Browse Topic: Vehicles and equipment

Items (42,741)
A great number of performances of an electric vehicle such as driving range, powering performance, and the like are affected by its configured batteries. Having a good grasp of the electrical and thermal behavior of the battery before the detailed design stage is indispensable. This paper introduces an experiment characterization method of a lithium-ion battery with a coolant system from cell level to pack level in different ambient conditions. Corresponding cell and pack simulation models established in AMESim that aimed to capture the electrical and thermal features of the battery were also illustrated, respectively. First, the capacity test and hybrid pulse power characterization (HPPC) test were conducted in a thermotank to acquire basic data about the battery cell. Next, based on acquired data, first-order equivalent circuit model (1C-ECM) was built for the battery cell and further combined with environmental boundary conditions to check the simulation accuracy. Then, hybrid
Zhou, ShuaiLiu, HuaijuYu, HuiliYan, XuYan, Junjie
With the global issue of fossil fuel scarcity and the greenhouse effect, interest in electric vehicles (EVs) has surged recently. At that stage, because of the constraints of the energy density and battery performance degradation in low-temperature conditions, the mileage of EVs has been criticized. To guarantee battery performance, a battery thermal management system (BTMS) is applied to ensure battery operates in a suitable temperature range. Currently, in the industry, a settled temperature interval is set as criteria of positive thermal management activation, which is robust but leads to energy waste. BTMS has a kilowatt-level power usage under high- and low-temperature environments. Optimizing the BTMS control strategy becomes a potential solution to reduce energy consumption and overcome mileage issues. An appropriate system simulation model provides an effective tool to evaluate different BTMS control strategies. In this study, a predictive BTMS control strategy, which adjusts
Huang, ZhipeiChen, JiangboTang, Hai
Increasing global pressure to reduce anthropogenic carbon emissions has inspired a transition from conventional petroleum-fueled internal combustion engines to alternative powertrains, including battery electric vehicles (EVs) and hybrids. Hybrids offer a promising solution for emissions reduction by addressing the limitations of pure EVs such as slow recharge and range anxiety. In a previous research endeavor, a prototype high-power density generator was meticulously designed, fabricated, and subjected to testing. This generator incorporated a compact permanent magnet brushless dynamo and a diminutive single-cylinder two-stroke engine with low-technology constructions. This prototype generated 8.5 kW of electrical power while maintaining a lightweight profile at 21 kg. This study investigates the performance and emissions reduction potential by adapting the prototype to operate on methanol fuel. Performance and emissions were experimentally evaluated under varying operating conditions
Gore, MattNonavinakere Vinod, KaushikFang, Tiegang
The use of electric vehicles (EVs) has been on the rise in recent years and this trend is expected to continue in the upcoming years. There are several reasons for the increasing popularity of EVs, including environmental concerns, advances in technology, and government incentives. The 2W/3W EV powertrain comprises components such as the battery, traction motor, motor controller, charger, and DC-DC converter, etc. Essential components which impact the power, efficiency, and range of the vehicle are a motor (generally PMSM or BLDC) and a motor controller. PMSMs can produce more output power than BLDC motors of the same size, making them suitable for high-power applications. While the EV powertrain allows for greater flexibility in designing electric vehicle architectures, it also exhibits new challenges in meeting all the essential requirements. When a motor rotates, as per Lenz’s law, an opposing voltage (Back-EMF) is generated in a motor whose magnitude is proportional to its angular
Mohan, MidhunShinde, RushikeshMagar, PradipDeo, Mayank PramodChaudhary, Pramod
With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for
Bao, ZhibinLang, HaoxiangLin, Xianke
Bendix® EC-80™ and certain EC-60™ ABS control units contain an event data recorder called the Bendix® Data Recorder (BDR). Raw BDR data is obtained using commercially available software, however, the translation of the raw data into an event report has only been performed by the manufacturer. In this paper, the raw data structures of the commercially available datasets are examined. It is demonstrated that the data follows uniform and repeatable patterns. The raw BDR data is converted into a conventional report and then validated against translation reports performed by the manufacturer. The techniques outlined in this research allow investigators to access and analyze BDR records independently of the manufacturer and in a way previously not possible.
