Charging Strategy Research Articles

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

An efficient partial charging and data gathering strategy using multiple mobile vehicles in wireless rechargeable sensor networks

Two‐layer dynamic economic nonlinear model predictive control for a Lithium‐ion battery charge process with random disturbances

AbstractThe charging process of lithium‐ion batteries is necessary for normal operation, and improper lithium‐ion battery charging strategy can cause side reactions, significant temperature rise, performance degradation, and safety concerns. This paper proposes a two‐layer dynamic economic nonlinear model predictive control economic model predictive control (EMPC) method for lithium‐ion battery charge management in which the non‐Gaussian random noise of voltage and current signal are taken into account. In the two‐layer EMPC, the upper layer finds the optimal reference trajectory, and the power in the future prediction horizon is chosen as an economic indicator to optimize the upper layer. The lower layer tracks the optimal trajectory, and model predictive control based on generalized correntropy is used. The economic cost of the system and the dynamic changes of the process are considered to greatly reduce the computational complexity and realize rapid battery charge management. Finally, the simulation results show that the tracking error of the two‐layer EMPC method based on generalized correntropy is stable around 0, while the tracking error of the two‐layer EMPC method based on MSE is stable around 0.003. The average control action times of the proposed two‐layer EMPC and single‐layer EMPC are 0.0052 and 0.0267 s, respectively. It is verified that the proposed method performs better for the lithium‐ion battery charging process with random disturbances.

Simulation Modeling and Analysis on the Value-Added Service of the Third-Party E-Commerce Platform Supporting Multi-Value Chain Collaboration

Service-oriented third-party e-commerce platforms have emerged as a new trend in the manufacturing industry. This paper aims to investigate the platforms’ value-added service (VAS) and charging strategies with a dynamic evolution analysis. Considering the change in the user numbers and characteristics of the e-commerce industry, this paper proposes a system dynamics model composed of multi-value chains and a third-party e-commerce platform. The simulation results indicate that the platform should reduce VAS investment and appropriately increase the VAS fee in the early development period. After the number of users stabilizes, the platform should appropriately increase its VAS investment and reduce the VAS fee. When the VAS fee is low, the platform profit first increases and then decreases as the VAS level increases. Differently, the platform profit will first decrease, then increase, and finally decrease as the VAS level improves when the VAS fee is low. This paper further finds that the strong cross-network effect of manufacturers is not always beneficial to the platform.

Intelligent, optimal charging with MSSC and PCM protocols for healthy and long life on Li-ion batteries

MSCC is one of the most efficient charging methods with the appropriate advantages over the CC/CV method most commonly used in the Li-ion battery charger market. Our approach to this optimal charging is based on the weak points of CC/CV charging, as well as the battery's reaction after each CC step in testing the new optimal current. Given the needs of the industrial market, which demands fast charging and safety against overcharging and side effects, we decided to develop a fast, optimal charging method aimed first and foremost at guaranteeing a long battery life cycle. Our intelligent charging strategy uses a combination of two charging protocols, MSSC and PCM, each with functionality and a communication link via an STM32 MCU. The principle is to find the optimum charging current for the battery at each stage of charging, depending on its state of health, and then apply the MSSC with a modern, optimized method based on methodological calculations, always respecting the temperature conditions.
 Finally, based on several experimental tests, we were able to prove that the optimal, intelligent method proposed in this article reduces charging time by 16.8% and temperature by 20%, compared with our own intelligent CC/CV+PCM method.

Adaptive Mobile Recharge Scheduling With Rapid Data Sharing in Wireless Rechargeable Networks

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

A New Charging Scheme Based on Mobile Charging Robots Cluster: A Three-Level Coordinated Perspective

A New Charging Scheme Based on Mobile Charging Robots Cluster: A Three-Level Coordinated Perspective

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

Design and simulation of 4 kW solar power-based hybrid EV charging station

Electric vehicles (EVs) have become an attractive alternative to IC engine cars due to the increased interest in lowering the consumption of fossil fuels and pollution. This paper presents the design and simulation of a 4 kW solar power-based hybrid EV charging station. With the increasing demand for electric vehicles and the strain they pose on the electrical grid, particularly at fast and superfast charging stations, the development of sustainable and efficient charging infrastructure is crucial. The proposed hybrid charging station integrates solar power and battery energy storage to provide uninterrupted power for EVs, reducing reliance on fossil fuels and minimizing grid overload. The system operates using a three-stage charging strategy, with the PV array, battery bank, and grid electricity ensuring continuous power supply for EVs. Additionally, the system can export surplus solar energy to the grid, reducing the load demand. The paper also discusses the use of MPPT techniques, PV cell modeling, and charge controller algorithms to optimize the performance of the hybrid charging station.

