World Electric Vehicle Journal

15 pages, 331 KiB

Open AccessReview

An Overview of Model-Free Adaptive Control for the Wheeled Mobile Robot

by Chen Zhang, Chen Cen and Jiahui Huang

World Electr. Veh. J. 2024, 15(9), 396; https://doi.org/10.3390/wevj15090396 - 29 Aug 2024

Control technology for wheeled mobile robots is one of the core focuses in the current field of robotics research. Within this domain, model-free adaptive control (MFAC) methods, with their advanced data-driven strategies, have garnered widespread attention. The unique characteristic of these methods is [...] Read more.

Control technology for wheeled mobile robots is one of the core focuses in the current field of robotics research. Within this domain, model-free adaptive control (MFAC) methods, with their advanced data-driven strategies, have garnered widespread attention. The unique characteristic of these methods is their ability to operate without relying on prior model information of the control system, which showcases their exceptional capability in ensuring closed-loop system stability. This paper extensively details three dynamic linearization techniques of MFAC: compact form dynamic linearization, partial form dynamic linearization and full form dynamic linearization. These techniques lay a solid theoretical foundation for MFAC. Subsequently, the article delves into some advanced MFAC schemes, such as dynamic event-triggered MFAC and iterative learning MFAC. These schemes further enhance the efficiency and intelligence level of control systems. In the concluding section, the paper briefly discusses the future development potential and possible research directions of MFAC, aiming to offer references and insights for future innovations in control technology for wheeled mobile robots. Full article

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20 pages, 1473 KiB

Open AccessArticle

Optimized Feature Selection for DDoS Attack Recognition and Mitigation in SD-VANETs

by Usman Tariq

World Electr. Veh. J. 2024, 15(9), 395; https://doi.org/10.3390/wevj15090395 - 28 Aug 2024

Viewed by 326

Abstract

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the [...] Read more.

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. Full article

(This article belongs to the Special Issue Recent Developments and Research in Vehicular Ad Hoc Networks (VANETs))

23 pages, 8240 KiB

Open AccessReview

Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility

by Sandeep Rawat, Sushabhan Choudhury, Devender Kumar Saini and Yogesh Chandra Gupta

World Electr. Veh. J. 2024, 15(9), 394; https://doi.org/10.3390/wevj15090394 - 28 Aug 2024

Viewed by 228

Abstract

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, [...] Read more.

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, is gaining traction among researchers as a viable alternative to achieve enhanced energy and power densities. This review paper provides a comprehensive overview of recent research and progress in this domain, emphasizing the significance of battery architectures in enabling the widespread adoption of electric mobility. Beginning with an exploration of fundamental principles underlying LIB systems, the paper discusses various architectures involving different cell form factors, like pouch cells, cylindrical cells, and prismatic cells, along with their advantages and limitations. Furthermore, it reviews recent research trends, highlighting innovations aimed at enhancing battery performance, energy density, and safety through advanced battery system architecture. Through case studies and discussions on challenges and future directions, the paper underscores the critical role of advanced battery system architecture in driving the evolution of e-mobility and shaping the sustainable transportation landscape. Full article

(This article belongs to the Topic Battery Design and Management)

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13 pages, 527 KiB

Open AccessSystematic Review

Backcasting Analysis of Autonomous Vehicle Implementation: A Systematic Review

by Fabricio Esteban Espinoza-Molina, Juan Diego Valladolid, Pablo Barbecho Bautista, Emilio Quinde, Ruffo Villa Uvidia, Javier Stalin Vazquez Salazar and Gustavo Javier Aguilar Miranda

World Electr. Veh. J. 2024, 15(9), 393; https://doi.org/10.3390/wevj15090393 - 28 Aug 2024

Viewed by 210

Abstract

The introduction of autonomous vehicles (AVs) has the potential to drastically change society, planning, design, and development strategies. This study uses the PRISMA protocol to carry out a systematic literature review, focusing on the backcasting method as an analytic tool. By examining. 21 [...] Read more.

The introduction of autonomous vehicles (AVs) has the potential to drastically change society, planning, design, and development strategies. This study uses the PRISMA protocol to carry out a systematic literature review, focusing on the backcasting method as an analytic tool. By examining. 21 studies published between 2003 and 2024, this paper highlights the phases of backcasting: visioning, policy packaging, and appraisal, and identifies critical factors necessary for the successful integration of AVs. Visioning for future driverless cities includes high-quality urban areas, active mobility, and innovative developments. Policies and Packaging suggested a focus on restricting vehicular access, transit-oriented development, and encouraging public transportation. Appraisal reveals skepticism about the positive impacts of AVs, urging policies that limit access to urban areas and promote sustainable modes of transportation. The main contribution of this study lies in its comprehensive application of backcasting to AV implementation, offering a structured approach to envisioning future urban scenarios, formulating supportive policies, and evaluating their impact. This analysis provides a solid foundation for future research, urging us to explore the intersection between AVs, citizen participation, and environmental sustainability to achieve more efficient and sustainable cities. Full article

