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Summary: Permanent magnet synchronous motors (PMSM) are widely employed in the application of electric vehicles (EVs) due to their simplicity of operation. Model predictive current control (MPCC) is an advanced technique used to control the PMSM owing advantages like multi‐variable cost function and good dynamic performance. Cost function of predictive current control (PCC) does not require the flux weighting factor; hence, it is simple in the selection of suitable voltage vector (VV). The conventional PCC (C‐PCC) applied to interior PMSM (IPMSM) contains more torque and flux ripples as it includes only one set of voltage vectors for all speed ranges. In proposed PCC (P‐PCC), the magnitude and location of voltage vectors is to be selected as large and small VVs to reduce torque ripples. The P‐PCC in this article utilizes two set of extended voltage vectors (large and small VVs) based on the applied speed change in dynamic conditions and hence reduces the ripple content. Incorporating maximum torque per ampere (MTPA) control in this article serves the purpose of optimizing the machine performance. By adding MTPA to the proposed method, it is aimed to enhance the machine performance with less ripples and improved torque response. Simulation results are presented for conventional and P‐PCC to highlight the effectiveness and efficacy of the P‐PCC. The P‐PCC is experimentally verified with 3.7 kW PMSM using d‐SPACE controller. [ABSTRACT FROM AUTHOR]

Ecuador, like every country in the world, urgently requires a conversion of transportation to electric power, both for economic and environmental reasons. This paper focuses on the technical and economic feasibility of a solar-powered electric charging station equipped with battery storage in Cuenca, Ecuador. By reviewing current literature, we assess the environmental impact of electric mobility and its potential to reduce fossil fuel dependence and generate energy savings. The analysis encompasses various factors, including EV energy consumption, solar energy system sizing, energy production, and battery storage capacity. Key findings indicate that integrating solar PV systems with EV charging stations efficiently supports a reliable and sustainable energy supply. Simulation results reveal seasonal variations in solar generation, highlighting the importance of proper system sizing to maintain charging supply reliability and manage surplus generation. The economic analysis of three scenarios underscores the financial viability of implementing PV systems without battery storage, yielding a positive Internal Rate of Return (IRR) and Net Present Value (NPV). However, scenarios with battery storage present negative NPV and long investment return periods, impacting economic viability negatively. These insights underscore the need for a balanced design to ensure sustainability and economic feasibility in the transition to electric mobility. [ABSTRACT FROM AUTHOR]

Understanding charging vehicles, charging stations, and built environment concerning idle connection times significantly guided the management of charging infrastructure. However, the interplay between these factors had remained incompletely understood. This study addressed this gap by investigating public charging stations in Changshu, Suzhou, China, as a case study. The random forest regression and partial dependence plots were employed to explore the nonlinear relationships between idle connection times of vehicles at public fast-charging stations and the built environment, charging stations, and charging vehicles. The exploration encompassed two typical scenarios: workdays and weekends. The findings reveal the distinct influences of various factors in different scenarios. Notably, catering service points of interests in the proximity of charging stations, significantly impact the idle connection time on both workdays and weekends. Furthermore, government groups and residential areas have a notable influence on idle connection times during workdays. Shopping service and Leisure sport have a significant impact on idle connection time during the weekends. Variables such as the charging start time and charged energy also exhibit significant effects. Importantly, these influencing factors demonstrate heterogeneity and exhibit different threshold effects. This research can offer valuable insights to planning authorities and charging facility operators for formulating strategies to enhance charging infrastructure utilization. [ABSTRACT FROM AUTHOR]

As public attention is increasingly drawn toward more sustainable transportation methods, the popularity of electric vehicles (EVs) as part of the solution is rapidly expanding. Operating conditions within EVs can be severe compared to standard combustion powertrains, and the risk of electrical arcing across mechanical surfaces from electric leakage currents incites additional concern. This study employed a series of electro-tribological tests utilizing various moving patterns to improve understanding of the driving conditions for electrically induced bearing damage (EIBD). Rolling ball-on-disk tests were performed with different polyurea-thickened greases. Rotational tests were initially run at various speeds and test durations, but electrical damage was limited. However, electrical damage was unmistakable when a reciprocating motion was used at different track lengths and speeds. These results suggest that the conditions associated with the track length, such as the number of directional changes and speed-dependent film thickness, play a considerable role in forming electrical damage. This work provides critical insights into the mechanisms of EIBD in EVs and other electrical systems. It highlights the importance of understanding the operational conditions that contribute to EIBD, which can lead to improved designs and maintenance practices, ultimately enhancing the efficiency and lifespan of these systems. [ABSTRACT FROM AUTHOR]

