Charging Strategy Research Articles

Optimization of the SOC-based multi-stage constant current charging strategy using coyote optimization algorithm

This study presents a new strategy which optimizes the multi-stage constant current (MSCC) charging algorithm with state-of-charge (SOC)-based transition conditions (MSCCSOC) by searching for the optimal values of transition state-of-charge (SOC) and charging currents using the coyote optimization algorithm (COA). The paper firstly uses the electrochemical impedance spectroscopy (EIS) analysis to construct the equivalent circuit model (ECM) of the lithium-ion battery, and particle swarm optimization (PSO) is utilized to determine the parameters of the battery ECM within each 1 % SOC. The study employs the COA for the first time to tackle the challenging multi-objective MSCC optimization problem, which involves nine parameters. By not relying on multiple charging experiments and not restricting the search range of SOC transition and charging current values, the proposed approach can identify the global optimal solution, thus being advantageous over existing methods. The proposed method considers both shortening the charging time and reducing the charging losses. The experimental results show that compared with the traditional 1C CC-CV charging method, the proposed strategy can improve the average temperature rise, charging time, and maximum temperature rise by 17.6 %, 34.0 %, and 26.0 %, respectively. Furthermore, the proposed method outperforms other state-of-the-art MSCC charging algorithms and optimization techniques with limited searching range. Therefore, the proposed strategy provides a promising solution for obtaining the optimal setting for MSCCSOC, which can lead to reduced charging time and charging losses, thereby improving the battery's performance and lifespan.

A convex multi-objective distributionally robust optimization for embedded electricity and natural gas distribution networks under smart electric vehicle fleets

The indisputable environmental concerns have forced the imminent proliferation of renewable energy sources (RES) and electric vehicles (EV). However, the high penetration of such uncertain and variable sources, can pose significant challenges for maintaining supply–demand balance in electrical distribution networks (EDNs). To address these challenges, this paper presents a distributionally robust optimization (DRO) method for multi-objective scheduling in integrated electricity and natural gas distribution networks (IENGDNs). The proposed approach aims to minimize environmental-economic objectives while taking into account the high penetration of EVs and RESs. The impact of a smart EV charging strategy is evaluated to reduce operating costs and maximize the use of RESs. Additionally, demand response programs (DRPs) are used in the EDN to prevent overlapping of peak load hours between the EDN and natural gas distribution network (NGDN). Linepack technology is also used to store natural gas in NGDN pipelines, which increases the short-term flexibility of the entire IENGDNs. The proposed problem is mathematically structured as a second-order conical programming (SOCP) model to benefit from the reliable and efficient convex optimization solution. The simulations were conducted on a 123-EDN and a 40-NGDN systems. Different simulation cases show that the proposed economic-environmental framework can bring down the total emissions by 10.02%.

A Novel Dynamic Li-Ion Battery Model for the Aggregated Charging of EVs

Implementing successful aggregated charging strategies for electric vehicles to participate in the wholesale market requires an accurate battery model that can operate at scale while capturing critical battery dynamics. Existing models either lack precision or pose computational challenges for fleet-level coordination. To our knowledge, most of the literature widely adopts battery models that neglect critical battery polarization dynamics favoring scalability over accuracy, donated as constant power models (CPMs). Thus, this paper proposes a novel linear battery model (LBM) intended specifically for use in aggregated charging strategies. The LBM considers battery dynamics through a linear representation, addressing the limitations of existing models while maintaining scalability. The model dynamic behavior is evaluated for the four commonly used lithium-ion chemistries in EVs: lithium iron phosphate (LFP), nickel manganese cobalt (NMC), lithium manganese oxide (LMO), and nickel cobalt aluminum (NCA). The results showed that the LBM closely matches the high-fidelity Thevenin equivalent circuit model (Th-ECM) with substantially improved accuracy over the CPM, especially at higher charging rates. Finally, a case study was carried out for bidding in the wholesale energy market, which proves the ability of the model to scale.

Research on Orderly Charging Strategy for Electric Vehicles Based on Electricity Price Guidance and Reliability Evaluation of Microgrid

With the increasing use of electric vehicles (EVs), EVs will be widely connected to the microgrid in the future. However, the influence of the disorderly charging behavior of EVs on the stable and reliable operation of the power grid cannot be ignored. To address these challenges, the charging load characteristic model is established to describe the charging behavior of EVs. Then, an EVs orderly charging strategy based on electricity price guidance is proposed, and the goal is to minimize the peak–valley difference ratio and the total cost of EV charging. The result shows that, compared with disorderly charging, the EV orderly charging strategy based on electricity price guidance proposed in this paper can effectively reduce the peaking and valley difference ratio of load, reduce user’s charging costs, and optimize the reliability level of the microgrid.