DiSogra, MatthewHirsch, JeffreyYeakley, Adam
This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of
Eldesouky, Dina M.MustafaAbdelaziz, Taha HelmyMohamed, Amr.E
The rotor position information is an important variable in the control system of dual three-phase motors, and ensuring the real-time and correct information is a prerequisite for the reliable operation of the dual three-phase motor system. The paper analyzes the structural characteristics of a dual three-phase permanent magnet synchronous motor (DTP-PMSM) with a 30°misalignment of dual Y windings, and proposes a new high-frequency symmetrical voltage injection method that can effectively identify rotor position information in a wide speed range. It forms heterogeneous information verification with the hardware signals of the dual three-phase motor rotor position sensors, improving the functional safety level of the control system. Firstly, a DTP-PMSM mathematical model was constructed under high-frequency voltage injection, and the mapping relationship between injection voltage and response current was derived, revealing the characteristic relationship between rotor position
Xu, LuhuiZhao, Zhiguo
This paper explores the integration of two deep learning models that are currently being used for object detection, specifically Mask R-CNN and YOLOX, for two distinct driving environments: urban cityscapes and highway settings. The hypothesis underlying this work is that different methods of object detection will work best in different driving environments, due to the differences in their unique strengths as well as the key differences in those driving environments. Some of these differences in the driving environment include varying traffic densities, diverse object classes, and differing scene complexities, including specific differences such as the types of signs present, the presence or absence of stoplights, and the limited-access nature of highways as compared to city streets. As part of this work, a scene classifier has also been developed to categorize the driving context into the two categories of highway and urban driving, in order to allow the overall object detection
Patel, KrunalPeters, Diane
With the growing energy crisis, people urgently need green energy sources to replace fossil ones. As a zero-emission clean energy source, the proton-exchange membrane fuel cell (PEMFC) has received growing attention from researchers due to its broad practical application. However, the large-scale application of PEMFC is currently impeded by their unsatisfying power output and high cost. PEMFC is composed of multiple components, among which the catalyst layer significantly affects the output power and cost of PEMFC. Drastically reducing the amount of platinum in the catalyst layer can bring great benefits to PEMFC, yet causing the large voltage loss associated with enlarged local oxygen molecule transport. Cutting down the platinum content in the catalyst layer can yield substantial cost savings for PEMFC. Developing an efficient catalyst possessing enhanced oxygen reduction reaction (ORR) catalytic performance is conducive to the commercialization of low-Pt proton exchange membrane
Liu, YuchenLiu, XinCai, XinDu, AiminLin, Rui
To take into account the drivers’ performance expectations in the comprehensive performance optimization of plug-in hybrid electric vehicles (PHEVs), we proposed an optimization method for the shift schedule of single-shaft parallel PHEVs considering drivers’ demands on both dynamic and economic performance. In accordance with torque distribution strategies developed for different working modes, the modes switching logic is formulated based on the demand torque along with the engine torque characteristics and the state of charge (SOC) of power battery. And a quantification model for driver’s intention is proposed using a fuzzy inference approach, which can compute the driver's dynamic and economic performance expectations using the driver's operation characteristics and vehicle status as input. With the help of a linear weighting method using the performance expectations as weights, a comprehensive performance evaluation function is constructed as the optimization objective of shift
Yin, XiaofengLi, HongZhang, JinhongLei, Yulong
Deadbeat Predictive Current Control (DPCC) has emerged as a highly effective control strategy, owing to its outstanding dynamic performance. However, the control effectiveness of traditional methods is limited by the machine parameters set in advance, which inevitably reduces the parameter robustness of the method. When machine parameters change due to factors like temperature, the discrepancy between the actual values and the parameters configured in the controller leads to a decline in DPCC performance, and cause system instability. To tackle the challenge of parameter dependence, this paper proposes an adaptive parameter-free model-free deadbeat predictive current control (PF-MFDPCC) method suitable for interior permanent magnet synchronous motors (IPMSM). The method estimates the actual gain parameters based on the sampled current values and reference values, and determines the required harmonic current injection by minimizing torque ripple. First, the relationship of the
Guo, RongGu, hongyang
A total of 368 frontal New Car Assessment Program (NCAP) tests (including 24 tests with Battery Electric Vehicles (BEVs)) with high-resolution load cell data were analyzed to investigate vehicle crash compatibility, especially between Internal Combustion Engine Vehicles (ICEVs) and BEVs. An Indirect Frontal Crash Model (IFCM) for Full-Overlap (FO) Vehicle-to-Moving Deformable Barrier (V2MDB) using load cell data from frontal NCAP tests was developed to assess vehicle aggressivity. An analytical solution of the IFCM for FO/V2MDB was obtained and used to develop a new aggressivity metric. In addition, the Max. Standard Deviation (SD) of load cell forces was used to assess vehicle front-end homogeneity. In the case studies, vehicle compatibility was assessed by the new aggressivity metric and Max. SD, along with typical frontal crash metrics.
