Real-time Charging Research Articles

Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

A Novel Charging Management and Security Framework for the Electric Vehicle (EV) Ecosystem

The EV charging network has witnessed significant growth in the UK in the last few years due to the net zero emission target of the government by 2030. The related literature in EV charging management mainly focuses on road-traffic-parameter-based optimization and lacks detail in terms of charging statistics and cyber–security-enabled charging management frameworks. In this context, this paper proposes a novel EV Charging Management and Security (EVCMS) framework using real-time charging statistics and an Open Charge Point Protocol (OCPP). Specifically, a system model for EVCMS is presented considering charging data management and security protocols. An EVCMS framework design is detailed, focusing on charging pricing, optimization, and charging security. The experimental implementation is described in terms of client–server and charge-box-based simulation. The performance of the proposed EVCMS framework is evaluated by considering different charging scenarios and a range of charging-related metrics. An analysis of results and comparative study attest to the benefits of the proposed EVCMS framework for enabling the EV charging ecosystem.

Implantable Zinc Ion Battery and Osteogenesis-Immunoregulation Bifunction of Its Catabolite.

Biocompatible batteries can power implantable electronic devices and have broad applications in medicine. However, the controlled degradation of implantable batteries, the impact of battery catabolites on surrounding tissues, and wireless charging designs are often overlooked. Here, we designed an implantable zinc ion battery (ZIB) using a gelatin/polycaprolactone-based composite gel electrolyte. The prepared ZIBs deliver a high specific capacity of 244.0 mA h g-1 (0.5C) and long cycling stability of 300 cycles (4C). ZIBs were completely degraded within 8 weeks in rats and 30 days in a phosphate-buffered saline lipase solution, demonstrating good biocompatibility and degradability. ZIBs catabolites induced macrophage M2 polarization and exhibited anti-inflammatory properties, with mRNA levels of the M2 markers Arg-1 and CD206 up-regulated 15.8-fold and 13.4-fold, respectively, compared to the blank control group. Meanwhile, the expressions of two typical osteogenic markers, osteopontin and osteocalcin, were up-regulated by 3.6-fold and 5.6-fold, respectively, demonstrating that designed ZIBs promoted osteogenic differentiation of bone marrow mesenchymal stem cells. Additionally, a wireless energy transmission module was designed using 3D printing technology to realize real-time charging of the ZIB in rats. The designed ZIB is a promising power source for implantable medical electronic devices and also serves as a functional material to accelerate bone repair.

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities

Electric vehicles (EVs) have become one of the promising power grid ancillary services. They can help reduce the intermittent of renewable energy sources by supporting active power and frequency control services. Additionally, bi-directional chargers included into EV batteries can supply/consume reactive power for voltage regulation. To improve voltage quality, a unique control approach is devised to offer fault ride-through. In this study, real-time charging optimization schemes based on particle swarm optimization are developed to coordinate the EVs charging/discharging power with the grid power dispatches depending on the charging priority and preferential electricity prices. Five different car user profiles including different initial state of charge and plug in state have been depicted. Different optimum issues are carried out with comprehensive simulations employing hardware in the loop via real-time simulation for a modified IEEE 14-bus test system. Where different frequency and voltage controls, management strategies, variant fault scenarios, benefits, and challenges for power system regulation with EVs are illustrated. According to the simulation results, the proposed optimized charging strategies reduce the total expenses of EV parking station by 40 % compared to other cases, hence maximizing EV owner satisfaction. The net load curve is smoothed and reduced by 11 %, particularly during peak period.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

Experiment-Based Determination of Optimal Parameters in Constant Temperature–Constant Voltage Charging Technique for Lithium-Ion Batteries Using Taguchi Method

Charging methods significantly affect the performance and lifespan of lithium-ion batteries. Investigating charging techniques is crucial for optimizing the charging time, charging efficiency, and cycle life of the battery cells. This study introduces a real-time charging monitoring platform based on LabVIEW, enabling observation of battery parameters such as voltage, current, and temperature. The proposed system allows the precise control of charging parameters via a user-friendly interface. Utilizing a programmable DC power supply, it delivers specific charging waveforms, while data acquisition instruments record temperature changes. Key performance metrics, including charging time, efficiency, and temperature rise, are analyzed. Moreover, this paper conducts in-depth research on the constant temperature–constant voltage (CT-CV) charging technique and applies the Taguchi method to identify key parameter configurations that achieve the objectives of the shortest charging time, highest charging efficiency, and lowest average temperature rise. A comprehensive evaluation compares the optimized CT-CV method with conventional constant current–constant voltage (CC-CV) charging. The results demonstrate a 10.7% reduction in charging time compared to the 1C CC-CV method, indicating the efficacy of CT-CV in shortening charging duration while managing temperature rise.

Synchronous Switching Strategy to Enhance the Real-Time Powering and Charging Performance of Triboelectric Nanogenerator.

Triboelectric nanogenerators (TENGs) are of great significance as sustainable power sources that harvest energy from the human body and environment. Nevertheless, due to TENG's impedance-dependent output voltage characteristics, in traditional strategy (TS), real-timely powering a sensor with TENG has a poor sensing on/off ratio (or response), and directly charging a capacitor with TENG shows a low charging efficiency. This degraded real-time powering and charging performance of TENG compared to a commercial constant voltage source is a huge challenge of the TENG field for a long time. This work proposes a synchronous switching strategy (SSS) for TENG to real-timely power sensors or charge capacitors without degrading its performance. Compared with TS, this new strategy enables sensors to have 5-7 times sensing on/off ratio enhancement when using TENG as a power source, reaching the powering ability of a commercial constant voltage source, it makes the powering performance of TENG stable under different driving frequency, improving the powering robustness of TENG. In addition, compared with TS, SSS can also enhance the charging efficiency of TENG in every charging cycle by up to 2.4 times when charging capacitors. This work contributes to real-timely powering or charging the distributed, mobile and wireless electronics using TENG.

