World Electr. Veh. J., Volume 15, Issue 9 (September 2024) – 51 articles

Li-ion batteries possess significant advantages like large energy density, fast recharge, and high reliability; hence, they are widely adopted in electric vehicles, portable electronics, and military and aerospace applications. Albeit having their merits, accurate battery modeling is subjected to problems like prior information on internal chemical reactions, complexity in problem formulation, a large number of unknown parameters, and the need for extensive experimentation. Hence, this article presents a reliable Spotted Hyena Optimizer (SHO) to determine the equivalent circuit parameters of lithium-ion (Li-ion) batteries. The methodology of the SHO is derived from the living and hunting tactics of spotted hyenas, and it is efficiently applied to solve the battery parameter estimation problem. Nine unknown battery model parameters of a Samsung INR 18650-25R are determined using this method. The model parameters estimated are endorsed for five different datasets with various discharge current values. Further, the effect of parameter range and its selection is also emphasized. Secondly, for validation, various performance metrics such as Integral Squared Error, mean best, mean worst, and Standard Deviation are evaluated to authenticate the superiority of the proposed parameter extraction. From the computed results, the SHO algorithm is able to explore the search area up to 89% in the case of larger search ranges. The chosen model and range of the SHO precisely predict the behavior of the proposed Li-ion battery, and the results are in accordance with the catalog data. Full article

In this paper, a flux–torque regulation hybrid excitation machine (FTRHEM) with axial–radial dual air gaps, which can increase torque and regulate magnetic flux by changing the exciting current, is studied. Dual air gaps have a huge impact on the magnetic flux and additional torque. The effect of the air gap reluctances on the magnetic flux of the machine is obtained by establishing equivalent magnetic network models, which show that the dual air gaps are the key component in the axial–radial magnetic circuit. This study examines the flux regulation ability and the enhanced torque performance of an FTRHEM with dual air gaps. The mechanism by which the dual air gaps affect the machine’s magnetic field is clarified, and the constraints and relationships between the dual air gaps are explained, offering a theoretical foundation for future machine optimization. As the axial air gap decreases from 0.95 mm to 0.35 mm, the flux regulation capability improves from 15.44% to 26.51%, while the additional torque increases by 40.77%. Ultimately, prototypes are manufactured for experimental testing to validate the viability of the structure and the accuracy of the FEA for the FTRHEM featuring an axial–radial magnetic circuit. Full article

In response to the problem of low optimization efficiency and low tracking accuracy in vehicle path tracking, a comprehensive optimization method is established based on the 3-DOF vehicle motion model. The outer layer adopts the adaptive particle swarm optimization (APSO) method for parameter optimization, and improves the adaptive inertia weight and adaptive particle exploration rate to improve the convergence efficiency and global search ability of the population. The inner layer adopts the segmented Gaussian pseudospectral method (GPM) to optimize the vehicle motion trajectory, and sets continuity constraints to ensure the continuity of the state and control variables at the segmentation points. The inner optimization results are fed back to the outer layer as a reference for the population updating fitness, achieving double-layer iterative optimization. The simulation results show that the proposed APSO-GPM optimization method can effectively solve the vehicle path tracking problem, with a high solving efficiency and stronger global optimization ability. Full article

Aiming at the problem of multiple constraints and low solving efficiency in the process of vehicle path tracking, an improved hp-adaptive Radau pseudospectral method (I-hp-ARPM) which uses a double-layer optimization iteration strategy and the residual of differential algebraic constraints at sampling points with a Gaussian distribution as the error evaluation criterion is proposed. Firstly, a four-DOF vehicle motion model is established. Secondly, on the basis of establishing algebraic differential constraints and path constraints and satisfying the optimization objective function, the I-hp-ARPM is used to transform the optimal control problem (OCP) into a general nonlinear programming problem for solution. Finally, the effectiveness of the proposed method is verified compared with the traditional hp-adaptive pseudospectral method. The simulation results and the virtual test show that there are peak values at 3.5 s and 4.8 s, as well as 6 s, for both the steering wheel angle and the sideslip angle with the condition of μ = 0.8. And also, there are peak values at the times of 3.5 s and 5.5 s, as well as 7.5 s, with the condition of μ = 0.4. This indicates the vehicle can track the reference path well with the control of the proposed algorithm. Both the initial and final constraints, as well as the path constraint, meet the requirements. The proposed method can generate the optimal trajectory that meets various constraint requirements. This method provides a design basis for path tracking of autonomous vehicles and has significance in engineering. Full article

