Charging Time Of Electric Vehicles Research Articles

Compact design of circularly polarized microstrip patch antenna with double slip ring resonator for vehicle-to-vehicle communication

The paper mainly focuses on the compact design of circularly polarized microstrip patch antenna for vehicle-to-vehicle communication. The charging time of electric vehicle is longer waiting time than compared to traditional gasoline-based vehicles. The charging is done at the public charging stations. When the vehicles need charging while driving, they communicate with Dedicated Short Range Communications (DSRC) band to send the information to the nearest vehicles and receive information from the charging station. Moreover, DSRC band is used in many applications area such as Intelligent Transportation System (ITS), Electronic Toll Collection (ETC), Collision Avoidance, and connected vehicles. The research problem in this study is that when installing an antenna on a vehicle, it is important to have a compact size and achieve circular polarization. The research solution for the problem statement in this study is emphasized on 45? inclined slot and corner trimmed is added in rectangular patch to make the current part longer and the impedance matching better, and to get circular polarization. After that, Swiss roll structure CSRR is added in ground plane to improve bandwidth. Finally, outer SRR is added in the ground plane to improve S11. The proposed design has been validated for car-to-car (5.85-5.925 GHZ) communication.

DC fast charging stations for electric vehicles: A review

AbstractThe expansion of the DC fast‐charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast‐charging stations. The review is closely aligned with current state‐of‐the‐art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

Vehicle-to-grid bidding for regulation and spinning reserve markets: A robust optimal coordinated charging approach

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.

Comparison of decentralised fast-charging strategies for long-distance trips with electric vehicles

Despite noticeable improvements in electric vehicle (EV) technology, EV charging time remains high, and the lack of fast-charging infrastructure extends the travelling time for long-distance trips even further. Charging strategies to coordinate EVs’ charging plans dynamically will help reduce station waiting times. This paper aims to quantify and compare the performances of two decentralised strategies using regular information-sharing between EVs and stations to estimate future waiting time in charging stations accurately. The communication strategy relies only on the communication of optimal charging plan computed by EVs to stations without any reservation. In contrast, the reservation strategy enables additionally the booking of optimal charging time slots. In a dedicated multi-agent simulation framework, we tested both strategies on different levels of infrastructure saturation. The communication strategy shows the best performance especially when combined with more frequent charging stops.

Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price

The rapid growth in the number of electric vehicles (EVs) has significantly increased the demand for electricity for residents. In addition, because the charging time of EVs highly coincides with the peak period of user electricity consumption, the disorderly charging of EVs will lead to the overload of the power grid transformer. Traditional control methods lack certain robustness and do not fully consider the uncertainty of EVs. As a result, the V2G participation rate of electric vehicles cannot be determined, and the control reliability is low. To solve the above problems, this paper designs a reinforcement learning framework of Long Short-Term Memory network and Improved Linear programming algorithm (LSTM-ILP) to control the V2G of EVs.This paper comprehensively considers the overall electric vehicle charging demand, discharge potential, large grid electricity price, aggregator, and users’ interests demands. Firstly, aiming to minimize the charging and discharging fee of EVs and the load peak-to-valley difference of the power grid, a dynamic electricity price based on Long Short-Term Memory neural network (LSTM) is established. Then, the improved linear programming algorithm (ILP) is used to solve the charging and discharging optimization problem of EV, and the results are fed back to the input of the next iterative update of the LSTM, and finally, the optimal electricity price and EV charging and discharging schedule are achieved. The simulation results show that the LSTM-ILP framework can not only reduce the charging fee of electric vehicles, but also achieve the Peak and valley trimming of the grid load. Charging costs for EV users were reduced by 42.1% compared with unordered charging, and by 22% percent compared with orderly charging.