Study on Distribution Path Planning of Electric Logistics Vehicles Under Partial Charging Strategy

The current situation of energy consumption and environmental pollution is becoming more and more serious, electric vehicles have gradually become an important distribution tool for logistics companies due to their advantages of low energy consumption and low pollution, however, the problems of limited battery capacity and long charging time are still the main obstacles to their effective promotion in the field of logistics and distribution. In order to improve the utilization rate of electric logistics vehicles, the charging method of the vehicle is studied in detail, and it is proposed to use partial charging strategy to optimize the distribution path scheme of electric logistics vehicles with soft time window, and to establish an optimization model with the goal of minimizing the total cost of distribution, and at the same time, consider the constraints of the vehicle's loading, the customer's soft time window, and the battery endurance; the genetic algorithm is improved by using the idea of destruction and repair in the large neighborhood search algorithm, and the problem is solving, and verify the effectiveness of the model and algorithm using the Solomon arithmetic set; and conduct a sensitivity analysis of the distribution cost by adjusting 2 parameters, the battery capacity and charging price of the electric vehicle. The results show that the partial charging strategy can effectively reduce the cost compared with the full charging strategy in the distribution activities of electric logistics vehicles. In the example, the total cost is reduced by 7.69%, the electricity cost is reduced by 8.85%, and the total distance is shortened by 7.4%. In this case, the total cost, electricity cost and time window cost increased by 6.22%, 19.83% and 6.83% respectively. At the same time, some charging strategies can also significantly improve the power utilization rate of the vehicle battery, and reduce the idle and waste of vehicle resources and power resources. This paper proposes a distribution route optimization model of electric logistics vehicles under the partial charging strategy, which can provide beneficial enlightenment for logistics enterprises in the scheme scheduling and distribution model selection in urban distribution, and is of great significance for reducing the distribution cost of logistics enterprises.

Decentralized restoration of distribution systems with coupling neighboring microgrids

In this study, a multi-agent system (MAS) is incorporated in a decentralized strategy to restore distribution systems while taking into account coupling neighboring microgrids (CNMGs). This provides modeling for renewable energy sources (RESs), electric vehicles (EVs), battery storage systems (BSS) and load. The desired and most favorable restoration path is found by the MAS, in which zone agents are dispersed across the distribution system. The MAS can also manage microgrids (MGs) overloaded as the unbalance operation of RESs, BSS, EVs, and load. This is realized by making a bridge between MGs and neighboring non-overloaded MGs. The suggested method adheres to voltage and power flow restrictions while operating according to expert system standards. The recommended approach is put to the test using a 33-bus radial distribution system. MATLAB calculations on agents and power flow are carried out in order to verify the validity of the choices made by agents. The proposed restoration plan is able to obtain the best power supply path with a low number of switching in the event of a fault so that the voltage magnitude is higher than 0.9 p.u. and free capacity is available for the distribution lines. The smart charging strategy of EVs reduces 93% of their turn off compared to the non-smart charging strategy. However, if the CNMG plan is established, all vehicles can be powered.

An Equivalent-Perceptional Intertemporal Choice Heuristics Model for Electric Operation Vehicle Charging Behavior

The inherent stochasticity of electric operation vehicle (EOV) charging poses challenges to the stability and efficiency of regional power distribution networks. Existing charging behavior decision-making models often prioritize revenue considerations, neglecting the influence of multi-time-span characteristics and the potential irrationality of EOV owners. To address these limitations, this study proposes a comprehensive framework encompassing three aspects. First, operational data are statistically analyzed to reconstruct EOV operation scenarios, establishing a dynamic charging scheme tailored to multi-time-span characteristics. Second, an improved ITCH model is developed using operational equivalent change to incorporate both gains and losses. Third, a WFL framework is employed to integrate the perceptual attenuation of revenue into the ITCH model. Simulation results show that decision-makers (DMs) demonstrate a preference for charging schemes with high equivalent perceived revenues and low time costs. Moreover, when the charging price is doubled, revenue perception attenuation leads decision-makers to postpone their charging behavior. Compared to other models, the equivalent perception intertemporal choice heuristics (EP-ITCH) charging model results in reduced load peaks, valleys, and variances on the grid side. This study highlights the model’s effectiveness and accuracy in optimizing EOV charging infrastructure.