The path tracking control of electric mining articulated vehicles (EMAVs), critical equipment commonly used for mining and transportation in underground mines, is a research topic that has received much attention. The path tracking control of EMAVs is subject to several system constraints, including articulation angle and articulation angular velocity. In light of this, many researchers have initiated studies based on model predictive control (MPC). The principal design schemes for existing MPC methods encompass linear MPC (LMPC) utilizing a single reference point, so named the single reference point LMPC (SRP-LMPC), and nonlinear MPC (NMPC). However, NMPC exhibits suboptimal real-time performance, while SRP-LMPC demonstrates inferior accuracy. To simultaneously improve the accuracy and real-time performance of the path tracking control of EMAV, based on the SRP-LMPC, a path tracking control method for EMAV based on nonlinear compensated multiple reference points LMPC (MRP-LMPC) is proposed. The simulation results demonstrate that MRP-LMPC simultaneously exhibits a commendable degree of accuracy and real-time performance. In all simulation results, the displacement error amplitude and heading error amplitude of MRP-LMPC do not exceed 0.2675 m and 0.1108 rad, respectively. Additionally, the maximum solution time in each control period is 5.9580 ms. The accuracy of MRP-LMPC is comparable to that of NMPC. However, the maximum solution time of MRP-LMPC can be reduced by over 27.81% relative to that of NMPC. Furthermore, the accuracy of MRP-LMPC is significantly superior to that of SRP-LMPC. The maximum displacement and heading error amplitude can be reduced by 0.3075 m and 0.1003 rad, respectively, representing a reduction of 65.51% and 73.59% in the middle speed and above scenario. Full article

The growing electric vehicle (EV) sector, while tackling climate issues, also encounters obstacles concerning resource usage and a rise in EV disposal. This study examines the circularity framework in the EV sector to address these problems, emphasizing the importance of reusing and upcycling resources for sustainability. Moreover, numerous nations have implemented recycling and R&D policies to offer legal and policy backing for the advancement of recycling technology. This report will investigate and contrast various recycling technologies. Furthermore, it involves an examination of key figures in the EV sector to identify deficiencies in the EV materials supply chain and tactics for minimizing waste. Conversations with a variety of EV stakeholders will provide perspectives on the creative methods businesses are implementing to tackle these obstacles. By embracing the principles of a circular economy, the EV industry can act as a model for a sustainable future, decreasing its environmental footprint and encouraging a more efficient use of resources. Full article

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network. Full article

Hybrid electric vehicles (HEVs) are set to play a critical role in the future of the automotive industry. To operate efficiently, HEVs require a robust energy management strategy (EMS) that decides whether the vehicle is powered by the engine or electric motors while managing the battery’s state of charge. The EMS must rapidly adapt to driver demands and optimize energy usage, ideally predicting battery charge rates and fuel consumption to adjust the powertrain in real time, even under unpredictable driving conditions. As HEVs become more prevalent, EMS technologies will advance to improve predictive capabilities. This analysis provides an overview of current EMS systems, including both rule-based and optimization-based approaches. It explores the evolution of EMS development through a technology roadmap, highlighting the integration of advanced algorithms such as reinforcement learning and deep learning. The analysis addresses the technologies that underly this evolution, including machine learning, cloud computing, computer vision, and swarm technology. Key advances and challenges in these technologies are discussed, along with their implications for the next generation of EMS systems for HEVs. The analysis of these technologies indicates that they will play a key role in the evolution of EMS technology, allowing it to better optimize driver needs and fuel economy. Full article

