Charging Process Of Electric Vehicles Research Articles

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Introduction: The rapid development of electric vehicle technology in the field of renewable energy has brought significant challenges to wireless charging systems. The efficiency of these systems is crucial for improving availability and sustainability. The main focus of the research is to develop an intelligent charging strategy that utilizes fuzzy logic to optimize the efficiency of wireless charging systems for electric vehicles.Method: Introduce a model that combines fuzzy logic algorithm with automatic control system to improve the wireless charging process of electric vehicles. The model adopts dynamic tracking and adaptive control methods by analyzing the characteristics of static wireless charging systems. Utilizing primary phase shift control and secondary controllable rectifier regulation, combined with optimized fuzzy control algorithm.Result and discussion: The experimental results show that when the secondary coil is stable, the model maintains a stable duty cycle of about 75.6% and a stable current of 5A. It was observed that when the mutual inductance values were set to 10, 15, and 20 uH, the efficiency of the primary coil before applying control decreased with increasing resistance.Conclusion: The proposed system has shown great potential for application in real-world electric vehicle charging systems, demonstrating good applicability and feasibility in controlling the charging process and tracking the optimal efficiency point. The integration of fuzzy logic enhances the system’s ability to adapt to different operating conditions, which may lead to wider implementation and improved operational efficiency.

Electromagnetic field simulation modeling method and distribution characteristics of electric vehicle wireless charging system

In recent years, wireless charging technology for electric vehicles has gained significant attention. To accurately analyze the distribution characteristics of the electromagnetic field during the wireless charging process of electric vehicles, a finite element-based electromagnetic analysis method was employed. Applied in the commercial simulation software, the electromagnetic environment of the resonant coil and electric vehicle model was simulated under high-power charging conditions, resulting in an overall electromagnetic field distribution for the electric vehicle. The results indicated that within the coil region, the magnetic induction intensity in the central area of the coil was zero, and it increased as the distance from the center of the coil grew. Outside the coil region, the magnetic induction intensity gradually decreased. The electric field intensity of the resonant coil was maximum in the central area of the coil, and it weakened as the distance from the center of the coil increased. When a magnetic shielding resonant coil was used, the electromagnetic field was confined between the shielding materials, and the magnetic field rapidly attenuated on both sides of the magnetic shield. The electromagnetic field energy of the electric vehicle body was mainly concentrated at the bottom of the vehicle near the coil. When the coil was located in the front of the car body, the maximum electric field intensity distribution in the car body was 8.50 V/m, and the maximum magnetic induction intensity was 0.024 μT. When the coil was located in the middle of the car body, the maximum electric field intensity was 2.31 V/m, the maximum magnetic induction intensity was 0.019 μT. As the distance from the coil position increased, the energy weakened.

A decentralized optimization approach to the power management of electric vehicles parking lots

To reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model.

Novel aggregated controller of wind and PV based grid connected charging station for electric vehicle

<span lang="EN-US">Novel technologies are adopting electric vehicles (EVs) day-by-day due to increasing interest in EVs. The charging process of EVs is a very important aspect when it is connected to the utility grid. Generally charging of an EV can be done at either home or in charging stations were connected to the utility grid. More harmonics and nonlinear currents are injecting into the utility grid during the charging process of the battery due to the existence of converters for power conversion in charging stations of EVs, which generally affects the quality of the power. In this situation, supplying the power to the utility grid from batteries existing in the vehicle through the charging station will provide a better solution and will charge again when there is less demand on the grid. Further using renewable energies in the charging stations can provide reliable power for both vehicles as well as the utility grid. To achieve better performance and maintain power quality at load bus, an aggregated controller is proposed in this paper. Moreover, reloading conditions are also incorporated to renewable energy sources under disconnection of the utility grid to maintain power balance. Hardware-in the–loop (HIL) based extensive results by using OPAL-RT modules are examined in this article under many situations to validate the proposed method.</span>

Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm

Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system. In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoT-based smart charging system for scheduling V2G connections for hybrid electrical vehicles. It allows for more precise and effective control and greater efficiency by enabling the system to learn from its surroundings.