In developing cities, transport activities have become one of the primary sources of CO2 emissions and energy consumption owing to rapid economic growth, urbanization, and motorization. Khon Kaen City, Thailand, was chosen as a representative mid-sized city of a developing country to investigate the potential influences of transit-oriented development (TOD), light rail transit (LRT), and electric vehicle (EV) policy integration scenarios on CO2 emission and energy consumption reductions in 2016, 2026, and 2046. The TOD did not significantly reduce CO2 emissions or energy consumption because it was only applied in one area of the city. The LRT development also had a small effect because of the small proportion of modal shifts to LRT. However, EV utilization offered the greatest potential for reducing both CO2 emissions and energy consumption. In addition, the integrated scenario combining the three policies had a promising effect, diminishing both CO2 emissions and energy consumption, because it gathered the potential merits and benefits of each individual policy. [ABSTRACT FROM AUTHOR]

Environmental concerns and the Paris agreements have prompted intensive efforts towards greener and more sustainable transportation. Persistent expansion of electric vehicles (EV) in the transportation sector requires electric vehicle charging stations (EVCSs) to accommodate the increased demand. Offsetting demand and alleviating the resultant electrical grid stress necessitates establishing grid-integrated renewable energy sources (RESs) where these sustainable strategies are accompanied by variable-weather-related obstacles, such as voltage fluctuations, grid instability, and increased energy losses. Strategic positioning of EVCSs and RES as distributed generation (DG) units is crucial for addressing technical issues. While technical constraints have received considerable attention, there is still a gap in the literature with respect to incorporating the additional complex optimization problems and decision-making processes associated with economic viability, social acceptance, and environmental impact. A possible solution is the incorporation of an appropriate multi-criteria decision analysis (MCDA) approach for feasible trade-off solutions. Such methods offer promising possibilities that can ease decision-making and facilitate sustainable solutions. In this context, this paper presents a review of published approaches for optimizing the allocation of renewable energy DG units and EVCSs in active distribution networks (ADNs). Promising published optimization approaches for the strategic allocation of multiple DG units and EVCSs in ADNs have been analyzed and compared. [ABSTRACT FROM AUTHOR]

The majority of urban CO2 emissions come from the transportation sector. To be able to reduce them, it is definitely necessary to replace Internal Combustion Engine (ICE) vehicles with electric ones. In this article, a public transport system is proposed, consisting of a supercapacitor (SC)-powered electric vehicle (EV) charged through a fuel cell-powered (FC) Inductive Power Transfer (IPT) system. The bus runs the usual route and it is charged each time it reaches the terminal, where the charging system is placed. The main advantages of the proposed system are related to the long-term cost of the EV, compared to a classic battery-powered system, to the aspects of ease of use and safety for charging operations and to the possibility of realizing a net-zero-energy transport system thanks to the use of green hydrogen. In addition, the proposed charging methodology allows for better energy utilization avoiding major changes to the main power grid. In this article, the system is presented considering a real case study; it is simulated at system and hardware level, and then validated through the realization of a scaled-down prototype. [ABSTRACT FROM AUTHOR]

Rapid growth in vehicle ownership in the developing world and the evolution of transportation technologies have spurred a number of new challenges for policymakers. To address these challenges, this study develops a system dynamics (SD) model to project the future composition of Iran's vehicle fleet, and to forecast fuel consumption and CO2 emissions through 2040. The model facilitates the exploration of system behaviors and the formulation of effective policies by equipping decision-makers with predictive insights. Under various scenarios, this study simulates the penetration of five distinct vehicle types, highlighting that an increase in fuel prices does not constitute a sustainable long-term intervention for reducing fuel consumption. Additionally, the model demonstrates that investments aimed at the rapid adoption of electric transportation technologies yield limited short-term reductions in CO2 emissions from transportation. The projections indicate that the number of vehicles in Iran is expected to surpass 30 million by 2040, with plug-in and hybrid electric vehicles (EVs and PHEVs) comprising up to approximately 2.2 million units in the base scenario. It is anticipated that annual gasoline consumption and CO2 emissions from passenger cars will escalate to 30,000 million liters and 77 million tons, respectively, over the next two decades. These findings highlight the need for a strategic approach in policy development to effectively manage the transition towards a lower-carbon vehicle fleet. [ABSTRACT FROM AUTHOR]