This study examines the thermal behavior of lithium-ion battery modules intended for Urban Air Mobility (UAM), a forthcoming urban transport system designed to facilitate efficient and secure passenger and cargo transport within city centers. UAM applications necessitate batteries with high energy densities capable of sustaining elevated discharge rates during critical phases such as takeoff and landing. The battery module evaluated in this study comprises four cells arranged in series and configured as a submodule for UAM applications. A three-dimensional thermal model was utilized to analyze the impact of external temperature fluctuations and high discharge rates on the performance of the battery module. The numerical findings indicated considerable variations in temperature and internal resistance among the cells, especially under high discharge rates at low temperatures, with a maximum temperature deviation of 32.952 °C observed at an 8 C discharge rate. These thermal non-uniformities were attributed to variations in cell capacity and internal resistance, which were amplified by manufacturing inconsistencies and operational conditions. The study underscores the necessity of robust thermal management strategies to mitigate the risk of thermal runaway and ensure the operational safety and reliability of UAM systems. The results emphasize the critical role of advanced Battery Management Systems (BMS) in monitoring and controlling cell voltage and temperature to achieve consistent performance across the battery module. This research contributes valuable insights into the design of more efficient and reliable battery modules for UAM, highlighting the importance of addressing cell-to-cell performance discrepancies to enhance overall module efficacy and durability. Full article

This study explores the obstacles to electric vehicle (EV) adoption in Indonesia, focusing on technological, environmental, economic, and regulatory factors. Despite government initiatives, such as the Presidential Regulation 55 of 2019, intended to encourage the adoption of EVs and mitigate air pollution, the EV market share in Indonesia remains low, at 1.47%. The main challenges include inadequate charging infrastructure, limited public revenue, and financial constraints. This research highlights the need for improved government policies, incentives for producers, and increased public awareness to encourage EV adoption. Factors influencing consumer decisions include operational costs, environmental concerns, and the availability of charging stations. Key findings suggest that electric motorcycle users have a lower understanding of technology than electric car users, with particular attention to initial costs, maintenance costs, and the accessibility of charging infrastructure. This study recommends that manufacturers and policymakers consider the different preferences of electric car and motorcycle users in their EV adoption promotion strategies. The study seeks to elucidate the determinants affecting EV adoption in Indonesia and propose potential solutions to accelerate the transition to electric mobility. Full article

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, each of which is an individual local learning controller at linear operational location that grows throughout the system’s nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentified dynamical configuration with a VRM drive. By formulating a policy iteration algorithm for VRM applications, the speed of the motor shows inside the machine model, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid would be updated and tuned. Additionally, a multivariate transition algorithm has been adopted to provide a seamless transition between the Q-centers. Finally, simulation and experimental results are presented to confirm the suggested control scheme’s efficacy. Full article

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm. Full article

The direct current (DC) motor is the core part of an electrical vehicle (EV). The unmatched perturbation of load torque is a challenging problem in the control of an EV system driven by a DC motor and hence a deep control concern is required. In this study, the proposed solution is to present two control approaches based on a backstepping control algorithm for speed trajectory tracking of EVs. The first control design is to develop the backstepping controller based on a quasi-sliding mode disturbance observer (BS-QSMDO), and the other controller is to combine the backstepping control with quasi-integral sliding mode control (BS-QISMC). In the sense of Lyapunov-based stability analysis, the ultimate boundedness of the proposed controllers has been detailedly analyzed, assessed, and evaluated in the presence of unmatched perturbation. A modified stability analysis has been presented to determine the ultimate bounds of disturbance estimation error for both controllers. The determination of ultimate bound and region-of-attraction for tracking and estimation errors is the contribution achieved by the proposed control design. The performances of the proposed controllers have been verified via computer simulations and the level of ultimate bounds for the estimation and tracking errors are the key measures for their evaluation. Compared to BS-QISMC, the results showed that a lower level of ultimate boundedness with a higher convergent rate can be reached based on BS-QSMO. However, a higher control effort can be exerted by the BS-QSMO controller as compared to BS-QISMC; and this is the price to be paid by the BS-QSMO controller to achieve lower ultimate boundedness with a faster convergence rate. Full article

The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research by providing a comprehensive analysis of factors influencing the selection of EV battery charging stations. This research focuses on integrating geographic, demographic, technical, and infrastructure considerations to inform strategic placement decisions. A quantitative approach was employed, using questionnaires distributed to 300 entrepreneurs in Thailand’s EV charging station sector. The data were analyzed using descriptive statistics and structural equation modeling (SEM) to evaluate the relationships among the influencing factors. The results reveal that technical and infrastructure factors significantly impact economic and financial considerations, which in turn influence the selection of charging stations. Additionally, geographic and demographic factors play a crucial role in shaping economic outcomes and the strategic placement of these stations. A holistic approach that integrates these diverse factors is essential for the strategic deployment of EV charging infrastructure, which supports increased EV adoption and contributes to environmental sustainability. Full article