A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles

As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid’s optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners.

Development of level three solar based fast charging station for electric vehicle

The whole research is based on the charging process of electric vehicle as this is the emerging future of Pakistan. to reduce the environmental pollution the whole world is transferring towards renewable energy, so the transportation sector is also a great source of causing environmental pollution so to reduce this problem the transportation section is moving toward pollution free vehicle as the electric vehicle is now a days in market. But the charging of electric vehicle is again must be pollution free to reduce pollution impact. In order to achieve this goal, the charging strategy must be pollution free so renewable energy is the best solution and to reduce the charging time the fast-charging station is presented to charge a battery in less time using renewable energy. The advanced used batteries are lithium-ion battery these batteries have two charging modes of charging combining these two will generate more effective results for charging in less time. By using two hybrid sources the system become reliable in case of weather dependent situation

Scheduling EV Charging Having Demand With Different Reliability Constraints

We develop a new algorithm, based on approximate dynamic programming, to efficiently schedule the charging processes of a large number of electric vehicles (EVs) with flexibility, which allows the EV charging service provider to charge the EVs between their minimum and target state of charge (SoC) within a time window. We assume that the charging demands have known distributions and the charging process of EVs can be scheduled preemptively. We model the scheduling problem as a dynamic program with state-dependent constraints and solve it utilizing the fitted value iteration. We propose a multi-stage decoupled algorithm to reduce the dimensions in the fitted value iteration to reduce the computation complexity, and provide sufficient conditions for achieving optimality.

Vehicle/Employee Rebalancing and Charging Scheduling in One-Way Car Sharing Systems

This paper studies the joint charging scheduling and rebalancing of employees and electric vehicles in one-way car sharing systems (OCS). Consider an OCS consisting of a fleet of vehicles and a team of employees responsible for charging low-battery level vehicles and rebalancing the idle vehicles in the system. The employees need also to rebalance themselves by service vehicles or by sharing a car with some customers. Firstly, a joint optimization method to ensure vehicle and employee balance is proposed, where the variation of the number of vehicles, customers and employees is described by fluid models, while the charging process of electric vehicles at charging stations is modeled by an M/M/S queue. Then, the well-posedness and equilibrium of the fluid model are proven, and the minimum fleet size and employee size for the system to reach equilibrium is given. Next, a rebalancing method is presented to minimize the number of empty vehicles and rebalancing employees traveling in the network. Numerical experiment and case study show that the proposed method can significantly improve the quality of service and reduce the number of rebalancing employees.

State-of-the-art power factor correction in EV charger

With the vigorous development of the automobile industry, the exploitation of oil resources has gradually deepened, which has led to the gradual depletion of oil resources. In this situation, electricity has come to replace oil resources in the automobile industry. However, in the charging process of electric vehicles, the use of the efficiency of electric energy is particularly important. If the efficiency is low, then a lot of power is lost in the power transmission line or alternating magnetic field conversion. And an important factor affecting efficiency is the power factor. The traditional AC-DC converter with a full bridge has low power utilization and efficiency, existing studies combine full-wave bridge with other circuits with different functions to form power factor correction (PFC) topologies. And these fundamental topologies continue to develop, derived from the bridgeless, interleaved PFC topologies, etc. This paper reviews the fundamental full-wave bridge PFC topologies and some derived bridgeless PFC topologies and interleaved parallel PFC topologies, including showing their circuit diagrams, explaining the operation principle of these circuits and comparing the advantages and disadvantages of each PFC topology.

