EV Charging Research Articles

Electric Railway System Integrated with EV Charging Station Realizing Train-to-Vehicle Technology

Electric Railway System Integrated with EV Charging Station Realizing Train-to-Vehicle Technology

GAO Optimized Sliding Mode Based Reconfigurable Step Size Pb&O MPPT Controller With Grid Integrated EV Charging Station

GAO Optimized Sliding Mode Based Reconfigurable Step Size Pb&O MPPT Controller With Grid Integrated EV Charging Station

Optimizing Urban Parking Utility: Strategic and Operational Planning of Fixed and Mobile EV Charging Services in Hybrid Parking Systems

Optimizing Urban Parking Utility: Strategic and Operational Planning of Fixed and Mobile EV Charging Services in Hybrid Parking Systems

Design of Blockchain-Based Multi-Domain Authentication Protocol for Secure EV Charging Services in V2G Environments

Design of Blockchain-Based Multi-Domain Authentication Protocol for Secure EV Charging Services in V2G Environments

Stabilized High Voltage Gain With Reduced Common Mode Voltage Using SN-USMC for Remote Green EV Charging

Stabilized High Voltage Gain With Reduced Common Mode Voltage Using SN-USMC for Remote Green EV Charging

Dual-Side Capacitor Tuning and Cooperative Control for Efficiency-Optimized Wide Output Voltages in Wireless EV Charging

Dual-Side Capacitor Tuning and Cooperative Control for Efficiency-Optimized Wide Output Voltages in Wireless EV Charging

Design and analysis of novel topology for PV-FED EV charging system

Design and analysis of novel topology for PV-FED EV charging system

Bivariate Detection based Dual-Mode Metal Object Detection System for Wireless EV Charging

Bivariate Detection based Dual-Mode Metal Object Detection System for Wireless EV Charging

Digital Twin of a battery-based EV charging station to support services

Digital Twin (DT) concepts and applications have been evolving to explore the potential to support Product-Service Systems (PSS). At the same time, DT newest developments include Electric Vehicle’ (EV) battery systems. However, the literature on Digital Twins of EV charging stations as service systems is still limited. This work presents a Digital Twin information architecture of an EV charging station aiming to support the related services. The proposed information architecture has been applied to a novel battery-based EV mobile charging platform under development. The prototyped application utilizes battery charge and discharge data to enable a scheduling service. Other services, such as reservation, SoX estimation, and lifecycle management, are also discussed. Results contribute to the expanding literature on the relationship between DT and PSS, focusing on mobility electrification. Such a service-oriented perspective of the Digital Twin application has the potential to foster innovative business models and highlight value for stakeholders.

A Case Study on the Intersection of EV Charge UX and User Onboarding Design

A Case Study on the Intersection of EV Charge UX and User Onboarding Design

Medium Voltage Direct Current Distribution System for an Electric Vehicle Fast Charging Park

There is an increasing shift towards the electrification of automobiles to meet zero-emission standards set by many nations. As electric vehicles become more common, their power demand on the power system becomes greater. A substantial modernization or upgrade of the current distribution power grid is required to meet such demand. Since most Level 3 fast chargers utilize DC power, medium voltage direct current (MVDC) provides a feasible alternative to the present AC distribution infrastructure. This study proposes an MVDC distribution model for powering a large EV park consisting of 40 EV charging stations with a 9.6-MW total power demand. Calculation and simulation are used to evaluate the model and compare it with an equivalent MVAC system. The outcomes show that implementing an MVDC distribution system is an efficient approach to meeting the increasing power demand for electric vehicles. The proposed 40-kV MVDC system power loss (13.1kW) is six times lower than that of the equivalent MVAC system (89.74kW). Further, since MVDC systems do not require AC step-down transformers and AC/DC converters at the equipment end, they can be a lower-cost option for powering large EV charging parks. The findings help enhance EV charging infrastructure, which expedites the adoption of EVs for reducing carbon emissions in the transportation sector.

Energy savings approach to optimal location of EV charging stations in microgrids

This paper presents an analytical approach to finding an optimal location for an EV charging station based on energy savings in a local microgrid. The analysis is carried out on days obtained by clustering yearly load data and by running an energy management system that runs on MATLAB interior point method. The microgrid is composed of both renewable and non-renewable energy sources. The charging station is equipped with a controlled charging feature and this study considers 2 EV charging strategies out of which the one benefitting the power system is adopted.