With the growing demand and projected shortage of rare earth elements in the near future, the urgent task of developing energy-efficient electrical equipment with less dependence on rare earth magnets has become paramount. The use of permanent magnet-assisted synchronous reluctance motors (PMaSynRMs), which reduce the consumption of rare earth magnets, can help solve this problem. This article presents a theoretical analysis of the characteristics of PMaSynRM in a subway train drive. Options with rare earth and ferrite magnets are considered. Optimization of the motor designs considering the train movement cycle is carried out using the Nelder-Mead method. Characteristics of the motors, such as losses, torque ripple, and inverter power rating, as well as the mass and cost of active materials, are compared. Full article

Accurate battery models are of great significance for the optimization design and management of lithium-ion batteries. This study uses a pseudo-two-dimensional electrochemical model combined with a three-dimensional thermal model to describe the electrodynamics and thermodynamics of commercial LIBs and adopts the concept of variable solid-state diffusion in the electrochemical model to improve the fitting ability of the model. Compared with the discharge curve without the VSSD concept, the progressiveness of the model is verified. On the other hand, by comparing the temperature distribution of batteries with different negative electrode thicknesses, it is found that the battery temperature decreases with the increase in battery thickness. At the same time, with the increase in active material volume fraction, the gradient of electrochemical performance is greater, and the heat generation rate is higher. This model can be used for online management of batteries, such as estimating charging status and internal temperature, and further constructing a lithium battery electrochemical capacity degradation model based on the VSSD concept to study the aging behavior of lithium batteries. Full article

LEVIS is an innovation project funded by the EU Horizon 2020 program. Its main objective is to develop lightweight multi-material solutions based on bio-based materials and carbon fiber thermoplastic composites for electric vehicle components and demonstrating the technical, operational, and economic feasibility of applying eco-design and circular-economy principles into the design process. The project demonstrates the application of these materials in four case studies: a suspension control arm, a battery box, a battery module housing, and a cross-car beam. All demonstrators achieved a 20%-to-40% reduction in component weight, but environmental assessment results varied significantly, with emissions changes ranging from an increase for suspension control arms to a 65.5% decrease for battery modules. Efficient use of materials, particularly in the battery box using hybrid solutions and bonding technologies, showed notable emissions reduction. In contrast, replacing steel with CFRPs in suspension control arms led to increased emissions, suggesting that CFRPs are more effective for replacing high-polluting materials like aluminum. Recycled carbon fibers proved more beneficial for low-polluting materials like steel. The environmental performance of technologies depends on the expected use of EVs and the electricity grid mix, with better outcomes in coal-reliant grids. Finally, no single recycling method is universally superior; the optimal method depends on the specific technologies and the energy required for recycled materials. Full article

Fuel cell hybrid systems, due to their combination of the high energy density of fuel cells and the rapid response capability of power batteries, have become an important category of new energy vehicles. This paper discusses energy management strategies in hydrogen fuel cell vehicles. Firstly, a detailed comparative analysis of existing PID control strategies and Adaptive Equivalent Consumption Minimization Strategies (A-ECMSs) is conducted. It was found that although A-ECMS can balance the energy utilization of the fuel cell and power battery well, the power fluctuations of the fuel cell are significant, leading to increased hydrogen consumption. Therefore, this paper proposes an improved Adaptive Low-Pass Filter Equivalent Consumption Minimization Strategy (A-LPF-ECMS). By introducing low-pass filtering technology, transient changes in fuel cell power are smoothed, effectively reducing fuel consumption. Simulation results show that under the 6*FTP75 cycle, the energy loss of A-LPF-ECMS is reduced by 10.89% (compared to the PID strategy) and the equivalent hydrogen consumption is reduced by 7.1%; under the 5*WLTC cycle, energy loss is reduced by 5.58% and equivalent hydrogen consumption is reduced by 3.18%. The research results indicate that A-LPF-ECMS performs excellently in suppressing fuel cell power fluctuations under idling conditions, significantly enhancing the operational efficiency of the fuel cell and showing high application value. Full article