Fuzzy Analytical Hierarchy Process for Strategic Decision Making in Electric Vehicle Adoption

In response to the requirement to address the global climate crisis in urban areas caused by the logistics sector, an increasing number of governments around the world have begun aggressive strategic actions to encourage manufacturers and consumers to adopt electric vehicle (EV) technology. One of the most beneficial aspects of driving an EV is that it reduces pollution while also reducing the use of fossil fuels, as well as improving public health by improving local air quality. Nevertheless, the level of EV adoption differs significantly across markets and geographies. EV adoption barriers slow the overall rate of electric mobility. This study ranks a list of obstacles and sub-hindrances to the adoption of electric vehicles in Thailand using the Fuzzy Analytical Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) technique. The results showed that infrastructure policy barrier (A), which had the highest weight of 0.6058, was the biggest barrier to EV adoption, followed by technological barrier (B) with a weight of 0.2657, and then by market barrier with a weight of 0.1285. Insufficient charging infrastructure network (A3), lack of proper government support/incentives and collaboration (A1), insufficient electric power supply (A2), high capital cost (C3), and EV charging time (B3) were key sub-barriers to EV adoption in Thailand. Decision Making Systems (DMS) have additionally been created to assist executives in making decisions about the aforementioned barriers. The DMS is based on the concept of computer-aided decision making in that it allows for direct user interaction, analysis, and the ability to change circumstances and the decision-making process based on the executives’ own experience and abilities. Thus, the findings of this study aid in the formulation of market strategies for relevant stakeholders and shed light on potential policy responses.

Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time

An efficient charging time forecasting reduces the travel disruption that drivers experience as a result of charging behavior. Despite the machine learning algorithm’s success in forecasting future outcomes in a range of applications (travel industry), estimating the charging time of an electric vehicle (EV) is relatively novel. It can help the end consumer plan their trip based on the estimation data and, hence, reduce the waste of electricity through idle charging. This increases the sustainability factor of the electric charging station. This necessitates further research into the machine learning algorithm’s ability to predict EV charging time. Foreign object recognition is an essential auxiliary function to improve the security and dependability of wireless charging for electric vehicles. A comparable model is used to create the object detection circuit in this instance. Within this research, the ensemble machine learning methods employed to estimate EV charging times included random forest, CatBoost, and XGBoost, with parameters being improved through the metaheuristic Ant Colony Optimization algorithm to obtain higher accuracy and robustness. It was demonstrated that the proposed Ensemble Machine Learning Ant Colony Optimization (EML_ACO) algorithm achieved 20.5% of R2, 19.3% of MAE, 21% of RMSE, and 23% of MAPE in the training process. In comparison, it achieves 12.4% of R2, 13.3% of MAE, 21% of RMSE, and 12.4% of MAPE during testing.

Thermal and electrical insulation properties of BN‐filled polymer‐based elastomer composites

AbstractTo make the current charging time of electric vehicles meet the technical requirements of rapid charging, the higher the charging current needs to be increased. Consequently, higher requirements are put forward for the heat dissipation capacity of the charging pile cable. Therefore, obtaining insulation materials with good electrical and thermal properties is vital to solving the heat dissipation problem of charging pile cables. This paper modified polypropylene (PP) with polystyrene‐ethylene‐butadiene‐styrene (SEBS). Boron nitride nanosheets (BNNS) and boron nitride nanospheres (BNNOS) were used as thermally conductive fillers to construct A‐B‐A‐B … A type thermally conductive pathways in SEBS/PP matrix. In SEM tests, it can be observed that the orientation of BNNS along the direction perpendicular to the pressure increases. DSC tests show that the introduction of BN particles lowers the melting point of the SEBS/PP matrix and increases the crystallization temperature of the composite. In the thermal conductivity experiment, with the rise of the content of BNNOS and the number of multilayer structures, the thermal conductivity gradually increases, up to 0.514 W/m K, which is three times the thermal conductivity of the SEBS/PP matrix. The tensile strength and elongation at the break of the composites decrease but the change of conductivity is not obvious.

Sliding mode-based control of an electric vehicle fast charging station in a DC microgrid

The fast-charging units have become a more efficient and attractive option recently for reducing the challenges due to the long charging time of electric vehicles (EVs). To evaluate the impacts of the EV fast charging stations (EVFCS) on the power grid and also to assess their contributions to the system operation through the vehicle-to-grid (V2G) technology, two control methods, namely, sliding mode control (SMC) and fuzzy logic control (FLC), are developed in this study for a DC microgrid including EVFCS and distributed generation sources. In these methods, the EV battery is used as a DC source of a distribution static compensator (D-STATCOM) with the objective of mitigating the voltage sag in the microgrid. Various simulations are conducted in MATLAB Simulink/SimPowerSytems environment in order to examine the effectiveness of the proposed control approaches in terms of ensuring the stability and improving the dynamic performance of the EVFCS. The results show that considerable improvements can be achieved, especially in the case of using the SMC method.

