Editor’s Choice Articles

This paper demonstrates the use of an Excel-based tool called the “Electric Vehicle-Charging Infrastructure Financial Analysis Spreadsheet Tool”, or “EVCI-FAST”, developed to analyze public–private partnership approaches to deliver publicly accessible EV-charging infrastructure that would not be commercially viable without a government subsidy. To demonstrate the use of this tool, we conducted a high-level screening analysis for a hypothetical bundle of publicly accessible EV-charging stations to assess the financial viability of delivering electric vehicle-charging infrastructure (EVCI) using alternative public–private partnership (P3) structures. This demonstration suggests that the EVCI-FAST could assist public agencies in determining whether their budgetary resources are adequate to support a proposed P3 for an EVCI project. The demonstration suggests that the EVCI-FAST could also help agencies decide which P3 structuring option would best meet their financial objectives. The results from the analysis of the hypothetical project suggest that public agencies could benefit considerably from a P3 structure that uses a minimum revenue guarantee to reduce revenue risk for the private partner. Full article

A new graduate degree programme, Master of Science in Electric Vehicles (MScEV), for engineering students is presented, which is timely and vital for modern society. The purpose of this programme is to provide graduate students with up-to-date knowledge and skills that can enhance their career prospects in the fast-growing electric vehicle (EV) community. The programme not only provides technological knowledge in system design, operation, and management of EVs, but also involves research training in specific EV topics. This paper first outlines the rationale of the programme and reveals the shortcomings of existing EV education. Then, the curriculum structure of the newly developed MScEV programme as well as the corresponding core and elective courses are discussed. Finally, the findings of this programme are evaluated, indicating that the programme is attractive to an overwhelming number of students from diverse engineering backgrounds, as evidenced by the applicants’ and admittees’ degree qualifications and work experiences. Full article

The transition to sustainable mobility is one of the most pressing and complex challenges for the automotive industry, with impacts that extend beyond the mere reduction of emissions. Electric vehicles, while at the center of this evolution, raise questions about the consumption of natural resources, such as lithium, copper, and cobalt, and their long-term sustainability. In addition, the introduction of advanced technologies, including artificial intelligence (AI) and autonomous systems, brings new challenges related to the management of components and materials needed for their production, creating a significant impact on supply chains. The growing demand for electric and autonomous vehicles is pushing the industry to rethink production models, favoring the adoption of circular economy principles to minimize waste and optimize the use of resources. To better understand the implications of this transition, this study adopts a multiple case study methodology, which allows in-depth exploration of different contexts and scenarios, and analysis of real cases of dismantling and recycling of internal combustion engines (ICEs) and electric vehicles (EVs). The research includes a financial simulation and a comparison of revenues from the dismantling of ICE and EV vehicles, highlighting differences in the value of recycled materials and the effectiveness of circular economy practices applied to the two types of vehicles. This approach provides a detailed overview of the economic benefits and challenges related to the management of the end of life of vehicles, helping to outline optimal strategies for a sustainable and cost-effective future in the automotive sector. Full article

A ubiquitous network of charging stations is vital to facilitate the adoption of electric vehicles (EVs) and the achievement of a low-carbon transportation system. Currently, the availability of EV infrastructure differs significantly between communities as planning procedures are not necessarily equitable. Understanding the charging behavior of EV users is a crucial step toward creating an electric vehicle service equipment (EVSE) infrastructure that serves users efficiently, equitably, and sustainably. Presently, public charging station deployment efforts differ across communities, with little context surrounding urbanicity. This study analyzes data from 66 public Level 2 charging stations across Rhode Island. Motivated by the significant disparities in infrastructure availability between urban and rural areas, the research explores behavioral differences to inform infrastructure planning. Key findings reveal that urban stations are predominantly used during weekdays, with longer charging durations and higher energy consumption, whereas rural stations are primarily utilized on weekends and exhibit shorter, more efficient charging sessions. On average, dwell times at rural stations are approximately 50% shorter, while average energy demand is only 7% less. These results provide actionable insights for optimizing charging station deployment and utilization across diverse communities to support the growing demand for EVs. Full article

The research objective of this paper is to analyse what is behind the self-driving offer implemented in Phoenix (Arizona) by Waymo and a normal rent-a-car company by modelling both in e³value. A gap analysis proposes a new model of the rent-a-car business with the integration of a shared autonomous vehicle ride-hailing service. The goal is to encourage the growth of additional global shared autonomous vehicle trials and their incorporation into conventional businesses. The primary objective is to enhance shared autonomous mobility options, resulting in increased road safety, decreased traffic, and decreased emissions in urban areas. As a result, modelling Waymo can serve as a foundation for expanding the use of shared autonomous vehicles by other businesses in different geographic areas. Full article