Climate change risks have triggered the international community to find efficient solutions to reduce greenhouse gas (GHG) emissions mainly produced by the energy, industrial, and transportation sectors. The problem can be significantly tackled by promoting electric vehicles (EVs) to be the dominant technology in the transportation sector. Accordingly, there is a pressing need to increase the scale of EV penetration, which requires simplifying the manufacturing process, increasing the training level of maintenance personnel, securing the necessary supply chains, and, importantly, developing the charging infrastructure. A new modular trend in EV manufacturing is being explored and tested by several large automotive companies, mainly in the USA, the European Union, and China. This modular manufacturing platform paves the way for standardised manufacturing and assembly of EVs when standard scalable units are used to build EVs at different power scales, ranging from small light-duty vehicles to large electric buses and trucks. In this context, modularising EV electric systems needs to be considered to prepare for the next EV generation. This paper reviews the main modular topologies presented in the literature in the context of EV systems. This paper summarises the most promising topologies in terms of modularised battery connections, propulsion systems focusing on inverters and rectifiers, modular cascaded EV machines, and modular charging systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are considered to be the most promising technology upgrades in the coming years to reduce the carbon emission, since the EVs could replace the conventional combustion vehicles as well as mitigate power fluctuations in power grids. This paper focuses on addressing the frequency stability issues in isolated hybrid grids (IHGs) by introducing a fractional order proportional-integral-derivative (FOPID) control for grid-connected power converters. A particle swarm optimization (PSO) technique is adopted to optimize the parameters of the controllers. The advantages of the proposed control method over the conventional proportional-integral-derivative (PID) control, proportional-integral with proportional-derivative (PI-PD) control, and proportional-integral-derivative with filter (PIDF) control schemes are verified by simulation case studies. Besides, a modified virtual rotor concept (MVRC) is proposed in this paper to enhance the inertia of the system. Furthermore, the performance of using EVs to regulate the frequency of IHGs is compared to the superconducting magnetic energy storage (SMES) units, capacitive energy storage (CES) units, and redox flow batteries (RFB) under fixed and variable load conditions. [ABSTRACT FROM AUTHOR]

The sources of fossil fuel are impoverishing in upcoming future. In the current research scenario, sincere effort has been taken worldwide to explore the use of renewable energy sources in electrical power system for the economic benefits and environmental consciousness. The main contribution of the proposed work is first, to find optimal hydro‐thermal scheduling (HTS) with wind, solar, and electric vehicles (EVs) for variable load. The target is to find out maximum utilization of renewable energy sources for economic power generation with less emission. Thus, a new approach of EV to grid has been adopted with wind–solar based HTS system for improving grid reliability and resilience. Second, there is a requirement to overcome the local optima problems with less convergence speed. This is obtained by employing a relatively new methodology, known as chaotic‐quasi‐opposition‐based whale optimization algorithm (WOA) (CQOWOA). The proposed algorithm is tested on HTS and wind–solar‐electric vehicle‐based HTS (HTWSVS) for three different cases. Different nonlinearities like valve point effect of thermal units, transmission losses, spillage rate of hydro reservoir units and uncertainties of wind, solar as well as EV are considered to judge the effectiveness of the proposed CQOWOA technique on realistic problems. The presence of wind, solar, and EV energy sources with HTS is evident from the test results of CQOWOA, for multi‐objective problem where cost and emission both are reduced significantly. The robustness of the proposed solution has been verified by implementing the statistical analysis on two systems with least variation of mean and optimal values of cost with the tolerance of less than 0.025%. The comparative analysis of CQOWOA with the other optimization techniques validates its superiority on both the test systems by minimizing the generation cost and emission. [ABSTRACT FROM AUTHOR]

With the development of renewable energy power systems, electric vehicles, as an important carrier of green transportation, are gradually having an impact on the power grid load curve due to their charging behavior. However, the significant influx of electric vehicles (EVs) and distributed power sources has led to multiple uncertainties, increasing the difficulty in making grid scheduling decisions. Traditional robust scheduling strategies tend to be overly conservative, resulting in poor economic performance. Therefore, this paper proposes a robust and economic dispatch strategy for park power grids based on the information gap decision theory (IGDT). Firstly, based on the probabilistic characteristics of the spatial and temporal distribution of EVs charging, the Monte Carlo method is used to generate typical electricity usage scenarios for EVs. Simultaneously, an economic dispatch model for the park power grid is established with the objective of minimizing operating costs. Taking into account the uncertainty of renewable energy output, simulation analysis is conducted through the IGDT model. Finally, through the verification of the improved IEEE-33 node test system and comparison with other methods, the proposed approach in this paper can reduce decision conservatism and effectively reconcile the contradiction. Through analysis, the proposed method in this paper can reduce the total operational cost of the system by up to 3.2%, with a computational efficiency of only 8.9% of the traditional stochastic optimization time. [ABSTRACT FROM AUTHOR]