(This article belongs to the Special Issue Changes in Travel Behavior When Autonomous Vehicles Are Integrated into the Existing Transport System in Urban Cities)

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28 pages, 1444 KiB

Open AccessArticle

A Novel Charging Management and Security Framework for the Electric Vehicle (EV) Ecosystem

by Safa Hamdare, David J. Brown, Yue Cao, Mohammad Aljaidi, Sushil Kumar, Rakan Alanazi, Manish Jugran, Pratik Vyas and Omprakash Kaiwartya

World Electr. Veh. J. 2024, 15(9), 392; https://doi.org/10.3390/wevj15090392 - 28 Aug 2024

Viewed by 216

Abstract

The EV charging network has witnessed significant growth in the UK in the last few years due to the net zero emission target of the government by 2030. The related literature in EV charging management mainly focuses on road-traffic-parameter-based optimization and lacks detail [...] Read more.

The EV charging network has witnessed significant growth in the UK in the last few years due to the net zero emission target of the government by 2030. The related literature in EV charging management mainly focuses on road-traffic-parameter-based optimization and lacks detail in terms of charging statistics and cyber–security-enabled charging management frameworks. In this context, this paper proposes a novel EV Charging Management and Security (EVCMS) framework using real-time charging statistics and an Open Charge Point Protocol (OCPP). Specifically, a system model for EVCMS is presented considering charging data management and security protocols. An EVCMS framework design is detailed, focusing on charging pricing, optimization, and charging security. The experimental implementation is described in terms of client–server and charge-box-based simulation. The performance of the proposed EVCMS framework is evaluated by considering different charging scenarios and a range of charging-related metrics. An analysis of results and comparative study attest to the benefits of the proposed EVCMS framework for enabling the EV charging ecosystem. Full article

(This article belongs to the Special Issue New Trends in Electrical Drives for EV Applications)

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18 pages, 5526 KiB

Open AccessArticle

State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter

by Qian Huang, Junting Li, Qingshan Xu, Chao He, Chenxi Yang, Li Cai, Qipin Xu, Lihong Xiang, Xiaojiang Zou and Xiaochuan Li

World Electr. Veh. J. 2024, 15(9), 391; https://doi.org/10.3390/wevj15090391 - 28 Aug 2024

Viewed by 221

Abstract

A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently [...] Read more.

A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence. Full article

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14 pages, 4612 KiB

Open AccessArticle

A Simplified 4-DOF Dynamic Model of a Series-Parallel Hybrid Electric Vehicle

by Lihong Dai, Peng Hu, Tianyou Wang, Guosheng Bian and Haoye Liu

World Electr. Veh. J. 2024, 15(9), 390; https://doi.org/10.3390/wevj15090390 - 28 Aug 2024

Viewed by 215

Abstract

To research the dynamic response of a new type of dedicated transmission for a hybrid electric vehicle, a detailed dynamics model should be built. However, a model with too many degrees of freedom has a negative effect on controller design, which means the [...] Read more.

To research the dynamic response of a new type of dedicated transmission for a hybrid electric vehicle, a detailed dynamics model should be built. However, a model with too many degrees of freedom has a negative effect on controller design, which means the detailed model should be simplified. In this paper, two dynamic models are established. One is an original and detailed powertrain dynamics model (ODPDM), which can capture the transient response, and it is validated that the ODPDM can be used to accurately describe the real vehicle in some specific operating conditions. The other is a simplified torsional vibration dynamics model to study the torsional vibration characteristics of the hybrid electric vehicle. Compared with the full-order model, which is based on the ODPDM, the simplified model has a very similar vibration in low frequency. This study provides a basis for further vibration control of the hybrid powertrain during the process of a driving-mode switch. Full article

(This article belongs to the Special Issue Dynamics, Control and Simulation of Electrified Vehicles)

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28 pages, 6157 KiB

Open AccessArticle

An Elderly-Oriented Form Design of Low-Speed New Energy Vehicles Based on Rough Set Theory and Support Vector Regression

by Zimo Chen

World Electr. Veh. J. 2024, 15(9), 389; https://doi.org/10.3390/wevj15090389 - 28 Aug 2024

Viewed by 230

Abstract

With the current trend of social aging, the travel needs of the elderly are increasingly prominent. As a means of urban transportation, low-speed new energy vehicles (NEVs) are widely used among the elderly. Many studies are devoted to exploring the function of cars [...] Read more.