In the Industry 4.0 era, integrating artificial intelligence (AI) with battery prognostics and health management (PHM) offers transformative solutions to the challenges posed by the complex nature of battery systems. These systems, known for their dynamic and nonl*-inear behavior, often exceed the capabilities of traditional PHM approaches, which struggle to account for the interplay of multiple physical domains and scales. By harnessing technologies such as big data analytics, cloud computing, the Internet of Things (IoT), and deep learning, AI provides robust, data-driven solutions for capturing and predicting battery degradation. These advancements address long-standing limitations in battery prognostics, enabling more accurate and reliable performance assessments. The convergence of AI with Industry 4.0 technologies not only resolves existing challenges but also introduces innovative approaches that enhance the adaptability and precision of battery health management. This perspective highlights recent progress in battery PHM and explores the shift from traditional methods to AI-powered, data-centric frameworks. By enabling more precise and scalable monitoring and prediction of battery health, this transition marks a significant step forward in advancing the field. Full article

This paper compares the electromagnetic performances of radial-flux, dual-rotor, permanent magnet (DRPM) machines with series (S) and parallel (P) magnetic circuits for two rotors, i.e., SDRPM and PDRPM, accounting for different slot/pole number combinations, stator winding configurations, and machine sizes. The machines are optimized using the finite element analysis (FEA) based on the genetic algorithm. It shows that the PDRPM machine with the tooth coil (TC) configuration has the highest permanent magnet (PM) utilisation compared to the PDRPM with toroidal winding (TW) configuration and the SDRPM machine with the TC configuration under different slot/pole number combinations. The scaling effects of the machine size on the torque have been investigated. The TW-PDRPM machine is suitable for large-radius and short-axial length applications due to the short end-winding length of the TW configuration, while the TC-PDRPM is better for small-radius and long-axial length applications. The TC-SDRPM performs well when both the machine outer radius and axial length increase. Finally, the TC-SDRPM and TW-PDRPM machines are prototyped and validated experimentally. Full article

Due to developments in vehicle engineering and communication technologies, vehicular networks have become an attractive and feasible solution for the future of electric, autonomous, and connected vehicles. Electric autonomous vehicles will require more data, computing resources, and communication capabilities to support them. The combination of vehicles, the Internet, and cloud computing together to form vehicular cloud computing (VCC), vehicular edge computing (VEC), and vehicular fog computing (VFC) can facilitate the development of electric autonomous vehicles. However, more connected and engaged nodes also increase the system’s vulnerability to cybersecurity and privacy breaches. Various security and privacy challenges in vehicular cloud computing and its variants (VEC, VFC) can be efficiently tackled using machine learning (ML). In this paper, we adopt a semi-systematic literature review to select 85 articles related to the application of ML for cybersecurity and privacy protection based on VCC. They were categorized into four research themes: intrusion detection system, anomaly vehicle detection, task offloading security and privacy, and privacy protection. A list of suitable ML algorithms and their strengths and weaknesses is summarized according to the characteristics of each research topic. The performance of different ML algorithms in the literature is also collated and compared. Finally, the paper discusses the challenges and future research directions of ML algorithms when applied to vehicular cloud computing. Full article

The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The model integrates advanced components such as a Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamic user interface. Important aspects include analyzing power consumption, forecasting urban energy demand, and monitoring the State of Charge (SoC) of FEV batteries using innovative algorithms validated through real-world applications in Valencia (Spain) and Ljubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) and energy demand forecasting (MSE ~6 × 10⁻⁴), demonstrating the model’s reliability and adaptability across diverse urban environments. This research contributes to the development of resilient, efficient, and sustainable smart city frameworks, emphasizing real-time data-driven decision-making in energy and mobility management. Full article

This study presents an adaptation of the YOLOv4 deep learning algorithm for 3D object detection, addressing a critical challenge in autonomous vehicle (AV) systems: accurate real-time perception of the surrounding environment in three dimensions. Traditional 2D detection methods, while efficient, fall short in providing the depth and spatial information necessary for safe navigation. This research modifies the YOLOv4 architecture to predict 3D bounding boxes, object depth, and orientation. Key contributions include introducing a multi-task loss function that optimizes 2D and 3D predictions and integrating sensor fusion techniques that combine RGB camera data with LIDAR point clouds for improved depth estimation. The adapted model, tested on real-world datasets, demonstrates a significant increase in 3D detection accuracy, achieving a mean average precision (mAP) of 85%, intersection over union (IoU) of 78%, and near real-time performance at 93–97% for detecting vehicles and 75–91% for detecting people. This approach balances high detection accuracy and real-time processing, making it highly suitable for AV applications. This study advances the field by showing how an efficient 2D detector can be extended to meet the complex demands of 3D object detection in real-world driving scenarios without sacrificing computational efficiency. Full article

