Vehicle Charging Research Articles

An optimization dispatching strategy for integrated electricity and natural gas systems with fast charging stations

With the increasing number of electric vehicles (EVs), a large amount of charging load will affect the safety and economic operation of the distribution network (DN). In this paper, an optimization dispatching strategy for integrated electricity and natural gas systems (IEGS) with fast charging stations (FCSs) is proposed to address the impact of fast charging loads. Firstly, the FCS load model is established based on the simulation of vehicle traveling and vehicle charging. Secondly, a natural gas supply model is established, which includes constraints such as the adjustment range, direction, and total number of supply flow adjustments. Then, an optimization dispatching strategy for IEGS based on energy storage and gas storage is proposed. Finally, the effectiveness of the proposed strategy is verified. The simulation results show that when the proposed strategy is applied, the voltage of FCS node will be stabilized within a good range. And the energy purchase cost will be reduced by more than 50%. The proposed method improves the carrying capacity of DN for FCSs.

Analysis and control design for input‐series output‐parallel multi‐channel inductive power transfer system

AbstractTo realize high‐power inductive power transfer (IPT) for fast charging of electric vehicles (EVs), an input‐series output‐parallel (ISOP) multi‐channel IPT system is analysed in this paper, and an output control strategy based on single neuron controller is proposed to improve the stability of the system. Firstly, the steady‐state operating conditions of ISOP‐IPT system is analysed based on different compensation networks which shows that the constant‐voltage‐output compensation networks are more suitable for the proposed circuit structure. Then, to improve the voltage equalization between channels, an open‐loop control method combining parameter design and phase‐shift control is proposed against different coil misalignments. At the same time, based on the single neuron controller, the system output closed‐loop control is realized without the need of accurate system modelling. Finally, a three‐channel ISOP‐IPT experimental system was constructed, which operated stably at 2.9 kW with a power efficiency of 93.38%. The system closed‐loop control with control time of 22 ms is realized. The voltage equalization control offset is less than 1%.

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

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

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones

Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones

Electric vehicle charging station demand prediction model deploying data slotting

Accurate prediction of energy requirement at charging station is essential for optimizing infrastructure usage, ensuring grid stability, and minimizing operational cost. Literatures suggest deployment of machine learning techniques to forecast the station demand. One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. The conventional pre-processing techniques fail to dislodge nonlinearities and highly random patterns that include very low or zero-charging. Employing such techniques affects the model's forecast accuracy. This article performs data-slotting during pre-processing stage and then selects the best among 1-h, 2-h, 3-h and 4-h slots, to frame the feature vectors. The 4-h data with minimum variance is suggested to frame the dataset. Four distinct datasets, comprising different combination of average and total demands as predictor and response respectively are considered. The created dataset is deployed in Random Forest, Categorical Boosting, Extreme Gradient Boosting and Light Gradient Boosting models. The article recommends Categorical Boosting Regression model with least mean absolute error, mean square error and root mean square error of 0.0726, 0.0112, and 0.1059 respectively. Furthermore, the use of feature vector comprising of aggregated load for prescribed slots and the response representing the aggregated demand is observed to provide the least prediction error by the suggested model. The suggested model fed by the proposed feature vector offers significant advantage to charging station operator by enhancing the operational efficiency while performing resource and cost management with strategic planning.

Game theoretic operation optimization of photovoltaic storage charging station considering uncertainty and carbon trading

With the advancement of energy conservation and emission reduction efforts, the orderly charging of electric vehicles and the operation of photovoltaic-storage-charging stations associated with electric vehicles have become increasingly important topics. This study constructs an optimization model for the operation of stations under the synergy of electricity and carbon markets from a game theory perspective. Firstly, Latin hypercube sampling and Monte Carlo sampling are employed to handle the uncertainties in photovoltaic output and the stochastic nature of electric vehicles charging. Secondly, a ladder-type carbon trading mechanism is introduced, and a charging optimization model based on Stackelberg game theory is developed to describe the benefit interaction between charging stations and electric vehicles users. In this model, the upper level represents the charging station operator aiming to maximize joint electricity and carbon revenue while minimizing load fluctuations, whereas the lower level represents electric vehicles users aiming to maximize consumer surplus. Finally, a genetic algorithm nested with mixed-integer linear programming is used to solve the optimization model. The simulation results validate the model's effectiveness and superiority. The results indicate that, compared to centralized optimization methods, the Stackelberg game mechanism can increase consumer surplus by 119.40 %, reduce carbon emissions by 217.92 %, and achieve a win-win situation for both parties. Compared to a fixed carbon trading value, the ladder-type carbon trading mechanism can reduce carbon emissions by 23.84 % and smooth load fluctuations.