From Grayscale Image to Battery Aging Awareness—A New Battery Capacity Estimation Model With Computer Vision Approach

Accurate detection of capacity degradation is critical to the safe and efficient utilization of battery systems. Many datadriven capacity estimators were proposed based on emerging intelligent algorithms, but their accuracy depends on the data of complete charged/discharged process and complex algorithm structures. This paper developed a computer vision (CV)-based method, constructing battery multidimensional aging features as the key image to estimate capacity using specific charging data segment. Specifically, the designed image-aging recognition method is used to extract multidimensional aging features from the partial charging current sequence and then establish map inputs for a computer vision model that recognizes the constructed feature maps. Consequently, the mapping relationship between the charging information and capacity degradation can be obtained as the two-dimensional grayscale images which contain massive extracted features in their small size hence greatly simplify the network structure in CV model so as to improve estimation accuracy and efficiency significantly. More importantly, since the model input is a specific charging current segment rather than the data of complete charging process, the model applicability to the random and incomplete charging process of electric vehicles can be greatly improved. Battery cycling data from different types of Li-ion cells was utilized for performance verification. Compared with the conventional estimation methods proposed previously, the proposed method demonstrates the great superiority in terms of the model applicability, estimation accuracy and computational efficiency for online capacity estimation in actual battery usage.

Voltage Sensorless Based Model Predictive Control With Battery Management System: For Solar PV Powered On-Board EV Charging

This work deals with a novel voltage sensorless based model predictive control (VSPC) scheme for continuous and quick maximum power harvesting (MPH) from photovoltaic (PV) array for solar-powered on-board EV (Electric Vehicle) charging system. In VSPC, the first model predictive control is used with a PV array to predict the system state in the horizon of time and to eliminate the voltage sensor. An adaptive concept is used for deciding the operating point, which accelerates the tracking process and improves dynamic performance during irradiation changes and shading pattern change in partial shaded condition. Moreover, VSPC also takes care of EV charging process using EV provided BMS (Battery Management System) command or threshold safety limits of the EV battery. The working principle of VSPC is based on a prediction of the future behavior of the system. It realizes on a selected time horizon, in an arbitrary number of samples, which is decided according to the complexity of the fitness function. In order to minimize or maximize the fitness function, it predicts the voltage of the solar PV array as well as tunes the present control signal, which forces it to converge or reach the convergence criteria. Moreover, cost and response time of current sensors are lower than voltage sensors. Therefore, the VSPC control gives a fast response and low power oscillations in steady-state compared to conventional techniques. This control technique is verified on a developed prototype of the PV system in different shading and irradiance conditions, as well as the system stability is analyzed through the Bode plot. The system performance is also compared with the state-of-the-art methods.

Characterising the flexibility of electric vehicle charging strategies: a systematic review and assessment

ABSTRACT The increasing uptake of electric vehicles (EVs) has raised concerns about the impact a large fleet could have on electricity markets and distribution grids alike. Charging strategies have emerged as a means to provide flexibility, especially to electricity distribution grids, by controlling the EV charging process. This paper presents a typification of charging strategies and introduces a conceptual framework for appraising their flexibility in distribution grids. This is underpinned by data collected through an aggregative systematic literature review. The framework is derived from an exploratory qualitative content analysis of the sampled data and encompasses four flexibility dimensions: time, duration, quantity, and location of charging. Structural elements of a charging strategy are also explored, complementing the framework. Finally, the paper also presents a quantitative data analysis assessing the level of flexibility provided by each charging strategy. Results show that flexibility dimensions are not equally exploited, direct control strategies do not strictly outperform other control alternatives and innovative charging structures are yet to thrive for flexibility supply to increase. These findings contribute to better-informed, evidence-based policy interventions.