This study aims to determine the type of vehicle with the lowest fuel consumption and greenhouse gas emissions by comparing spark ignition commercial vehicles against hybrid vehicles. The data were obtained through the OBD Link MX+ interface under traffic conditions in the Metropolitan District of Quito to determine the consumption and emissions delivered by each studied vehicle. Measurements were made while driving on two high-traffic routes during peak hours, with a duration of 2 to 3 h of stalling, and the engine fuel consumption parameters of each vehicle were obtained using 85 octane gasoline. Five measurements were generated per route and for each vehicle tested to reduce uncertainty and strengthen the prediction model with a factor of less than 10%. Statistical analysis was implemented to obtain a numerical model that allowed to analyse the estimate of the variation in fuel economy in each vehicle. The numerical model compared the values of fuel consumption measured with those calculated on all the routes with the highest traffic, finally indicating which vehicle with the smallest cylinder capacity is optimal, with an average consumption of 14 km/l on each route compared to a hybrid vehicle with an average consumption of 8.5 km/l per route, for better fuel performance within the Metropolitan District of Quito, in heavy traffic conditions. This study conducts a comparison of the consumption between a hybrid vehicle and spark ignition vehicles through the real driving cycle on routes considered to be of greater influx, to determine which vehicle has lower consumption and, therefore, greater energy efficiency in Quito City. Full article

Modern vehicles are equipped with various electronic control units (ECUs) for safety, entertainment, and autonomous driving. These ECUs operate independently according to their respective roles and generate considerable data. However, owing to capacity and security concerns, most of these data are not stored. In contrast, Tesla vehicles, equipped with multiple sensors and designed under the software-defined vehicle (SDV) concept, collect, store, and periodically transmit data to dedicated servers. The data stored inside and outside the vehicle by the manufacturer can be used for various purposes and can provide numerous insights to digital forensics researchers investigating incidents/accidents. In this study, various data stored inside and outside of Tesla vehicles are described sequentially from a digital forensics perspective. First, we identify the location and range of the obtainable storage media. Second, we explain how the data are acquired. Third, we describe how the acquired data are analyzed. Fourth, we verify the analyzed data by comparing them with one another. Finally, the cross-analysis of various data obtained from the actual accident vehicles and the data provided by the manufacturer revealed consistent trends across the datasets. Although the number of data points recorded during the same timeframe differed, the overall patterns remained consistent. This process enhanced the reliability of the vehicle data and improved the accuracy of the accident investigation. Full article

This comprehensive state-of-the-art literature review investigates the status of the electric vehicle (EV) market share and the key factors that affect EV adoption with a focus on the shared vision of vehicle electrification and the smart city movement. Investigating the current scenarios of EVs, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people’s socio-economic features (e.g., age, gender, income, education, vehicle ownership, home ownership, and political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SOC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and people’s demand can help policymakers leverage the smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the factors of EV adoption, outlying their role in achieving smart cities, suggesting policy recommendations to integrate EVs into smart cities, and proposing suggestions for future research directions. Full article

The transition to electric vehicles (EVs) is hindered by the insufficient development of charging infrastructure (CI) networks, particularly in urban areas. The existing literature highlights significant advancements in highway CI modeling, yet urban-specific models remain underdeveloped, due to the complexity of diverse driver behaviors and evolving environmental factors. To address this gap, this study investigates the influence of urban sprawl on future urban CI demand. Using a vector field analysis methodology, we first define the urban environment to capture its heterogeneity. A conceptual framework is then developed to analyze how changes in urban environments affect critical factors influencing CI demand. The results demonstrate that urban sprawl significantly impacts key variables shaping CI demand, including population distribution, transportation patterns, and land use. To quantify these impacts, geospatial metrics are derived from highly cited literature and integrated into the analysis, offering a novel approach to incorporating sprawl effects into CI planning. This study concludes that urban sprawl has a profound influence on future CI demand and emphasizes the importance of monitoring geospatial metrics over time. The proposed methodology provides a theoretical framework that enables stakeholders to anticipate changes in CI demand, thereby facilitating more effective infrastructure planning to accommodate urban sprawl. Full article

While IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combines IL with DRL by incorporating expert demonstration data to control AV in roundabout and right-turn intersection scenarios. Instead of employing CNNs, we integrate a ViT into the perception module of the SAC algorithm to extract key features from environmental images. The ViT algorithm excels in identifying relationships across different parts of an image, thereby enhancing environmental understanding, which leads to more accurate and precise decision making. Consequently, our approach not only boosts the performance of the DRL model but also accelerates its convergence, improving the overall efficiency and effectiveness of AVs in roundabouts and right-turn intersections with dense traffic by a achieving high success rate and low collision compared to RL baseline algorithms. Full article

This research addresses the challenges and opportunities for electric vehicle charging stations in Latin America. The transition to electric mobility is crucial to reduce greenhouse gas emissions, modernize the quality of life in urban areas, update public policies related to transportation, and promote economic development. However, this is not an easy task in this region; it faces several obstacles, such as a lack of liquidity in governments, a lack of adequate infrastructure, high implementation costs, the need for clear regulatory frameworks, and limited public awareness of the benefits of electric mobility. To this end, the current panorama of electric mobility in the region is analyzed, including current policies, the state of the charging infrastructure, and the prospects for growth regarding electric vehicles in Latin America. Factors that could lead to their successful implementation are promoted, highlighting the importance of public policies adapted to Latin American countries, collaboration between the public–private industry, the industry’s adoption of new technologies in this region, and the education of the population, and the benefits of these policies are considered. Successful case studies from the region are presented to provide us with an idea of practices that can be carried out in other countries. The implementation of a charging system in Latin America is also studied; the successful implementation of charging systems is found to depend largely on the existence of integrated public policies that address aspects other than the charging infrastructure. Finally, the value of the work and the research findings are presented to indicate what this study can help with. These strategies are key to overcoming the challenges and maximizing the benefits of electric mobility in Latin America. Full article