Park, Chung-Kyu
The advancements in vehicle connectivity and the increased level of driving automation can be leveraged for the development of Advanced Driver Assistance Systems (ADAS) that improve driver safety and comfort while optimizing the energy consumption of the vehicle. In the development phase of energy-efficient ADAS, modeling and simulation are used to assess the potential benefits of these technologies on energy consumption. However, there is a lack of standardized simulation or test frameworks to quantify the benefits. Moreover, the driving scenario and the traffic conditions are often not explicitly modeled when simulating energy-efficient ADAS, even though they have a major impact on the attainable energy benefits. This paper presents the development and implementation of a closed-loop traffic-in-the-loop simulator designed to evaluate the performance of vehicles under realistic traffic conditions. The primary objective is to qualitatively assess how varying traffic conditions
Grano, EliaVillani, ManfrediAhmed, QadeerCarello, Massimiliana
The modern luxurious electric vehicle (EV) demands high torque and high-speed requirements with increased range. Fulfilling these requirements, arises the need for increased electric current supply to motors. Increased amperage through the stator causes higher losses resulting in elevated temperature across the motor components and its housing. In most of the cases, stator is mounted on the housing through interference fit to avoid any slippage during operation conditions. High temperature across the stator and housing causes significant thermal expansions of the components which is uneven in nature due to the differences in corresponding coefficient of thermal expansion (CTE) values. Housings are generally made of aluminium and tends to expand more having higher value of CTE than that of steel core of stator which may give rise to a failure mode related to stator slippage. To address this slippage if the amount of interference fit is increased, that’ll result in another failure mode
Karmakar, NilankanPrasad, Praveen
Test procedures such as EuroNCAP, NHTSA’s FMVSS 127, and UNECE 152 all require specific pedestrian to vehicle overlaps. These overlap variations allow the vehicle differing amounts of time to respond to the pedestrian’s presence. In this work, a compensation algorithm was developed to be used with the STRIDE robot for Pedestrian Automatic Emergency Braking tests. The compensation algorithm uses information about the robot and vehicle speeds and positions determine whether the robot needs to move faster or slower in order to properly overlap the vehicle. In addition to presenting the algorithm, tests were performed which demonstrate the function of the compensation algorithm. These tests include repeatability, overlap testing, vehicle speed variation, and abort logic tests. For these tests of the robot involving vehicle data, a method of replaying vehicle data via UDP was used to provide the same vehicle stimulus to the robot during every trial without a robotic driver in the vehicle.
