World Electr. Veh. J., Volume 15, Issue 8 (August 2024) – 54 articles

To improve the ride comfort and driving stability of automobiles, an optimal sliding mode control (OSMC) strategy based on the enhanced African vultures optimization algorithm (EAVOA) is proposed. Firstly, the structure and operating principle of a semi-active suspension system (SASS) with a magnetorheological damper (MRD) is comprehensively introduced. Secondly, the OSMC is designed based on a quarter-vehicle suspension model with two degrees of freedom (DOF) to meet the Hurwitz stability theory. Simultaneously, the EAVOA is employed to optimize the control coefficients of the sliding mode surface and the control law parameters. Finally, the EAVOA-OSMC control strategy is utilized to construct the simulation model in MATLAB/Simulink (R2018b), providing a comprehensive analysis for passive suspension systems (PSSs) and suspensions with SMC control. The simulation results demonstrate that the EAVOA-OSMC control strategy outperforms SMC controllers, offering a better control performance in real application. Full article

Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges. Full article

The electric vehicle (EV) market is expanding rapidly, highlighting the need for enhanced customer perceived value to foster loyalty and competitive differentiation. This study investigates how experience management tools can improve customer experience management in the EV sector with an emphasis on sustainable business practices and environmental sustainability. The research explores existing customer experience management methods, the necessary functions of these tools, and their effectiveness in enhancing management capabilities from the perspective of customer perceived value. A thorough literature review and empirical analysis were conducted to design and evaluate tailored experience management tools. The findings suggest that these tools can enhance customer satisfaction and loyalty by addressing key elements of perceived value, such as price perception, quality perception, and brand image. Additionally, improved customer experience management may encourage sustainable consumer behaviors by making eco-friendly EVs more appealing, supporting environmental sustainability. This research aims to bridge the gap between customer perceived value theory and its practical application in the EV industry. It offers insights for manufacturers and marketers seeking to create more engaging and sustainable customer experiences. The implications extend beyond the EV market, providing a potential framework for various industries to enhance customer perceived value through effective and sustainable experience management. Full article

To establish battery electric vehicles as an attractive alternative to internal combustion vehicles, charging times of 15 min or less are increasingly demanded. This is especially challenging for lower battery temperatures, as this exacerbates the risk of accelerated battery degradation due to lithium plating. Therefore, active battery heating is utilized in state-of-the-art electric vehicles. To evaluate the impact of such heating strategies at vehicle level, we deployed an electrochemical battery model coupled with a longitudinal vehicle dynamics model. Using anode potential control to prevent lithium plating, we assess the time-saving potential versus the energy cost of different preconditioning and fast-charging strategies. The results reveal substantial energy saving and charge speed increase potential through optimal charge-stop planning, preconditioning timing, cost-adjusted thermal management thresholds, and considering driving behavior. This emphasizes the need for advanced operation strategies, taking into account both battery-level electrical and thermal restrictions, as well as vehicle integration and route planning. Full article

Spoke-type permanent magnet (STPM) machines have high power density and low cost due to flux concentrated effect and high air-gap flux density, but they can cause high cogging torque and torque ripple. To reduce the cogging torque, the analytical model considering a rotor slot is established and compared with the finite element mothed (FEM). Then, the cogging torque production mechanism is revealed and analyzed under different conditions, which provides direction to optimize the cogging torque STPM machines. The harmonic content of cogging torque under different conditions is obtained based on the freezing permeability (FP) method. It is found that the fundamental waves mainly generate the cogging torque under a no-load condition, and it is mainly generated by the second harmonics under an on-load condition. In addition, the optimization method is introduced and researched, including rotor slot width, uneven rotor core, and so on. Finally, a 50 kW STPM machine prototype is manufactured and tested to verify the accuracy and efficiency of the analysis method. Full article

This comprehensive systematic review explores the multifaceted impacts of electric vehicle (EV) adoption across technological, environmental, organizational, and policy dimensions. Drawing from 88 peer-reviewed articles, the study addresses a critical gap in the existing literature, which often isolates the impact of EV adoption without considering holistic effects. Technological advancements include innovations in the battery technology and energy storage systems, enhancing EV performance and mitigating range anxiety. The environmental analysis reveals substantial reductions in greenhouse gas emissions, with lifecycle assessments showing significant reductions for EVs compared to internal combustion engine vehicles, particularly when charged with renewable energy sources. Key comparisons include lifecycle emissions between mid-size battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), and global average lifecycle emissions by powertrain under various policy scenarios. The organizational implications are evident, as businesses adopt new models for fleet management and logistics, leveraging EVs for operational efficiency and sustainability. Policy analysis underscores the crucial role of government incentives, regulatory measures, and infrastructure investments in accelerating EV adoption. The review identifies future research areas such as efficient battery recycling methods, the potential impact of EVs on grid stability, and long-term economic implications. This study offers insights for stakeholders aiming to foster sustainable transportation and achieve global climate goals. Full article