Analysis of Charging Load Acceptance Capacity of Electric Vehicles in the Residential Distribution Network

After large-scale electric vehicles (EVs) are connected to the residential distribution network, community charging has become one of the main bottlenecks at present, especially in old residential areas. Therefore, the current residential distribution network’s ability to accept charging load and when and how the distribution network needs to be transformed have become meaningful research points. Based on the characteristics of the EVs’ charging load of residential areas on a typical day and the size of the target annual charging load, this paper analyzes the acceptance capacity of the charging load of the distribution network on typical weekdays and weekends. By taking the charging load characteristics, the charging time of EVs, the voltage of each node of the distribution network, the line capacity, the transformation capacity of the distribution station as constraints, and the maximum capacity of the residential distribution network to accept the charging load as the objective function, the charging load capacity of the residential distribution network is analyzed. The particle swarm optimization algorithm is employed to solve the optimized mathematical model. The simulation uses an actual residential distribution network as an analysis example, and the partition optimization results prove the correctness and feasibility of this proposed method.

Optimal planning of photovoltaic-storage fast charging station considering electric vehicle charging demand response

The charging demand response of electric vehicle(EV) users will affect the social and economic benefits of fast charging services, so it is an important factor in EV charging station planning. In this paper, a photovoltaic-storage fast charging station(PSFCS) planning method considering charging demand response is proposed. Firstly, the EV fast charging behaviors and the factors influencing the admitting ability of PSFCS to the fast charging load are discussed, and the interaction characters between them are analyzed. Based on the above analysis, a three-stage dual-objective optimization model of PSFCS planning considering charging demand response is proposed and solved by the elitist non-dominated sorting genetic algorithm(NSGA-II). Finally, the case study is formulated based on the 21 power nodes-12 traffic nodes network, simulation results show that the proposed method can effectively improve the profit of charging service providers and reduce the total charging time of EVs.

Assessment of dynamic wireless charging based electric road system: A case study of Auckland motorway

• Calibration, and validation of a part of Auckland traffic network using AIMSUN. • Estimation of dynamic wireless charging length requirement in a traffic corridor. • Impact of traffic and energy parameters on the wireless charging length requirement. • Financial feasibility of DWC system with the plug-in charging system. Urbanization and the need to improve green mobility have paved the way for electric vehicle (EV) adoption that reduces greenhouse gas emissions. Investors have a critical role in incentivizing charging infrastructure to meet EV's charging needs immediately. Dynamic wireless charging (DWC) offers a viable solution to mitigate issues related to the limited driving range, higher battery capacity (resulting in higher cost), and longer charging time of EVs. This paper examines the impact of a DWC-based electric road system (ERS) on EV flow and energy consumption. A traffic simulation-based methodological framework is developed to evaluate the effect of DWC facilities on road networks. A case study is conducted for the Auckland motorway network using various scenarios to evaluate the performance of the ERS, including the financial feasibility of the DWC system compared with the existing plug-in charging system. It provides insights into the minimum length of DWC facilities and the spatial and temporal variations in energy demand, which can be helpful to public and private stakeholders in deciding the optimal strategy to implement DWC in transportation networks on a large scale.

Effects of Fast Charging Modes on Thermal Performance of Lithium‐Ion Battery

With the fast development and popularity of electric vehicles (EVs), their relatively slow charging speed has been paid more and more attention to. Fast charging technology can significantly increase the charging speed and shorten the charging time, but the heat generation and temperature rise of the lithium-ion battery during the fast charging process will seriously affect its electrochemical characteristics and cycle life, and even cause safety issues. Therefore, it is necessary to analyze the thermal performance of the lithium-ion battery under fast charging conditions, and investigate the effects of different fast charging modes on it. In this paper, a NMC lithium-ion cell was taken as the object, and the electrochemical-thermal coupling (ETC) model was built and verified. Based on this model, the heat generations and temperature rises of the cell in different fast charging modes were analyzed and compared, including constant current (CC) charging, constant voltage (CV) charging, constant current-constant voltage (CC-CV) charging, stage CC charging and pulse charging. It was found that the peak temperature of the cell appeared at different moments in the above charging modes. And the peak temperature during CV charging was much higher than those during charging of other modes. The results could provide references for the design and selection of charging modes and battery thermal management systems toward fast charging technology. This article is protected by copyright. All rights reserved.