Bartholomew, MeredithNguyen, AnHelber, NicholasHeydinger, Gary
Vehicle sideslip is a valuable measurement for ground vehicles in both passenger vehicle and racing contexts. At relevant speeds, the total vehicle sideslip, beta, can help drivers and engineers know how close to the limits of yaw stability a vehicle is during the driving maneuver. For production vehicles or racing contexts, this measurement can trigger Electronic Stability Control (ESC). For racing contexts, the method can be used for driver training to compare driver techniques and vehicle cornering performance. In a fleet context with Connected and Autonomous Vehicles (CAVS) any vehicle telemetry reporting large vehicle sideslip can indicate an emergency scenario. Traditionally, sideslip estimation methods involve expensive and complex sensors, often including precise inertial measurement units (IMUs) and dead reckoning, plus complicated sensor fusion techniques. Standard GPS measurements can provide Course Over Ground (COG) with quite high accuracy and, surprisingly, the most
Hannah, AndrewCompere, Marc
The accurate extraction of internal operating parameters associated with multi-physicochemical processes forms the basis for precise modelling of solid oxide fuel cells (SOFCs), which serves as the foundation for predicting performance degradation and estimating the lifespan of SOFCs. In this work, a novel integration of the teaching-learning based optimization (TLBO) and collective intelligence (CI), referred as the teaching-learning based collective intelligence algorithm (TLBCI), is introduced. This algorithm utilizes diverse characteristic patterns, including current-voltage (I-V) curves and sequential output data, to enhance the overall identification of degradation process. Experimental data was gathered from a 3-cell SOFC short stack during a 640-hour durability test. The proposed parameter identification algorithm employs a collective intelligence framework, wherein sub-optimizers are based on genetic algorithm (GA) and individually tasked with processing specific formats of
Wang, ZheyuShen, YitaoSun, AoTongHan, BeibeiMa, XiaoShuai, Shijin
Due to advantages such as high efficiency, low emissions, and fuel flexibility, solid oxide fuel cells (SOFCs) have garnered significant attention as promising power sources for automotive applications. Nickel/yttria-stabilized zirconia (Ni/YSZ) is one of the most widely used anode materials in SOFCs, as it can catalyze both chemical and electrochemical reactions of carbon-containing fuels. However, the direct use of carbon-containing fuels can lead to carbon deposition on the Ni/YSZ anode, negatively impacting the performance and reliability of automotive SOFC systems. The diffusion of carbon atoms within nickel plays a crucial role in the carbon deposition process and requires further investigation. The oxygen atoms that spillover from YSZ also participate in main reactions such as carbon deposition and electrochemical reactions in Ni. Molecular dynamics (MD) is one of the main methods for studying atomic diffusion in crystalline structures. In this study, reactive force field
Du, HaoyuZhang, KaiqiXiao, MaZhang, XiaoqingShuai, Shijin
Plug-in Hybrid Electric Vehicles (PHEVs) combine the benefits of electric propulsion and storage with the extended range of conventional internal combustion engines to reduce fuel consumption and greenhouse gas emissions. However, optimizing the efficiency of PHEVs in real-world driving conditions remains a challenge due to the uncertainties of environmental and driving conditions. Connectivity and automation technologies can offer a unique opportunity to enhance the efficiency of PHEVs by enabling real-time interaction with surrounding vehicles and infrastructure. By leveraging these technologies, significant reductions in energy consumption for PHEVs can be achieved. However, most existing works primarily rely on simulation-based analyses to evaluate energy savings offered by connected and automated PHEVs. This study advances the understanding of the energy-saving potential of connected and automated PHEVs by incorporating experimental validation alongside simulation-based analyses
Kibalama, DennisOzkan, Mehmet FatihStockar, StephanieCanova, MarcelloRizzoni, Giorgio
The number of electric vehicles (EVs) has significantly increased in recent years. Safety performance of EVs is at least at the same level as that of conventional vehicles. To evaluate battery safety and ensure passenger protection, several standard tests and regulations for EV batteries have been established, including IEC 62660-3, ISO 6469-1, and UN/ECE/R100 Revision 3. ISO 6469-1:2019/Amd 1 specifies thermal propagation (TP) test to evaluate battery robustness against thermal runaway (TR) in a single cell. Moreover, UN/ECE/R100 Revision 3 aims to provide sufficient egress time to protect passengers in the event of a TR in a single cell. Typically, these tests initiate TR in a cell within a battery pack using either a heater or nail. In the heater method, if the gap between cells is larger than the heater’s thickness and there are no installation constraints due to components, almost any cell can be chosen as the initiating cell. However, if the gap between cells is smaller than the