The field of autonomous vehicle technology has experienced remarkable growth. A pivotal trend in this development is the enhancement of tracking performance and stability under high-speed conditions. Model predictive control (MPC), as a prevalent motion control method, necessitates an extended prediction horizon as vehicle speed increases and will lead to heightened online computational demands. To address this, a path preview strategy is integrated into the MPC framework that temporarily freezes the vehicle state within the prediction horizon. This approach assumes that the vehicle state will remain consistent for a specified preview distance and duration, effectively extending the prediction horizon for the MPC controller. In addition, a stability controller is designed to maintain handling stability under high-speed conditions, in which a square-root cubature Kalman filter (SRCKF) estimator is employed to predict tire forces to facilitate the cornering stiffness estimation of vehicle tires. The double lane change maneuver under high-speed conditions is conducted through the Carsim/Simulink co-simulation. The outcomes demonstrate that the SRCKF estimator could provide a reasonably accurate estimation of lateral tire forces throughout the whole traveling process and facilitates the stability controller to guarantee the handling stability. On the premise of ensuring handling stability, integrating the preview strategy could nearly double the prediction horizon for MPC, resulting in the limited increase of online computation burden brought while maintaining path tracking accuracy. Full article

Autonomous driving is a rising technology expected to revolutionize commuting. Even if the spread of autonomous vehicles is slower than expected some years ago, their progress will not stop and will become a reality shortly. Therefore, we must manage them both technologically and by considering their impact on other aspects such as safety, economy, society, and environment. Of these, trust in these vehicles by society is a crucial element that must be accounted for when designing the interaction between human passengers and autonomous vehicles. Economical and social impacts derived from the diffusion of autonomous vehicles hold both promises and challenges, as different sectors and professions might undergo considerable changes, along with our idea of transport infrastructure. This paper aims to analyze future developments and effects of this technology by starting with a review of the related work. For this purpose, we have analyzed several papers with contrasting perspectives and conclusions. This paper is not limited to summarizing them but also points out relevant research directions. Full article

Recently, the operation of electric charging stations has stopped being solely dependent on the state or centralised energy companies, instead depending on the decentralization of decisions made by the operators of these stations, whose goals are to maximise efficiency in the distribution and supply of energy for electric vehicles. Therefore, the operations of charging stations are exposed to increased complexity, leading to a growing need for decision-making based on more reliable and sustainable models. This research presents a review of key aspects, technologies, protocols, and case studies on the current and future trends of electric charging stations. A taxonomy of the technologies applied to charging stations and their applications in elements such as intelligent energy supply, electric vehicles, sustainability, the Industrial Internet of Things, and energy demand management is developed. Thus, this work synthesizes the essential features found in recent research regarding charging stations, aiming for a systemic approach that can lead toward sustainability in electromobility. Full article

Due to the significant increase in the number of EVs, this manuscript presents a study of the total ownership cost of electric vehicles in Romania. The total cost of ownership (TCO) includes the initial purchase price, maintenance costs, power prices, and government incentives or subsidies unique to the market in Romania. The TCO was calculated for battery electric vehicles (BEVs) and internal combustion vehicles (ICEs). Several vehicles were selected for the study, representing the models with the highest sales in Romania and a similar price range. The results show that EVs have a lower TCO compared with internal combustion vehicles if the battery replacement cost for EVs is not considered in the analysis. If this cost is considered, the TCO for the BEVs has a significant increase due to the high cost of the battery. Another analysis performed regards the CO₂ emissions. These are higher for ICEs compared to BEVs, so the BEVs help reduce emissions. Full article

Electrification of private cars is a key mechanism for reducing transport emissions and achieving net zero. Simultaneously, the development of public electric vehicle (EV) charging networks is essential for an equitable transition to EVs. This paper develops and analyses an extensive, nationally representative dataset of EV-charging sessions taking place on a key public charging network in Scotland between 2022 and 2024 to gain insights that can support the development of public charging infrastructure. Data were collated from 2786 chargers and analysed to establish a detailed characterisation of the network’s organisation and utilisation. The network considered is government-owned and was fundamental to the Scottish rollout of public chargers. Key insights from our analysis of the developed dataset include quantified disparities between urban and rural charger use-time behaviours, with the most rural areas tending to have charging activity more concentrated towards the middle of the day; an analysis of the numbers of deployed chargers in areas of greater/lesser deprivation; utilisation disparities between charger technologies, with 35% of slower chargers being used at least once daily compared to 86% of rapid/ultra-rapid chargers; and demonstration that charging tariff introductions resulted in a 51.3% average decrease in sessions. The implications of our findings for policy and practice are also discussed. Full article

