Reliability Of Vehicles Research Articles

Enhancing Electric Vehicle Reliability: Advanced Fault Detection for Demagnetization Faults in PMSM Motor Using Artificial Neural Network

Enhancing Electric Vehicle Reliability: Advanced Fault Detection for Demagnetization Faults in PMSM Motor Using Artificial Neural Network

Intrusion Detection System for Autonomous Vehicles Using Non-Tree Based Machine Learning Algorithms

This research work highlights significant achievements in the domain of intrusion detection systems (IDSs) for autonomous vehicles, which are crucial in enhancing their safety, reliability, and cybersecurity. This study introduces an approach that leverages non-tree-based machine learning algorithms, such as K-nearest neighbors and ensemble learning, to develop an IDS tailored for autonomous vehicles. These algorithms were employed because of their ability to process complex and large datasets with less likeliness for overfitting, their scalability, and their ability to adapt to changing conditions in real time. These algorithms effectively handle imbalanced data, enhancing the detection accuracy of both normal and intrusive instances. The IDS’s performance was validated through the utilization of three real-world datasets, CAN intrusion, CICIDS2017, and NSL-KDD, where the proposed non-tree-based IDS (NTB-MTH-IDS) was measured with the standard measurement metrics: accuracy, precision, F1-score, and recall, including specificity and sensitivity. Notably, the results indicate that K-nearest neighbors and stacking, as part of NTB-MTH-IDS, has an accuracy of 99.00%, 98.57%, and 97.57%, and F1-scores of 99.00%, 98.79%, and 97.54% in the CICIDS2017, NSL-KDD, and CAN datasets, respectively. The results of this research can lead to establishing a robust intrusion detection framework, thereby ensuring the safety and reliability of autonomous vehicles. Through this achievement, road users, passengers, and pedestrians are safeguarded against the consequences of potential cyber threats.

Seamless Micro-Electro-Mechanical System-Inertial Navigation System/Polarization Compass Navigation Method with Data and Model Dual-Driven Approach.

The integration of micro-electro-mechanical system-inertial navigation systems (MEMS-INSs) with other autonomous navigation sensors, such as polarization compasses (PCs) and geomagnetic compasses, has been widely used to improve the navigation accuracy and reliability of vehicles in Internet of Things (IoT) applications. However, a MEMS-INS/PC integrated navigation system suffers from cumulative errors and time-varying measurement noise covariance in unknown, complex occlusion, and dynamic environments. To overcome these problems and improve the integrated navigation system's performance, a dual data- and model-driven MEMS-INS/PC seamless navigation method is proposed. This system uses a nonlinear autoregressive neural network (NARX) based on the Gauss-Newton Bayesian regularization training algorithm to model the relationship between the MEMS-INS outputs composed of the specific force and angular velocity data and the PC heading's angular increment, and to fit the integrated navigation system's dynamic characteristics, thus realizing data-driven operation. In the model-driven part, a nonlinear MEMS-INS/PC loosely coupled navigation model is established, the variational Bayesian method is used to estimate the time-varying measurement noise covariance, and the cubature Kalman filter method is then used to solve the nonlinear problem in the model. The robustness and effectiveness of the proposed method are verified experimentally. The experimental results show that the proposed method can provide high-precision heading information stably in complex, occluded, and dynamic environments.

ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

Construction and Method Study of the State of Charge Model for Lithium-Ion Packs in Electric Vehicles Using Ternary Lithium Packs as an Example

Accurate and real-time estimation of pack system-level chips is essential for the performance and reliability of future electric vehicles. Firstly, this study constructed a model of a nickel manganese cobalt cell on the ground of the electrochemical process of the packs. Then, it used methods on the grounds of the unscented Kalman filter and unscented Kalman particle filter for system-level chip estimation and algorithm construction. Both algorithms are on the ground of Kalman filters and can handle nonlinear and uncertain system states. In comparative testing, it can be seen that the unscented Kalman filter algorithm can accurately evaluate the system-level chip of the nickel manganese cobalt cell under intermittent discharge conditions. The system-level chip was 0.53 at 1000 s and was reduced to 0.45 at 1500 s. These results demonstrate that the evaluation of the ternary lithium battery pack’s performance is time-dependent and indicate the accuracy of the algorithm used during this time period. These data should be considered in the broader context of the study for a comprehensive understanding of their meaning. In the later stage, the estimation error of the recursive least-squares unscented Kalman particle filter method for system-level chips began to significantly increase, gradually exceeding 1%, with a corresponding root-mean-square error of 0.002171. This indicates that the recursive least-squares optimization algorithm, the unscented Kalman particle filter algorithm, diminished its root mean square error by 27.59%. The unscented Kalman filter and unscented Kalman particle filter are effective in estimating the system-level chip of nickel manganese cobalt cells. However, UPF performs more robustly in handling complex situations, such as pack aging and temperature changes. This study provides a new perspective and method that has a high reference value for pack management systems. This helps to achieve more effective energy management and improve pack life, thereby enhancing the reliability and practicality of electric vehicles.