With the current trend of social aging, the travel needs of the elderly are increasingly prominent. As a means of urban transportation, low-speed new energy vehicles (NEVs) are widely used among the elderly. Many studies are devoted to exploring the function of cars and the travel modes that meet the needs of older people. However, in addition to product performance, the Kansei needs of users also play a key role in communication between enterprises and users. Therefore, the problem of how to improve car shapes in the initial stage of design to meet the Kansei needs of elderly users remains to be solved. In order to fill this gap, the design of low-speed NEVs are selected as the study objects so as to explore the relationship between the visual perception of elderly users and car design; thus, a design method for the form of elderly-oriented cars is proposed. Firstly, using the research framework of Kansei engineering, factor analysis is used to cluster elderly-oriented Kansei factors. Second, the cars’ appearances are deconstructed by morphological analysis, and the key design features affecting elderly-oriented satisfaction are identified by a rough set attribute reduction algorithm. Finally, support vector regression is used to establish a mapping model of elderly-oriented Kansei factors and the key design features to predict the elderly-oriented form design of optimal low-speed NEVs. The research results show that selecting “Hub6”, “Headlight9”, “Car side view2”, “Rearview mirror9”, and “Front door10” in the form deconstruction table for low-speed NEVs can elicit optimal emotions in elderly users. The research results enable enterprises to more effectively understand the emotional cognition of elderly users related to the form of low-speed NEVs and improve the purchase desire and satisfaction of elderly users, providing references and guidance for the elderly-oriented design and development of intelligent transportation tools. Full article

(This article belongs to the Special Issue Design, Modelling and Control Strategies for Hybrid and Electric Vehicles)

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14 pages, 9376 KiB

Open AccessArticle

Research on Motion Control and Compensation of UAV Shipborne Autonomous Landing Platform

by Xin Liu, Mingzhi Shao, Tengwen Zhang, Hansheng Zhou, Lei Song, Fengguang Jia, Chengmeng Sun and Zhuoyi Yang

World Electr. Veh. J. 2024, 15(9), 388; https://doi.org/10.3390/wevj15090388 - 27 Aug 2024

Viewed by 334

Abstract

As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV [...] Read more.

As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV landing platform to address the impact of complex sea conditions on the stability of UAV landing platforms. Firstly, the parallel Stewart platform was introduced as the landing platform, and its structure was analyzed with forward and inverse kinematic calculations conducted in Matlab to verify its accuracy. Secondly, a least-squares recursive AR prediction algorithm was designed to predict the future attitudes of ships under varying sea conditions. Finally, the prediction algorithm was combined with the platform’s control strategy and a dual-sensor system was adopted to ensure the stability of the UAV landing process. The experimental results demonstrate that these innovative improvements enhanced the compensation accuracy by 59.6%, 60.3%, 48.4%, and 47.9% for the rolling angles of 5° and 10° and the pitching angles of 5° and 10°, respectively. Additionally, the compensation accuracy for the roll and pitch in sea states 2 and 5 improved by 51.2%, 59.4%, 58.7%, and 55.9%, respectively, providing technical support for UAV missions such as maritime rescue and exploration. Full article

(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)

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25 pages, 6088 KiB

Open AccessArticle

Optimized Longitudinal and Lateral Control Strategy of Intelligent Vehicles Based on Adaptive Sliding Mode Control

by Yun Wang, Zhanpeng Wang, Dapai Shi, Fulin Chu, Junjie Guo and Jiaheng Wang

World Electr. Veh. J. 2024, 15(9), 387; https://doi.org/10.3390/wevj15090387 - 27 Aug 2024

Viewed by 229

Abstract

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode [...] Read more.

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode control (ASMC). First, a three-degree-of-freedom (3-DOF) vehicle dynamics model was established. Next, under the fuzzy adaptive Unscented Kalman filter (UKF) theory, the vehicle state parameter estimation and road adhesion coefficient (RAC) observer were designed to estimate vehicle speed (VS), yaw rate (YR), sideslip angle (SA), and RAC. Then, a layered control concept was adopted to design the path-tracking controller, with a target VS, YR, and SA as control objectives. An upper-level adaptive sliding mode controller was designed using RBF neural networks, while a lower-level tire force distribution controller was designed using distributed sequential quadratic programming (DSQP) to obtain an optimal tire driving force. Finally, the control strategy was validated using Carsim and Matlab/Simulink software under different road adhesion coefficients and speeds. The findings indicate that the optimized control strategy is capable of adaptively adjusting control parameters to accommodate various complex conditions, enhancing the tracking precision and robustness of vehicles even further. Full article

(This article belongs to the Special Issue Advanced Vehicle System Dynamics and Control)

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19 pages, 636 KiB

Open AccessArticle

The Decision-Making Processes for Consumer Electric Vehicle Adoption Based on a Goal-Directed Behavior Model

by Xiuhong He and Yingying Hu

World Electr. Veh. J. 2024, 15(9), 386; https://doi.org/10.3390/wevj15090386 - 26 Aug 2024

Viewed by 343

Abstract

Electric vehicles (EVs) are increasingly recognized as a viable strategy for mitigating energy consumption and reducing greenhouse gas emissions within the transportation sector. In order to facilitate the advancement of EVs, this study expands upon the model of goal-directed behavior by integrating the [...] Read more.