The development of electric vehicles has facilitated intelligent transportation, which requires the swift and effective detection and tracking of moving vehicles. To satisfy this demand, this paper presents an enhanced DeepSORT algorithm. By selecting YOLO-SSFS as the front-end detector and incorporating a lightweight and high-precision feature training network called FasterNet, the proposed method effectively extracts vehicle appearance attributes. Besides this, the noise scale adaptive Kalman filter is implemented and the conventional cascade matching process is substituted with global join matching, thereby enhancing overall performance and tracking accuracy. Validation conducted on the VisDrone dataset demonstrates the superiority of this method compared to the original DeepSORT algorithm, exhibiting a 4.76% increase in tracking accuracy and a 3.10% improvement in tracking precision. The findings reveal the advantages of the algorithms in the domain of vehicle detection and tracking, allowing significant technological advancements in intelligent transportation systems. Full article

The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. Full article

With increasing attention being paid to electric vehicles, hybrid braking systems combining regenerative braking and conventional friction braking have become a hot research topic. Although some important advancements have been achieved in the field of hybrid braking system control, these have not been fully summarized. In order to fill this gap and provide a comprehensive perspective for other researchers, this paper surveys a wide range of research works reported in the literature on hybrid braking system control. We identify the advantages and limitations of existing hybrid brake control strategies via comparative analysis. Through analysis, we find that the control strategy used for brake torque distribution and braking systems’ coordinated control in current hybrid braking systems are usually designed separately. In order to ensure braking stability, most of the current hybrid braking control strategies are designed relatively conservatively, and it is difficult to fully leverage the advantages of hybrid braking systems. Comprehensively considering the coordinated control of braking torque distribution and braking systems is a good research direction for hybrid braking control research. Overall, this survey summarizes the existing research relevant to hybrid brake control methods and also discusses the research challenges and new research directions. Full article

This study uses an evolutionary game model to analyze the interplay between Macao residents’ willingness to purchase electric vehicles (EVs) and the government’s promotion strategies. It assesses the effectiveness of incentives like tax exemptions and price reductions. Despite these initiatives, challenges such as high initial costs, limited vehicle range, and long charging times continue to hinder the widespread adoption of EVs in Macao. Government subsidies increase the appeal of EV purchases, but if not managed carefully, they risk creating dependency. Simulation analysis shows that an active purchasing strategy by Macao residents can stabilize the model’s development. However, to achieve wider market penetration and environmental goals, this study highlights the need for the government to align subsidies with market dynamics and for residents to increase their environmental awareness. This study outlines actionable strategies for policy-makers, emphasizing the importance of infrastructure improvements and financial incentives in promoting electric mobility. Policy-makers should focus on expanding the network of charging stations to enhance the convenience and viability of EV usage. Additionally, implementing targeted financial incentives, such as subsidies or tax breaks, can lower the cost barrier for potential EV buyers, thereby increasing the attractiveness and adoption of electric vehicles. Full article

In this paper, a time-varying delay output feedback control method based on the potential barrier function is proposed, which can solve the communication delay and field-of-view (FOV) constraints of Unmanned Ground Vehicle (UGV) clusters when communicating with ultra-wide-angle cameras. First, a second-order oscillator and an output feedback controller are utilized to feed back the position and direction of neighboring vehicles by exchanging control quantities and to solve the time-varying delay in the position computation of the ultra-wide-angle camera. Due to the limited target radiation range perceived by the camera, an FOV-constrained potential function is adopted to optimize the design of the sliding mode surface. The stability of the closed-loop control system is analyzed by applying the Lyapunov method. Finally, simulation experiments are conducted to verify the effectiveness of the consensus scheme in addressing the communication delay and FOV constraint problem under two different initial conditions. Full article

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model’s focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model’s practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection. Full article

