EV Charging Research Articles

Performance improvement of front-end PFC controller with small DC-link capacitance for light EV charger applications

Performance improvement of front-end PFC controller with small DC-link capacitance for light EV charger applications

Loss analysis of three-level flying capacitor converter-based EV chargers using hybrid WBG-Si devices

Loss analysis of three-level flying capacitor converter-based EV chargers using hybrid WBG-Si devices

Cloud-IoT framework for EV charge station allocation and scheduling: A spotted hyena jellyfish search optimization approach

Cloud-IoT framework for EV charge station allocation and scheduling: A spotted hyena jellyfish search optimization approach

The potential of V2G considering EV charging behaviors, battery lifespan, and distribution networks

The potential of V2G considering EV charging behaviors, battery lifespan, and distribution networks

The road to net zero in a renewable energy-dominated electricity system: Impact of EV charging and social cost of emission on the optimal economic dispatch

The road to net zero in a renewable energy-dominated electricity system: Impact of EV charging and social cost of emission on the optimal economic dispatch

Machine learning approaches for the prediction of public EV charge point flexibility

Machine learning approaches for the prediction of public EV charge point flexibility

Three-Stage Energy Management of Urban Rail Transit-Based Microgrid and EV Charging Station With V2T Technology

Three-Stage Energy Management of Urban Rail Transit-Based Microgrid and EV Charging Station With V2T Technology

EV charging stations multipulse rectifiers based on passive harmonic reduction circuits: A critical survey

EV charging stations multipulse rectifiers based on passive harmonic reduction circuits: A critical survey

EV charging habits and their impact on public charging infrastructure usage

EV charging habits and their impact on public charging infrastructure usage

Evaluating the Damage Cost of Vehicle to Grid Integration in Indonesia Power Grid for Sustainable Energy System

The cost incurred in the EV Charging is currently only from the energy consumed consideration. While there is extensive research on the impact of EV charging on grid infrastructure and operation, few studies quantify these effects in terms of explicit 'damage costs'. However, besides energy problems, the analysis of the environmental impact of EVs has not been quantified monetarily. This paper aimed to investigate the damage cost (DC) of EVs in comparison with Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) based on Indonesia Java Bali's grid conditions. Using the existing data in Indonesia, the characterisation parameter were determined using cradle-to-gate life cycle analysis (LCA) and ReCiPe 2016 then converting the endpoint indicators impacts to DC using a monetary weight factor (MWF) of those two EVs. Cradle to gate LCA is a method for evaluating the environmental impact of battery from the extraction of raw material to the factory gate and ReCiPe 2016 is a widely used of LCA method, which converts emission and resources extraction into certain indicators. Results show that DC for a 20 kWh BEV = 0.12 ± 0.01 USD/kWh compared to an 8 kWh PHEV = 0.08 ± 0.005 USD/kWh. Therefore, the DC of an extra 0.02 USD/kWh for BEVs and 0.015 USD/kWh for PHEVs should be considered in formulating the EV charging tariff.

Predictive optimization using long short-term memory for solar PV and EV integration in relatively cold climate energy systems with a regional case study

The global shift toward sustainable energy and electric mobility addresses environmental concerns related to fossil fuels. While these alternatives are increasingly utilized in residential and commercial sectors, integrating renewable energy in building systems presents significant challenges. This is particularly evident in cold regions where unpredictable resource availability complicates energy reliability. The study emphasizes the need for innovative approaches to address these complexities and ensure consistent energy performance in dynamic conditions. This research explores the energy dynamics within a residential community located in a relatively cold climate region (Tabriz). The study begins by estimating the energy requirements of individual buildings, including the additional demand generated by electric vehicles. It then evaluates the potential for solar energy generation from photovoltaic systems. Finally, a machine learning-based approach (i.e., LSTM, Long Short-Term Memory) is employed to optimize the management of energy supply and demand across the community. This study demonstrates that heating demands in a cold climate are substantially higher than cooling needs, with solar energy providing sufficient (~ 32.1%) coverage during warmer months but requiring grid support in colder seasons. The prediction of EV charging patterns using LSTM models achieved over 93% accuracy, enabling improved energy demand forecasting and load management. These findings highlight the potential for optimizing renewable energy use, reducing grid dependency, and enhancing energy efficiency through effective production-demand management.