Grey wolf optimizer-based machine learning algorithm to predict electric vehicle charging duration time

ABSTRACT Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models.

Pricing iterative optimization for multi‐agent simulation of setting electric vehicle charging model in public parking lots

AbstractDespite the increasing scale of the electric vehicle market in recent years, in view of the long charging time of EVs (Electric Vehicles), the accessibility of charging facilities is still an obstacle to the rapid development of EVs. Therefore, the Chinese government has promulgated a one‐size‐fits‐all construction strategy which means constructing charging piles in parking lots with a fixed proportion. This paper mainly simulates the actual demand and adjusts the charging price of charging stations to reduce the uneven spatial distribution of charging demand. In particular, this paper constructs a multi‐agent system of the road network, vehicle, and charging station to simulate the charging behaviour in the mixed scenario of EVs and traditional fuel vehicles. The impact of charging pricing adjustment on individuals and charging stations is fed back in real‐time by using the characteristics of agents to respond to dynamic changes and make intelligent decisions. This paper proposes an iterative optimization method to obtain the optimal pricing strategy for charging stations to reduce the imbalance of charging demand. Taking the Wulin Square business district in Hangzhou as an example, the results show that the proposed pricing optimization method can improve the overall utility.

Deep reinforcement learning-based operation of fast charging stations coupled with energy storage system

Fast charging stations (FCSs) can reduce the charging time of electric vehicles (EVs) and thus can help in the widespread adoption of EVs. However, FCSs may result in the power system overload. Therefore, the deployment of the battery energy storage system (BESS) in FCSs is considered as a potential solution to avoid system overload. However, the optimal operation of FCSs equipped with BESS is challenging due to the involvement of several uncertainties, such as EV arrival/departure times and electricity prices. Therefore, in this study, a deep reinforcement learning-based method is proposed to operate FCSs with BESS under these uncertainties. The state-of-the-art soft actor-critic method (SAC) is adopted and the model is trained with one-year data to cover seasonality and different types of days (working days and holidays). The performance of SAC is compared with two other deep reinforcement learning methods, i.e., deep deterministic policy gradient and twin delayed deep deterministic policy gradient. A comprehensive reward function is devised to train the model offline, which can then be used for the real-time operation of FCS with BESS under different uncertainties. The trained model has successfully reduced the peak load of the FCS during both weekdays and holidays by optimizing the operation of the BESS. In addition, the robustness of the proposed model against different EV arrival scenarios and extreme market price scenarios is also evaluated. Simulation results have shown that the proposed model can reduce the peak load of the FCS under diverse conditions in the desired fashion.

A Deep Learning Approach for Electric Vehicle Charging Duration Prediction at Public Charging Stations: The Case of Morocco

The adoption of electric vehicles (EVs) is increasing worldwide as it may help reduce reliance on fossil fuels and greenhouse gas emissions. However, the large-scale use of charging stations for electric vehicles poses some challenges to the power grid and public infrastructure. To overcome the problem of extended charging time, the simple solution of increasing the charging station and increasing the charging capacity does not work due to the load and space limitation of the power grid. Therefore, researchers focused on developing intelligent planning algorithms to manage the demand for public charging based on predicting the charging time of electric vehicles. As a result, this paper proposes a deep learning approach for predicting the duration of charging sessions. These approaches are validated using a real-world dataset of charging processes collected at public charging stations in Morocco. Numerical results show that the gated recurrent units (GRU) regression method slightly outperforms the other methods in predicting the charging sessions duration. Accurate prediction of electric vehicles charging duration has many potential applications for utilities and charging operators, including grid reliability, scheduling, and smart grid integration. In the case of Morocco, the massive deployment of EVs can cause a variety of problems to the electrical system due to the considerable charging power and stochastic charging behaviors of electric vehicle drivers. Thanks to this study's results, we can assess the expected impact of additional EVs on the grid, considering specific characteristics of the Moroccan power system.