Grid-Tied Solar Integrated Electric Vehicle Charging System with Advanced LMS Control

Grid-Tied Solar Integrated Electric Vehicle Charging System with Advanced LMS Control

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Optimizing Microgrid Load Fluctuations through Dynamic Pricing and Electric Vehicle Flexibility: A Comparative Analysis

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Dynamic Determination Method of Line Drop Compensator Parameters for Voltage Regulators Based on Mixture of Experts Using Real‐Time Information

In this paper, a method for determining line drop compensator (LDC) parameters for step voltage regulators and on‐load tap changers is proposed to avoid voltage violations in distribution systems with voltage fluctuations due to photovoltaic (PV) generation and electric vehicle (EV) charging. Distribution system operators need to effectively solve voltage problems caused by the widespread installation of distributed energy resources with the existing voltage regulators to construct an efficient and rational infrastructure. Our focus was on improving the method for determining the LDC parameters for the existing voltage regulators based on LDC control. A mechanism to dynamically change the LDC parameters depending on the situations in the distribution system was developed by using a mixture of experts, one of the machine learning techniques, and real‐time voltage and power flow information from information technology switches. The power flow calculations were performed using a distribution system model constructed based on the topology, loads, and generation characteristics of an actual distribution system in the Hokuriku region. The effectiveness of the proposed method was evaluated in terms of its improved effect on PV and EV hosting capacity, as well as the impact on the frequency of tap operations of voltage regulators. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Analog One-Cycle Control Strategy for Stable Battery Charging in Photovoltaic Systems

The growing adoption of photovoltaic systems for direct electricity generation from solar radiation has underscored the importance of efficient energy storage solutions. Batteries are the predominant method for storing energy from photovoltaic systems, especially for applications such as electric vehicle (EV) charging. However, these systems face significant challenges, particularly regarding battery longevity, as fluctuations in solar irradiation can lead to harmful voltage spikes. This paper introduces an Analog One-Cycle Control (OCC) strategy designed to mitigate these issues by stabilizing the battery voltage against varying input voltages. The proposed control scheme aims to enhance battery life by preventing damage caused by voltage overshoots. Experimental results demonstrate the effectiveness of the OCC strategy in maintaining steady-state conditions and optimizing power management.

Simultaneous Allocation of Electric Vehicle Charging Station and Power Filters in Power Distribution Network Using Marine Predators Algorithm

Simultaneous Allocation of Electric Vehicle Charging Station and Power Filters in Power Distribution Network Using Marine Predators Algorithm

Characterization of plugging and unplugging process for electric vehicle charging connectors based on Force/Torque measurements

Understanding the magnitude and profile of forces and torques (F/T) to properly execute the plugging and unplugging for electric vehicles (EVs) charging is crucial for designing reliable robots in automated charging stations (ACS). Previous research focuses on F/T measurement for controlling force during robotic tasks. In contrast, this study aims to refine automation workflows for smoother operation. By utilizing the comprehensive F/T measurements, it becomes possible to extract precise information during the plugging and unplugging process. This data provides insight into interaction dynamics, improving the robot’s ability to perform tasks more accurately and efficiently. Through experiments involving 40 participants, anthropometric and F/T data were analysed using statistics, uncovering key relationships between height, weight, and forces. The F/T profiles were predicted using polynomial, Fourier series, and Gaussian models, with the Gaussian proving the most accurate. Peak force and impulse are significantly higher during plugging (74.15 Ns) than unplugging(57.92Ns).