Wireless power transfer for electric vehicles : Design and efficiency analysis

Wireless Power Transfer (WPT) could make EV charging safer and more efficient by replacing physical connections. Electric car WPT system design and efficiency are the subject of this article. The implications of misalignment on coil topologies, resonance coupling, and charging efficiency are examined. After thorough computation and experimental validation, we provide a best WPT design that minimizes losses, improves energy transfer efficiency, and operates well in practice. WPT may speed up EV charging, according to study. We evaluate current and proposed methods to increase electric vehicle wireless charging system efficiency.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the "carbon peaking and carbon neutrality goals", the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network - transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

Einstein Aggregation Operators with Cubic Fermatean Fuzzy Sets

The study goals are to address the compound, multi-criteria decision-making tests associated with choosing the ideal scope for electric vehicle charging stations. This difficulty ascends due to the interaction of numerous quantitative and qualitative issues and the uncertainty characteristic in such assessments. Aliya et al. [37] introduced the concept of the cubic fermatean fuzzy sets, including their operational laws and score function. We develop the three aggregation operators including CFEFWA, CFEFOWA, and CFEFHWA operators with the cubic fermatean fuzzy set. Furthermore, uncertainty usually arises in Cubic Fermatean fuzzy sets and challenges related to selecting charging stations for electric vehicles. Therefore, the current study aims to provide an integrated decision-making method for handling multi-criteria CFFS electric vehicle charging stations. For CFFS, we propose some innovative Einstein operations to aggregate the decision data. To illustrate the effectiveness of our proposed technique, we apply it to an applied case of choosing EV charging positions and associate the outcomes with existing practices. The efficiency of the proposed method is assessed by comparing its fallouts with those obtained from existing methodologies. The results indicate that the introduced technique proposes extra value and is well aligned with current observations. Introduce three aggregation operators based on cubic fermatean fuzzy set to improve the decision-making for EV charging station locations. Propose an MCDM method with cubic fermatean fuzzy set To define the effectiveness of the proposed method through a practical case study and numerical example

A Condition-Monitoring Method of a DC-Link Capacitor in a V2X Bidirectional EV Charger

A Condition-Monitoring Method of a DC-Link Capacitor in a V2X Bidirectional EV Charger

Adaptive federated learning framework for predicting EV charging stations occupancy

Adaptive federated learning framework for predicting EV charging stations occupancy

Multi-stage adaptive expansion of EV charging stations considering impacts from the transportation network and power grid

Multi-stage adaptive expansion of EV charging stations considering impacts from the transportation network and power grid

Multi-view graph contrastive representative learning for intrusion detection in EV charging station

Multi-view graph contrastive representative learning for intrusion detection in EV charging station

A six - stage evaluation model for optimizing urban EV charging and swapping station locations

A six - stage evaluation model for optimizing urban EV charging and swapping station locations

Design and Controlled for Bridgeless Zeta-Luo Converter for EV Charging

Abstract: Thewidespreadadoptionofelectricvehicles(EVs)hasnecessitatedthedevelopmentofefficientand reliable charging infrastructure. Conventional EV chargers often employ bridge rectifiers, which can lead to significant conduction losses due to the presence of high-voltage diodes. To address this limitation, a novel EV charger based on a bridgeless (BL) isolated Zeta-Luo converter with built-in power factor (PF) preregulation capability at the supply side has been proposed. The BL isolated ZetaLuo converter is a combination ofa Zeta converter and aLuo converter, each operating during alternatehalves of the supply voltage cycle. This approach eliminates the need for bridge rectifiers, reducing conduction losses and improving overall efficiency. Additionally, the shared output inductors of the Zeta and Luo converters furthercontributeto the compact sizeand lightweight design ofthecharger.Thebuilt-in PFPreregulation capability ensures that the charger operates with a near-unity power factor, minimizing reactive power consumption and improving grid stability. This is achieved by regulating the input current waveform to closely match the input voltage waveform. The proposed EV charger offers several advantages over conventional chargers, including: The BL topology and shared output inductors significantly reduce semiconductor losses, leading to improved efficiency. The built-in PF Preregulation capability ensures near-unity power factor operation, minimizing reactive power consumption and improving grid stability. The elimination of bridge rectifiers and the sharing of output inductors result in a compact and lightweight charger design. Enhanced performance under sudden voltage variations: The proposed charger exhibits robust performance under sudden line voltage fluctuations, ensuring reliable EV charging even during unstable grid conditions. The Bridgeless Isolated Zeta–Luo Converter-Based EV Charger With PF Preregulation presents a promising solutionforefficientandreliableEVcharging.Itscombinationofhighefficiency,improvedpowerfactor, reduced size, and enhanced performance under voltage variations makes it an attractive choice for future EV charging infrastructure.