Maeda, KiyotakaTakahashi, Masashi
Precisely understanding the driving environment and determining the vehicle’s accurate position is crucial for a safe automated maneuver. vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLOv8 image segmentation, trained on a diverse world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to world measurements. This design minimizes
Karuppiah Loganathan, Nirmal RajaPoovalappil, AmanNaber, JeffreyRobinette, DarrellBahramgiri, Mojtaba
SAE J3230 provides Kinematic Performance Metrics for Powered Standing Scooters. These performance metrics include many tests which require specific conditions including flat pavement with a near zero slope, drivers of specific height and weights, and data acquisition equipment. In order to determine the efficacy of replicating SAE J3230 tests in a laboratory setting, a device called the Micromobility Device Thermo-Electric Dynamometer was used alongside outdoor tests to provide a comparison of scooter performance in these two testing applications. Based on the testing outcomes, it can be determined whether SAE J3230 and similar standards for other micromobility devices can be replicated in a lab-based setting, saving time, operator hazard, and providing more thorough data outputs.
Bartholomew, MeredithAndreatta, DaleZagorski, ScottHeydinger, Gary
As a crucial tool for lunar exploration, lunar rovers are highly susceptible to instability due to the rugged lunar terrain, making control of driving stability essential during operation. This study focuses on a six-wheel lunar rover and develops a torque distribution strategy to improve the handling stability of the lunar rover. Based on a layered control structure, firstly, the approach establishes a two-degree-of-freedom single-track model with front and rear axle steering at the state reference layer to compute the desired yaw rate and mass center sideslip angle. Secondly, in the desired torque decision layer, a sliding mode control-based strategy is used to calculate the desired total driving torque. Thirdly, in the torque distribution layer, the optimal control distribution is adopted to carry out two initial distributions and redistribution of the drive torque planned by the upper layer, to improve the yaw stability of the six-wheeled lunar rover. Finally, a multi-body dynamics
Liu, PengchengZhang, KaidiShi, JunweiYang, WenmiaoZhang, YunqingWu, Jinglai
As the main power source for modern portable electronic devices and electric vehicles, lithium-ion batteries (LIBs) are favored for their high energy density and good cycling performance. However, as the usage time increases, battery performance gradually deteriorates, leading to a heightened risk of thermal runaway (TR) increases, which poses a significant threat to safety. Performance degradation is mainly manifested as capacity decline, internal resistance increase and cycle life reduction, which is usually caused by internal factors of LIBs, such as the fatigue of electrode materials, electrolyte decomposition and interfacial chemical reaction. Meanwhile, external factors of LIBs also contribute to performance degradation, such as external mechanical stresses leading to internal structural damage of LIBs, triggering internal short-circuit (ISC) and violent electrochemical reactions. In this paper, the performance degradation of LIBs and TR mechanism is described in detail, as well
Zhou, JingtaoZhong, XiongwuWang, KunjunZhou, YouhangYou, GuojianTang, Xuan
The proliferation of intelligent technologies in the future battlefield necessitates an exploration of crew workload balancing strategies for human-machine integrated formations. Many current techniques to measure cognitive workload, through qualitative surveys or wearable sensors, are too brittle for the harsh, austere operational environments found in military settings. Non-invasive workload estimation techniques, such as those that analyze physiological effects from video feeds of the crew, present a way forward for workload-aware Soldier-machine interfaces that could trigger events – such as task reallocation – if limits on crew or individual workload are exceeded. One such technique that is being explored is the use of facial expression analysis for workload estimation. We present the performance results of regression and classification models developed from supervised machine learning algorithms that predict pNN50, a common heart rate variability metric used as a physiological
Mikulski, ChristopherRiegner, Kayla