Optimal planning depends on precise and exact estimation of the operation costs of mobile robots. Unfortunately, determining the current and future state of a vehicle implies identifying all the parameters in its model. Rather than broadening the number of factors, in this work we adopt the approach of using a higher-level abstraction model to identify only a few cost parameters. Based on the observation that arc travel times accurately reflect the effect of physical states, this work proposes using them as the key parameters to compute accurate path traversal costs in the context of indoor transportation. This approach eliminates the need to model all factors in order to derive the cost for every robot. The resulting model organizes those parameters in a bilinear state-space form and includes the evolution of actual travel times with changing states. We show that the proposed model accurately estimates arc travel times with respect to actual observations gathered from real robots traversing a few arcs of a traffic network until battery exhaustion. We experimentally obtained minimum-cost paths from random origin and destination nodes when using heuristics and the “closer-to-reality” (bilinear-state version of our model) path costs, finding that it can save an average of 15% in transportation time compared to conventional methods. Full article

Historically, practitioners and researchers have used selected count station data and survey-based methods along with demand modeling to forecast vehicle miles traveled (VMT). While these methods may suffer from self-reporting bias or spatial and temporal constraints, the widely available connected vehicle (CV) data at 3 s fidelity, independent of any fixed sensor constraints, present a unique opportunity to complement traditional VMT estimation processes with real-world data in near real-time. This study developed scalable methodologies and analyzed 238 billion records representing 16 months of connected vehicle data from January 2022 through April 2023 for Indiana, classified as internal combustion engine (ICE), hybrid (HVs) or electric vehicles (EVs). Year-over-year comparisons showed a significant increase in EVMT (+156%) with minor growth in ICEVMT (+2%). A route-level analysis enables stakeholders to evaluate the impact of their charging infrastructure investments at the federal, state, and even local level, unbound by jurisdictional constraints. Mean and median EV trip lengths on the six longest interstate corridors showed a 7.1 and 11.5 mile increase, respectively, from April 2022 to April 2023. Although the current CV dataset does not randomly sample the full fleet of ICE, HVs, and EVs, the methodologies and visuals in this study present a framework for future evaluations of the return on charging infrastructure investments on a regular basis using real-world data from electric vehicles traversing U.S. roads. This study presents novel contributions in utilizing CV data to compute performance measures such as VMT and trip lengths by vehicle type—EV, HV, or ICE, unattainable using traditional data collection practices that cannot differentiate among vehicle types due to inherent limitations. We believe the analysis presented in this paper can serve as a framework to support dialogue between agencies and automotive Original Equipment Manufacturers in developing an unbiased framework for deriving anonymized performance measures for agencies to make informed data-driven infrastructure investment decisions to equitably serve ICE, HV, and EV users. Full article

Solid-state batteries (SSBs) offer significant improvements in safety, energy density, and cycle life over conventional lithium-ion batteries, with promising applications in electric vehicles and grid storage due to their non-flammable electrolytes and high-capacity lithium metal anodes. However, challenges such as interfacial resistance, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This study conducts a comprehensive scientometric analysis, examining 131 peer-reviewed SSB research articles from IEEE Xplore and Web of Science databases to identify key thematic areas and bibliometric patterns driving SSB advancements. Through a detailed analysis of thematic keywords and publication trends, this study uniquely identifies innovations in high-ionic-conductivity solid electrolytes and advanced cathode materials, providing actionable insights into the persistent challenges of interfacial engineering and scalable production, which are critical to SSB commercialization. The findings offer a roadmap for targeted research and strategic investments by researchers and industry stakeholders, addressing gaps in long-term stability, scalable production, and high-performance interface optimization that are currently hindering widespread SSB adoption. The study reveals key advances in electrolyte interface stability and ion transport mechanisms, identifying how solid-state electrolyte modifications and cathode coating methods improve charge cycling and reduce dendrite formation, particularly for high-energy-density applications. By mapping publication growth and clustering research themes, this study highlights high-impact areas such as cycling stability and ionic conductivity. The insights from this analysis guide researchers toward impactful areas, such as electrolyte optimization and scalable production, and provide industry leaders with strategies for accelerating SSB commercialization to extend electric vehicle range, enhance grid storage, and improve overall energy efficiency. Full article

In this paper, a fast and efficient control method is proposed for a pure sine wave inverter used in an electric vehicle system, which can provide better performance under transient and steady-state conditions. The proposed control technique consists of a fast terminal attractor (FTA) and a fractional multivariable grey model (FMGM). The FTA with finite time convergence offers a faster convergence rate of the system state and a singularity-free solution. However, if the uncertain system boundaries are overestimated or underestimated, chatter/steady-state errors can occur during the FTA, which can lead to significant harmonic distortion at the output of the pure sine wave inverter. A computationally efficient FMGM is incorporated into the FTA to solve the chatter/steady-state error problem when an uncertain estimate of the system boundary cannot be satisfied. Simulation results show that the proposed control technique exhibits low total harmonic distortion. Experimental results of a prototype pure sine wave inverter are presented to support the results of the simulation and mathematical analysis. Since the proposed pure sine wave inverter outperforms the classical TA (terminal attractor)-controlled pure sine wave inverter in terms of convergence speed, computational efficiency, and harmonic distortion elimination, this paper will serve as a useful reference for electric vehicle systems. Full article