Land vehicle gravity anomaly measurement relies heavily on global navigation satellite system (GNSS). However, when gravity measurement is carried out in special environments such as forests, valleys and tunnels, GNSS observation quality will inevitably decline, which directly affects the accuracy of gravity anomaly measurement. From the point of view of the gravity anomaly measurement principle, obtaining accurate elevation information of the test line is the premise to ensure the accuracy of gravity anomaly measurement. Thus, this paper proposes a strapdown land vehicle dynamic gravity anomaly measurement method combining an odometer and a geo-information system. In this method, strapdown inertial navigation errors are suppressed by observing the velocity of the odometer output. Then, the position information obtained by the combined navigation is entered into the geo-information system to obtain the elevation. The results of a single test line show that the external coincidence accuracy of the proposed method is 1.65 mGal, and the accuracy is comparable to the traditional GNSS assisted land vehicle gravimetry method. In addition, compared with the odometer assisted land vehicle gravimetry method, the external coincidence accuracy is increased by 30%. Full article

This study examines the management of noise, vibration, and harshness (NVH) in electric vehicle (EV) powertrains, considering the challenges of the automotive industry’s transition to electric drivetrains. The growing popularity of electric vehicles brings new NVH challenges as the lack of internal combustion engine noise makes drivetrain noise more prominent. The key to managing NVH in electric vehicle powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces and high-frequency noise generated by inverters, significantly impacts overall NVH performance. This article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. The methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics, the finite element method, and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. This study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components. Full article

This paper presents a comprehensive framework for optimising vehicle performance, integrating advanced simulation techniques with optimisation methodologies. The aim is to find the best racing line, as well as the optimal combination of parameters and control inputs to make a car as fast as possible around a given track, with a focus on energy deployment and recovery, active torque distribution and active aerodynamics. The problem known as the Minimum Lap Time Problem is solved using optimal control methods and direct collocation. The solution covers the modelling of the track, vehicle dynamics, active aerodynamics, and a comprehensive representation of the powertrain including motor, engine, transmission, and drivetrain components. This integrated simulator allows for the exploration of different vehicle configurations and track layouts, providing insights into optimising vehicle design and vehicle control simultaneously for improved performance and energy efficiency. Test results demonstrate the effect of active torque distribution on performance under various conditions, enhanced energy efficiency and performance through regenerative braking, and the added value of including parameter optimisation within the optimisation framework. Notably, the simulations revealed interesting behaviours similar to lift-and-coast strategies, depending on the importance of energy saving, thereby highlighting the effectiveness of the proposed control strategies. Also, results demonstrate the positive effect of active torque distribution on performance under various conditions, attributed to the higher utilization of available adherence. Furthermore, unlike a simpler single-track model, the optimal solution required fine control of the active aerodynamic systems, reflecting the complex interactions between different subsystems that the simulation can capture. Finally, the inclusion of parameter optimisation while considering all active systems, further improves performance and provides valuable insights into the impact of design choices. Full article

The present work is devoted to the analysis of the most important reliability indicators of components of electrical devices of mining dump trucks, and analytical methods of their evaluation are proposed. A mathematical model for calculating the reliability of electrical devices integrated into the electrical systems of quarry dump trucks is presented. The model takes into account various loads arising in the process of operation and their influence on reliability reduction. Optimisation of maintenance and repair schedules of electrical equipment has revealed problems for research. One of them is the classification of electrical equipment by similar residual life, which allows the formation of effective repair and maintenance cycles. The analysis of statistical data on damages revealed the regularities of their occurrence, which is an important factor in assessing the reliability of electrical equipment in mining production. For quantitative assessment of reliability, it is proposed to use the parameter of the average expected operating time per failure. This parameter characterises the relative reliability of electrical equipment and is a determining factor of its reliability. The developed mathematical model of equipment failures with differentiation of maintained equipment by repeated service life allows flexible schedules of maintenance and repair to be created. The realisation of such cycles makes it possible to move from planned repairs to the system of repair according to the actual resource of the equipment. Full article

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed. The findings underscore artificial intelligence’s potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications. Full article