A cost-effectiveness comparison of renewable energy pathways for decarbonizing heavy-duty vehicles in China

Decarbonizing heavy-duty vehicles is becoming increasingly important, yet multiple renewable-based decarbonization pathways have not been compared systematically. To fill this gap, this study develops a techno-economic and environmental model that integrates the fuel cycle and vehicle cycle to assess these pathways for China. The model includes two energy storage technologies: batteries and hydrogen, three energy transmission options, and two vehicle types: fuel cell electric vehicles and battery electric vehicles. Five distinct low-carbon pathways are evaluated on a ton-kilometer basis, including cost, greenhouse gas emissions, and abatement cost relative to conventional diesel trucks. Results indicate that the most cost-effective battery electric vehicle pathway offers a 7 % lower ton-km cost than diesel trucks for a 49-ton gross vehicle weight. Battery electric vehicle charging combined with hydrogen for power generation is more cost-effective than direct use of hydrogen in fuel cell electric vehicles. All studied pathways can achieve over 80 % emission reduction compared to diesel trucks, with fuel cell electric vehicle pathways having the highest abatement costs of 103–117 USD/tonCO2. Analysis of the impact of gross vehicle weight and vehicle range indicates that battery electric vehicle pathways outperform fuel cell electric vehicle pathways across most dimensions. Costs of battery electric vehicle pathways do increase sharply for vehicle ranges beyond about 700 km, and fuel cell electric vehicles become more cost-effective relative to battery electric vehicles for ranges above approximately 1100 km, although all pathways are much more costly than diesel trucks at long ranges. The sensitivity analysis highlights the importance of powertrain efficiency and cost projections show that fuel cell electric heavy-duty vehicles can achieve cost-parity before 2030. These results highlight the desirability of promoting the adoption of heavier battery electric vehicles and providing more targeted subsidies for long-range fuel cell electric vehicles.

The Evolution of Electric Vehicle Charging Technologies in Nigeria: Prospects and Challenges

The Evolution of Electric Vehicle Charging Technologies in Nigeria: Prospects and Challenges

Intelligent Energy Management for Electric Vehicle Charging

Intelligent Energy Management for Electric Vehicle Charging

Photo-voltaic system with battery employing parallel converters operation for charging stations and distributed loads amidst anomalous grid

In this work, solar photovoltaic array-based generation unit is connected to non-ideal distribution network and powering plug-in electric vehicle (PEV) charging infrastructure and other local loads connected to AC common bus while ensuring reliable operation of parallel connected voltage source converters under dynamics. A photovoltaic array connected via voltage source inverter, which feeds power to loads and battery is used to act as power management entity and provides reactive power and harmonic power support via converter. In context of widespread incorporation of PEVs and renewable energy sources (RES) into grid, inherent intermittency and dynamic behavior pose significant challenges in establishing a resilient and flexible control framework. Consequently, a solution is proposed that employs dual low pass filter integrated along an adaptive noise elimination filtering algorithm, serving as a primary processing stage. Additionally, integrator-based frequency locked loop control strategy is implemented in conjunction to mitigate power quality concerns, regulate voltage levels, and correct power factor discrepancies. Furthermore, system ensures continuous operation, providing uninterrupted power to local loads even during fluctuating conditions while simultaneously functioning as a seamless PEV charging station. Additionally, a comparative analysis of controller performance is summarized in a tabular format, alongside a power quality assessment. The analysis includes the evaluation of total harmonic distortion (THD) for both grid currents and grid voltages, which are measured at 2.36 % and 3.60 %, respectively. These values are well within the limits established by IEEE 519 standard, demonstrating compliance with the required power quality criteria. This RES based charging structure topology is tested using a hardware prototype under dynamics and impactful performance is observed through obtained results.

A Novel Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks

Wireless sensor networks can monitor the environment to detect anomalies and reduce the risk of maritime traffic. Energy is necessary for low-power conditions where wireless sensor networks are used. Ensuring the lifespan of energy constraints and providing continuous environmental observation, data collecting, and communication requires management. Battery replacement and energy consumption issues can be resolved with path planning and energy-efficient autonomous underwater vehicle charging for sensor nodes. The nearest neighbour technique is used in this study to solve the energy-aware path planning problem of an autonomous underwater vehicle. Path planning simulations show that the nearest neighbour strategy converges faster and produces a better result than the genetic algorithm. We develop robust and energy-efficient path-planning algorithms that efficiently acquire sensor data while consuming less energy, allowing the monitoring system to respond to anomalies more rapidly. Increased sensor connectivity lowers energy usage and increases network longevity. This study also considers the situation when it is recommended to avoid taking direct travel paths between particular node pairs for a variety of reasons. This recommendation is considered in this study. We present a strategy based on a modified Nearest Neighbour-based Approach from the Nearest Neighbour method to address this more challenging scenario. The direct pathways between such nodes are constrained within the context of this technique. The modified version of Nearest Neighbor-based approach performs well even in that particular situation.