Lithium titanate oxide cells are gaining attention in electric vehicle applications due to their ability to support high-current charging and their enhanced thermal stability. However, despite these advantages, safety concerns, particularly thermal runaway, pose significant challenges during abuse conditions such as overcharging. In this study, we investigated the effectiveness of various dielectric fluids in mitigating thermal runaway during overcharge abuse tests of cylindrical LTO cells with a capacity of 10 Ah. The experimental campaign focused on overcharging fully charged cells (starting at 100% State of Charge) at a current of 40A (4C). The tests were conducted under two conditions: the first benchmark test involved a cell exposed to air, while the subsequent tests involved cells submerged in different dielectric fluids. These fluids included two perfluoropolyether fluorinated fluids (PFPEs) with boiling points of 170 °C and 270 °C, respectively, a synthetic ester, and a silicone oil. The results were analyzed to determine the fluids’ ability to delay possible thermal runaway and prevent catastrophic failures. The findings demonstrate that some dielectric fluids can delay thermal runaway, with one fluid showing superior performance with respect to the others in preventing fire during thermal runaway. The top-performing fluid was further evaluated in a simulated battery pack environment, confirming its ability to mitigate thermal runaway risks. These results provide important insights for improving the safety of battery systems in electric vehicles. Full article

This case report explores the UTVs (utility terrain vehicles) transition from internal combustion engines to electric drive and how the shift will impact the assembly tooling industry. A multiple-case study at manufacturing plants was complemented by an exploratory survey with key stakeholders in the industry. The findings showed that the transition to electric drive is still in its infancy and is likely to accelerate soon. Electric vehicles were generally found to contain fewer components and thus have fewer applications for tightening tools in their assembly. Much of the difference comes from the fact that electric engines require far fewer tightening operations compared to internal combustion engines. However, the assembly of electric components and battery packs requires new advanced tooling solutions. When transitioning to electric drives, manufacturers were found to source their battery packs and electric engines most commonly from external suppliers. This can displace the tooling industry’s business within the segment. Several opportunities and challenges for assembly tool suppliers were identified. Firstly, the transition to electric drive will likely generate significant tooling needs on the manufacturers side. Electric vehicles tend to require more advanced tools and solutions, which likely will benefit premium tool suppliers with Industry 4.0 solutions. There are, however, long-term challenges as electric UTVs have fewer components and fewer tightenings in their assembly process. One long-term opportunity that could potentially offset the decline in tightenings within final assembly is battery pack assembly. This process does not only require a lot of advanced tightenings, but there are also opportunities for other joining techniques. Thus, the assembly tooling business’ biggest opportunities within the UTV industry are likely to shift from the vehicle’s final- to battery pack assembly. Full article

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

This paper presents an optimized method for evaluating and enhancing the crashworthiness of an electric vehicle (EV) battery frame, leveraging finite element model (FEM) simulations with minimal computational effort. The study begins by utilizing a publicly available LS-DYNA model of a conventional Toyota Camry, simplifying it to include only the structures relevant to a side pole crash scenario. The crash simulations adhere to FMVSS214 and UNR135 standards, while also extending to higher speeds of 45 km/h to evaluate performance under more severe conditions. A dummy frame with virtual mass is integrated into the model to approximate the realistic center of gravity (COG) of an EV and to facilitate visualization. Based on the side pole crash results, critical parameters are extracted to inform the development of load cases for the EV battery. The proposed battery frame, constructed from aluminum, houses a representative volume of battery cells. These cells are defined through a homogenization process derived from individual and pack of cell crash tests. The crashworthiness of the battery frame is assessed by measuring the overall intrusion along the Y-axis and the specific intrusion into the representative volume. This method not only highlights the challenges of adapting conventional vehicle platforms for EVs or for dual compatibility with both conventional and electric powertrains but also provides a framework for developing and testing battery frames independently. By creating relevant load cases derived from full vehicle crash data, this approach enables battery frames to be optimized and evaluated as standalone components, offering a method for efficient and adaptable battery frame development. This approach provides a streamlined yet effective process for optimizing the crash performance of EV battery systems within existing vehicle platforms. Full article

Since the advent of rare-earth permanent magnet (PM) materials, PM synchronous machines (PMSMs) have become popular in power generation, industrial drives, and e-mobility. However, rare-earth PMs in PMSMs are prone to temperature- and operation-related irreversible demagnetization. Additionally, faults can endanger components like inverters, batteries, and mechanical structures. Designing a fault-tolerant machine requires considering these risks during the PMSM design phase. Traditional transient finite element analysis is time-consuming, but fast analytical simulation methods provide viable alternatives. This paper evaluates methods for analyzing dynamic three-phase short-circuit (3PSC) events in PMSMs. Experimental measurements on a PMSM prototype serve as benchmarks. The results show that accounting for machine saturation reduces discrepancies between measured and predicted outcomes by 20%. While spatial harmonic content and sub-transient reactance can be neglected in some cases, caution is required in other scenarios. Eddy currents in larger machines significantly impact 3PSC dynamics. This work provides a quick assessment based on general machine parameters, improving fault-tolerant PMSM design. Full article