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius. Full article

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities. Full article

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers. Full article

The increasing volume of personal motorized vehicles (PMVs) in cities has become a serious issue leading to congestion, noise, air pollution and high land consumption. To ensure the sustainability of urban transportation, it is imperative to transition the current transportation paradigm toward a more sustainable state. Transitions within socio-technical systems often arise from niche innovation. Therefore, this paper pursues the technical optimization of such a niche innovation by applying a technical sustainability perspective on an innovative mobility and logistics concept within a case study. This case study is based on a centrally managed connected, automated, shared and electric (CASE) vehicle fleet which might replace PMV use in urban city centers of the future. The key technical system components of the envisioned mobility and logistics concept are analyzed and optimized with regard to economic, ecological and social sustainability dimensions to maximize the overall sustainability of the ecosystem. Specifically, this paper identifies key challenges and proposes possible solutions across the vehicle components as well as the orchestration of the vehicles’ operations within the envisioned mobility and logistics concept. Thereby, the case study gives an example of how different engineering disciplines can contribute to different sustainability dimensions, highlighting the interdependences. Finally, the discussion concludes that the early integration of sustainability considerations in the technical optimization efforts of innovative transportation systems can provide an important building block for the transition of the current transportation paradigm to a more sustainable state. Full article

In order to address the challenges posed by the integration of regional electric vehicle (EV) clusters into the grid, it is crucial to fully utilize the scheduling capabilities of EVs. In this study, to investigate the energy storage characteristics of EVs, we first established a single EV virtual energy storage (EVVES) model based on the energy storage characteristics of EVs. We then further integrated four types of EVs within the region to form EV clusters (EVCs) and constructed an EVC virtual energy storage (VES) model to obtain the dynamic charging and discharging boundaries of the EVCs. Next, based on the dispatch framework for the participation of renewable energy sources (RESs) and loads in the distribution network, we established a dual-objective optimization dispatch model, with the objectives of minimizing system operating costs and load fluctuations. We solved this model with NSGA-II and TOPSIS, which guided and optimized the charging and discharging of EVCs. Finally, the simulation results show that the system operating cost was reduced by 7.81%, and the peak-to-valley difference of the load was reduced by 3.83% after optimization. The system effectively achieves load peak shaving and valley filling, improving economic efficiency. Full article

The sealing technology of proton exchange membrane fuel cells (PEMFCs) is a critical factor in ensuring their performance, impacting driving safety and range efficiency. To guarantee the safe operation of PEMFCs in complex environments, it is essential to conduct related sealing research. The structure of the fuel cell sealing system is complex, with components in close contact, and identifying factors that affect its sealing performance is crucial for the development and application of the cells. This paper briefly describes the sealing mechanism of PEMFCs and introduces four typical sealing structures. It considers both the assembly and operation processes, summarizing assembly errors, sealing gaskets, and sealing leaks as well as vibration, cyclic temperature and humidity, and cyclic assembly. The research status of the sealing system in simulations and experiments is reviewed in detail. The key factors affecting the sealing performance of fuel cells are emphasized, highlighting the significance of dynamic detection of the gasket status, stack performance improvement under cumulative errors, and multi-objective optimization models combining contact pressure with the characteristics of stack components. Full article

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases. Full article

The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular networks and Controller Area Network (CAN) protocol leaves vehicles exposed to intrusions. One common attack type is the message injection attack, which inserts fake messages into original Electronic Control Units (ECUs) to trick them or create failures. Therefore, this paper tackles the pressing issue of cyber-attack detection in modern IoV systems, where the increasing connectivity of vehicles to the external world and each other creates a vast attack surface. The vulnerability of in-vehicle networks, particularly the CAN protocol, makes them susceptible to attacks such as message injection, which can have severe consequences. To address this, we propose an intelligent Intrusion detection system (IDS) to detect a wide range of threats utilizing machine learning techniques. However, a significant challenge lies in the inherent imbalance of car-hacking datasets, which can lead to misclassification of attack types. To overcome this, we employ various imbalanced pre-processing techniques, including NearMiss, Random over-sampling (ROS), and TomLinks, to pre-process and handle imbalanced data. Then, various Machine Learning (ML) techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and K-Nearest Neighbors (k-NN), are employed in detecting and predicting attack types on balanced data. We evaluate the performance and efficacy of these techniques using a comprehensive set of evaluation metrics, including accuracy, precision, F1_Score, and recall. This demonstrates how well the suggested IDS detects cyberattacks in external and intra-vehicle vehicular networks using unbalanced data on vehicle hacking. Using k-NN with various resampling techniques, the results show that the proposed system achieves 100% detection rates in testing on the Car-Hacking dataset in comparison with existing work, demonstrating the effectiveness of our approach in protecting modern vehicle systems from advanced threats. Full article