The electrification of mobility has introduced considerable challenges to distribution networks due to varying demand patterns in both time and location. This underscores the need for adaptable tools to support strategic investments, grid reinforcement, and infrastructure deployment. In this context, the present study employs real-world datasets to propose a comprehensive spatial–temporal energy model that integrates a traffic model and geo-referenced data to realistically evaluate the flexibility potential embedded in the light-duty transportation sector for a given study region. The methodology involves assessing traffic patterns, evaluating the grid impact of EV charging processes, and extending the analysis to flexibility services, particularly in providing primary and tertiary reserves. The analysis is geographically confined to the Lombardy region in Italy, relying on a national survey of 8.2 million trips on a typical day. Given a target EV penetration equal to 2.5%, corresponding to approximately 200,000 EVs in the region, flexibility bands for both services are calculated and economically evaluated. Within the modeled framework, power-intensive services demonstrated significant economic value, constituting over 80% of the entire potential revenues. Considering European markets, the average marginal benefit for each EV owner is in the order of 10 € per year, but revenues could be higher for sub-classes of users better fitting the network needs. Full article

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR’s mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles. Full article

Until recently, electrified aircraft propulsion (EAP) technology development has been driven by the dual objectives of reducing greenhouse gas (GHG) emissions and addressing the depletion of fossil fuels. However, the increasing severity of climate change, posing a significant threat to all life forms, has resulted in the global consensus of achieving net-zero GHG emissions by 2050. This major shift has alerted the aviation electrification industry to consider the following: What is the clear path forward for EAP technology development to support the net-zero GHG goals for large commercial transport aviation? The purpose of this paper is to answer this question. After identifying four types of GHG emissions that should be used as metrics to measure the effectiveness of each technology for GHG reduction, the paper presents three significant categories of GHG reduction efforts regarding the engine, evaluates the potential of EAP technologies within each category as well as combinations of technologies among the different categories using the identified metrics, and thus determines the path forward to support the net-zero GHG objective. Specifically, the paper underscores the need for the aviation electrification industry to adapt, adjust, and integrate its EAP technology development into the emerging new engine classes. These innovations and collaborations are crucial to accelerate net-zero GHG efforts effectively. Full article

The suspension of a car has different structural forms but usually consists of springs, shock absorbers, guiding mechanisms, etc. As a vehicle moves, the terrain often induces a multifaceted non-white noise vibration within the vehicle. Research on this type of vibration often uses the operational modal analysis (OMA) method, due to its advantages of not requiring knowledge of excitation signals. The disadvantage is that it can only analyze systems under white noise excitation, otherwise it will bring errors. So, this paper proposes a frequency domain fitting algorithm (FDFA) based on colored noise excitation. Initially, an exposition on the foundational principles of the FDFA technique was provided, followed by a demonstration of the modal identification approach. Subsequently, a simulation scenario involving a cantilever beam, akin to a suspension system, was chosen for examination in three instances, revealing that the frequency discrepancies are under 2.94%, and for damping coefficients, they are less than 2.76%. In conclusion, the paper’s introduced FDFA technique, along with the frequency–spatial domain decomposition (FSDD) approach, were employed to determine the modal characteristics of aluminum cantilever beams subjected to four distinct colored noise stimulations. The findings indicate that when utilizing the FDFA technique, the error in modal frequency is kept below 2.5%, while the error for the damping ratio does not exceed 15%. Compared with FSDD, the accuracy was improved. Full article

This paper extensively explores the intricate nuances surrounding the delayed transition to new business models for electric vehicles. While there is commendable clarity regarding stakeholders, model possibilities, emission-reduction strategies, state aid initiatives, and citywide prohibitions, the central challenge lies in the gradual pace of this transition. Notably, the persistent high costs of electric vehicles, primarily attributed to exorbitant battery prices and the raw materials involved, represent a formidable hurdle to widespread adoption. In this article, a comprehensive examination of the multifaceted aspects contributing to the delays in the shift towards electrified transport is proposed. By meticulously scrutinizing the intricacies of this delay, the aim is to provide valuable insights that can contribute to accelerating the adoption of electric vehicles. The exploration of these challenges is essential for fostering a nuanced understanding of the impediments hindering the transition and, subsequently, for devising effective strategies to overcome them. The analysis presented herein not only identifies the hurdles but also seeks to offer potential solutions and strategies that can drive the transformative change needed in the realm of electric transportation. Understanding and mitigating the barriers impeding the transition is crucial for fostering a rapid and successful shift towards electric mobility in Spain, ensuring a sustainable and efficient transportation landscape for the future. Full article

