Electric Vehicle Users Research Articles

PV integrated multi-leg powered constant quasi-dynamic charging system for low-speed vehicles

The global adoption of electric vehicles (EVs) is gaining momentum as countries strive to achieve sustainable development goals. Establishing charging infrastructure is of paramount importance to promote increased EV usage. The cost and weight of the batteries are factors that reduce the sales volume and popularity of EVs. A dynamic charging system has also been developed to enhance the driving range of EVs and mitigate the need for heavy storage requirements. The energy demand of charging infrastructure is increasing day by day. Many countries are installing solar arrays along the roadside to meet the power demand of highway lighting applications and achieve sustainable development goal (SDG) 7. To further enhance this system, this manuscript proposes integrating PV technology with the dynamic charging system. The PV arrays and energy storage system (ESS) collaborate to power the dynamic charging system. A multi-leg inverter is employed to energize the charging couplers, while a resonant network improves the power transfer capability of the couplers. On the receiver side, a resonant network ensures the delivery of constant voltage and constant current by tuning the network topology. The 3.3 kW five-legged inverter is developed to energize the four double-D-shaped charging couplers. The common DC bus delivers 350 V to the inverter, and the charging system delivers 350 V, 8.85 A to the EV batteries.

Are Residents Willing to Pay for Electric Cars? An Evolutionary Game Analysis of Electric Vehicle Promotion in Macao

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

Experimental Investigation of a Distributed Architecture for EV Chargers Performing Frequency Control

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

Sizing Methodology of Dynamic Wireless Charging Infrastructures for Electric Vehicles in Highways: An Italian Case Study

The necessity of pushing the road mobility towards more sustainable solutions has become of undeniable importance in last years. For this reason, both research and industry are constantly investigating new technologies able to make the usage of battery electric vehicles(BEV) as accessible and usable as traditional internal combustion engine vehicles (ICEV). One of the most limiting issues concerns the short range of electric vehicles, which complicates their use for long distances, such as for highway travels. A promising solution seems to be the “charge-while-driving” approach, by exploiting the inductive dynamic wireless power transfer (DWPT) technology. Nevertheless, such systems show different issues, first of all, high investment and maintenance costs. Furthermore, it is not clear how extensive a potential dynamic wireless charging infrastructure needs to be to make a real advantage for electric vehicle drivers. As a consequence, the aim of this paper is to introduce a new methodology to estimate the number and length of wireless charging sections necessary to allow the maximum number of electric vehicles to travel a specific highway without the need to stop for a recharge at a service area. Specifically, the methodology is based on a algorithm that, starting by real traffic data, simulates vehicle flows and defines the basic layout of the wireless charging infrastructure. This simulator can provide a decision support tool for highway road operators.

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Establishing a rational pricing mechanism for electric vehicles (EVs) can incentivize users to engage in charging activities, foster positive consumption behaviors, and indirectly benefit the grid by increasing load demand. Therefore, this paper conducts a Stackelberg game study between the grid and EV users by utilizing real charging and discharging data of EV users in F city. First, the price elasticity of EV users' electricity consumption behavior is analyzed. Second, the Stackelberg game model is constructed, multiple tariff schemes are designed to calculate the equilibrium tariff of the game, and the benefits of the grid and EV users are analyzed in comparison with the tariff policy that will be implemented in F city. Lastly, different seasonal scenarios are constructed to extend the applicability of the research results. It is found that Scheme 3 effectively incentivizes charging load growth, increases cross-subsidies and transmission and distribution fees, and protects the level of charging and discharging costs for residents; furthermore, lowering charging and discharging tariffs during seasons of high price elasticity for residents to increase demand is an optimal strategy for grid pricing.

Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China

The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.

Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks.

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm's effectiveness in meeting EV users' bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

Challenges of electric vehicle adoption in Indonesia: Revealing the hidden factors affecting purchase intention

