. To overcome this limitation, this study leverages the ONNX format (Open Neural Network Exchange), a universally supported format among machine learning frameworks, and the Functional Mock-up Interface (FMI), a standard interface format for simulation tools, to enable general-purpose embedded technology with ROM. This study employs a vehicle model in engine surge simulations to validate AI-based reduced-order modeling for MBD.

Reducing consumption of fossil fuels and resulting emissions remains a goal of the worlds’ population. Perhaps as an aid to encourage more fuel-efficient driving style, many modern motor vehicles are equipped with digital displays of average and instantaneous display of fuel efficiency in miles per gallon (mileage) or liters per 100 kilometers. The display may be interesting to drivers concerned with fuel efficiency, but may not yield desired or best results. What is missing is impact on time of travel; what is the fuel- and time-efficient way to accelerate, what steady speed to travel, and how to decelerate? The author has defined a new fuel efficiency measure described as Dynamic Fuel Cost, in units of currency such as dollars, per travel time in hours, and a microprocessor to compute and display same. Using this display, vehicle operators can choose a maximum steady highway speed, accelerate and decelerate in ways that are fuel-, time- and cost-effective and minimize emissions.

Hardware-in-the-loop (HIL) testing is part of automotive V-design which is commonly used in automotive industries for the development of Electronic Control Unit (ECU). HIL test platform provides the capacity to test the ECU in a controlled environment even with scenarios that would be too dangerous or impractical to test on real situation, also the ECU can be tested even before the actual plant under building. This paper presents a HIL test platform for the validation of a seat ECU. The HIL platform can also be used for control and diagnostics algorithm development. The HIL test platform consists of three parts: a real time target machine (dSPACE SCALEXIO AutoBox), an ECU (Magna Seating M12 Module), and a signal conditioning unit (Load Box). The ECU produces the control commands to the real-time target machine through load box. The real time target machine hosts the plant model of the power seat which includes the kinematics and dynamics of the seat movements.

Automotive hybrid vehicle controls development is an increasingly complex and challenging task. Therefore, to adequately verify and validate the control algorithms prior to its deployment onto real world testing platforms a robust, scalable, low-maintenance simulation platform is most necessary. The currently available test properties pose major challenges in setup, accessibility, maintenance, complexity, and reusability. The aim of this paper is to present a systematic approach of the initial setup, the adaptation to a vehicle program, and the maintenance of a purely MATLAB/Simulink based simulation platform that alleviates the concerns highlighted above. The platform follows the approach of a level 1 virtualization platform for production intent application software components - without the Run-Time Environment (RTE), Basic Software (BSW), and Microcode Abstraction (MCAL) layers.

Lithium-ion batteries are the ubiquitous energy storage device of choice in portable electronics and more recently, in electric vehicles. However, there are numerous lithium-ion battery chemistries and in particular, several cathode materials that have been commercialized over the last two decades. In recent time several automakers have followed trend by announcing their own plans to move their EV production to LFP, due to its high intrinsic safety, fast charging, and long cycle life and cobalt free batteries as well as avoiding other supply chain constrained metals like nickel. Accurate estimation of the state-of-charge (SOC) is crucial for efficient and safe battery applications.

Multiple experimental studies were performed on galling intiation for variety of tooling materials, coatings and surface treatments, sheet materials with various surface textures and lubrication. Majority of studies were performed for small number of samples in laboratory conditions. In this paper, the methodology of screening experiment using different combinations of tooling configurations and sheet material in the lab followed by the high volume small scale U-bend performed in the progressive die on the mechanical press is discussed. The experimental study was performed to understand the effect of the interface between the sheet metal and the die surface on sheet metal flow during stamping operations. Aluminum sheet AA5754 2.5mm thick was used in this experimentation. The sheet was tested in laboratory conditions by pulling between two flat insert with controllable clamping force and through the drawbead system with variable radii of the female bead.

Roller offsetting is an incremental forming technique used to generate offset stiffening or mating features in sheet metal parts. Compared to die forming, roller offsetting utilizes generic tooling to create versatile designs at a relatively lower forming speed, making it well-suited for low volume productions in automotive and other industries. However, more significant distortion can be generated from roller offset forming process resulting from springback after forming. In this work, we use particle swarm optimization to identify the tool path and resulting feature geometry that minimizes distortion. In our approach, time-dependent finite element simulations are adopted to predict the distortion of each candidate tool path using a quarter symmetry model of the part. A multi-objective fitness function is used to both minimize the distortion measure while constraining the minimal radius of curvature in the tool path.

A model-based torque control strategy which is simple and easily adaptable to various types of engines is developed in this paper. A torque model is derived from constant-volume combustion model, and applications of the model to engine torque control problem are also discussed. As examples, the torque model is calibrated with experimental data collected from two different engines, and simulation and experimental results from the torque control strategy are presented as well.

