Charging Strategy Research Articles

Towards unifying perturbative and Holographic Light-Front QCD via holomorphic coupling

We construct a QCD coupling A(Q2) in the Effective Charge (ECH) scheme of the canonical part d(Q2) of the (inelastic) polarised Bjorken Sum Rule (BSR) Γ¯1p−n(Q2). In the perturbative domain, the coupling A(Q2) practically coincides with the perturbative coupling a(Q2) [≡ αs(Q2)/π] in the four-loop ECH renormalisation scheme. In the deep infrared (IR) regime, A(Q2) behaves as suggested by the Holographic Light-Front QCD up to the second derivative. Furthermore, in contrast to its perturbative counterpart a(Q2), the coupling A(Q2) is holomorphic in the entire complex Q2-plane with the exception of the negative semiaxis, reflecting the holomorphic properties of the BSR observable d(Q2) [or: Γ¯1p−n(Q2)] as dictated by the general principles of the Quantum Field Theory. It turns out that the obtained coupling, used as ECH, reproduces quite well the experimental data for Γ¯1p−n(Q2) in the entire Nf = 3 regime 0 < Q2 ≲ 5 GeV2.

Charging Strategies for Electric Vehicles Using a Machine Learning Load Forecasting Approach for Residential Buildings in Canada

The global electric vehicle (EV) market is experiencing exponential growth, driven by technological advancements, environmental awareness, and government incentives. As EV adoption accelerates, it introduces opportunities and challenges for power systems worldwide due to the large battery capacity, uncertain charging behaviors of EV users, and seasonal variations. This could result in significant peak–valley differences in load in featured time slots, particularly during winter periods when EVs’ heating systems use increases. This paper proposes three future charging strategies, namely the Overnight, Workplace/Other Charging Sites, and Overnight Workplace/Other Charging Sites, to reduce overall charging in peak periods. The charging strategies are based on predicted load utilizing a hybrid machine learning (ML) approach to reduce overall charging in peak periods. The hybrid ML method combines similar day selection, complete ensemble empirical mode decomposition with adaptive noise, and deep neural networks. The dataset utilized in this study was gathered from 1000 EVs across nine provinces in Canada between 2017 and 2019, encompassing charging loads for thirty-five vehicle models, and charging locations and levels. The analysis revealed that the aggregated charging power of EV fleets aligns and overlaps with the peak periods of residential buildings energy consumption. The proposed Overnight Workplace/Other Charging Sites strategy can significantly reduce the Peak-to-Average Ratio (PAR) and energy cost during the day by leveraging predictions made three days in advance. It showed that the PAR values were approximately half those on the predicted load profile (50% and 51%), while charging costs were reduced by 54% and 56% in spring and winter, respectively. The proposed strategies can be implemented using incentive programs to motivate EV owners to charge in the workplace and at home during off-peak times.

Novel variable charging pricing strategy applied to the multi-objective planning of integrated fast and slow electric vehicle charging stations and distributed energy resources

Electric vehicle charging stations are increasingly being deployed to accommodate the rising demand for electric vehicles. However, this additional load on the distribution system can lead to significant challenges, including increased power losses and undervolage issues. Therefore, this paper proposes the optimized planning of electric vehicle charging stations, along with the integration of distributed energy resources considering the power system operator’s perspective. The approach focuses on minimizing the costs associated with public charging stations and distributed generators, as well as reducing power losses and voltage deviation of the system. Additionally, this study incorporates both fast and slow chargers to address user requirements, while promoting the use of slow charging to help reduce the peak load demand caused by the electric vehicle charging stations. A novel variable pricing strategy for slow charging is also proposed to decrease the operator’s expenses while promoting this mode of charging. The proposed methodology is validated through the integration of a 33-bus distribution test system with a 25-node traffic system. The analysis was conducted using three multi-objective optimization methods: Multi-Objective Cuckoo Search, Multi-Objective Flower Pollination Algorithm, and Non-dominated Sorting Genetic Algorithm II. From the results, it is possible to conclude that the proposed approach provides benefits not only to the system operator but also to EV users, given the reduction in their travel costs. Despite the increased charging demand, the optimization results show a reduction in voltage deviation by 11.57%–16.94% and power losses by 19.71%–24.20% compared to the original system.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Development of an advanced current mode charging control strategy system for electric vehicle batteries