The development of connected and automated vehicles (CAVs) is rapidly increasing in the next generation and the automotive industry is dedicated to enhancing the safety and efficiency of CAVs. A cooperative control strategy helps CAVs to collaborate and share information among the neighboring CAVs, improving efficiency, optimizing traffic flow, and enhancing safety. This research proposes a safe cooperative control framework for CAVs designed for highway merging applications. In the urban transportation system, highway merging scenarios are high-risk collision zone, and the CAVs on the main and merging lanes should collaborate to avoid potential accidents. In the proposed framework, the on-ramp CAVs merge at 40 mph within the same and opposite directions to the main lane CAVs. The proposed framework includes the consensus controller, safety filter, and quadratic programming (QP) optimization method. The consensus controller incorporates the communication between CAVs and designs the
Chang, PeiYuBhatti, SidraJaved, Nur UddinAhmed, Qadeer
Amphibious vehicles are widely used in civil and military scenarios due to their excellent driving performance in water and on land, unique application scenarios and rapid response capabilities. In the field of civil rescue, the hydrodynamic performance of amphibious vehicles directly affects the speed and accuracy of rescue, and is also related to the life safety of rescuers. In the existing research on the hydrodynamic performance of amphibious vehicles, seakeeping performance has always been the focus of research by researchers and amphibious vehicle manufacturers, but most of the existing research focuses on the navigation performance of amphibious vehicles in still water. In actual application scenarios, amphibious vehicles often face complex water conditions when performing emergency rescue tasks, so it is very important to study the navigation performance of amphibious vehicles in waves. Aiming at the goal of studying the navigation performance of amphibious vehicles in waves
Zhang, Yu
A key challenge for manufacturers of automotive systems, hardware components and software products with no contribution to driving automation is the stringent requirements imposed on elements while being integrated into vehicles with driving automation. The result is increased development cost and low reusability. For such elements or components with no contribution to driving automation, their functions and failure modes remain unchanged when comparing vehicle integration with and without driving automation. The influence of driving automation is not accounted for in the current approach of classifying risk while conducting a Hazard Analysis and Risk Assessment (HARA). Functional safety standards for on-road vehicles rely on human intervention as a parameter to classify risk. Since current safety standards for on-road vehicles are not inclusive of driving automation concepts, classification of risk, based on existing definitions of parameters such as controllability, leads to
Shah, MihirIbarra, Ireri
The electric vehicle market, vehicle ECU computing power, and connected electronic vehicle control systems continue to grow in the automotive industry. The results of these advanced and expanded vehicle technologies will provide customers with increased cost savings, safety, and ride quality benefits. One of these beneficial technologies is the tire wearing prediction. The improved prediction of tire wear will advise a customer the best time to change tires. It is expected that this prediction algorithms will be essential part for both the optimization of the chassis control systems and ADAS systems to respond to changed tire performance that varies with a tire’s wear condition. This trend is growing, with many automakers interested in developing advanced technologies to improve product quality and safety. This study is aimed at analyzing the handling and ride comfort characteristics of the tire according to the depth of tire pattern wear change. The handing and ride comfort
Kim, ChangsuKwon, SeungminSung, Dae-UnRyu, YonghyunKo, Younghee
In order to improve the safety and reliability of the inverter used in hybrid vehicles and reduce the risk of inverter failure, based on the functional safety ISO26262 development process and software architecture, a safe shutdown path scheme is designed in this paper. Firstly, after entering the initialization mode, on the basis of adding the inverter control signal feedback mechanism on the inverter control system, this scheme designs the control methods and specific processes of the shutdown path test and insulation detection. The shutdown path test and insulation detection designed in this scheme are implemented during the control initialization process, including designing the hardware diagnostic safety mechanism and the unique output shutdown path test method. If the shutdown path test or insulation detection fails, the risk of IGBT out of control can be avoided; the detection mechanism of this system can effectively reduce the failure rate and potential failure rate of faults
Jing, JunchaoLiu, YiqiangZuo, BotaoHuang, WeishanDai, Zhengxing