Deep reinforcement learning approach with hybrid action space for mobile charging in wireless rechargeable sensor networks

Mobile charging is feasible to deal with the energy-constrained problem in wireless rechargeable sensor networks (WRSNs). The mobile chargers (MCs) are usually employed to charge the sensors sequentially according to the charging schemes. Existing studies assume that each sensor should be charged to its maximum energy capacity or to a fixed upper threshold before the next one can be charged. However, they neglect to control the charging time adaptively of each sensor according to the charging demand. Hence, in this paper, we assume that the charging time of each sensor can be controlled, and we study the joint optimization of charging sequence and charging time problem (JCSCT). Correspondingly, we propose a novel deep reinforcement learning with hybrid action space approach for JCSCT (DRLH-JCSCT), which utilizes deep q-network (DQN) to generate the charging sequence, and adopts deep deterministic policy gradient (DDPG) to determine the charging time. An attention-based encoder–decoder model is integrated in the actor network of DDPG, and a modified bi-directional gate recurrent unit network (MBGRU) is utilized as the decoder. We also design a novel reward function to evaluate the quality of the charging actions. Simulations demonstrate the improved charging performance of the proposed approach, with a longer network lifetime and fewer failed sensors compared with the existing mobile charging scheduling approaches.

A novel navigation and charging strategy for electric vehicles based on customer classification in power-traffic network

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles.

A Model for Electrifying Fire Ambulance Service Stations Considering Practical Service Data and Charging Strategies

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs’ return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs.

Measuring acceptance of tradable credit scheme and its effect on behavioral intention through theory of planned behavior

Public support is crucial for the successful implementation of congestion charging schemes. The Tradable Credits Scheme (TCS), a novel concept built upon the cap-and-trade principle, is believed to possess qualities of effectiveness and fairness that should lead to greater public approval. This study investigates the level of acceptance of TCS among the public and its impact on the propensity to shift travel modes in the TCS framework. In this study, we used the Theory of Planned Behavior, encompassing components such as attitudes toward TCS, subjective norms and perceived behavioral control and assessed the perceived effectiveness and equity of TCS. Data were acquired through an online survey involving 544 participants in China. The findings indicate that public acceptance of TCS is significantly influenced by subjective norms and attitudes toward TCS. In turn, behavioral intention is indirectly shaped by these factors, with acceptance serving as a mediator. Perceived behavioral control exerts a direct influence on behavioral intention, while perceived effectiveness and equity positively impact both the acceptance of TCS and behavioral intention. Moreover, latent profile analysis was used to determine the influence of various factors on TCS acceptance, following which we categorized the participants into four distinct groups. The results show that age, city scale, income, holding a driving license or not, car ownership, necessity through congested areas, main transportation modes, and the frequency of using different travel modes all affect the distribution of individuals in support or rejection groups Notably, over one-third of participants express TCS acceptance, with most holding a neutral stance, indicative of TCS's potential for public acceptance with effective guidance and comprehensive explanation. In summary, this research provides novel insights into the multifaceted factors that influence individuals' acceptance of TCS and offers valuable assistance in promoting an innovative traffic demand management policy.

Smart and Sustainable Wireless Electric Vehicle Charging Strategy with Renewable Energy and Internet of Things Integration

This study addresses the challenges associated with electric vehicle (EV) charging in office environments. These challenges include (1) reliance on manual cable connections, (2) constrained charging options, (3) safety concerns with cable management, and (4) the lack of dynamic charging capabilities. This research focuses on an innovative wireless power transfer (WPT) system specifically designed for use in office parking areas. This system incorporates renewable energy resources (RERs) and uses the transformative power of the Internet of Things (IoT). It employs a mix of solar energy systems and battery storage solutions to facilitate a sustainable and efficient energy supply to EVs. The integration of IoT technology allows for the automatic initiation of charging as soon as an EV is parked. Additionally, the implementation of the Blynk application offers users real-time access to information regarding the operational status of the photovoltaic system and the battery levels of their EVs. The system is further enhanced with IoT and RFID technologies to provide dynamic updates on the availability of charging slots and to implement strict security protocols for user authentication and protection. The research also includes a case study focusing on the application of this charging system in office settings. The case study achieves a 95.9% IRR, lower NPC of USD 1.52 million, and 56.7% power contribution by RERs, and it reduces annual carbon emissions to 173,956 kg CO2.