Purpose – Recently, electric vehicles (EVs) have seen significant development worldwide. Indonesia has responded by implementing various policies to support the adoption of EVs, which are touted as the future environmentally friendly transportation solution. However, in Indonesia, the purchase intention for EVs still needs to reach expectations, reaching only 14,93% of the target in 2023. This study analyzes the antecedents of benefit factors determining the intention to buy an electric vehicle. This study will fill the gap in earlier research by comprehensively examining the factors that influence EV purchase intention in Indonesia.Design/methodology/approach – This study will fill the gap in earlier research by comprehensively analyzing the factors that influence EV purchase intention in Indonesia. This study will empirically examine the relationship between perceived usefulness, perceived ease of use, perceived risk, EV knowledge, public involvement, and purchase intention, as well as the antecedents of perceived usefulness, perceived ease of use, and perceived risk. This study uses a quantitative method with a survey design to gain an in-depth understanding of the factors influencing the purchase intention of electric vehicles in Indonesia. The survey involved 957 potential electric vehicle users in Indonesia who have never bought an EV but have sufficient interest and knowledge about EVs. The research instrument was a validity and reliability-tested questionnaire used to collect accurate and consistent data.Findings – This study found that although respondents had strong perceptions of the benefits of electric vehicles (perceived usefulness), this did not automatically lead to high purchase intentions. Another surprising finding was that perceived risk also did not directly affect purchase intention. This suggests the presence of other factors inhibiting purchase intention, which need to be identified and addressed.Research limitations/implications – This research contributes significantly to the literature on adopting electric vehicles (EVs). Perceived ease of use has a positive effect on purchase intention. The results of this study indicate that perceived ease of use does not significantly affect the purchase intention of electric vehicles.Practical implications – The findings of this study provide several practical implications for electric vehicle manufacturers, marketers, and policy makers.Originality/value – This research offers a comprehensive approach to understanding the factors that influence electric vehicle (EV) purchasing interest in Indonesia, by including previously overlooked variables such as risk perception, public involvement, and knowledge about EVs. Additionally, this research focuses on the Indonesian context, providing valuable insights into the unique challenges and opportunities in EV adoption in the country, which can form the basis for strategies and policies to promote sustainable transportation.

Transitioning to electric vehicles: Opportunities and challenges

The rapid improvements in India’s financial status and the inadequate transport system have significantly contributed to the surge in cars and two-wheelers. However, these motor vehicles, which predominantly run on petrol and diesel, pose a severe threat to the environment as they emit greenhouse gases such as carbon dioxide, carbon monooxide , Methane , sulphur oxides, and nitrous oxides and particulate matter etc.. These emissions contribute significantly to air pollution, posing a grave threat to human health, especially in urban areas. It is imperative to take action now to safeguard the planet from future catastrophes and ensure a safer environment for both current and future generations. One significant step we can take is transitioning to electric vehicles (EVs), which have far less environmental impact compared to those running on petrol and diesel. The Government of India has initiated several initiatives to promote EV usage. According to Statista, as of March 2023, there were 225,103,000 units operational EVs of all types in India .Electric vehicles emit significantly lower carbon emissions than gasoline vehicles, making them a crucial part of reducing environmental harm. According to recent studies, by 2030, EVs are expected to reduce nitrogen oxide by 17%, particulate matter by 18%, carbon monoxide by 18%, and carbon dioxide by 846.3 million tons. This paper high lights the transitioning towards zero emission public transport and the positive impacts of Electric vehicles on environment and the challenges before EV industry.

Injuries With Electric vs Conventional Scooters and Bicycles.

Micromobility, the use of small vehicles (primarily scooters and bicycles), has become a standard transportation method in the US. Despite broad adoption of electric micromobility vehicles, there is a paucity of data regarding the injury profiles of these vehicles, particularly in the US. To characterize micromobility injury trends in the US, identify demographic characteristic differences in users of electric and conventional vehicles, and identify factors associated with hospitalization. This cross-sectional study queried the National Electronic Injury Surveillance System, a comprehensive database that collates injury data associated with consumer products from emergency departments across the US to provide national estimates, from calendar year 2017 to 2022. Data on micromobility vehicle injuries (bicycles, scooters, electric bicycles [e-bicycles], and electric scooters [e-scooters]) were obtained. Trends in injury and hospitalization counts, injury characteristics, and factors associated with hospitalization. From 2017 to 2022, the US recorded 2 499 843 bicycle (95% CI, 1 948 539-3 051 147), 304 783 scooter (95% CI, 232 466-377 099), 45 586 e-bicycle (95% CI, 17 684-73 488), and 189 517 e-scooter (95% CI, 126 101-252 932) injuries. The median age of the riders was 28 (IQR, 12-51) years; 72% were male, 1.5% Asian, 13% Black, 12% Hispanic, and 49% White. Annual e-bicycle and e-scooter injuries increased from 751 (95% CI, 0-1586) to 23 493 (95% CI, 11 043-35 944) and injuries increased from 8566 (95% CI, 5522-11 611) to 56 847 (95% CI, 39 673-74 022). Compared with conventional vehicles, electric vehicle accidents involved older individuals (median age, 31 vs 27 years; P < .001) and a higher proportion of Black riders (25% vs 12%; P < .001). Helmet use was less in electric vehicle incidents compared with conventional vehicles (43% vs 52%; P = .02), and injuries were more common in urban settings (83% vs 71%; P = .008). Age-adjusted odds of hospitalization among all Black individuals compared with White individuals was 0.76 (95% CI, 0.59-0.98; P = .04). In this cross-sectional study of micromobility vehicles, an increased number of injuries and hospitalizations was observed with electric vehicles compared with conventional vehicles from 2017 to 2022. These findings suggest the need for change in educational policies, infrastructure, and law to recenter on safety with the use of micromobility vehicles.