Nonlinear vibrations of a linkage mechanism: theory and experiment

Linkage mechanisms have a wide range of applications in truck-mounted cranes, folding mechanical arms of transport vehicles and so on due to their simple and effective design. Vibration problems in the linkage mechanisms seriously affect the stability and reliability of crane machines or transport vehicles. Therefore, in this study, an effective nonlinear dynamic model of a linkage mechanism with spring-damping systems is developed and experimentally validated to explore the nonlinear vibration properties. The geometric nonlinearity of the linkage mechanism is considered and the Lagrange equation is employed to establish the nonlinear dynamic equations. Based on the harmonic balance method (HBM), the nonlinear vibration properties of the structural system are analyzed. The nonlinear primary resonance response of the linkage mechanism calculated from the HBM is compared with those of the Runge–Kutta method and the experimental result, which validates the effectiveness and accuracy of the proposed nonlinear dynamic model. The influences of some parameters, such as the spring stiffness, the lengths of connecting rods, and the installation positions of the spring-damping system, on the nonlinear vibration characteristic of the linkage mechanism are analyzed. Based on the numerical results, the folding mechanical arms or truck-mounted cranes can be conveniently designed to avoid irreparable damage to the structure caused by nonlinear primary resonance and instability phenomena, which provides new insight into the stability analysis and design of various linkage mechanisms required in engineering applications.

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper.

Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

In the rapidly evolving electric vehicle industry, the reliability of electronic systems is critical to ensuring vehicle safety and performance. Printed circuit boards (PCBs), serving as a cornerstone in these systems, necessitate efficient and accurate surface defect detection. Traditional PCB surface defect detection methods, like basic image processing and manual inspection, are inefficient and error-prone, especially for complex, minute, or irregular defects. Addressing this issue, this study introduces a technology based on the YOLOv5 network structure. By integrating the Convolutional Block Attention Module (CBAM), the model’s capability in recognizing intricate and small defects is enhanced. Further, partial convolution (PConv) replaces traditional convolution for more effective spatial feature extraction and reduced redundant computation. In the network’s final stage, multi-scale defect detection is implemented. Additionally, the normalized Wasserstein distance (NWD) loss function is introduced, considering relationships between different categories, thereby effectively solving class imbalance and multi-scale defect detection issues. Training and validation on a public PCB dataset showed the model’s superior detection accuracy and reduced false detection rate compared to traditional methods. Real-time monitoring results confirm the model’s ability to accurately detect various types and sizes of PCB surface defects, satisfying the real-time detection needs of electric vehicle production lines and providing crucial technical support for electric vehicle reliability.

A new method for assessing the reliability of multi-purpose vehicles based on test results in extreme conditions

The article presents materials on methods and means of reducing testing time and improving their quality in terms of accuracy, reliability and reproducibility of results, regardless of changing road, soil and climatic conditions using a new reliability indicator: instead of average time between failures — time to 1st failure with probability of failure-free operation is 0.95. A method is presented for assessing the reliability of test results with different volumes of mileage in extreme road conditions by comparing normal test data with a reference scientific and methodological platform. Keywords multi-purpose vehicles, road tests, reliability, failure, method, reliability assessment

A mathematical model for reliability management of agricultural machinery

The article examines the current and practically significant task of managing the reliability of agricultural machinery used in the production activities of Russian Federation penal system institutions. A model for optimal management of technical maintenance and repairs of technical equipment is proposed in order to increase their reliability. Additionally, the necessary optimality conditions for solving the optimization problem under consideration are formulated. An algorithm for the numerical solution of the formulated optimal control problem has been developed, and the results of testing the implemented algorithm are presented. The implementation of the proposed approach in the form of an automated decision support system serves to solve the problem of increasing the operational reliability of vehicles used in agricultural activities of correctional institutions.

Future-proof welding simulation tools to improve vehicle operational reliability

The article discusses technologies for modeling welding operations using modern design tools, which will improve the quality of repairs with the existing resource base, as well as the operational reliability of vehicles after repairs. Keywords resistance Spot Welding (RSW), welding simulation, transport system safety, operational reliability

Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization

Global navigation satellite systems (GNSSs) applied to intelligent transport systems in urban areas suffer from multipath and non-line-of-sight (NLOS) effects due to the signal reflections from high-rise buildings, which seriously degrade the accuracy and reliability of vehicles in real-time applications. Accordingly, the integration between GNSS and inertial navigation systems (INSs) could be utilized to improve positioning performance. However, the fixed GNSS solution uncertainty of the conventional integration method cannot determine the fluctuating GNSS reliability in fast-changing urban environments. This weakness becomes solvable using a deep learning model for sensing the ambient environment intelligently, and it can be further mitigated using factor graph optimization (FGO), which is capable of generating robust solutions based on historical data. This paper mainly develops the adaptive GNSS/INS loosely coupled system on FGO, along with the fixed-gain Kalman filter (KF) and adaptive KF (AKF) being taken as comparisons. The adaptation is aided by a convolutional neural network (CNN), and the feasibility is verified using data from different grades of receivers. Compared with the integration using fixed-gain KF, the proposed adaptive FGO (AFGO) maintains the 100% positioning availability and reduces the overall 2D positioning error by up to 70% in the aspects of both root mean square error (RMSE) and standard deviation (STD).

An Overview of Influence of Hybridization in Automobiles on its Performance and Environment

The rapid advancements in transportation technology have led to the development of Hybrid Electric Vehicles (HEVs) and smart hybrid electric vehicles (S-HEVs) as potential solutions for reducing fuel consumption, emissions, and dependency on fossil fuels. These vehicles combine conventional internal combustion engine propulsion systems with electric propulsion systems, offering various driving modes and the ability to adjust operation points for increased efficiency. Additionally, HEVs and S-HEVs contribute to the creation of alternative power sources for household applications, provide ancillary services to the grid, and integrate intermittent resources for vehicle charging. The reliability of Electric Vehicle (EV) batteries is a crucial aspect, involving failure recognition, testing methods, and life prediction techniques to ensure prolonged battery life. As countries worldwide strive to transition from gasoline vehicles to EVs, practical limitations such as "range anxiety" due to inadequate charging infrastructure and high costs of long-ranged EVs arise. One potential solution to address range anxiety is the use of range extenders, optimizing driving range, costs, and vehicle performance. These advancements in eco-friendly, safer, and cost-effective transportation contribute significantly to reducing carbon emissions and promoting sustainable development globally.

Faz Değişim Malzemesi Olarak Parafinin Bir Batarya Termal Yönetim Sisteminin Soğutma Performansı Üzerindeki Etkisinin Deneysel Olarak İncelenmesi

Internal combustion engines (ICEs) are largely dependent on fossil fuels, and both the risk of depletion of fossil fuels and the harmful exhaust emissions emitted by ICEs have led researchers to become interested in electric vehicles (EVs). As the EV industry develops day by day, battery thermal management systems (BTMS) have become indispensable in solving the high-temperature problem of batteries, which are the most important component of EVs. The cost and reliability of electric vehicles are affected by parameters such as the life cycle, capacity, charging time, durability, and warranty cost of the battery pack used. The heat produced in the battery pack is removed by gas or liquid cooling in active cooling, and by phase change materials (PCM) in passive cooling. The high energy storage density of PCMs and the fact that there is no need for fan or pump power in cooling using PCMs are some of the reasons why PCMs are preferred for BTMSs. In this study, a battery pack consisting of 18 lithium-ion batteries, 6 in series and 3 in parallel, was first charged and discharged without any cooling system and then with the addition of PCM at a current strength of 1C, and the effect of the BTMS was examined. It has been observed that the BTMS has a positive effect of approximately 8% for charging experiments and 23% for discharge experiments in terms of the maximum temperature value in the battery pack.

Analysis of Internal Short Circuit and Thermal Runaway of Li-Ion Battery Due to Mechanical Abuse

Due to the growing electric vehicle market and new trends to reduce fuel consumption for a healthy environment, safe and reliable vehicles are desirable for reasons beyond EV and new technology. Although certain safety standards and legislation are in place and continuously improving but several concerns including battery safety, battery performance, vehicle structure, and battery design to be considered as these affect the overall reliability of Electric vehicles (EVs). Lithium-ion battery technology is considered a good solution to power electric vehicles but potential overheating due to stressful conditions such as mechanical abuse, overcharge, over-discharge, short circuit, and excessive heat from external environment gives rise to safety issues and the occurrence of one or more than one of these can lead to thermal runaway. In most cases, an electric short circuit is a necessary but not sufficient condition for the occurrence of thermal runaway after mechanical abuse. Chemistry of the cell, the resistance of separator to heat, size of the fractured part, and rate of heat transfer all play a role in processes leading to a thermal runaway, if the cell has not gone to thermal runaway right away it can still go into a slow process of electrochemical reaction, releasing gases that eventually could lead to a catastrophic event. This abstract investigates the mechanical behaviour of the separator in the LiCoO2/Graphite cylindrical 18650 cells. Internal short circuit (ISC) behaviour, strain rate dependency, and electrochemical status of the cells (i.e. SOC dependency) are studied to understand failure patterns. Furthermore, a simple and effective constitutive model for the separator layer is formed, facilitating further mechanical analysis and numerical simulation of lithium-ion battery study. The occurrence of ISC is investigated by jellyroll deformation where the casing is removed, and quasi-static load is applied. The numerical simulation model is developed to further investigate sequential structural failures and temperature changes. Simulation results showed good accuracy with experimental results and are useful to predict structural failure of cells. The number of failures including electrolyte leakage, change in shape, sudden voltage drop/temperature rise, and gas venting are observed.