A simulation-based investigation into the charging performance of lithium-ion and lead-acid batteries in electric vehicles

The transportation sector constitutes a major source of global greenhouse gas emissions, and the adoption of electric vehicles (EVs) can help mitigate this issue. This paper presents a simulation study examining the performance of lithium-ion and lead-acid batteries for EVs when charged using direct current (DC) chargers. A simulation model is employed to observe the charging behavior of these batteries and analyze the effects of various DC chargers. Factors such as the state of charge (SOC) and charging rate influence the charging behavior of batteries in EVs. The simulation results are compared and analyzed to ascertain the optimal charging strategy and infrastructure for EVs. The performance of different EVs is evaluated in terms of charging time, energy efficiency, and SOC. According to the findings, implementing the appropriate charging strategy and infrastructure for lithium-ion and lead-acid batteries in EVs can significantly enhance performance and encourage the widespread adoption of EVs.

Lithium-Ion Battery Optimal Charging Using Moth-Flame Optimization Algorithm and Fractional-Order Model

Considering the increasingly serious environmental pollution and energy crisis, new energy electric vehicles have good application prospects in future transportation. The lithium-ion battery is a kind of energy storage device widely used in electric vehicles. An efficient and optimal charging strategy is the premise of its extensive use. In this paper, a fractional model-based multi-stage charging strategy using Moth-flame Optimization (MFO) Algorithm is presented, in which the charging current is divided into several stages. Considering the convenience and safety of the charging process, the charging time, temperature rise, and energy loss are selected as optimization objectives. The MFO algorithm, which has the advantages of simple structure and excellent local search capacity, is employed to optimize the charging performance. Through experiments and simulations, the impacts of the current stage number, cut-off voltage, and weights of each fitness function part on the charging effect are discussed respectively. This paper provides guidance for optimal charging of lithium-ion batteries under different demands in practice.

Techno-economic and environmental impact assessment of a hybrid renewable energy system employing an enhanced combined dispatch strategy

Developing countries face challenges in maintaining a reliable power supply due to factors such as ageing infrastructure and rapid urbanization. Relying on backup diesel generators during outages is not only ecologically hazardous but also economically inefficient. Integrating multiple renewable sources with conventional energy systems is crucial to meeting growing energy demands and reducing carbon emissions. This study assesses dispatch strategies for optimal operation in hybrid renewable energy systems (HRES) connected to an unreliable national grid (GRD). An enhanced combined dispatch (ECD) strategy is introduced for effective energy distribution, considering load demands, energy resource availability, and grid unreliability. Compared to load following (LF) and cycle charging (CC) strategies, the ECD strategy proves superior, resulting in an optimized HRES configuration with a 248 kW solar PV array, a 2 kW wind turbine (WDT), a 22 kW biogas generator (BGG), a 92 kW diesel generator (DiG), and a 658 kWh battery storage (BSS). Achieving a low Levelized Cost of Energy (LCOE) at 0.148 USD per kilowatt-hour and a Net Present Cost (NPC) of 1.99 million USD. Adopting the ECD strategy also exhibits substantial reductions in CO2, CO, SO2, and NOx emissions when compared to CC and LF. ECD achieves approximately 25% lower CO2 emissions, 34% lower CO emissions, and a 40% reduction in SO2 and NOx emissions. These findings highlight the ECD strategy's potential for effective, economically viable, and environmentally conscious energy solutions, particularly relevant in developing nations like Nigeria, where Hybrid Renewable Energy Systems could play a crucial role in the energy sector.

A Fuel-Efficient BLDC Motor-Driven Light Electric Vehicle With Single-Stage Onboard Charging System

This work proposed a simple, compact, and efficient scheme for an on-board charging configuration for light plug-in electric vehicles (LPEVs) with a dual charging arrangement i.e., utility grid and solar panel. The system is targeted to develop an LPEV with a low DC bus voltage (<100V) and on-board solar support using a brushless dc motor (BLDCM) drive due to its economic controller, compact drive arrangement, and high efficiency. The designed integrated converter works well under all operating modes like charging, propulsion (PP), and regenerative braking (RB) operations. The developed converter works as a power factor correction (PFC) converter during grid charging as well as performs as maximum power point tracking (MPPT) converter when charging through solar power. A new approach of the controller in regenerative mode exploiting both the drive and proposed integrated control is unique for this system. As a result, the designed system becomes compact and efficient, making it a competitive solution for an on-board charging scheme for LEVs.