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the real-time charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region.

Balancing Awareness Fast Charging Control for Lithium-Ion Battery Pack Using Deep Reinforcement Learning

Minimizing charging time without damaging the batteries is significantly crucial for the large-scale penetration of electric vehicles. However, charging inconsistency caused by inevitable manufacture and usage inconsistencies can lead to lower efficiency, capacity, and shorter durability due to the “cask effect”. This goal can be achieved by solving a series of constrained optimization problems with the model-based framework. Nevertheless, the high computational complexity, identifiability, and observability issues still limit their fidelity and robustness. To overcome these limitations and provide end-to-end learning strategies, this paper proposes a balancing-aware fast-charging control framework based on deep reinforcement learning. In particular, a cell-to-pack equalization topology is first introduced to dispatch energy among in-pack cells. Then, the balancing awareness fast charging problem is formulated as a multiobjective optimization problem by considering charging time, consistency, and over-voltage safety constraints. Thirdly, a deep reinforcement learning framework using a deep Q-network is established to find the optimal policy. By using the generalization of a neural network, the learned policies can be transferred to real-time charging and balancing control, thus improving the applicability of the proposed strategy. Finally, numerous comparative simulations and experimental results illustrate its effectiveness and superiority in terms of charging rapidity and balancing.

Two-stage distributionally robust offering and pricing strategy for a price-maker virtual power plant

This paper presents a two-stage distributionally robust approach for offering and pricing strategy of a virtual power plant (VPP) acting as a price-maker in the day-ahead market. The model incorporates uncertainties related to distributed photovoltaic (PV) output, real-time market price and charging stations demand. In the first stage, the VPP determines its day-ahead market bids/offers as a price maker and releases the retail price to the charging stations based on the worst case of probability distribution realizations. In the second stage, the VPP optimizes energy storage operations and the power transactions in the real-time market to maximize its revenue according to the optimal first stage decisions. To efficiently solve the two-stage model, a customized column-and-constraint generation algorithm (C&CG) is designed that decomposes non-convex subproblems into several small-scale linear programming problems that are independent of each other. The effectiveness of the proposed strategy is validated through case studies conducted with the IEEE 33-bus system.

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Rolling Horizon Approach for Real-Time Charging and Routing of Autonomous Electric Vehicles

Rolling Horizon Approach for Real-Time Charging and Routing of Autonomous Electric Vehicles

Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines.

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

Multiple-layer energy management strategy for charging station optimal operation considering peak and valley shaving

Existing vehicle-to-grid (V2G) applications are aimed at the power grid and the government. It is difficult for charging stations (CSs) to execute the schedules in real time. To figure out the multiple-layer energy management from the perspective of CS, the dispatch potential assessment model is constructed based on the EV users’ charging demand and Minkowski summation. And the optimal energy management schedule model of CS with ESS is proposed considering peak shaving and valley filling under the time-in-use tariff. Besides, the real-time charging control model of EVs in CS is designed under the premise of meeting the charging needs. The simulation results show that the proposed strategy can promote CS operation revenues and track the scheduling plan of CS. The arbitrage of tariffs and peak shaving ancillary services are realized while the charging loads of CSs are smoothed by the charging/discharging of ESS. The proposed strategy is applicable for the CS aggregators and can help the grid operators for dispatch schedules.

Analysis on Rapid Charging for Electrified Transportation Systems

Background: To improve system resilience when charging electric vehicles, a new control mechanism for converters that convert voltage from sources in micro-grids is presented. Methods: This deals with an evolving continuous current and stable voltage charging method for electric automobiles (EVs) with the objectives of speedy charging, constant voltage stability, deviation from voltage reduction, and cost reduction. Results: The study uses spectrum evaluation and state of health evaluations to look at the effects of different voltage sag thresholds on the automotive charging parking’s. Conclusions: In the end, the research offers a power management algorithm created for a green energy-based electric vehicle charging station that maximizes the use of sources of Clean Conventional Energy Systems (CCES) with best process while optimizing real-time charging prices.

Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques

Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.

Mobile Charging Scheduling Approach for Wireless Rechargeable Sensor Networks Based on Multiple Discrete-Action Space Deep Q-Network

Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WRSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where MC is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches.

Load management, energy economics, and environmental protection nexus considering PV-based EV charging stations

Integrating electric vehicles (EVs) into the electric power system poses significant challenges to grid operation and planning due to the potential constraints on the power system. To minimize grid congestion, the best option is to increase the production of photovoltaic (PV) energy for domestic use and EV charging stations. This paper addresses the nexus of technical, financial, and environmental effects of customer involvement in economic development and load management. In this paper, a new model design of solar-powered EV charging stations is proposed and implemented in HOMER Grid, and a case study has explored how economic, technical, and energy management benefits can be achieved through customer energy involvement and the integration of PV-based charging stations. The proposed PV-based charging stations contribute toward the energy management of the region, and the study observes the real-time optimal charging and discharging strategy of PV-based grid-connected charging stations. The case study results show that the investigated area can produce 1,070,804.096 MWh/year of energy through maximum customer involvement, consequently reducing overall energy costs. Moreover, the study demonstrates that the selected region can produce 86,961,688 kWh/year through the PV system, achieving 363,899 charging sessions per year, offering maximum advantages and facilitation for EV charging. The proposed model applicability shows that large-scale customer involvement can bring significant techno-economic benefits and attract regional investments.

EV Charging Path Distribution Solution Based on Intelligent Network Connection

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.