Optimization of Charging Station Capacity Based on Energy Storage Scheduling and Bi-Level Planning Model

With the government’s strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to the problems of high electricity costs, long waiting times, and increased grid load during peak hours. To address these issues, a dual-layer optimization model was constructed and solved using the Golden Sine Algorithm, balancing the construction cost of CSs and user costs. In addition, the problem was alleviated by combining energy storage scheduling and the M/M/c queue model to reduce grid pressure and shorten waiting times. The study shows that energy storage scheduling effectively reduces grid load, and the electricity cost is reduced by 6.0007%. The average waiting time is reduced to 2.1 min through the queue model, reducing the electric vehicles user’s time cost. The bi-level programming model and energy storage scheduling strategy have positive implications for the operation and development of bus CSs.

An Analytical Framework for Assessing Equity of Access to Public Electric Vehicle Charging Stations: The Case of Shanghai

With the unprecedented growth of electric vehicles usage, the equitable population-based provision of public charging services has become an important concern in high-density urban centers. To address sustainability concerns, this study explores an analytical framework for assessing the equity of access to public charging services. By comprehensively analyzing factors such as accessibility, the Gini coefficient, the correlation coefficient, and supply–demand matching, we investigated the unequal access to public charging stations within 24 types of sites in central Shanghai. The spatial distribution and accessibility were visualized to illustrate differences in service access. Subsequently, social equity was assessed by considering the population distribution and identifying areas of supply–demand imbalance. The results show that 81% of households share only 10% of public charging services, suggesting a generalized inequality within areas and facilities. Residents of large-scale, low-density, low-grade neighborhoods have difficulties accessing services. Nearly 66.96% of subdistricts have supply and demand conflicts. In addition, priority types of improvement were identified and directions for improvement were suggested, as well as recommendations for the integration of PCSs with exterior built places. We also found significant differences in accessibility and equity at both the district and subdistrict level. The findings of this study will help urban planners assess and locate unequal areas and provide insights and the basis for further expansion into the analysis methods adopted at different stages to achieve sustainable development.

Online Spatial-Temporal EV Charging Scheduling with Incentive Promotion

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users’ charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this paper. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users’ charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users’ willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users’ implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users’ feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.

Solar Charging Station for Easy Bikes in Bangladesh: Validation and Post-Model Evaluation

The significance of renewable sources of energy is enormous in reducing the demand, use, and also price of fossil fuels following recent advances in related technologies allowing for the development of solar-powered cars. The number of users of electric vehicles (EVs) is increasing rapidly day by day all over the world. Due to the growing number of users of EVs, our existing electrical network is facing a lack of electric power because of the overload. As the number of electric and hybrid vehicles increases, efficient and integrated charging stations must be developed to solve the overload issues in existing networks. To address this, the paper proposes using onsite renewable energy sources such as solar energy and existing networks to improve the efficiency and effectiveness of charging stations. Combining photovoltaic with battery storage in a grid-connected system ensures the reliable functioning of the hybrid charging station. The paper focuses on the post-model evaluation, charging station distribution and location, business model, limitations, and software simulation and corresponding calculation. The results show that hybrid-powered charging stations can be economical, dependable, and efficient enough to fulfill the various demands of electric vehicle loads. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 1-9

Big Data Technology Usage in Electric Transportation Industry

In the context of critical challenges related to global warming and the necessity of reducing carbon footprint, the electric car sector is experiencing significant growth. This progress inevitably leads to the need for expansion and modernization of the charging station infrastructure. This article conducts a detailed analysis of how big data processing technologies can contribute to the optimization of this infrastructure’s use, the efficiency of charging stations, and the development of personalized services for electric vehicle users. Strategies for solving current problems, particularly in the areas of data security and standardization, are discussed, along with the impact of big data on the formation of new commercial models in the electric transport sector. Special attention is given to the analysis of existing scientific works and publications, which reveal a noticeable deficit of research focused on adapting big data technologies to specific regional conditions and analyzing the behavioral models of electric vehicle consumers. The article identifies key directions for future research aimed at exploring the potential of big data for the intelligent optimization of electric vehicle transport, particularly in areas such as demand forecasting, effective management of charging stations, development of new user services, and integration with broader urban transport management systems. Additionally, the article highlights challenges related to ensuring the confidentiality and security of collected data, the need to integrate diverse data and systems, and the current demand for qualified professionals in the field of big data analysis. The final section of the article focuses on the prospects of using big data in electric transport, their potential contribution to the development of smart city concepts, infrastructure improvement, and enhancing the quality of services for end consumers. Recommendations are provided for key stakeholders in the industry, with the aim of facilitating the adoption of strategic decisions that would take into account future opportunities and challenges. An analytical review of current literature sources and online publications is included, emphasizing the innovative nature of the research conducted.