The purpose of this study is to gain a better understanding of the difficulty in measuring consumer acceptance of emergent technologies where artificial intelligence is present in autonomous vehicles (AVs). Using the Technology Acceptance Model (TAM) as our theoretical lens, survey data of US adult consumers are used to better understand consumer acceptance of AVs. Results from Partial Least Squares–Structural Equation Modeling (PLS-SEM) show that the certainty of product performance and interest are positively related to usage. Surprisingly, the relationship between two variables, internal locus of control and ease of use and usage, was not significant, which could be explained by AVs being self-driving and the ease of use therefore not being important in this context. Internal locus of control was negatively related to willingness to buy, and interest and usage were positively related to willingness to buy. Mediation analysis further explains these relationships. This research calls into question the TAM, long used as a measurement for the acceptance of information systems, as an acceptable model for measuring consumer acceptance where the intent is to purchase technology that contains artificial intelligence. Full article

Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability. Full article

Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery’s characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs. Full article

Lithium-ion battery systems are a core component for electric mobility, which has become increasingly important in the last decade. The rising number of new manufacturers and model variants also increases competitive pressure. Competition is shortening development times. At the same time, the range of technology options for batteries is growing steadily. Fast and well-founded concept development is becoming even more essential in this increasingly complex environment. For this purpose, various model-based systems engineering (MBSE) methods are analyzed and evaluated. Based on this, the battery modeling framework is derived and described, tailored to the needs of battery development. The validation of the methodological approach is demonstrated by the simulation workflow from an electrical cell characterization to the thermal evaluation of different cooling methods. Full article

Throughout this article, an alternative depreciation method for electric vehicles (EVs) is presented, addressing the challenge of information asymmetry—a common issue in secondary markets. The proposed method is contrasted with traditional models, such as the Straight-Line Method (SLM), the Declining Balance Method, and the Sum-of-Years Digits (SYD) method, as these classic approaches fail to adequately consider key factors such as mileage and secondary aspects like battery degradation and rapid technological obsolescence, which critically impact the residual value of used EVs. The presented approach employs an adverse selection model that incorporates buyers’ and sellers’ perceptions of vehicle quality from the information recorded on e-commerce platforms, improving the depreciation estimation. The results show that the proposed method offers greater accuracy by leveraging asymmetric information extracted from web portals. Specifically, the method identifies a characteristic intersection point, marking the moment when the model aligns most closely with the data obtained through traditional methods in terms of precision. The analysis through the density of price estimations by vehicle model year indicates that, beyond 1.8 months, the proposed model provides more reliable results than traditional methods. The proposed model allows buyers to identify undervalued assets and sellers to obtain a fair market value, mitigating the risks associated with adverse selection, reducing uncertainty, and increasing market transparency and trust. It fosters equitable pricing between buyers and sellers by addressing the implications of adverse selection, where sellers—possessing more information about the vehicle’s condition than buyers—can dominate market transactions. This model restores balance by ensuring fairer valuation based on vehicle usage, primarily addressing the lack of critical data available on e-commerce platforms, such as battery certifications, among others. Full article

The knowledge of battery aging is an indicator that allows controlling the performance of large battery banks. State of Health (SOH) is typically the metric used, encompassing all possible mechanisms in a percentage indicator, with the Coulomb Counting as the most common method. Hence, an in-depth study of aging based on known models provides proper information for correctly managing batteries. This article proposes an aging-sensitive 3-RC-array-equivalent electrical circuit model to characterize the behavior of batteries throughout their useful life, identifying parametric changes as complementary information to the state of health. This model was validated based on experimental tests with 2 V and 6 Ah VRLA batteries aged according to the manufacturer’s recommended use. The results reveal a proportionality through capacity degradation. Then, a control group of batteries was subjected to overcharge and over-discharge conditions. The information given by Coulomb Counting SOH and the proposed method were evaluated. The proposed method provides additional information to the SOH, enhancing the distinguishing capability between typical aging performance and misused aging performance, resulting in a useful tool capable of identifying the aging associated with parametric changes in a time-invariant system where aging is treated as an imminent multiplicative fault. Full article

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB^®/Simulink^® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications. Full article

The motor characteristics control method using the winding changeover technique can improve the matching ratio between the most frequent operating point of electric vehicle (EV) and the motor’s high-efficiency operating point, thereby enhancing the overall average efficiency of the drive system. This technology reduces back electromotive force and winding resistance by adjusting the effective number of motor winding turns according to the EV’s operating speed, ultimately improving the average efficiency. In this paper, we propose a winding changeover circuit and control method that maximizes the average efficiency in the main driving regions to extend the driving range per charge and improve the fuel efficiency of EVs. The proposed circuit is constructed using thyristor switching devices, offering the advantage of relatively lower overall system losses compared to conventional circuits. Due to the characteristics of the thyristor switching devices used in the proposed circuit, seamless winding changeover is possible during motor operation. Additionally, no extra snubber circuits are required, and the relatively low switch losses suggest the potential for improved efficiency and lightweight design in EV drive systems. To verify the proposed winding changeover circuit and control scheme, experiments were conducted using a dynamometer with an 80 kW permanent magnet motor, inverter, and the developed prototype of the winding changeover circuit. Full article