Researh on dynamic time-sharing tariff orderly charging strategy based on NSGA2 in PV-Storage-Charging stations

Researh on dynamic time-sharing tariff orderly charging strategy based on NSGA2 in PV-Storage-Charging stations

Charging Type Selection for Electric Buses Using Interval-Valued Neutrosophic Decision Support Model

Using electricity to fuel buses has always been known as a green option for the public bus operations considering the recent regulations on environmental and climate concerns. Emission-free public transportation modes such as electric buses improve local air quality and reduce noise pollution and also contribute to achieving sustainability goals by developing a sustainable transportation system. Many projects have been carried out using different charging strategies for electric buses around the world. However, selecting the most appropriate charging strategy for the electric buses in the public transportation is a challenging and multidimensional decision-making problem for transportation management sector. Therefore, this article proposes a decision support model based on the interval-valued neutrosophic set which uses Shannon&#x0027;s entropy and mixed aggregation by comprehensive normalization technique to choose the most appropriate charging type for urban electric buses among alternatives such as opportunity, depot, inductive, and no shift. This article presents a real-life case study in Turkey to illustrate the applicability and advantages of the proposed model. Extensive sensitivity analysis and comparative analysis tests are conducted to show the robustness of the proposed methodology. The results show that depot charging is the most suitable charging strategy for Istanbul&#x0027;s bus operator system.

The Effect of Voltage Changes Toward Time and Temperature Charging of Electrical Vehicle Battery

Technology in electric vehicles (EVs) has emerged as a major topic in the development of environmentally friendly and efficient energy. The battery is one of the most important components of an EV. Using the CCCV charging method, the battery will be more stable when charging. The performance of an EV mainly depends on its battery charging strategy. Consumers want fast charging with long-distance travel and good acceleration. Various filling techniques are currently in use, but research is still ongoing to find which one is more optimal and reliable. Modelling and simulation are very important to find out how to get optimal charging current or voltage, as well as determine the pattern of the charging circuit to shorten the charging time for EVs. This paper proposes a design and method that is carried out by changing the output voltage with a boost converter circuit to see the effect of time and temperature during charging and the simulation results are obtained regarding the length of time for charging Lithium-ion batteries. With the 420 V input voltage and 153 A, the battery is fully charged for 4500 seconds with a maximum temperature of 38° at a voltage of 420 V and the fastest charging time is 1200 seconds at an output voltage of 450 V with a maximum temperature of 62°. A 10 V voltage change resulted in a temperature increase of 6°C and a charging time of 600 seconds for each cycle.

A cost-effective and high-efficient EV shared fast charging scheme with hierarchical coordinated operation strategy for addressing difficult-to-charge issue in old residential communities

Charging infrastructures are being deployed widely in urban and rural areas to solve the so-called “last one-kilometer” charging problem. However, for old residential communities, the existing difficult-to-charge issue is not easily solved solely by the widespread deployment of charging infrastructure owing to limited spaces, complex reconstruction, and huge costs. In this context, this paper introduces a cost-effective and high-efficient shared fast charging scheme for old residential communities. The comprised registration-based queuing charging mechanism is proposed to schedule electric vehicle (EV) charging. An integrated and multi-stage power electronic device called smart interlinking unit (SIU) is introduced for grid reconstruction. The SIU-based charging station architecture features flexible control capability, enabling DC power supply for terminal EV chargers. For this architecture, a novel hierarchical coordinated operation strategy is designed, comprising the upper-level real-time vehicle-to-grid (V2G) dispatching and lower-level coordination strategies. Case studies demonstrate that the proposed scheme could accommodate 40 % EV penetration, while exhibiting a significant decline in system investment and occupied area compared to the slow charging scheme. Additionally, a 1.3 % improvement in average transfer efficiency is verified under this architecture. The proposed strategy is proven to realize a prominent power peak-shaving and valley-filling, effective transformer load balance, and DC voltage restoration.