This analysis compares the energy efficiency and operational costs of combustion vehicles (Hyundai Accent 1.6 L and Chevrolet Sail 1.5 L) with the Nissan Leaf, an electric vehicle, under current fuel and electricity pricing in Ecuador. Combustion vehicles, converting gasoline into mechanical energy, demonstrate substantial energy losses, leading to higher operational costs, especially with recent gasoline price hikes to USD 2.722 per gallon. In stark contrast, the Nissan Leaf exhibits significantly greater energy efficiency, consuming only 15–20 kWh per 100 km, which translates to lower running costs (USD 11.20 to fully charge a 40 kWh battery). Despite the clear economic and environmental benefits of electric vehicles, their adoption in Ecuador is hampered by geographical challenges such as diverse terrain that can affect vehicle range and battery longevity. Moreover, the limited and uneven distribution of EV charging stations, mostly concentrated in urban areas, poses significant barriers. For broader implementation, a strategic expansion of the EV infrastructure and careful consideration of the national energy grid’s capacity to support increased electric vehicle uptake are essential. Addressing these challenges is crucial for realizing the full potential of electric vehicles in enhancing Ecuador’s sustainability and energy independence. Full article

The global shift towards electric vehicles (EVs) has necessitated a paradigm shift in engineering education, emphasizing hands-on experiences and innovative learning approaches. This review article presents a comprehensive analysis of the design and fabrication process of an educational EV prototype, highlighting its significance in preparing future engineers for the rapidly evolving EV industry. The article delves into the historical development and recent trends in EVs, providing context for the growing importance of practical skills in this field. A detailed examination of the key components and systems in modern EVs, such as battery packs, electric motors, transmission systems, and chassis design, lays the foundation for understanding the complexities involved in EV prototype development. The methodology section explores the research approach, conceptual design, simulations, material selection, and construction techniques employed in the creation of an educational EV prototype. The evaluation and testing phase assesses the prototype’s performance, safety, and reliability, offering valuable insights into the lessons learned and areas for improvement. The impact of such projects on engineering education is discussed, emphasizing the importance of hands-on learning experiences and interdisciplinary collaboration in preparing students for future careers in the EV industry. The article concludes by addressing common challenges faced during EV prototype projects and providing recommendations for future educational initiatives in this field. Full article

For electric vehicle (EV) charging in North America, three AC connectors are standardized, resulting in a proliferation of charging stations which can only charge one of the three types of EV. We propose a “Universal EV Outlet” that works with an EV “carry along” charging cable—one end of the cable has a connector specific to that user’s EV, the other a plug for the Universal EV Outlet. This proposal does not interfere with, nor require change to, any existing charging stations. It does not require any new types of inlets on EVs. The components are already standardized. Eight use cases are examined to illustrate the advantages, and some limitations, of the Universal EV Outlet. The use cases illustrate how this solution: resolves the problem of multiple AC charging connectors, makes today’s “EV Ready” building codes more adaptable, lowers capital and maintenance costs, creates a solution to curbside and urban charging, increases energy efficiency, enables higher power three-phase AC charging for heavy vehicles, and facilitates use of EVs for building backup power and for vehicle-to-grid. Finally, we propose a standards-based active cable used with the Universal EV Outlet, which would allow fast and secure EV identification for curbside or other shared charging locations, usable today without modifications to current EVs. Full article

The energy management system of a fuel cell electric robot should be highly responsive to provide the required power for various tactical operations, navigation of different routes, and acceleration. This paper presents a new multi-level online energy management strategy for a fuel cell electric robot based on the proposed functions of equivalent hydrogen fuel value evaluation, classification of the battery state of charge via the squared combined efficiency function, identification of the robot maneuver condition based on the proposed operation state of robot function, improvement of the overall energy efficiency based on the proposed function of the battery overcharge control, and separation of the functional points of the fuel cell based on the operational mode control strategy. The simulation study of the proposed online multi-level energy management strategy was carried out with MATLAB R2018b software to verify its superiority by comparing with other strategies. The results indicate a reduction in hydrogen consumption, reduction in fuel cell power fluctuations, prevention of battery overcharging, and incrementation in the total energy efficiency of energy storage systems compared to other energy management strategies. Full article

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero. Full article