The paper presents a robust adaptive control technique for precise regulation of a port fuel injection + direct injection (PFI+DI) system, a dual fuel injection configuration adopted in modern gasoline engines to boost performance, fuel efficiency, and emission reduction. Addressing parametric uncertainties on the actuators, inherent in complex fuel injection systems, the proposed approach utilizes an indirect model reference adaptive control scheme. To accommodate the increased control complexity in PFI+DI and the presence of additional uncertainties, a nonlinear plant model is employed, incorporating dynamics of the exhaust burned gas fraction. The primary objective is to optimize engine performance while minimizing fuel consumption and emissions in the presence of uncertainties. Stability and tracking performance of the adaptive controller are evaluated to ensure safe and reliable system operation under various conditions.

When an SI engine is equipped with individual cylinder pressure transducers, combustion timing of each cylinder can be precisely controlled by adjusting spark timing in real-time. In this paper, a novel method based on principal component analysis (PCA) is introduced to control the combustion timing with a significantly less computational burden than a conventional method.

This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.

SLAM (Simultaneous Localization and Mapping) plays a key role in autonomous driving. Recently, 4D Radar has attracted widespread attention because it breaks through the limitations of 3D millimeter wave radar and can simultaneously detect the distance, velocity, horizontal azimuth and elevation azimuth of the target with high resolution. However, there are few studies on 4D Radar in SLAM. In this paper, RI-FGO, a 4D Radar-Inertial SLAM method based on Factor Graph Optimization, is proposed. The RANSAC (Random Sample Consensus) method is used to eliminate the dynamic obstacle points from a single scan, and the ego-motion velocity is estimated from the static point cloud. A 4D Radar velocity factor is constructed in GTSAM to receive the estimated velocity in a single scan as a measurement and directly integrated into the factor graph. The 4D Radar point clouds of consecutive frames are matched as the odometry factor.

One of the main challenges of autonomous driving is to integrate different modules, such as perception, planning, control, and communication, that work together to enable the vehicle to drive safely and efficiently. A key module of autonomous driving is the vehicle localization system, which estimates the vehicle's position in the environment, and provides guidance for the optimal route. The vehicle localization system is essential for ensuring the safety of autonomous driving. This paper proposes a vehicle localization method based on visual simultaneous localization and mapping (SLAM) using a monocular camera. The method captures images of the environment with a monocular camera and extracts ORB (Oriented FAST and rotated BRIEF) features from them. It then tracks the features across the images and constructs a sparse map of the scene. The map is used to estimate the vehicle's pose, which is the position and orientation of the vehicle, in local coordinates.

This paper investigates the utilization of Low Earth Orbit (LEO) satellites for vehicle localization and conducts a comparative analysis with traditional Global Navigation Satellite Systems (GNSS)-based methods. With the rise of LEO satellite constellations, such as Starlink, LEO-based vehicle localization may offer solutions to GNSS-related challenges. With a large number of satellites and short communication distance, the LEO-based method has great potential to improve accuracy, reduce warm-up time, and provide a robust localization solution for vehicle applications. In this paper, a dedicated LEO satellite simulator is presented, adaptable to various LEO constellations, making it relevant for evolving technologies beyond older LEO systems like Orbcomm or Iridium. The simulator includes satellite trajectory generation, observable satellite identification, and vehicle localization.

The cylinder bore in an engine block is deformed under the assembling stress of the cylinder head and thermal stress. This distortion exacerbates the piston skirt friction and piston slap. Through a numerical and experimental study, this article analyzes the effect of an optimized bore profile on the engine performance. The piston skirt friction was estimated in a three-dimensional elastohydrodynamic (EHD) friction analysis. An ideal cylindrical bore under the rated load condition was assumed as the optimal bore profile that minimized the piston skirt friction without compromising the piston slap. The simulation study revealed that secondary motion of the piston immediately after firing the top dead center can be mitigated by narrowing the piston–bore clearance at the upper position of the cylinder.

High cycle-to-cycle variations (CTCV) in a Hydrogen-Fueled Internal Combustion Engine (H2-ICE), especially in the lean-burn condition, not only lower the engine’s efficiency but also increase emissions and torque variations. High CTCV are mainly due to the variations in: mixture motion within the cylinder at the time of spark, amount of air and fuel fed to the cylinder, and mixing of the fresh mixture and residual gases within the cylinder during each cycle. In this article, multiple cycle-based methodologies were compared and analyzed specifically for H2-ICEs based on systematic experimentation. The experimental test campaign was performed on a Port Fuel Injection (PFI) H2-ICE designed by PUNCH Torino and data is processed with MATLAB. A MATLAB code is also proposed as a tool for comparing multiple methodologies for the analysis of CTCV specifically for H2-ICE.

Typically, modern automotive engine designs include separate cylinder heads and cylinder blocks and utilize a multilayer steel head gasket (MLS) to seal the resulting joint. Cylinder head bolts are used to hold the joint together and the non-linear properties of head gasket provide capability to seal the movement within the joint, which is essential for engine durability and performance. The current design of cylinder head gasket mainly evaluates the sealing performance in hot and cold state through finite element analysis. The sealing performance of cylinder head gasket is mainly determined by sealing pressure, fatigue and lateral movement in the joint, which have been widely studied [1]. However, no one has been involved in the study of factors affecting sealing pressure and lateral movement in the joint.