Enhancing the utilization of renewable generation on the highway with mobile energy storage vehicles and electric vehicles

The growth of electric vehicles (EVs) and renewable generation on the highway will magnify the imbalance between the energy supply and traffic electricity demand. Reshaping EV charging loads to address the above imbalance is challenging. Scheduling mobile energy storage vehicles (MESVs) to consume renewable energy is a promising way to balance supply and demand. Therefore, leveraging the spatiotemporal transferable characteristics of MESVs and EVs for energy, we propose a co-optimization method for the EV charging scheme and MESV scheduling on the highway, considering locational marginal price, renewable generation, and EV users’ benefit. Furthermore, by building a time–space–energy model for MESVs and EVs, we develop a bi-level optimization model to simulate the interaction between EV users and the highway operator (HO). In detail, the upper-level model optimizes EV charging pricing and MESV scheduling strategies to maximize HO profit, while the lower-level model considers EV users’ characteristics to minimize their charging-parking costs. The column-and-constraint generation algorithm is adopted due to the objective conflict between two levels. The study shows a 32.0% increase in renewable energy utilization and a reduction of 3190.1 kWh in electricity purchased from the main grid, promoting environmental and economic sustainability for the HO.

Multi-objective optimization for multi-stage constant current charging for Li-ion batteries

Fast charging is a key challenge for the widespread adoption of electric vehicles (EVs), as it can make EVs more convenient and appealing to consumers. Therefore, different charging methods are proposed to enhance the performance of lithium-ion batteries (LIBs). Multi-stage constant current (MSCC) charging can improve LIB's performance in several aspects, including charging time, charged capacity, temperature rise, average temperature rise, and charging energy efficiency. However, achieving a multi-objective performance during LIB charging is challenging. In this paper, the Taguchi method is used to determine the multi-objective optimal (MOO) charging profile for the MSCC charging strategy. The Orthogonal experiments are designed and conducted to find the optimal solutions for each performance parameter for a five-stage constant current (5SCC) charging profile. A comparison is conducted among the performance-based selective charging profiles to validate the optimal charging profile. Furthermore, a comparison is made between the obtained MOO charging profile and the classical equivalent constant current constant voltage (CCCV) method. Experimental results demonstrated that the MOO charging profile reduces the charging time with comparable temperature rise to the CCCV, making it promising for the future alternative efficient EV charging. These findings highlight the effectiveness of the MSCC approach in improving LIB performance.

Enhanced EV charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types

The increasing penetration of Electric Vehicles (EVs) presents significant challenges in integrating EV chargers. To address this, precise smart EV charging strategies are imperative to prevent a surge in peak power demand and ensure seamless charger integration. In this article, a smart EV charging pool algorithm employing optimal control is proposed. The main objective is to minimize the charge point operator’s cost while maximizing its EV chargers’ flexibility. The algorithm adeptly manages the charger pilot signal standard and accommodates the non-ideal behavior of EV batteries across various vehicle types. It ensures the fulfillment of vehicle owners’ preferences regarding the departure state of charge. Additionally, we develop a data-driven characterization of EV workplace chargers, considering power levels and estimated battery capacities. A novel methodology for computing the EV battery’s arrival state of charge is also introduced. The efficacy of the EV charging algorithm is evaluated through multiple simulation campaigns, ranging from individual charger responses to comprehensive charging pool analyses. Simulation results are compared with those of a typical minimum-time strategy, revealing cost reductions and significant power savings based on the flexibility of EV chargers. This novel algorithm emerges as a valuable tool for accurately managing the power demanded by an EV charging station, offering flexible services to the electrical grid.

Drone-based hybrid charging for multiple sensors: A distributionally robust optimization approach

The drone-based charging is emerging as a promising way to supply electricity to sensors that keep the Internet of Things working. One-to-one and one-to-many charging strategies can both be adopted. In this background, how to dispatch drones and decide the charging strategy becomes an important problem. In this paper, we first investigate the electricity consumption uncertainty during drone travel, and then develop a distributionally robust hover location and routing optimization model with hybrid charging strategies. After that, we transform the established model into a tractable mixed-integer linear programming model based on dual theory. In order to solve it, we introduce a pre-screening process based on greedy algorithm to select the candidate hover locations. Finally, we conduct extensive numerical experiments and sensitive analysis to verify the efficacy and advantages of the proposed method. We find that the optimized charging strategies and drone dispatching schemes can vary with different sensor distribution patterns. Valuable managerial insights are also provided for drone-based hybrid charging in practice.