The transport sector is experiencing a notable transition towards sustainability, propelled by technological progress, innovative materials, and a dedication to environmental preservation. This study explicitly examines the incorporation of electric vehicles (EVs) into the power grid, with a particular emphasis on passenger automobiles. Our analysis emphasises the vital importance of updated transport infrastructure in decreasing greenhouse gas emissions and aiding carbon reduction efforts in electricity networks. The analysis uncovers that adopting electric vehicles offers significant advantages, including enhanced grid efficiency and decreased emissions. However, it also brings issues concerning the design and operation of power systems at both the transmission and distribution levels. Key players are crucial in tackling these difficulties to improve electric vehicle integration into the grid. The study determines the most effective ways for distributing and providing electric vehicle charging infrastructure, and investigates the efforts made to establish common standards in order to solve current challenges. This research contributes to the advancement of sustainable mobility and energy systems by conducting a thorough examination of the impact of electric vehicles on power systems and offering appropriate integration solutions.

Vehicle to grid (V2G) is one the most important ways to effectively integrate electric vehicles (EVs) with electric power systems. The important benefits can be made by V2G such as reducing/increasing the cost/revenue of EV owners and technically sup-porting electric power systems. Concerning technical and regulatory constraints, EV owners must participate in electricity mar-kets via aggregators. This paper proposes a robust optimal coordinated charging (OCC) model including bidding ancillary ser-vices for regulation and spinning reserve markets. The presented work handles the uncertain behavior of the electricity market that are ancillary service prices and their deployment signals by the robust optimization approach. The aim of optimization is the maximization of the aggregator's profits from V2G by joining the ancillary services markets. The recommended robust OCC model which is a robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. An assumed set of 10000 EVs in the electric reliability council of Texas (ERCOT) electricity markets is considered for doing simulations. Employing the presented model in this test system shows the efficacy of the proposed model in comparison to other deterministic and stochastic models. [ABSTRACT FROM AUTHOR]

The global shift towards sustainable transportation, exemplified by the increasing adoption of Electric Vehicles (EVs), represents a vital response to contemporary environmental and energy challenges. This study investigates the determinants of consumers’ Desire to Purchase EVs within the unique context of the Medan market, where consumer preferences and market dynamics differ from more globally examined settings. Additionally, it explores the mediating roles of Consumer Awareness Campaigns and Local Industry Partnerships in shaping purchase intent. Through a comprehensive survey of 832 respondents in Medan, Indonesia, the study reveals crucial insights. Government incentives, including tax breaks and rebates, emerge as influential factors, significantly increasing consumers’ inclination to adopt EVs. Equally vital is the accessibility and availability of charging infrastructure, which is pivotal in bolstering consumer confidence in EVs. Consumer perceptions and attitudes have a significant bearing on purchase intent, with positive perceptions regarding environmental benefits, cost savings, and driving experiences contributing to the heightened desire for EV ownership. Effective financial management strategies also play a positive role, emphasizing the significance of sound financial planning and resource allocation. Further-more, this research emphasizes the instrumental role of Consumer Awareness Campaigns in shaping purchase intent. These campaigns are a powerful tool for elucidating the multifaceted advantages of EV ownership, encompassing environmental consciousness, economic feasibility, and enhanced driving experiences. Local Industry Partnerships within the Medan EV market equally contribute to heightened purchase intent, reflecting the synergistic effects of collaborative initiatives.

This paper handles the problem of optimal charging control for electric vehicle (EV) aggregators in energy and ancillary service markets under uncertainties. We propose a robust optimal coordinated charging (OCC) model that formulates a robust linear programming (RLP) for the EV aggregator formed on unidirectional vehicle-to-grid (V2G) and coordinates the provision of regulation and spinning reserves. Our model considers many uncertainties in the electricity markets, like ancillary service prices and the placement signals, as well as accurate parameters associated with EV behavior, like EV availability, time of trips, and trip duration. The objective of the optimization is to maximize the aggregator's income from V2G by contributing to the ancillary services markets. The proposed robust OCC model, a robust linear problem (RLP) model, is simulated using the CPLEX solver in GAMS software. We compare our model with the existing deterministic models and show that our model increases the aggregator's revenues and then reduces the deviation between predicted and realized revenues. We propose a new and effective method for EV aggregators to bid into energy and ancillary service markets under uncertainties. Our model shifts the charging time of EVs from peak to off-peak hours, resulting in an improved final state of charge.