Electric vehicles (EVs) face customer hesitancy due to challenges in locating fast charging stations, lengthy recharging times, and incompatible charging ports. This research addresses these issues by proposing a novel current mode control strategy for EV battery charging. Traditional charging methods often result in suboptimal rates, battery degradation, and safety risks. The primary objective is to enhance charging efficiency, safety, and battery lifespan by optimizing parameters such as voltage and current. Control mode charging offers significant advantages over plug-in charging by minimizing stress factors that contribute to degradation, such as high temperatures and excessive charging cycles. This approach aims to extend the lifespan of EV batteries while ensuring safe, efficient, and fast charging. The control system offers three charging modes: slow (0.49 A, 6.31 W, 264 mins), medium (2.74 A, 34.85 W, 50 mins), and fast (4.62 A, 50.80 W, 30 mins) using a 12 V single-phase supply. This advanced strategy significantly improves EV charging system efficiency, with fast charging achieving 80% higher efficiency than slow charging in both simulations and experimental testing. The key contribution of this research is the development of a tailored current mode charging strategy that optimizes charging efficiency while ensuring battery longevity and safety.

Hierarchical Game Theory-Based Optimal Power Management for Both Seaports and Ships

Unlike land-based power systems, a seaport microgrid is not only equipped with extensive electrification but also connected with ships, which turns maritime power management into a comprehensive transportation-power coordination problem. To fully exploit the potential of shipboard power systems for energy efficiency improvement of seaports, a hierarchical Stackelberg game-based framework is developed in this paper to achieve efficient power management for both seaports and ships. In this framework, the seaport microgrid serves as a leader to manage the local sources and determine the price for ships to maximize revenue, considering the influence of both the power side and transportation side. On the other hand, the ships act as followers to decide the charging and service strategies for an optimal trade-off between the benefits from onboard battery charging and the ship service cost. The proposed game is solved as a bilevel optimization problem via mathematical programming with the equilibrium constraints method. The proposed algorithm's efficiency is evaluated through numerical simulations. The simulation results demonstrate that the proposed power management method can simultaneously increase the economic profits of seaports and improve the service of ships at berth. Compared to traditional energy management methods, the comprehensive benefits can be increased by approximately 36%.

Flexibility through power-to-heat in local integrated energy systems with renewable electricity generation and seasonal thermal energy storage

In heating dominated regions, the flexibility obtained through coupling heating and power sectors is particularly beneficial for the integration of high shares of variable renewable energy sources. This study concerns the design of an energy system for a new neighborhood in Norway, including a seasonal thermal energy storage storing excess heat from waste incineration, a seawater heat pump, and local power generation. Two supply temperature scenarios are considered for the local heating network: medium-temperature (70 °C), where all heating demands are covered through the network; and low-temperature (45 °C), where booster heat pumps are applied for hot water production. Both scenarios are more cost-effective than if heat demands were to be met through import from the district heating network, however, the difference between the two scenarios is small. The low-temperature scenario has the highest degree of self-sufficiency, and the advantage of additional flexibility gained through the local heat pumps with hot water storage. Cost-optimal charging strategy for the seasonal storage was highly dependent on the pricing of excess heat with respect to the electricity prices. Unlimited sharing of electricity among all users in the neighborhood should be promoted to gain full benefits of local flexibility.

Optimization of charging infrastructure and strategy for an electrified public transportation system

Government policies and incentives aimed at reducing the carbon footprint are increasingly focusing on the electrification of public transportation, particularly transit buses. However, electrification faces significant challenges, including optimizing the charging infrastructure, battery size and type, and charging strategies. Addressing these challenges is crucial for the effective deployment and operation of electric bus fleets. This study presents an innovative method for optimizing these aspects of electric bus systems under diverse route conditions. By leveraging general transit feed specification (GTFS) and GeoTIFF data, the developed approach ensures scalability and is grounded in real-world data. A detailed physical model, especially concerning battery degradation, adds a unique dimension to the study, providing more accurate and reliable results. The optimization method employed in this study is dynamic programming (DP), which allows for a comprehensive evaluation of various factors influencing the performance and efficiency of electric buses. The proposed approach has been validated through three distinct case studies. The findings of this study indicate that the optimized solution can lead to a substantial cost reduction of nearly 35% for operators compared to current state-of-the-art practices in Zurich, which underscores the potential of the proposed approach to contribute to more sustainable and cost-effective public transportation systems.

