Charging Strategy Research Articles

Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

An Intelligent Charging Scheme for Lithium-Ion Batteries of Electric Vehicles Considering Internal Attenuation Modes

Charging control is one of the essential functions of battery management systems. Battery charging involves behavioral changes such as electricity, heat, and aging. Balancing these factors is crucial for the safe and efficient operation of batteries. This work proposes an intelligent charging scheme for lithium-ion batteries that considers charging time, temperature rise, and health losses. Firstly, charging aging experiments are conducted to investigate the effect of charging rate on battery aging. Specifically, half cell experiments are carried out to construct an electrode open circuit voltage model to explore battery aging modes. The aging mode attenuation model is proposed based on empirical formulas. Then, the charging multi-objective function is established based on the equivalent circuit, thermal, and aging empirical models. A multi-stage constant current charging optimization scheme has been developed based on the multi-objective grey wolf optimizer. Finally, the feasibility of three targets and dual targets fast charging schemes is verified through simulation and experiments.

Mental models guide electric vehicle charging

To discover conceptual and behavioral barriers to electric vehicle (EV) charging, in-depth interviews were conducted in person with experienced and novice EV users. Mental models were found to affect vehicle charging strategies. Novice EV users drew from their existing mental models for petrol refueling and misapplied them to EV charging. Most experienced users had developed new mental models appropriate for the physical and temporal realities of EV charging—they are adapted to diverse rates of charge, EVs' longer energy filling duration, co-location of EV charging with certain user activities, and EVs drivers’ shorter equipment engagement time. Three predominant mental models for EV charging were found: 1. Monitor the fuel gauge, when low, seek a refill location (from liquid-fueling model), 2. Prior to a trip, plan where and when to charge, 3. Event-triggered charging (unique to the EV mental model). Misapplication of liquid fuel models to EVs has detrimental effects: more effort and more frequent anxiety regarding recharging, buyer choice of EV characteristics mismatched to need, manufacturer oversizing of batteries, policy overemphasis on fast public charging, and restricted opportunity to reduce load on the grid. Solutions are suggested to forestall or minimize these detrimental effects and facilitate EV adoption.

Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy

Renewable energy and electric vehicles (EVs) are crucial technologies for achieving sustainable cities. However, intermittent power generation from renewable energy sources and increased peak load due to EV charging can pose technical challenges for the power systems. Improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). A city on the west coast of Sweden is used as a case study. The smart charging and V2G schemes aim to minimize the mismatch between generation and load, and are formulated as quadratic programming problems. The simulation results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance in the optimal net-zero energy city is increased from 68% with opportunistic charging to 73% with smart charging and further to 84% with V2G. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings indicate that EVs have a high potential to provide flexibility to urban energy systems.

Charging non-deterministic mobile nodes in a transfer learning approach

Wireless rechargeable sensor networks (WRSNs) provide a solution to the energy problem in wireless sensor networks by introducing chargers to recharge the sensor nodes with rechargeable batteries. Most of the existing studies on WRSN focus on charging static nodes or nodes with certain mobility, while a few works investigate charging non-deterministic mobile nodes, where the movement pattern of nodes is unknown. The main challenge in the study of charging non-deterministic nodes is how to find the energy-hungry nodes, because the movement of the nodes is uncertain. The existing schemes assume that the historical trajectories of each node are known and predict the future locations of the nodes by analyzing the trajectory data. However, these schemes require a large amount of trajectory data to train the prediction model, and it takes a considerable amount of time for the network to run to obtain this trajectory data. If there is a node energy depletion during this time, these schemes cannot perform the energy replenishment to the nodes. To address this problem, we propose a novel charging scheme to provide charging services for non-deterministic nodes by introducing the transfer learning technology to predict the future locations of non-deterministic nodes. In the proposed scheme, a backbone Graph Convolutional Network (GCN) is pre-trained with other trajectory datasets, based on which only a small amount of trajectory data of nodes in the target network is needed to complete the location prediction task. In addition, a new scheduling algorithm is proposed, which considers multiple states of the nodes, including the energy consumption rate, the remaining energy state and the future locations, to select the charging target nodes. Simulation results show that the proposed scheme is able to achieve energy replenishment of non-deterministic mobile nodes when the network is just starting to operate.

Optimized ANN for LiFePO4 battery charge estimation using principal components based feature generation

Electric vehicles (EVs) have gained prominence in the present energy transition scenario. Widespread adoption of EVs necessitates an accurate State of Charge estimation (SoC) algorithm. Integrating predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions, marking a crucial milestone in sustainable electric vehicle technology. In this research study, machine learning methods, particularly Artificial Neural Networks (ANN), are employed for SoC estimation of LiFePO4 batteries, resulting in efficient and accurate estimation algorithms. The investigation first focuses on developing a custom-designed battery pack with 12 ​V, 4 Ah capacity with a facility for real-time data collection through a dedicated hardware setup. The voltage, current and open-circuit voltage of the battery are monitored with computerized battery analyzer. The battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry Pi. Principal components are derived for the collected battery data set and analyzed for feature engineering. Three principal components were generated as input parameters for the developed ANN. Early Stopping for the ANN was also implemented to achieve faster convergence of the ANN. While considering eleven combinations for ten different optimizers loss function is minimized. Comparative analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of 0.9998. The proposed algorithm was also implemented for two different types of datasets, a UDDS drive cycle and a standard cell-level dataset. The results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