Navigating Tomorrow Non-Linearly: A Holistic Exploration of Smart and Secure EV Charging Infrastructure in Parking Facilities

Navigating Tomorrow Non-Linearly: A Holistic Exploration of Smart and Secure EV Charging Infrastructure in Parking Facilities

Research on EV charging load forecasting and orderly charging scheduling based on model fusion

The randomness and uncertainty of charging behavior of EV users bring great challenges to the short-term prediction of charging load of EV charging stations. Aiming at the low precision of short-term load forecasting of EV charging station under the influence of many factors, this paper takes the optimization of EV charging loss as the objective of optimization, firstly, the time series attributes are combined with the nonlinear attributes, and PLSR and LightGBM are used to extract the attributes from EV charging load series, and the fusion prediction model is established. Secondly, the Bayesian optimization method is introduced to optimize hyper-parameters of the model, and the self-adaptive adjustment of the hyper-parameters are realized, which improves performance of the algorithm and the prediction precision. Finally, taking the actual load data of EV charging pile in a certain area as an example, the experimental analysis is carried out, the second-order cone optimal dispatching scheme is adopted to optimize the EV charging schedule. The results show that the EV prediction model constructed by this paper has higher prediction accuracy than other artificial intelligence prediction algorithms, and provides more accurate data for the second-order cone optimal scheduling scheme, the utility model effectively realizes the safe and stable operation of the power grid.

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%).

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.

Electric vehicle charging scheduling with mobile charging stations

Mobile charging station (MCS) is a brand-new technology to electric vehicle charging, which is a supplementary service for addressing the shortcomings associated with fixed charging station (FCS), such as prolonged waiting time of charging, charging congestion of FCS, and user travel anxiety. While MCSs offer convenience to electric vehicle users, the challenge faced in the MCS operation is that MCS equipped with large batteries to provide charging services to users by visiting them one by one not only leads to high operating costs, but also prolongs the waiting time for users to utilize MCS services. To overcome the issues, a novel framework is proposed by optimizing the MCS operation in economic efficiency. In this study, a variant of mixed integer linear programming (MILP) model is developed to maximize the MCS operator's profits with considering the user's perspective, including clustering, and covering user demands, setting temporary charging location for users, dispatching MCSs for charging and discharging, and scheduling EV charging. As the scale of the problem increases, the solution time of using the proposed MILP model is inefficient. Whereas an improved genetic algorithm is developed for solving a large-scale of the proposed model. Solomon's benchmarking instance data is adopted to evaluate the performance and validity of the proposed algorithm, complemented by various sensitivity analyses aimed at providing managerial insights into MCS operations.

Policy sequencing can increase public support for ambitious climate policy

Abstract Public support for ambitious climate policies and carbon prices that have direct costs for voters may depend on policy sequencing. Policy sequencing theory suggests that the strategic ordering of policies into sequences that initially create benefits can subsequently increase support for higher carbon prices. However, systematic quantitative evidence about the effects of sequencing on public support is lacking. We provide novel theoretical and empirical insights on the mechanisms through which strategic policy sequencing affects public support for climate policies. We generated these insights using geospatial data and a representative conjoint experiment with Swiss voters conducted just before the popular vote on an amendment to the Federal Act on the Reduction of Greenhouse Gas Emissions in June 2021. Our evidence shows that the perceived effectiveness of prior policy-induced benefits is related to more public support for higher carbon prices across sectors. Moreover, we find that more opportunity structures for low-emission alternatives—like higher EV charging station density—are associated with increased public support for carbon prices in the sector where the former material benefits occur. Our results also imply that positive policy perceptions of prior climate policies are related to increased support, particularly among conservative voters and those who do not regard climate change as a salient issue. Thus, strategic policy sequencing could be an effective strategy for broadening public support for ambitious climate policies.

A Novel Dynamic Li-Ion Battery Model for the Aggregated Charging of EVs

Implementing successful aggregated charging strategies for electric vehicles to participate in the wholesale market requires an accurate battery model that can operate at scale while capturing critical battery dynamics. Existing models either lack precision or pose computational challenges for fleet-level coordination. To our knowledge, most of the literature widely adopts battery models that neglect critical battery polarization dynamics favoring scalability over accuracy, donated as constant power models (CPMs). Thus, this paper proposes a novel linear battery model (LBM) intended specifically for use in aggregated charging strategies. The LBM considers battery dynamics through a linear representation, addressing the limitations of existing models while maintaining scalability. The model dynamic behavior is evaluated for the four commonly used lithium-ion chemistries in EVs: lithium iron phosphate (LFP), nickel manganese cobalt (NMC), lithium manganese oxide (LMO), and nickel cobalt aluminum (NCA). The results showed that the LBM closely matches the high-fidelity Thevenin equivalent circuit model (Th-ECM) with substantially improved accuracy over the CPM, especially at higher charging rates. Finally, a case study was carried out for bidding in the wholesale energy market, which proves the ability of the model to scale.