In extremely cold environments, when battery electric vehicles (BEVs) are navigating urban roads at low speeds, the limited heating capacity of the on-board heat pump system and positive temperature coefficient (PTC) device can lead to an inevitable decline in battery temperature, potentially falling below its permissible operating range. This situation can subsequently result in vehicle malfunctions and, in severe cases, traffic accidents. Henceforth, a novel battery self-heating method during driving is proposed to maintain battery temperature. This approach is ingeniously embedded within the heating mechanism within the motor driving system without any necessity to alter or modify the existing driving circuitry. In the meantime, the battery voltage can be regulated to prevent it from surpassing the limit, thereby ensuring the battery’s safety. This method introduces the dead zone into the space vector pulse width modulation (SVPWM) algorithm to form the newly proposed dSVPWM algorithm, which successfully changes the direction of the bus current in a PWM period and forms AC, and the amplitude of the battery alternating current (AC) can also be controlled by adjusting the heating intensity defined by the ratio of the dead zone and the compensation vector to the original zero vector. Through the Simulink model of the motor driving system, the temperature hysteresis locking strategy, grounded in the field-oriented control (FOC) method and employing the dSVPWM algorithm, has been confirmed to provide controllable and sufficiently stable motor speed regulation. During the low-speed phase of the China Light Vehicle Test Cycle (CLTC), the battery temperature fluctuation is meticulously maintained within a range of ±0.2 °C. The battery’s minimum temperature has been successfully locked at around −10 °C. In contrast, the battery temperature would decrease by a significant 1.44 °C per minute without the implementation of the temperature-locking strategy. The voltage of the battery pack is always regulated within the range of 255~378 V. It remains within the specified upper and lower thresholds. The battery voltage overrun can be effectively avoided. Full article

The increasing reliance of Vehicular Ad-hoc Networks (VANETs) on the BeiDou Navigation Satellite System (BDS) for precise positioning and timing information has raised significant concerns regarding their vulnerability to spoofing attacks. This research proposes a novel approach to mitigate BeiDou spoofing attacks in VANETs by leveraging a hybrid machine learning model that combines XGBoost and Random Forest with a Kalman Filter for real-time anomaly detection in BeiDou signals. It also introduces a geospatial message authentication mechanism to enhance vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication security. The research investigates low-cost and accessible countermeasures against spoofing attacks using COTS receivers and open-source SDRs. Spoofing attack scenarios are implemented in both software and hardware domains using an open-source BeiDou signal simulator to examine the effects of different spoofing attacks on victim receivers and identify detection methods for each type, focusing on pre-correlation techniques with power-related metrics and signal quality monitoring using correlator values. The emulation results demonstrate the effectiveness of the proposed approach in detecting and mitigating BeiDou spoofing attacks in VANETs, ensuring the integrity and reliability of safety-critical information. This research contributes to the development of robust security mechanisms for VANETs and has practical implications for enhancing the resilience of transportation systems against spoofing threats. Future research will focus on extending the proposed approach to other GNSS constellations and exploring the integration of additional security measures to further strengthen VANET security. Full article

The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities. This paper proposes a comprehensive framework using the Levenberg–Marquardt algorithm (LMA) for validating and identifying lithium-ion battery model parameters to improve the accuracy of state of charge (SOC) estimations, using only discharging measurements in the N-order Thevenin equivalent circuit model, thereby increasing computational efficiency. The framework encompasses two key stages: model parameter identification and model verification. This framework is validated using experimental measurements on the INR 18650-20R battery, produced by Samsung SDI Co., Ltd. (Suwon, Republic of Korea), conducted by the Center for Advanced Life Cycle Engineering (CALCE) battery group at the University of Maryland. The proposed framework demonstrates robustness and accuracy. The results indicate that optimization using only the discharging data suffices for accurate parameter estimation. In addition, it demonstrates excellent agreement with the experimental measurements. The research underscores the effectiveness of the proposed framework in enhancing SOC estimation accuracy, thus contributing significantly to the reliable performance and longevity of lithium-ion batteries in practical applications. Full article