Problem of Patient Transport Route Planning for Battery Electric Vehicles Considering a Flexible Charging Strategy

In view of the current problem of non-emergency patient transport route planning, the research is mainly carried out on fuel vehicles, and in the related research on the vehicle routing problem of electric vehicles, the relevant research results considering flexible charging strategies of different charging technologies are relatively weak. Therefore, this paper studies the non-emergency patient transport route planning problem based on pure electric vehicles, introduces a flexible charging strategy, constructs a mathematical model with the goal of maximizing the average patient satisfaction and minimizing the total cost of patient transport, and designs a hybrid heuristic algorithm to solve the problem. Then, different algorithms are used to solve the mathematical model considering patient satisfaction and the mathematical model not considering patient satisfaction. The feasibility and applicability of the mathematical model and the hybrid algorithm are verified by comparing and analyzing the solution results of different algorithms. Finally, based on the hybrid heuristic algorithm, the mathematical model of non-emergency patient transport route planning considering different charging strategies is solved. The applicability and reliability of the hybrid algorithm are further verified by comparing the solution results of different charging strategies, which shows that the flexible charging strategy can not only achieve a better balance between patient satisfaction and the total cost of patient transport, but also effectively improve the utilization efficiency of the remaining power of the vehicle.

Modelling of Charging Demand for Electric Vehicle based on Person-trip Survey Data

In electricity distribution networks, voltage might be dropped significantly by simultaneous charging of high penetration Electric Vehicle (EV). The impact of EV on voltage drop can be different among areas depending on various factors such as original electricity demand profile without EV, EV penetration level and usage pattern, etc. For the statistical assessment of impact of high penetration EV in various distribution networks, this study proposes a model to estimate time-series data of electricity demand including EV charging. The proposed model is based on Grid Square Statistics Data on geographical distribution of population, employee, etc. and Person-trip Survey Data. As an example using the proposed model, this study compares three EV charging strategies in suburban area of Nagoya, Japan. As a result, if the EV charging starts simultaneously at 23:00 at which the night-time discount electricity rate is applied, the night-time electricity demand can be much larger than the day-time peak demand without EV. The increase can be mitigated by autonomous charging control according to the time and SOC of EV returning home in most areas. Finally, by using the calculated electricity demand profile, this study assesses the impact of EV charging on voltage profile. The result shows that the impact of EV charging on distribution network voltage can be avoided by applying the proposed autonomous charging control scheme.

Day-ahead bidding and dispatch strategy of novel battery charging and swapping station under multiple uncertainties

The novel battery charging and swapping station (NBCSS) has great operational flexibility due to its integration of wind power, photovoltaic power, gas turbine and energy storage. This paper presents a day-ahead bidding and dispatch strategy of NBCSS under multiple uncertainties, which consists of battery demand, market price, renewable energy and load. Firstly, the optimized backup method is used to address the uncertainty of battery demand. Then, a two-stage stochastic robust optimization model is further applied to address the uncertainties of market price, renewable energy and load. The model’s goal is to minimize the total operational cost in the worst-case scenario, which is limited by the corresponding uncertainty set. Especially, to control the conservatism of the model, the total adjustable budget and temporal adjustable budget of uncertainties are considered together. Next, the two-stage model is solved efficiently based on the strong duality theory and column-and-constraint generation (C&CG) method. Finally, case studies are given to verify the effectiveness and performance of the proposed model. The results demonstrate that the NBCSS can be efficiently deployed, enabling market arbitrage and reducing the overall cost. Additionally, the conservatism of the proposed model can be adjusted according to the budgets of uncertainties.

Novel structure-preserving schemes for stochastic Klein–Gordon–Schrödinger equations with additive noise

Stochastic Klein–Gordon–Schrödinger (KGS) equations are important mathematical models and describe the interaction between scalar nucleons and neutral scalar mesons in the stochastic environment. In this paper, we propose novel structure-preserving schemes to numerically solve stochastic KGS equations with additive noise, which preserve averaged charge evolution law, averaged energy evolution law, symplecticity, and multi-symplecticity. By applying central difference, sine pseudo-spectral method, or finite element method in space and modifying finite difference in time, we present some charge and energy preserved fully-discrete schemes for the original system. In addition, combining the symplectic Runge-Kutta method in time and finite difference in space, we propose a class of multi-symplectic discretizations preserving the geometric structure of the stochastic KGS equation. Finally, numerical experiments confirm theoretical findings.