Performance Evaluation of Battery Swapping Stations for EVs: A Multi-Method Simulation Approach

This study presents an optimisation framework for operating a battery swapping station (BSS) to enhance efficiency and sustainability in electric vehicle (EV) infrastructure. A hybrid modelling approach combines agent-based discrete event simulation and linear programming to model the dynamic behaviour of batteries and operational processes within the BSS. The model considers factors such as charging speed, battery degradation, grid power constraints, customer behaviour, and range anxiety. The agent-based model simulates the interaction between vehicles, batteries, and the station, capturing the stochastic nature of EVs’ arrivals and battery demand with the discrete event simulation. The linear programming component optimises battery state transitions to minimise degradation and ensure that the demand is met while respecting the power limits of the grid. Different battery types are considered based on vehicle category, each with specific capacity and usage patterns, reflecting real-world market conditions. The results demonstrate that the proposed optimisation framework can effectively manage the complex operational needs of a BSS. The proposed framework effectively balances service quality with resource efficiency by employing a strategic mix of charging modes and inventory management, reducing operational and degradation costs. This approach supports a more sustainable EV infrastructure, highlighting BSS as a viable solution to enhance the efficiency and sustainability of EV operations. Furthermore, the analysis highlights the critical role of power limits in determining charging strategies and their impact on operational efficiency. The findings suggest that with optimised operations, BSS can play a critical role in accelerating the adoption of EVs by offering a faster, more reliable, and sustainable alternative.

Enhancing Urban Energy Resilience: Vehicle-to-Grid (V2G) Strategies within Electric Scooter Battery Swapping Ecosystems

With the global rise in electric scooter adoption and the expansion of battery swapping stations (BSSs), particularly in Taiwan—home to the world's densest network of these facilities—these stations are increasingly recognized as crucial energy storage hubs that enhance grid stability. This study introduces a smart energy management system designed for optimal charge and discharge management of BSSs, using Taiwan as a practical example. Central to our approach is a vehicle-to-grid (V2G) strategy, which, through hourly resolution analyses, assesses the impacts on grid peak demand, operational costs, and emissions. Our results indicate that V2G significantly reduces peak power demand by 110.1% compared to traditional charging methods and lowers operating costs by 18.9% through energy arbitrage. Although current emission reductions are modest due to Taiwan's limited renewable installed capacity, projections demonstrate that emissions could rise by 2% without strategic emission management. Our proposed V2G strategy targets both peak shaving and low-emission charging periods, potentially reducing emissions by 12.8% and supporting national sustainability targets. Furthermore, a comparative analysis shows that V2G is effective in both urban and rural settings, with rural areas achieving greater reductions in peak power usage and costs due to their higher idle battery capacity. This emphasizes the importance of tailored energy management strategies and underscores the critical role of well-designed BSS charging strategies in moving towards a sustainable, net-zero future.

Optimization Research on the Impact of Charging Load and Energy Efficiency of Pure Electric Vehicles

In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.

Research on the Smooth Switching Control Strategy of Electric Vehicle Charging Stations Based on Photovoltaic–Storage–Charging Integration

To facilitate seamless transitions between grid-connected and islanded modes in PV–storage–charging integration, an energy storage system converter is designated as the subject of investigation, and its operational principles are examined. Feed-forward decoupling, double closed-loop, constant-power (PQ), constant-voltage–constant-frequency (V/F), and constant-voltage charge and discharge control strategies are developed. The PQ and V/F control framework of the energy storage battery comprises an enhanced common current inner loop and a switching voltage outer loop. The current reference value output by the voltage outer loop and the voltage signal output by the current inner loop are compensated. The transient impact is reduced, and the smooth switching of the microgrid from the grid-connected mode to the island mode is realized, which significantly improves the power quality and ensures the uninterrupted charging of electric vehicles and the stable operation of the key load of the system. By constructing a simulation model of the photovoltaic energy storage microgrid on the MATLAB/Simulink platform, the practicability of the control strategy proposed in this paper is verified.