This paper aims to improve the efficiency, affordability, and safety of Wireless Power Transfer (WPT) devices. While wireless inductive charging is common in devices like smartphones, charging electric vehicles presents significant challenges due to high frequency electromagnetic fields that can be dangerous for users and those nearby. Current systems are expensive due to the use of specialized materials and components. By developing WPT systems with drastically reduced frequency levels, this research has the potential to significantly impact the widespread adoption of affordable, safe, and efficient WPT devices for high-power applications like electric vehicle charging. In fact, using lower frequencies allows us to build WPT systems with far less expensive materials, e.g. no need of Litz wires and ferrites that can be replaced by single core copper wires and classical magnetic steels. The effectiveness of the method is demonstrated through simulation using MATLAB Simulink® and experimental tests. The results indicate that it is possible to maintain good performance, reduce limitations and costs, and improve user acceptance of WPT systems by considerably lowering the frequency of electromagnetic fields that are classically used in such systems. [ABSTRACT FROM AUTHOR]

Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time- varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system. [ABSTRACT FROM AUTHOR]

In order to maintain environmental sustainability, it is crucial to address the transportation sector's explosive growth and the emissions that accompany it. Therefore, understanding the derivation factor of transportation emissions is of utmost importance. In the face of increasing transport emissions, it is important to expand the role of electric vehicles (EVs). An eco-friendly economy and low-carbon economy are made possible by electric automobiles. Energy consumption models that can accurately and consistently forecast energy use are critical to the successful deployment of electric vehicles. Enhancing EVs' energy efficiency will help reduce driver anxiety a great deal and offer a foundation for organizing, operating, and managing the infrastructure for charging. To overcome the challenges in estimating the energy consumption of electric vehicles, data was collected in Aichi Prefecture, Japan. In the study, traditional machine learning models, Multi Output, Gradient Boosting, XGBoost and Random Forest were used to predict the energy consumption of electric vehicles. The prediction models were assessed using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The forecasting results reveal that the regression model with the combination of Gradient Boosting and Multi Output performs well. Gradient Boosting and Multi Output based models with higher R2 values and lower MAE and RMSE values are proved to be more accurate To demonstrate the effect and relative influence of various input variables on the energy consumption forecast of electric vehicles, a thorough important feature analysis is carried out. The findings demonstrate that a sophisticated machine learning model can enhance the accuracy of electric car energy consumption predictions. [ABSTRACT FROM AUTHOR]

Under the impetus of national energy-saving and emission-reduction policies, electric vehicles (EVs) are increasingly entering the mainstream automotive market, signaling the gradual decline of the internal combustion engine era. However, the challenge of extending the range of EVs remains substantial. Addressing this core obstacle necessitates a dual approach: advancing battery technology and optimizing aerodynamic performance through streamlined design to reduce drag and enhance driving stability. This approach is crucial for enhancing the competitiveness of car manufacturers, expanding the market penetration of EVs, and supporting environmental sustainability. This study concentrates on pure EVs, employing three-dimensional, steady-state, incompressible fluid mechanics principles alongside advanced FLUENT simulation tools to systematically evaluate the external flow characteristics and overall aerodynamic efficiency of the vehicle. The research integrates streamlined design concepts, implementing micro-level aerodynamic modifications to the vehicle body structure to maximize driving efficiency. By combining precise computational fluid dynamics (CFD) simulations with empirical wind tunnel testing, the direct impact of design changes on the vehicle's aerodynamic performance is meticulously analyzed. Highlights of the research include the utilization of a 1:25 scale accurately scaled 3D printed vehicle model and comprehensive aerodynamic evaluations conducted in a lowspeed wind tunnel laboratory using a direct-current suction-type wind tunnel device. This methodology robustly verifies the accuracy of simulation predictions, providing scientific evidence and practical guidance for further exploring the application of streamlined design in enhancing the aerodynamic performance of EVs. Consequently, this research is expected to accelerate advancements at the technological frontier of the field. [ABSTRACT FROM AUTHOR]

