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World Electr. Veh. J., Volume 15, Issue 12 (December 2024) – 6 articles

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15 pages, 8288 KiB

Open AccessArticle

Optimization of Shift Strategy Based on Vehicle Mass and Road Gradient Estimation

by Huijun Yue, Haobo Jing, Zhenkun Dai, Jinyu Lin, Zihan Ma, Changtong Zhao and Pan Zhang

World Electr. Veh. J. 2024, 15(12), 545; https://doi.org/10.3390/wevj15120545 - 22 Nov 2024

Abstract

For electrically driven commercial vehicles equipped with three-speed automatic mechanical transmission (AMT), the transmission control unit (TCU) without vehicle mass and road gradient estimation function will lead to frequent shifting and insufficient power during vehicle full-load or grade climbing. Therefore, it is necessary to estimate the mass and road gradient for the electrically driven commercial vehicles equipped with the three-speed AMT, and to adjust the shift rule according to the estimation results. Given the above problems, this paper focuses on the control and development of the electrically driven three-speed AMT and takes the shift controller with the vehicle mass and road gradient estimation as the research goal. The mathematical model and simulation model of vehicle dynamics are established to verify the shift function of TCU. The least squares method and calibration techniques are applied to estimate the vehicle mass and road gradient. According to the estimation results, the existing shift strategy is optimized, and the software-in-the-loop simulation of the transmission controller is carried out to verify the function of the control algorithm software. The hardware-in-the-loop test model is established to verify the shift strategy’s optimization effect, which shortens the controller’s forward development cycle. According to the estimation results of mass and gradient, the error result of the proposed method is controlled within 4.5% for mass and 8.6% for gradient. The experiment verifies that the optimized shift strategy can effectively improve the dynamic performance of the vehicle. The HIL experimental results show that the vehicle can maintain low gear while climbing the hill, and the vehicle speed does not decrease significantly. Full article

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25 pages, 7898 KiB

Open AccessArticle

Rolling Bearing Fault Diagnosis Based on Optimized VMD Combining Signal Features and Improved CNN

by Yingyong Zou, Xingkui Zhang, Wenzhuo Zhao and Tao Liu

World Electr. Veh. J. 2024, 15(12), 544; https://doi.org/10.3390/wevj15120544 - 22 Nov 2024

Abstract

Aiming at the problem that the vibration signals of rolling bearings in high-speed rail traction motors are often affected by noise when they are in a fault state, which makes it very difficult to extract the fault features during fault diagnosis and causes obstruction in fault classification. The article proposes a rolling bearing fault diagnosis based on optimized variational mode decomposition (VMD) combined with signal features and an improved convolutional neural network (CNN). The golden jackal optimization (GJO) algorithm is employed to optimize the key parameters of the VMD, enabling effective signal decomposition. The decomposed signals are then filtered and reconstructed using criteria based on kurtosis and interrelationship measures. The time-domain features of the reconstructed signals are computed, and the feature vectors are constructed, which are used as inputs to the deep learning network; the CNN combined with the support vector machine (SVM) network model is used for the extraction of the features and the classification of the faults. The experimental results show that the method can effectively extract fault features in noise-covered signals, and the accuracy is also significantly improved compared with traditional methods. Full article

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20 pages, 11953 KiB

Open AccessArticle

Direct Power Control of Vienna Rectifier Based on Fractional Order Sliding Mode Control

by Tao Wang, Shenhui Chen, Xin Li, Jihui Zhang and Jinghao Ma

World Electr. Veh. J. 2024, 15(12), 543; https://doi.org/10.3390/wevj15120543 - 22 Nov 2024

Abstract

Taking a Vienna rectifier as the research object, the power mathematical model based on a switching function is established according to its working principle. A sliding mode variable structure control algorithm based on the reaching law is examined in order to address the issues of the slow response speed and inadequate anti-interference of classical PI control in the face of abrupt changes in the DC-side load. In response to the sluggish convergence rate and inadequate chattering suppression of classical integer order sliding mode control, a fractional order exponential reaching law sliding mode, direct power control approach with rapid convergence is developed. The fractional calculus is introduced into the sliding mode control, and the dynamic performance and convergence speed of the control system are improved by increasing the degree of freedom of the fractional calculus operator. The method of including a balance factor in the zero-sequence component is employed to address the issue of the midpoint potential equilibrium in the Vienna rectifier. Ultimately, the suggested control is evaluated against classical PI control through simulation analysis and experimental validation. The findings indicate that the proposed technique exhibits rapid convergence, reduced control duration, and enhanced robustness, hence augmenting its resistance to interference. Full article

(This article belongs to the Special Issue Intelligent Electric Vehicle Control, Testing and Evaluation)