Differential Game Theory in Electric Vehicle Charging Optimization

Differential Game Theory is a strong way to solve the hard optimization problems that come up when charging electric vehicles (EVs) in smart grids. This method takes into account the strategic exchanges between many parties, such as EV users, charging station operators, and grid operators, each of whom has their own goals. In this study, we look at how differential game theory can be used to find the best charging plans for electric vehicles. Our goal is to lower total energy costs, make the grid less crowded, and get more energy from green sources. We take into account how power prices change, the different types of green energy that are available, and how EVs move around by modeling the charging process as a difference game. The game-theoretic method lets you come up with plans where each player maximizes their own reward while taking into account what other players do. To find the best price methods for different situations, such as peak and off-peak hours, different amounts of green energy usage, and different pricing plans, we use Nash equilibrium solutions. Our findings show that differential game theory can successfully balance the different needs of parties, which can lead to EV charging systems that work better and last longer. Furthermore, the suggested way not only makes smart grids work better, but it also helps lower carbon emissions by supporting the use of clean energy. In addition, this method helps us understand how to create reward systems that match individuals' goals with world improvement goals. This makes it possible for EV charging strategies to work together better. This study shows that differential game theory could be a very useful tool in the development of smart and environmentally friendly transportation systems.

Charging scheduling optimisation of battery electric buses with charging setup time

Battery electric buses (BEBs) are recognised as sustainable modes of transportation. Because of its increasing range, efficient and convenient overnight charging has become crucial. The limited number of charging stations and variability in setup times require the optimisation of BEB charging schedules. This study proposes an optimal overnight centralised charging schedule that considers setup time and battery-degradation costs. We model this as a multi-travelling salesman problem with sojourn time to minimise operating costs, including electricity, setup time, and battery wear, while adhering to the bus-schedule constraints. We introduce a local search grouping genetic algorithm with a 2-opt operator local search to address the complexities of public-transport networks. Our extensive numerical analysis, grounded in real-world data, shows a 4.48% reduction in operating costs using our optimised strategy compared with current methods. Moreover, our charging-station allocation analysis provides insights for resource optimisation, advancing sustainable public transport, and charging strategies. This study contributes to the field of sustainable public transportation and charging optimisation.

Vehicle-To-Grid (V2G) Charging and Discharging Strategies of an Integrated Supply–Demand Mechanism and User Behavior: A Recurrent Proximal Policy Optimization Approach

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.

Simulation and Optimization of a Hybrid Photovoltaic/Li-Ion Battery System

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems.

Simulation and Optimization of Automated Guided Vehicle Charging Strategy for U-Shaped Automated Container Terminal Based on Improved Proximal Policy Optimization

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Improving energy efficiency for suburban railways: A two-stage scheduling optimization in a rail-EV smart hub

As the scale of suburban rail and electric vehicles (EVs) continues to expand with the revolution of electrification of transportation, park and ride (P&R) facilities are increasingly recognized as critical energy coupling points between suburban rail traction transformers and EV charging stations. However, flexible coordination of the energy distribution among the bidirectional power flow of multiple trains and EVs’ charging demand becomes an urgent issue. In this paper, we establish a rail-EV Smart Energy Hub (SEH) framework integrating trains, ultra-capacitors (UC), and battery-based EVs. An emendable two-stage optimization model is proposed, enabling railways to provide R2X (railway-to-anything) services. The first stage determines the optimal train trajectory and adjusts timetables to minimize the energy consumption of multiple trains. In the second stage, the charging strategy of the EV is coordinated with the charging/discharging scheme of the UC, which takes the train power flow determined in the first stage as input. Meanwhile, the voltage unbalance caused by the railway is constrained to comply with the limits set by IEC/TR 61000-3-13. Case studies based on actual suburban railway lines in China demonstrate that the proposed scheduling optimization approach can significantly reduce the energy consumption of both railways and EVs.