With the intensifying global energy crisis and environmental issues, electric vehicles (EVs) have emerged as a crucial direction for future transportation development due to their high efficiency and zero emissions. However, EVs still face numerous technical challenges in practical use, particularly in braking energy management and thermal management system optimization. This paper aims to deeply analyze the regenerative braking process of EVs based on the second law of thermodynamics to uncover the irreversible losses and optimization pathways in energy conversion. Furthermore, a comprehensive vehicle thermal management system model is established, with detailed parameter settings and optimization to ensure efficient operation under various conditions. The research results indicate that the thermodynamics-based braking energy management strategy can significantly enhance the energy efficiency and range of EVs. Additionally, optimizing the vehicle's thermal management system contributes to improving overall vehicle performance and reliability. [ABSTRACT FROM AUTHOR]

In this study, a three-phase AC/DC neutral point clamped (NPC) power factor corrected (PFC) multilevel converter for off-board chargers of Electric Vehicles (EVs) is evaluated. The rectifier analysis has been tested by extensive simulations in combination with isolated DC-DC converters. This rectifier provides unity input power factor and efficiency above 96% for DC fast chargers rated at power levels up to 100 kW. The voltage ripple is below 0.5% for 800 Volt DC voltage. The current ripple is below 0.1% for 125 Ampere DC current. When the EV fast charger is evaluated with both isolated DC-DC converters, the NPC rectifier has less than 1% THD. This multilevel converter is highly efficient in terms of capacitor voltage balancing. The system has a simple control structure and does not require an extra PFC circuit. The converter operation and design calculations are presented with simulation studies. The simulation results of the system evaluated for isolated DC-DC converters demonstrate the validity and flexibility of the proposed charging system. [ABSTRACT FROM AUTHOR]

Hybridizing renewable energy sources with electric vehicles is more important to maintain a clean and emission-less environment. Electric vehicles (EVs) are incorporated into the curtailed incentive-based demand response program (CIBDR) and microgrid system to minimize thermal and transaction costs. In this study, a novel choice-based decision-making mechanism (CBDMM) is proposed. In CBDMM, charging and discharging of EVs power are generated based on the availability of renewable, pricing and state of charge. The objective of the proposed work is to minimize the grid burden by minimizing the thermal, transaction cost and maximizing the customer benefits. To test the effectiveness of the proposed work, three case studies (three, six, and ten units) are considered. A modified whale optimization algorithm (MWOA) is proposed to satisfy the power balance constraints. When incorporating electric vehicles into the system, thermal and transaction cost is reduced by 2.108% and 13.5%, respectively. At the same customer, incentives are increased to 2.49%, and objectives are satisfied. Scheduling interval is used over a 24-h period. Ethereum and ganache used to write the smart contract and validate the transaction, respectively. Metamask is used to verify and view the status of complete transaction. The energy transaction between EVs and the grid is achieved using blockchain technology by hashing mechanism. [ABSTRACT FROM AUTHOR]

The rising Greenhouse Gas (GHG) emissions stemming from the extensive use of automobiles across the globe represent a critical environmental challenge, contributing significantly to phenomena such as global warming and the deterioration of air quality. To address these challenges, there is a critical need for research and development in electric vehicles (EVs) and their associated charging infrastructure, including off-board and on-board chargers (OBCs). This paper aims to bridge the gaps in existing review literature by offering a comprehensive review of both integrated and non-integrated OBCs for EVs, based on the authors' knowledge at the time of writing. The paper begins by outlining trends in the EV market, including voltage levels, power ratings, and relevant standards. It then provides a detailed analysis of two-level and multi-level power converter topologies, covering AC-DC power factor correction (PFC) and isolated DC-DC topologies. Subsequently, it discusses single-stage and two-stage non-integrated OBC solutions. Additionally, various categories of integrated OBCs (iOBCs) are explored, accompanied by relevant examples. The paper also includes comparison tables containing technical specifications and key characteristics for reference and analysis. [ABSTRACT FROM AUTHOR]