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16 pages, 6355 KiB

Open AccessArticle

The Impact of Wafters on the Thermal Properties and Performance of In-Wheel Motor

by Muhammad Hasan Albana, Ary Bachtiar Khrisna Putra and Harus Laksana Guntur

World Electr. Veh. J. 2024, 15(12), 542; https://doi.org/10.3390/wevj15120542 - 21 Nov 2024

Viewed by 401

Abstract

Electric vehicle (EV) proliferation is accelerating, characterized by the rising quantity of electric automobiles on global roadways. The electric machine is a crucial component of an EV, and the heat generated within the motor requires consideration as it impacts performance and longevity. A prevalent form of machine in EV is the in-wheel motor (IWM), which is notable for its compact size. However, it presents more significant cooling challenges. This research offers a new cooling method to cool the IWM. The system consists of wafters mounted on the housing of the IWM. Testing was conducted to determine the effect of wafters on the thermal properties and performance of IWMs. The machine used in this research is a brushless direct current (BLDC) motor featuring an outer rotor configuration and a peak power output of 1.5 kW. Testing was carried out experimentally and by simulation, and the simulation used Ansys Motor-CAD software. The research results show that applying wafers to IWM reduces the temperature of IWM components by up to 13.1%. IWM with wafters results in a torque increase of 0.14%, a power increase of 0.64%, and an efficiency improvement of 0.6% compared to IWM without wafters. Full article

(This article belongs to the Special Issue Design, Analysis and Optimization of Electrical Machines and Drives for Electric Vehicles, 2nd Edition)

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17 pages, 1206 KiB

Open AccessArticle

Multi-Criteria Analysis of Electric Vehicle Motor Technologies: A Review

by Emmanuel Kinoti, Mosetlhe Thapelo and Yusuff Adedayo

World Electr. Veh. J. 2024, 15(12), 541; https://doi.org/10.3390/wevj15120541 - 21 Nov 2024

Viewed by 356

Abstract

The electric vehicle market is constantly evolving, with the research and development efforts to improve motor technologies and address the current challenges to meet the growing demand for sustainable transportation solutions well underway. Electric vehicles are crucial to the global initiative to reduce carbon emissions. The core component of an electric vehicle is its motor drive technology, which has undergone significant advancements and diversification in recent years. Although alternating-current motors, particularly induction and synchronous motors, are widely used for their efficiency and low maintenance, direct-current motors provide high torque and cost-effectiveness advantages. This study examines various electric motor technologies used in electric vehicles and compares them using several parameters, such as reliability, cost, and efficiency. This study presents a multi-criteria comparison of the various electric motors used in the electric traction system to provide a picture that enables selecting the appropriate electrical motor for the intended application. Although the permanent magnet synchronous motor appears to be the popular choice among electric car makers, the proposed comparative study demonstrates that the induction motor matches the essential requirements of electric vehicles. Full article

(This article belongs to the Special Issue Dynamic Modeling, Identification, and Advanced Control of Intelligent Electric Vehicles)

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18 pages, 1518 KiB

Open AccessArticle

VAS-3D: A Visual-Based Alerting System for Detecting Drowsy Drivers in Intelligent Transportation Systems

by Hadi El Zein, Hassan Harb, François Delmotte, Oussama Zahwe and Samir Haddad

World Electr. Veh. J. 2024, 15(12), 540; https://doi.org/10.3390/wevj15120540 - 21 Nov 2024

Viewed by 436

Abstract

Nowadays, driving accidents are considered one of the most crucial challenges for governments and communities that affect transportation systems and peoples lives. Unfortunately, there are many causes behind the accidents; however, drowsiness is one of the main factors that leads to a significant number of injuries and deaths. In order to reduce its effect, researchers and communities have proposed many techniques for detecting drowsiness situations and alerting the driver before an accident occurs. Mostly, the proposed solutions are visually-based, where a camera is positioned in front of the driver to detect their facial behavior and then determine their situation, e.g., drowsy or awake. However, most of the proposed solutions make a trade-off between detection accuracy and speed. In this paper, we propose a novel Visual-based Alerting System for Detecting Drowsy Drivers (VAS-3D) that ensures an optimal trade-off between the accuracy and speed metrics. Mainly, VAS-3D consists of two stages: detection and classification. In the detection stage, we use pre-trained Haar cascade models to detect the face and eyes of the driver. Once the driver’s eyes are detected, the classification stage uses several pre-trained Convolutional Neural Network (CNN) models to classify the driver’s eyes as either open or closed, and consequently their corresponding situation, either awake or drowsy. Subsequently, we tested and compared the performance of several CNN models, such as InceptionV3, MobileNetV2, NASNetMobile, and ResNet50V2. We demonstrated the performance of VAS-3D through simulations on real drowsiness datasets and experiments on real world scenarios based on real video streaming. The obtained results show that VAS-3D can enhance the accuracy detection of drowsy drivers by at least

7.5 %

(the best accuracy reached was

95.5 %

) and the detection speed by up to

57 %

(average of

0.25

ms per frame) compared to other existing models. Full article

(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)

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