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid. Full article

Previous work presented a reference model for shared autonomous vehicles in urban areas supported by a systematic literature review and topic modeling. The proposed reference model was then evaluated with two real-world demonstrations: the service provided by Waymo in Phoenix and another offered by Baidu in Beijing. In this paper, we present another evaluation based on a survey conducted with a group of potential stakeholders belonging to the mobility industry who were asked about their agreement with each of the concepts in the reference model. The resulting artifact is stronger and more reliable because it reflects the feedback of mobility experts. Full article

As the number of electric vehicles (EVs) on North American roads continues to rise, driven by the shift toward sustainable transportation, understanding the economic implications of this transition is crucial. This review paper prioritizes an evaluation of the Total Cost of Ownership (TCO) for various types of EVs, providing insights into how different driving profiles align with the financial benefits of EV adoption. It demonstrates that at-home charging and government incentives are pivotal in reducing TCO. The analysis also offers a comprehensive overview of the factors driving EV growth, including declining operating and maintenance costs. Additionally, the paper explores adoption rates, charging infrastructure, and other non-monetary factors that influence consumer decisions in the shift to EVs. Conclusions emphasize that while EVs offer a financial advantage for many drivers, the success of broader adoption depends on decreasing the initial cost of EVs, developing charging infrastructure, and investing in charging networks. Full article

Artificial neural networks (ANNs) are artificial intelligence techniques that have made autonomous driving more efficient and accurate; however, autonomous driving faces ongoing challenges in the accuracy of decision making based on the analysis of the vehicle environment. A critical task of ANNs is steering angle prediction, which is essential for safe and effective navigation of mobile robots and autonomous vehicles. In this study, to optimize steering angle prediction, NVIDIA’s architecture was adapted and modified along with the implementation of the Swish activation function to train convolutional neural networks (CNNs) by behavioral cloning. The CNN used human driving data obtained from the UDACITY beta simulator and tests in real scenarios, achieving a significant improvement in the loss function during training, indicating a higher efficiency in replicating human driving behavior. The proposed neural network was validated through implementation on a differential drive mobile robot prototype, by means of a comparative analysis of trajectories in autonomous and manual driving modes. This work not only advances the accuracy of steering angle predictions but also provides valuable information for future research and applications in mobile robotics and autonomous driving. The performance results of the model trained with the proposed CNN show improved accuracy in various operational contexts. Full article

This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes. Full article

This article introduces a novel approach for developing an electrical model of the lithium batteries used in an electric ultralight aircraft. Currently, no method exists in the technical literature for accurately modeling the electrical characteristics of batteries in an electric aircraft, making this study a valuable contribution to the field. The proposed method was validated with an all-electric ultralight aircraft designed and constructed at the Pascual Bravo University Institution. To build the detailed model, a kinematic analysis was first conducted through takeoff tests, where data on the speed, acceleration, time, and distance required for takeoff were collected, along with measurements of the current and power consumed by the batteries. The maximum speed and acceleration of the aircraft were also recorded. These kinematic results were obtained using two batteries made from Samsung INR-18650-35E lithium-ion cells, and different wing configurations of the aircraft were analyzed to assess their impacts on the battery energy consumption. Additionally, the discharge cycles of the batteries were evaluated. In the second phase, laboratory tests were performed on the individual battery cells, and the Peukert coefficient was estimated based on the experimental data. Finally, using the Peukert coefficient and the kinematic results from the takeoff tests, the electrical model of the battery was fine tuned. This model allows for the creation of charging and discharging equations for ultralight lithium batteries. With the final electrical model and energy consumption data during takeoff, it becomes possible to determine the energy usage and flight range of an electric aircraft. The model indicated that the aircraft did not require a long distance to takeoff, as it reached the necessary takeoff speed in a very short time. The equations used to simulate the discharge cycles of the batteries and lithium cells accurately described their energy capacities. Full article

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices. Full article

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization. Full article

Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies. Full article

The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess the effect of EV market penetration on component manufacturing employment over time. Key drivers of employment in the conventional and the EV component industries were identified and incorporated into the model. The results indicate a negative impact of EV penetration on employment of 18.3% when considering 20.0% EV sales (EV20) in 2040. Scenario analyses highlighted the influence of individual components, battery localisation, and load shedding on labour. Tyre and wheel manufacturing was found to be the most labour impactful component in the conventional industry against electrical engines in the EV counterpart. Localising 25.0% of battery production could increase employment by 6.9% and 2.7% in the EV40 and EV20 Scenarios. Load shedding has a detrimental effect on the country’s economy, assumed to reduce employment by 30.0%. However, strategic industry and policy interventions can mitigate the adverse effects of this transition. Full article

Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%. Full article

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction. Full article