The surge in electric vehicle usage has expanded the number of charging stations, intensifying demands on their operation and maintenance. Public charging stations, often exposed to harsh weather and unpredictable human factors, frequently encounter malfunctions requiring prompt attention. Current methods primarily employ data-driven approaches or rely on empirical expertise to establish warning thresholds for fault prediction. While these approaches are generally effective, the artificially fixed thresholds they employ for fault prediction limit adaptability and fall short in sensitivity to special scenarios, timings, locations, and types of faults, as well as in overall intelligence. This paper presents a novel fault prediction model for charging equipment that utilizes adaptive dynamic thresholds to enhance diagnostic accuracy and reliability. By integrating and quantifying Environmental Influence Factors (EF), Scenario Influence Factors (SF), Fault Severity Factors (FF), and
Wang, HaoWang, NingLi, YuanTang, Xinyue
Utilization of fiber-reinforced composite laminates to their full potential requires consideration of angle-ply laminates in structural design. This category of laminates, in comparison with orthotropic laminates, imposes an additional degree of challenge, due to a lack of material principal axes, in determination of elastic laminate effective properties if the same has to be done experimentally. Consequentially, there is a strong inclination to resort to the usage of “CLPT” (Classical Laminated Plate Theory) for theoretically estimating the linear elastic mechanical properties including the cross-correlation coefficients coupling normal and shear effects. As an angle-ply laminate is architecturally comprised of layers of biased orthotropic laminas (based on unidirectional or woven bidirectional fibers), an essential prerequisite for the application of CLPT is an a-priori knowledge of elastic mechanical properties of a constituent lamina. It is natural to expect that the properties of
Tanaya, SushreeDeb, Anindya
In order to effectively improve the chassis handling stability and driving safety of intelligent electric vehicles (IEVs), especially in combing nonlinear observer and chassis control for improving road handling. Simultaneously, uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IEVs become a hot topic in both academia and industry. To issue the above mentioned, a fuzzy sliding mode control method based on phase plane stability domain is proposed to enhance the vehicle’s chassis performance during complex driving scenarios. Firstly, a two-degree-of-freedom vehicle dynamics model, accounting for tire non-linearity, was established. Secondly, combing with phase plane theory, the stability domain boundary of vehicle yaw rate and side-slip phase plane based
Liao, YinshengWang, ZhenfengGuo, FenghuanDeng, WeiliZhang, ZhijieZhao, BinggenZhao, Gaoming
Hybrid vehicles are driven by the vehicle controller, engine controller and motor controller through torque control, and there may be unexpected acceleration or deceleration of the vehicle beyond the driver's expectation due to systematic failure and random hardware failure. Based on the torque control strategy of hybrid vehicles, the safety monitoring model design of torque control is carried out according to the ISO 26262 safety analysis method. Through the establishment of safety goals and the analysis of safety concepts, this paper conducts designs including the driver allowable torque design for safety monitoring, the driver torque prediction design for safety monitoring, the rationality judgment design of driver torque for safety monitoring, the functional safety degradation design, and the engine start-stop status monitoring, enabling the system to transition to a safe state when errors occur. Firstly, the design of the driver's allowable torque includes the allowable requested
Jing, JunchaoWang, RuiguangLiu, YiqiangHuang, WeishanDai, Zhengxing
As global warming and environmental problems are becoming more serious, tires are required to achieve a high level of performance trade-offs, such as low rolling resistance, wet braking performance, driving stability, and ride comfort, while minimizing wear, noise, and weight. However, predicting tire wear life, which is influenced by both vehicle and tire characteristics, is technically challenging so practical prediction method has long been awaited. Therefore, we propose an experimental-based tire wear life prediction method using measured tire characteristics and the wear volume formula of polymer materials. This method achieves practical accuracy for use in the early stages of vehicle development without the need for time-consuming and costly real vehicle tests. However, the need for improved quietness and compliance with dust regulations due to vehicle electrification requires more accuracy, leading to an increase in cases requiring judgment through real vehicle tests. To address
Ando, Takashi
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