In the realm of urban logistics, optimizing vehicle routes for varied cargo types—including refrigerated, fragile, and standard cargo—poses significant challenges amid complex urban infrastructures and heterogeneous vehicle capacities. This research paper introduces a novel model for the multi-type capacitated vehicle routing problem (MT-CVRP) that harnesses an advanced ant colony optimization algorithm, dubbed Lévy-EGACO. This algorithm integrates Lévy flights and elitist guiding principles, enhancing search efficacy and pheromone update processes. The primary objective of this study is to minimize overall transportation costs while optimizing the efficiency of intricate route planning for vehicles with diverse load capacities. Through rigorous simulation experiments, we corroborated the validity of the proposed model and the effectiveness of the Lévy-EGACO algorithm in optimizing urban cargo transportation routes. Lévy-EGACO demonstrated a consistent reduction in transportation costs, ranging from 1.8% to 2.5% compared to other algorithms, across different test scenarios following base data modifications. These findings reveal that Lévy-EGACO substantially improves route optimization, presenting a robust solution to the challenges of MT-CVRP within urban logistics frameworks. Full article

The trolley problem has long posed a complex ethical challenge in the field of autonomous driving technology. By constructing a general trolley problem model, this paper demonstrates that the default loss assumption is a necessary condition for the occurrence of trolley problems. However, an analysis of the differences between classical trolley problems and autonomous driving scenarios reveals that this assumption is not supported in the design of autonomous driving systems. This paper first provides a detailed definition of the trolley problem within the context of autonomous driving technology and establishes a general trolley problem model to better analyze the issue. We then discuss two solutions: the first solution acknowledges the existence of the trolley problem in the context of autonomous driving technology but does not recognize the existence of a “most acceptable decision”; the second solution denies that decision-makers are limited to a finite number of decisions, each resulting in a corresponding loss. Based on the second solution, we propose a “sufficient time” solution, illustrating that the interaction between planning and control systems in autonomous driving can avoid ethical dilemmas similar to the trolley problem. Finally, we analyze from a philosophical perspective why the trolley problem does not arise in the context of autonomous driving technology and discuss the ethical responsibilities associated with autonomous driving. The design goal of autonomous driving technology should be a zero-accident rate, which contradicts the unavoidable loss assumption in the traditional trolley problem. Therefore, the existence of the trolley problem is unrealistic in the practical application of autonomous driving technology. Full article

The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the trajectory, we formulate an objective function that balances the time required for lane changes with the peak acceleration experienced during the maneuver. The proposed method addresses key challenges such as driver discomfort and prolonged lane change durations by considering the entire lane change process rather than just the initiation point. Utilizing a fifth-order polynomial for trajectory planning, the strategy ensures smooth and continuous vehicle movement, reducing the risk of collisions. The effectiveness of the method is validated through comprehensive simulations and real-world vehicle tests, demonstrating significant improvements in lane change performance. Despite its advantages, the model requires further refinement to address limitations in mixed traffic conditions. This research provides a foundation for developing intelligent vehicle systems that prioritize safety and adaptability. Full article

The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transportation sector. Promising autonomous cars for innovating modern transportation systems are anticipated to address many long-standing transporting challenges related to congestion, safety, parking, and energy conservation. Choosing the optimal AI architecture for autonomous vehicles is a multi-attribute decision-making (MADM) dilemma, as it requires making a complicated decision while considering a number of attributes, and these attributes can have two-dimensional uncertainty as well as indiscernibility. Thus, in this framework, we developed a novel mathematical framework “complex intuitionistic fuzzy rough set” for tackling both two-dimensional uncertainties and indiscernibility. We also developed the elementary operations of the deduced complex intuitionistic fuzzy rough set. Moreover, we developed complex intuitionistic fuzzy rough (weighted averaging, ordered weighted averaging, weighted geometric, and ordered weighted geometric) aggregation operators. Afterward, we developed a method of MADM by employing the devised operators and investigated the case study “Selection of optimal AI architecture for autonomous vehicles” to reveal the practicability of the devised method of MADM. Finally, to reveal the dominance and supremacy of our proposed work, a benchmark dilemma was used for comparison with various prevailing techniques. Full article