Remedying driving range and refueling challenges in electric mobility: Consumer adoption of battery-swappable electric vehicles

Driving range (mileage) limitations and long charging times associated with electric vehicle (EV) usage have necessitated the emergence of battery-swappable electric vehicles (BSEV) for sustainable mobility. In enhancing BSEV proliferation, a thorough comprehension of consumer adoption is needed. Understanding consumer adoption or hesitancy of BSEV is important for environmental sustainability. Consequently, this study explores the adoption intention of BSEVs by extending an amalgamated technology acceptance model (TAM) and the innovation resistance theory (IRT) with knowledge. Using 552 responses obtained through a survey, hypotheses were assessed using a structural equation model (SEM). Results from the study expressed that the conceptual framework explained 70.2 % of the overall variance, better than the individual TAM (40.7 %) and IRT (20.2 %), as well as the integrated model (60.7 %). Perceived ease-of-use, attitude, and perceived usefulness were positive in influencing BSEV. Value barrier and usage barrier were negatively significant in influencing BSEV adoption intention. However, the barrier of risk was insignificant in affecting the adoption intention of BSEVs. Tradition and image barriers were negative in influencing attitude. Perceived usefulness and attitude were discovered as mediators. This research provides policymakers and stakeholders with apt recommendations to promote acceptance and mitigate BSEV adoption hesitancy.

A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory

With “charging anxiety” gradually replacing “range anxiety” as the major obstacle for consumers to purchase electric vehicles (EVs), the quantity-driven rather than quality-driven charging station development model barely meets the development of the EV market. It is of great practical significance that a comprehensive and in-depth insight into charging stations’ customer requirements (CRs) to uncover their pain points and improvement measures. Therefore, this study develops a decision framework for improving EV charging service quality based on quality function deployment (QFD). First, CRs are extracted based on charging station online reviews and classified by the revised importance performance analysis (IPA) method to identify the charging service’s main highlights and pain points. Then, the process of achieving consensus among experts on evaluating the correlation between CRs and service design requirements (DRs) is considered as an evolutionary game of coalition, with a meritocratic Fermi rule considering affinities (MFA) being introduced. Finally, the importance of DRs is determined by combining the Shapley value and risk aversion theory. The results indicate that regulation and convenience are the main highlights of charging stations to satisfy EV users, while reliability and assurance are primary pain points. The charging station operators should prioritize their limited resources on providing efficient customer and maintenance services, installing charging stations with international standards, and regularly inspecting and updating charging infrastructures. This study is beneficial for charging station operators to understand what customers want and how to satisfy them.

Suitable Site Selection of Public Charging Stations: A Fuzzy TOPSIS MCDA Framework on Capacity Substation Assessment

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.

Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&amp;KANO and MVO in PV-Connected Distribution Network

Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&amp;KANO and MVO in PV-Connected Distribution Network

Modeling, design and validation of DC-DC landsman converter for low-power electric vehicle battery charging applications

As the world moves towards sustainable development, the reduction of air pollution is considered an important factor. Consequently, the transportation sector is moving from conventional internal combustion engine vehicles to electric vehicles (EVs). To encourage EV usage, governments are working towards installing more public charging systems. These systems invariably use a DC-DC converter to ensure the supply of an appropriate voltage to charge the battery. In developing countries, the majority of users prefer electric bikes and electric rickshaws, which require low power. Therefore, it becomes necessary to use low-power converters suitable to charge these batteries. In such systems, non-isolated DC-DC converters play an important role. This work is intended to explore the use of the Landsman converter for EV battery charging. The motivation behind the work is explained, and the state of the art of EV battery charging research is reviewed to show the research gap. The operation of the proposed converter is explained in detail with its dynamic loop and nodal equations. The gain equation and the memory elements design are detailed with the corresponding equations. The model of the proposed converter is provided with the loss calculation. The system is simulated on MATLAB R2022b software, and the simulation results are provided to show the behavior of voltage and current in each component. To validate the design, a 100 W hardware prototype is implemented using discrete components to supply a resistive load, and to charge a 48 V Li-ion battery. The corresponding results are provided to validate the design and operation. A comparison of the proposed converters’ performance parameters with other non-isolated converters is provided. The topology is seen to provide an instantaneous efficiency of 88% during the prototype testing for resistive load, and an efficiency of 89.6% while charging a 48 V Li-ion battery, wherein, the efficiency magnitudes are the experimental values obtained through real-time measurements on Keysight IntegraVision Power Analyzer PA2203A, during the testing of the hardware prototype, as presented in section . Consequently, Landsman converter is seen to be an attractive converter for EV battery charging applications.