Determination of maximum additional load for EV charging station considering practical security limits

Nowadays, the allocation of electric vehicle charging stations (EVCS) for the charging of electric vehicles (EVs) is essential, as random allocation may cause significant energy loss for the radial distribution system (RDS). Uncoordinated charging of EVs dominates the load profile of the RDS. Although a single EV has a negligible impact on the system, the combined effect of charging an EV cluster may violate the voltage security constraints of the RDS. Therefore, to avoid such circumstances, the loadability limit of each node of the RDS must be determined. Previously, conventional analytical methods were used to calculate the maximum loadability of the RDS node, where voltage magnitudes become less than the critical voltage limit. However, these approaches are not acceptable for the power industry, as it may push the system towards a blackout. Therefore, the appropriate capacity of the EVCS can only be determined if the voltage security limit of each node of the RDS follows its critical limit. Hence, in this paper, a modified forward-backward sweep (MFBS) algorithm is formulated to find the maximum additional load (MAL) of each node of the RDS considering non-unity power factor during the EV charging process. This algorithm may help the EVCS to determine its capacity or optimal number of charging ports directly during installation at an optimal location of RDS. For validation, the 33-bus and 69-bus RDSs have been used, and it can be seen that the EVCSs have been successfully installed in the optimal location of the test systems mentioned above without violating the voltage security limit of each node of the RDS.

Reliability Enhancement of Fast Charging Station under Electric Vehicle Supply Equipment Failures and Repairs

This work focuses on the enhancement of the charging reliability of both scheduled (SEVs) and opportunistic (UEVs) electric vehicle (EV) users in an EV fast charging station (FCS). The proposed charging coordination strategies allow UEVs to exploit unused charging resources to optimally utilize the limited charging resources of FCS. However, the optimum utilization of limited charging resources of an FCS while assuring a reliable charging process for plugged-in EVs under random failures of electric vehicle supply equipment (EVSE) is a real challenge for the FCS controller. When the FCS admits UEVs in addition to SEVs, assuring a satisfactory quality of service to both EV user categories is also dispensable. Therefore, we analyze the performance of reservation of off-board mobile chargers (MOBCs) to enhance the charging reliability of EV users while achieving high charging resource utilization. This work proposes resource allocation and charging coordination strategies for an FCS where MOBCs are used to enhance the charging reliability of both SEVs and UEVs. Moreover, the proposed dynamic charging resource coordination strategies are analyzed with a continuous time Markov-chain (CTMC). The presented results from the CTMC model demonstrate that the proposed strategies outperform the EV charging process of the FCS in terms of high resource utilization and reliability while guaranteeing a satisfactory quality of service to EV users.

Statistical Analysis of Electric Vehicle Charging Based on AC Slow Chargers

Regarding DC fast chargers, various studies, such as the charge scheduling, have been conducted. On the other hand, research on AC slow chargers has rarely been conducted due to the predictable and simple usage pattern. Despite the long charging times of AC slow chargers, which use the existing electric outlets with relatively low supplied power, these chargers are suitable for daily home charging of electric vehicles (EVs) during the night. Due to their low installation costs, they are likely to be the dominant type of charging equipment. In this paper, the EV charging process based on AC slow chargers, which supply a maximum power of 3 kW from an AC 220 V outlet, is analyzed by constructing a simple charging model. The charging time and fees are statistically derived and investigated. Furthermore, power load curves for charging EVs with the 3 kW charger are observed. From the statistical analyses, we conclude that daily charging of EVs can be an appropriate scenario in using the AC slow chargers, and the power load can be spread without employing any demand response schemes.

Preemptive scheduling of EV charging for providing demand response services

We develop a new algorithm for scheduling the charging process of a large number of electric vehicles (EVs) over a finite horizon. We assume that EVs arrive at the charging stations with different charge levels and different flexibility windows. The arrival process is assumed to have a known distribution and that the charging process of EVs can be preemptive. We pose the scheduling problem as a dynamic program with constraints. We show that the resulting formulation leads to a monotone dynamic program with Lipschitz continuous value functions that are robust against perturbation of system parameters. We propose a simulation based fitted value iteration algorithm to determine the value function approximately, and derive the sample complexity for computing the approximately optimal solution.