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

The electric vehicle thermal management system (EVTMS) plays a crucial role in ensuring battery efficiency, driving range, and passenger comfort. However, EVTMSs still face unresolved challenges, such as accurate modeling, compensating for temperature variations, and achieving efficient control strategies. Addressing these issues is crucial for enhancing the performance, reliability, and energy efficiency of electric vehicles. Therefore, this study presents a cooling EVTMS model, considering both the battery pack temperature and the cabin comfort, and utilizes the prediction of neural network as a feedforward in a fuzzy PI controller to compensate for the model temperature variations. The simulation results reveal that, compared with PI controller and MPC, the neural network fuzzy PI (NN-Fuzzy PI) controller can well predict and compensate for the system’s nonlinear characteristics as well as the time-delay caused by heat transfer, achieving superior control performance and reducing energy consumption. The battery pack temperature and PMV fluctuations are effectively constrained within [−0.5, 0.5] and [−0.1, 0.1], reducing up to 150% and 164%, and the energy consumption of the pump and compressor are reduced by up to 0.23 and 100.1 KJ, with ranges of 18% and 2.68%. Meanwhile, the neural network feedforward also works effectively in different controllers. The findings of this research can provide valuable insights for TMS engineers to select advanced control strategies.

UNDERSTANDING CONSUMERS INTENTION TO PURCHASE GREEN VEHICLE: A LITERATURE REVIEW

Global warming is one of the main issues discuss by nations leaders. It is a crucial problem that needs to be address immediately. One of the solutions to global warming is green vehicles. Hybrids and electric vehicles are not new products in the automobile market. Unfortunately, the sales percentage for both types of green vehicles are still behind the petrol engine vehicles. Regardless of the world leaders’ dream to fill the street with green vehicles by 2035 in developed countries, the dream is still far from reality. As for developing countries, the people are more familiar with hybrid vehicles compare to electric vehicles. Other than that, the consumer perceptions regarding green vehicles are not all in positive aspect. Green vehicles are more associated as luxury goods in consumer perspective. There are also critics on the reliability and durability of the green vehicles as consumers find trouble to believe that green vehicles could compete with petrol engine vehicles. This paper identifies factors that could influence consumer to purchase green vehicles. Thus, providing a better insight in promoting green vehicles to consumers.

The reliability of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras for estimating chlorophyll content, plant height, canopy area, and fruit total number of Lemons (Citrus limon)

Monitoring lemon production requires appropriate and efficient technology. The use of UAVs can addressed these challenges. The purpose of this study was to determine the best vegetation indices (VIs) for estimating chlorophyll content, plant height (PH), canopy area (CA), and fruit total numberas (FTN). CCM 200 was used as a tool to measure the chlorophyll content index (CCI), the number of fruits was measured by hand-counter, and other variables were recorded in meters. The UAV used was a Phantom 4 with a multispectral camera capable of capturing five different bands. The VIs was obtained via analysis of digital numbers generated by the multispectral camera. Then, the VIs was correlated with the CCI, PH, CA and FTN. VIs tested included the following: the normalized difference vegetation index (NDVI), the normalized difference vegetation index-green (NDVIg), the normalized different index (NDI), green minus red (GMR), simple ratio (SR), the Visible Atmospherically Resistant Index (VARI), normalized difference red edge (NDRE), simple ratio red-edge (SR<sub>RE</sub>), the simple ratio vegetation index (SR<sub>VI</sub>), and the Canopy Chlorophyll Content Index (CCCI). The best model for predicting CCI was obtained using the NDVIg (R<sup>2</sup>=0.8480; RMSE=6.1665 and RRMSE=0.0908). Meanwhile, SR turned out to be the best model for predicting PH (R<sup>2</sup>=0.8266; RMSE=15.6432 and RRMSE=0.0883), CA (R<sup>2</sup>=0.6886; RMSE= 0.8826 and RRMSE=0.1907), and FTN (R<sup>2</sup>=0.6850; RMSE=24.5574 and RRMSE=0.3503). The implication of these results for future activities includes establishing early monitoring and evaluation systems for lemon yield and production. This model was developed and tested in this specific location and under these environmental conditions.

Parameterized coefficient fine-tuning-based polynomial chaos expansion method for sphere-biconic reentry vehicle reliability analysis and design

Parameterized coefficient fine-tuning-based polynomial chaos expansion method for sphere-biconic reentry vehicle reliability analysis and design