Electric vehicles (EVs) are increasingly recognized as a viable strategy for mitigating energy consumption and reducing greenhouse gas emissions within the transportation sector. In order to facilitate the advancement of EVs, this study expands upon the model of goal-directed behavior by integrating the novel concept of perceived consumer effectiveness (PCE) to examine the decision-making process of consumers regarding EV adoption. The model was empirically tested using data gathered from 398 participants in China. The results indicate that factors such as attitude, subjective norm, anticipated positive and negative emotions, and PCE significantly enhance consumers’ desire to adopt EVs, which subsequently affects their behavioral intentions. Notably, the relationship between desire and behavioral intention is moderated by PCE. The insights derived from this study enhance the understanding of EV adoption behaviors and offer strategic recommendations for promoting electric vehicles. Full article

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23 pages, 5012 KiB

Open AccessArticle

State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model

by Raid Mohsen Alhazmi

World Electr. Veh. J. 2024, 15(9), 385; https://doi.org/10.3390/wevj15090385 - 25 Aug 2024

Viewed by 400

Abstract

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the [...] Read more.

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model’s performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH. Full article

(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)

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30 pages, 8447 KiB

Open AccessReview

Aircraft Electrification: Insights from a Cross-Sectional Thematic and Bibliometric Analysis

by Raj Bridgelall

World Electr. Veh. J. 2024, 15(9), 384; https://doi.org/10.3390/wevj15090384 - 24 Aug 2024

Viewed by 364

Abstract

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, [...] Read more.

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. This study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations. Full article

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19 pages, 1384 KiB

Open AccessArticle

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

by Omer Melih Gul

World Electr. Veh. J. 2024, 15(9), 383; https://doi.org/10.3390/wevj15090383 - 24 Aug 2024

Viewed by 285

Abstract

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be [...] Read more.

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach. Full article

(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)

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19 pages, 6138 KiB

Open AccessArticle

Visual Detection of Traffic Incident through Automatic Monitoring of Vehicle Activities

by Abdul Karim, Muhammad Amir Raza, Yahya Z. Alharthi, Ghulam Abbas, Salwa Othmen, Md. Shouquat Hossain, Afroza Nahar and Paolo Mercorelli

World Electr. Veh. J. 2024, 15(9), 382; https://doi.org/10.3390/wevj15090382 - 23 Aug 2024

Viewed by 456

Abstract

Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 [...] Read more.

Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency. Full article

(This article belongs to the Special Issue Vehicle Safe Motion in Mixed Vehicle Technologies Environment)

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44 pages, 973 KiB

Open AccessReview

A Review on State-of-Charge Estimation Methods, Energy Storage Technologies and State-of-the-Art Simulators: Recent Developments and Challenges

by Tawanda Kunatsa, Herman C. Myburgh and Allan De Freitas

World Electr. Veh. J. 2024, 15(9), 381; https://doi.org/10.3390/wevj15090381 - 23 Aug 2024

Viewed by 471

Abstract

Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and [...] Read more.

Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and are usually monitored by management systems to achieve optimal use and protection. Coming up with effective methods for battery management systems that can adequately estimate the state-of-charge of batteries has become a great challenge that has been studied in the literature for some time. Hence, this paper analyses the different energy storage technologies, highlighting their merits and demerits. The various estimation methods for state-of-charge are discussed, and their merits and demerits are compared, while possible applications are pointed out. Furthermore, factors affecting the battery state-of-charge and approaches to managing the same are discussed and analysed. The different modelling tools used to carry out simulations for energy storage experiments are analysed and discussed. Additionally, a quantitative comparison of different technical and economic modelling simulators for energy storage applications is presented. Previous research works have been found to lack accuracy under varying conditions and ageing effects; as such, integrating hybrid approaches for enhanced accuracy in state-of-charge estimations is advised. With regards to energy storage technologies, exploring alternative materials for improved energy density, safety and sustainability exists as a huge research gap. The development of effective battery management systems for optimisation and control is yet to be fully exploited. When it comes to state-of-the-art simulators, integrating multiscale models for comprehensive understanding is of utmost importance. Enhancing adaptability across diverse battery chemistries and rigorous validation with real-world data is essential. To sum up the paper, future research directions and a conclusion are given. Full article

(This article belongs to the Special Issue Sustainable EV Rapid Charging, Challenges, and Development)

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World Electr. Veh. J., Volume 15, Issue 9 (September 2024) – 16 articles

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