Self-driven liquid metal cooling connector for direct current high power charging to electric vehicle

Direct current-based high power charging (DC-HPC) technology is expected to shorten the charging time of electric vehicles (EV) significantly and lessen the range anxiety. However, a high charging current would cause a remarkable temperature rise of the charging connector due to the Joule heat, which must be adequately removed to keep its durability and safety. This paper reported a novel concept of the self-driven liquid metal (LM) cooling connector (LMCC) used for DC-HPC. The room-temperature LM of Galinstan filled in the flexible tube is applied as the current-carrying conductor and circulated-cooling fluid simultaneously. The circulation-flow LM is driven by the designed electromagnetic force through coupling the charging current and magnetic field from a pair of permanent magnets, which does not need additional pumping devices. A principle experiment is conducted to demonstrate the driving and cooling performances of LMCC, which shows excellent circulation flow rate (with the flow rate of 0.75 L/min and the driven pressure head of 44.7 kPa) of the LM in the flexible tube (diameter of 6 mm and length of 2.65 m) and only achieves a temperature rise of about 13 °[email protected] Both 3D multi-physics simulation and theoretical models are also established and demonstrated by experiment results to accurately evaluate the impacts of the charging current, magnetic field, and flexible tube size on the performance of self-driven LMCC. The results indicate that increasing the diameter of the flexible tube could significantly reduce its temperature rise induced by a high charging current and does not affect its flexibility. A super-low temperature rise of 10.6 °[email protected] for the charging cable (the diameter of 10 mm and length of 3 m) is designed. This work provides a new strategy for the heat dissipation technology of DC-HPC charging connectors.

Electric vehicle charging schedule considering shared charging pile based on Generalized Nash Game

Electric vehicle (EV) will not only effectively promote the popularization of charging facilities, but also reduce the dependence on fuel vehicles. However, large-scale charging behaviours of EVs will bring new challenges to match between charging facilities and EVs. Due to the limited capacity of the centralized charging stations, it is difficult to meet the charging demands of EVs in certain areas, especially during the peak load periods. To solve the insufficiency of charging capacity caused by the mismatch between charging stations and EV charging loads, this paper proposes a hierarchical scheduling model of EVs considering shared charging piles. The upper scheduling model determines the charging time of EVs based on the charging demands. The lower scheduling model coordinates charging stations and shared charging piles to determine the charging locations of EVs. The sharing scheme of private charging piles is determined by sharing capacity model based on the generalized Nash game. The quantum particle swarm algorithm with the dynamic feedback mechanism is adopted for a better convergence of the solving process of the hierarchical scheduling model. The simulation results on IEEE 33-node system are conducted to validate the effectiveness of the proposed model and solution algorithm, which show the extensive potential applications in EV scheduling.

Reservation-based EV charging recommendation concerning charging urgency policy

Electric Vehicles (EVs) are environmental friendly comparing with traditional internal combustion vehicles (ICVs), and have great application potential to achieve green transportation. However, due to the battery technology under development, the charging time of EVs is still longer than refuelling time of ICVs. Importantly, CS-Selection scheme (which/where to charge) and charging scheduling (when/whether to charge) are key solutions, for coping with long charging time and uneven distribution of Charging Stations (CSs) in urban city. In this paper, we propose an Urgency First Charging (UFC) scheduling policy, which orders EVs via their charging urgency (calculated by their charging demand and remaining parking duration). With the underlying UFC policy, we further propose a reservation-based CS-Selection scheme that selects the optimal CS with the minimum trip duration (summation of travelling time through CS, and the charging time spent at CS), where the EVs would further report their reservations to help anticipate the service congestion status of CSs in future. We have conducted simulations through Helsinki’s city traffic scenarios. The simulation results show that our proposed CS-Selection scheme has advantages in improving users quality of experience, which shortens the overall trip duration of EVs and fully charges more EVs before departure deadline.