With the rapid growth in the number of EVs, a huge number of EVs are connected to the power grid for charging, which places a great amount of pressure on the stable operation of the power grid. This paper focuses on the development of V2G applications, based on the current research status of V2G technology. Firstly, the standards on V2G applications and some pilot projects involving more representative V2G systems are introduced. Comparing V2G applications with ordered charging and unordered charging, the social and economic benefits of V2G applications are highlighted. Analysis of the social benefits of V2G applications concerns three points: the grid demand response, personalized charging, and the coordination of renewable energy sources. And analysis of the economic benefits of V2G applications is divided into three parties: the grid, the aggregator, and individuals. From the perspective of innovative EVs expanding the application scenarios through V2G technology, V2G applications for commercial EVs, emergency power applications, and vehicle-to-vehicle energy trading are introduced. The current challenges related to V2G applications are presented: users' willingness to participate in V2G applications, battery loss, charging and discharging tariffs, privacy and security, and power loss. Finally, some research recommendations for the development of V2G applications are given and the current state of research in regard to those recommendations is presented. [ABSTRACT FROM AUTHOR]

The idea of electric-drive-reconstructed onboard charger (EDROC) systems, along with the concept of dual-channel charging, offers a novel design, thought to enhance the integration and fault tolerance of the charging system of electric vehicles (EVs). This article investigates a dual-channel EDROC incorporating an asymmetrical six-phase permanent magnet synchronous machine (ASPMSM). A unique operation mode, called the unbalanced charging voltage operation mode, exists in this topology, in case the voltages of the two batteries are unequal. This unbalance results in different winding currents following through two channels, leading to an undesired charging torque in the machine. To ensure the safety of the system, an effective charging torque elimination method, based on dual-channel winding current balance, is proposed, which achieves a dot-shaped current path of torque generation-associated subspace (i.e., α–β subspace) by balancing the dual-channel charging power. Eventually, a controller is designed for the system and a prototype is created, to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The surge in electric vehicle adoption has resulted in a significant rise in end-of-life batteries, which are unsuitable for demanding EV applications. Repurposing these batteries for secondary applications presents a promising avenue to tackle environmental and economic challenges associated with their disposal. The second-life battery (SLB) approach emerges as a mechanism to manage this massive amount of retired EV batteries. However, this approach poses significant challenges in determining and monitoring battery degradation and performance. After evaluating different scenarios for reusing or recycling retired EV batteries, this paper examines the main challenges associated with SLBs, including techno-economic aspects, uncertainty from first life, safety, characterization and screening, battery-management systems, and secondary applications. A comprehensive review of current state-of-the-art SLB research and implementations is provided, particularly emphasizing battery characterization and the requisite evaluation processes for SLB eligibility. This paper explores diverse measurement techniques for assessing SLB performance, evaluating them based on accuracy, complexity, and time consumption, which are essential for achieving cost-effective SLB applications. The overarching objective is to thoroughly understand the principal challenges associated with repurposing EV batteries and delineate the research imperatives necessary for their successful implementation and prolonged lifespan. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being introduced to lessen greenhouse gas (GHG) emissions, air pollution, and reliance on fossil fuels. As a result of the government's aggressive promotion of EVs and rising environmental consciousness, EVs are quickly rising to the top of the low-carbon transportation market. Several viewpoints suggested that shifting to electric vehicles has been seen as a potential way to achieve sustainable mobility. Nevertheless, many studies discussed the obstacles and hurdles that obstruct the embracing of various electric-mobility (E-mobility) as EVs and electricscooters (E-scooters) as eco-friendly means. Herein, we discussed these hurdles and determined them through surveys for prior studies. Therefore, appraising these hurdles is the objective of our study. Best Holistic Adaptable Ranking of Attributes Technique - version 2 (BHARAT -v2) as a novel Multi-Criteria Decision Making (MCDM) technique is leveraged as an appraiser technique for these hurdles. Tree Soft (TrS) methodology is utilized for modeling these hurdles. Hence, we hybridized and integrated two methodologies for constructing BHARAT v2 Tree Soft (Bv2TrS) as an appraiser model. Subsequently, we discussed the findings of the Bv2TrS appraiser model. [ABSTRACT FROM AUTHOR]