Event-Based EV Charging Scheduling in a Microgrid of Buildings

With the popularization of electric vehicles (EVs), EV charging demand is becoming an important load in the building. Considering the mobility of EVs from building to building and their uncertain charging demand, it is of great practical interest to control the EV charging process in a microgrid of buildings to optimize the total operation cost while ensuring transmission security between the microgrid and the main grid. We consider this important problem in this article and make the following contributions. First, we formulate this problem as a Markov decision process (MDP) to capture the uncertain supply and EV charging demand in the microgrid of buildings. Besides reducing the total operation cost of buildings, the model also considers the power exchange limitation to ensure transmission security. Second, this model is reformulated within an event-based optimization (EBO) framework to mitigate the impact of large state and action space. A randomized parametric event-based control policy is proposed for the microgrid controller to determine the charge ratio for each building. Then, a selecting-to-charging principle is implemented for each building controller to determine the charge control of each EV in the building. Third, a constrained gradient-based policy optimization method is developed to find the optimal event-based control policy. Furthermore, an adjustment mechanism is also introduced to ensure transmission security during optimization. Numerical experiments are conducted to analyze the structure and the performance of the event-based control policy for EV charging in a microgrid of three buildings.

Optimal Utilization of Charging Resources of Fast Charging Station with Opportunistic Electric Vehicle Users

The key challenge with the rapid proliferation of electric vehicles (EVs) is to optimally manage the available energy charging resources at EV fast-charging stations (FCSs). Furthermore, the rapid deployment of fast-charging stations provides a viable solution to the potential driving range anxiety and charging autonomy. Costly grid reinforcements due to extra load caused by fast charging can be omitted using a dedicated energy storage and/or renewable energy system at the FCS. The energy supply and fixed number of EV supply equipment (EVSE) are considered as the limited charging resources of FCS. Amidst various uncertainties associated with the EV charging process, how to optimally utilize limited charging resources with opportunistic ultra-fast charging EV users (UEVs) is studied in this work. This work proposes resource allocation and charging coordination strategies that facilitate UEVs to dynamically exploit these limited charging resources with defined liabilities when pre-scheduled users (SEVs) do not occupy them to utilize limited charging resources maximally. Moreover, the proposed dynamic charging coordination strategies are analyzed with a Monte Carlo simulation (MCS). The presented numerical results reveal that the major drawbacks of under-utilization of limited charging resources by SEVs can be significantly improved through dynamic charging resource allocation and coordination along with UEVs. With the proposed charging coordination strategies in this study, the maximum charging resource utilization of considered FCS with 10 EVSE has been improved to 90%, which bounds to 78% only with SEVs.

Grid-connected hybrid renewable energy systems for supermarkets with electric vehicle charging platforms: Optimization and sensitivity analyses

Coupling energy and transport sectors is critical for optimum utilization of renewable energy. The present work proposes a grid-connected photovoltaic/wind/battery hybrid power system to generate electricity for supermarkets. The supermarkets are located at three geographically distinct cities in Morocco and integrate electric vehicle (EV) charging stations in their parking areas. HOMER grid software is used for the optimal design of the proposed hybrid renewable energy system (HRES) with an accurate utility tariff corresponding to the Moroccan scenario. Dakhla, which is a high windy site, is found to be the best implementation site with maximum renewable energy fraction (REF), minimum COE and operating costs of 71.66%, 0.0841 $/kWh, and 0.124 M$/year, respectively. The optimal grid-connected system consisted of 107 kW p of PV, 300 kW of wind, 12 batteries, and 65 kW capacity inverter for Dakhla site. The next identified best site is Casablanca while Fez remained at third place based on COE and annual operating costs. At all of these sites, the EV charging process is achieved, almost completely, with 7300 charging sessions annually. A series of sensitivity analyses is performed based on supermarket energy intensity, renewable energy availability, and carbon pricing and impact on the viability of HRES was evidenced.