Optimisation ofEnergy Accumulation for Renewable Energy sources

This project focuses on optimisation of energy accumulation for various types of distributed renewable energy sources. The main goal is to prepare charging - discharging strategy depending on actual power consumption and prediction of consumption and production of utilised renewable energy sources for future period. The simulation is based on real long term data measured on photovoltaic system, wind power station and meteo station between 2004 - 2021 . The data from meteo station serve as the input for the simulation and prediction ofthe future production while the data from Pv system and wind turbine are used either as actual production or as a verification of the predicted values. various parameters are used for trimming of the optimisation process. Influence of the charging strategy discharging strategy, values and shape of the demand from the grid and prices is described on typical examples of the simulations. The main gal is to prepare and verify the system in real conditions with real load chart and real consumption defined by the model building with integrated renewable energy sources. The system can be later used in general installations on commercial or residential buildings

Transformer performance enhancement by optimized charging strategy for electric vehicles

Transformer performance enhancement by optimized charging strategy for electric vehicles

Charging Between PADs: Periodic Charging Scheduling in the UAV‐Based WRSN With PADs

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV’s service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem’s definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency.

A Multi-Criteria Decision Making TOPSIS Fusion Approach for Selection Best Strategy Charging for Electric Bus Systems

An essential part of electrifying bus networks is deciding on the appropriate charging strategy among the many available alternatives, such as opportunistic (rapid) charging techniques and overnight (slow) charging. The broad usage of electric buses in public transportation networks and the increasing demand for environmentally friendly transportation options have elevated the significance of this step. This research establishes a multi-criteria decision-making (MCDM) fusion method for choosing the optimal electric bus charging strategy by considering various variables, including operational, quality-of-service, social, environmental, and economic factors. To determine what matters most for making decisions in this field, we surveyed electric bus specialists and reviewed the literature extensively. We used the TOPSIS method as an MCDM fusion method to combine the criteria and alternatives. We compute the weights of criteria by the average method. Then, the TOPSIS fusion method selects and ranks the alternatives. We collected the 20 criteria and five alternatives in this study. We show that overnight is the best charging strategy in the electric bus system. We performed a sensitivity analysis to show the different cases in criteria weights, then ranked the alternatives under different weights to establish the stability of the results.

Towards intelligent electric vehicle power batteries and multi-scenario application vehicle operation safety charging strategies: a review

Herein, we introduce the characteristics of different charging strategies and their equalization control technologies based on battery cells and modules and present an overview of the charging mode of the whole vehicle in detail.

Local Differential Privacy-Based Privacy-Preserving Data Range Query Scheme for Electric Vehicle Charging

Local Differential Privacy-Based Privacy-Preserving Data Range Query Scheme for Electric Vehicle Charging

A Non-Cooperative Pricing Strategy for UAV-Enabled Charging of Wireless Sensor Network

A Non-Cooperative Pricing Strategy for UAV-Enabled Charging of Wireless Sensor Network

Wireless Charging Scheme for an Intraoral Sensing System with 2.45-GHz Quasi-electrostatic Field Power Transfers

Wireless Charging Scheme for an Intraoral Sensing System with 2.45-GHz Quasi-electrostatic Field Power Transfers

Research on the application of wireless energy transmission technology in smart grid

Abstract With the continuous development of wireless energy transmission technology, the role of wireless rechargeable sensor networks (WRSNs) in smart grids has become more and more important. In this paper, after fully investigating the application of wireless energy transmission technology in smart grids, the solution vector of SFLA is discretized, and the IMSFLA model based on the hybrid frog jump algorithm is proposed to solve the wireless energy transmission planning problem in the smart grid. Finally, it is applied to the optimal design of the charging scheme of WRSNs, and the simulation results of the IMSFLA algorithm are compared and analyzed with those of the Taboo Search (TS) algorithm after exploring its wireless transmission performance. In terms of energy utilization, when the number of nodes is increased to 550, the energy utilization of IMSFLA algorithm is improved by 7% compared to TS algorithm, and when the number of nodes is increased to 550-800, the optimized results of TS algorithm deviates significantly more compared to IMSFLA algorithm. This indicates that the IMSFLA algorithm designed in this paper has a better effect on the optimization of the charging scheme of wireless rechargeable sensor networks, which is of great practical significance for improving the stability of the smart grid.

Optimal Spatial–Temporal Scheduling for EVs With Stochastic Behaviors and Possible Inauthentic Information

This paper studies the spatial-temporal scheduling for electric vehicles with stochastic charging behaviors. A new spatial-temporal scheduling model is proposed to achieve both the spatial coordination and temporal coordination for EV users and is modeled as a non-convex optimization problem which can be solved by the coordinate descent method. Further, a new billing method of charging cost is proposed based on the Vickrey-Clarke-Groves to avoid possible inauthentic information from users. Specially, the individual rationality and incentive compatibility of the method are theoretically proved. Moreover, a stochastic optimization problem is proposed to describe the stochastic charging behavior of users, which can fit the real application better than the deterministic one. Consequently, an efficient charging strategy is obtained via chance constraint method. Simulation results and comparison studies show the effectiveness of both the model and the billing method.