Electric vehicles are finding it difficult to make faster inroads into the markets, and one of the most cited barriers to the faster adoption of electric vehicles in the academic literature is the lack of charging infrastructure and the associated range anxiety. However, densifying the charging infrastructure network is cost-intensive and should be meticulously planned. This study estimates discrete choice models with workplace, leisure place and highway as the location choice alternatives to investigate the electric vehicle public charging location preferences of the potential electric vehicle buyers. Mixed multinomial logit models and integrated choice and latent variable models are developed based on the attributes of the charging stations, viz. charging time, waiting time, charging cost, distance to the nearest charging station, emissions, and the characteristics of the individuals such as age, gender, income, and daily travel distance. This study finds significant negative utility associated with higher values of charging times, waiting times, charging costs, distance to the nearest charging station, and emissions. The results also indicate that the marginal disutility related to waiting time is higher than that of charging time. In terms of socio-demographics, females and higher income groups are found to prefer the workplace as their place of charging. However, as the age increases, the inclination towards highway charging stations increases. This study also discusses some important policy implications that can help decision-makers and stakeholders better plan electric vehicle charging infrastructure. • A first of its kind study to explore the preferences for the locations of electric vehicle charging stations. • Explores the influence of charging station attributes on the choice of charging station location. • Waiting time has higher disutility as compared to charging time. • Charging time at highways is perceived to be more onerous than charging time at workplaces or leisure places. [ABSTRACT FROM AUTHOR]

This article proposes a two-stage reliability-based model for the economic dispatch (ED) of thermal units (TUs) and wind turbines (WTs) in the presence of a demand-side response program (DSRP). In the first stage, the well-being analysis (WBS) is performed to determine the power generation and spinning reserve (SR) of the TUs regarding the timely power generation of WTs. In the second stage, the adoption of the responsive load consumption with various conditions of the generation system in the power pool market is established using the cost of expected energy not served criterion. This optimization problem is solved at two stages using the genetic algorithm. To validate the proposed model, numerical studies have been applied to the generation part of 24-Bus IEEE standard test power system including 11 TUs, one wind farm, and 1000 EVs. It is found from simulation results that an 8%–10% shift and increase in the energy consumption with responsive loads (RLs) participation especially EVs during low-load and off-peak hours can lead to more than 53.83% saving in total reliability cost of power system. In addition, the daily smooth load profile causes to savings in total load ED on TUs in the presence of WTs due to removing the unnecessary startup and shot-down costs during a day. [ABSTRACT FROM AUTHOR]

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Electrification of transport industry in China presents several new prospects to fulfil requirements, which are necessary to encounter increasing issues of energy security, air quality, and to lower the dependence on fossil fuels. The Chinese government is paying significant attention in increasing EV market penetration and consumer adoption through numerous demonstration programs/plans with attractive transportation policies. In this study, key factors included in adoption barriers and EV policies are comprehensively reviewed, which can enhance consumer intention to adopt EVs. This research study extensively demonstrates the significant and positive impact of two distinguished types of EV policies including financial policies and preferential policies on consumer's intention to adopt EVs by implementing an extended and improved version of Theory of Planned behavior (TPB). A case study in Shanghai is performed through survey of 314 respondents, which is further evaluated by structural equation modelling (SEM) to assess the aspects of EV policies on consumer adoption. In particular, construct items of TPB including attitude, subjective norm (SN), and perceived behavioral control (PBC) are investigated in detail to present their joint impact on purchasing intention of consumers. The confirmatory factor analysis (CFA) and structural equation modelling are performed in AMOS for assessment of survey findings. The research findings from survey analysis reveal that financial and preferential EV policies have a considerable impact towards shaping the attitude of consumers and significantly related to adoption intention of EVs. However, for Shanghainese, preferential policies are more positively associated with consumer purchase intention in comparison with financial policies. Consequently, preferential policies are playing a crucial role in controlling the adoption intention of EVs in China. The principal policy suggestions for various aspects provide multifaceted perceptions for stakeholders to envision electrified transportation.

This themed edition of the Proceedings of the Nigerian Academy of Science delves into the pivotal topic of transport decarbonization, a critical component in the global strategy to combat climate change and promote environmental sustainability. It amalgamates a rich tapestry of research, case studies, and policy analyses focused on the multifaceted challenges and opportunities inherent in transitioning to a low-carbon transport system. Key areas of discussion include the complexities of electric vehicle (EV) adoption, the potential for invigorating local manufacturing sector through EV and e-bike production, the transformative impact of removing fuel subsidies on the uptake of solar power, and the intricate policy landscapes that underpin transport decarbonization efforts. By spotlighting Nigeria's unique context, from its policy strides to innovative local initiatives, this edition not only contributes to the academic discourse on sustainable transport but also aims to inspire actionable insights and collaborative efforts towards achieving a decarbonized transport future. This compilation seeks to foster a deeper understanding of the interplay between technological innovation, policy frameworks, and community engagement in driving the shift towards sustainable mobility solutions, with the hope of catalyzing further research and implementation of sustainable transport systems globally.