Optimal Electric Vehicle Parking Lot Energy Supply Based on Mixed-Integer Linear Programming

E-mobility represents an important part of the EU’s green transition and one of the key drivers for reducing CO2 pollution in urban areas. To accelerate the e-mobility sector’s development it is necessary to invest in energy infrastructure and to assure favorable conditions in terms of competitive electricity prices to make the technology even more attractive. Large peak consumption of parking lots which use different variants of uncoordinated charging strategies increases grid problems and increases electricity supply costs. On the other hand, as observed lately in energy markets, different, mostly uncontrollable, factors can drive electricity prices to extreme levels, making the use of electric vehicles very expensive. In order to reduce exposure to these extreme conditions, it is essential to identify the optimal way to supply parking lots in the long term and to apply an adequate charging strategy that can help to reduce costs for end consumers and bring higher profit for parking lot owners. The significant decline in photovoltaic (PV) and battery storage technology costs makes them an ideal complement for the future supply of parking lots if they are used in an optimal manner in coordination with an adequate charging strategy. This paper addresses the optimal power supply investment problem related to parking lot electricity supply coupled with the application of an optimal EV charging strategy. The proposed optimization model determines optimal investment decisions related to grid supply and contracted peak power, PV plant capacity, battery storage capacity, and operation while optimizing EV charging. The model uses realistic data of EV charging patterns (arrival, departure, energy requirements, etc.) which are derived from commercial platforms. The model is applied using the data and prices from the Croatian market.

High Performance Rotating Triboelectric Nanogenerator with Coaxial Rolling Charge Pump Strategy.

With the development of society and the advancement of technology, the emergence of the Internet of Things (IoT) has changed people's lifestyles and raised the demand for energy to a new level. However, there are some drawbacks in terms of energy supply for IoT sensors, such as limited battery capacity and limitations in replacement and maintenance. Therefore, it has become urgent to develop a sustainable green energy source (wind energy) using the surrounding environment. Meanwhile, triboelectric nanogenerators (TENGs) with advantages such as flexible structure, low manufacturing cost, and environmental friendliness provide enormous potential for constructing self-powered sensing systems. In this work, we present a novel coaxial rolling charge pump TENG (CR-TENG) based on wind energy to enhance the output performance and durability. The rolling friction charge pump TENG directly injects positive and negative charges into the main TENG, which is more wear-resistant compared to sliding friction, and greatly increases the charge density and output power. In addition, the charge pumping part and the main TENG adopt the coaxial design, reducing the complexity of the structural design. On comparing the output performance of the CR-TENG under the initial state, rectifier bridge supplemental charge strategy, and charge pump supplemental charge strategy, results shown that the output voltage performance of the CR-TENG can be improved by 5800% under the charge pump supplemental charge strategy. Moreover, the output performance of the CR-TENG remains stable after 72,000 cycles. The output power of the CR-TENG can reach 1.21 mW with a load resistance of 3 × 107 Ω. And the CR-TENG can charge a 0.1 μF capacitor to 5 V in just 1.6 s. This work provides new insights for the rotary durable high output charge pump compensating a triboelectric nanogenerator and demonstrates the important potential of harvesting environmental energy to supply intelligent IoT nodes.

Integrated optimization of automated guided vehicles and yard cranes considering charging constraints

To improve the operational efficiency of automated container terminals and the coordination between multiple operations, this article studies the integrated scheduling optimization problem of automated guided vehicles (AGVs) and yard cranes. The impact of charging constraints on AGV task allocation and scheduling is considered. With the goal of minimizing the maximum completion time of all tasks, a mixed integer programming model is proposed. A solution method based on a dynamic programming algorithm is designed, where a heuristic algorithm is used to assign tasks to the yard cranes, and the dynamic programming method is used to assign tasks to the AGVs based on the task assignment results of the yard cranes. Finally, the validity of the model and algorithm is tested by numerical experiments. Furthermore, the influence of the quantity of AGVs on the terminal operational efficiency and the impact of AGV charging strategies on AGV scheduling are analysed.

Online Fast Charging Model without Lithium Plating for Long-Dimensional Cells in Automotive Applications

The internal negative electrode potential in lithium-ion batteries (LIBs) is intricately linked to the lithium-ion intercalation and plating reactions occurring within the cell. With the expansion of cell sizes, the internal negative electrode potential distribution gradually becomes inconsistent. However, the existing negative electrode potential estimation models and fast charging strategies have not yet considered the impact of consistency, and the model estimation accuracy will be greatly influenced by different temperatures and charging rates. This study proposes an online lithium-free fast charging equivalent circuit model (OLFEM) for estimating the negative electrode potential terminal voltage and developing fast charging strategies of long-dimensional LIBs in real vehicles. This study employs distributed reference electrodes integrated into long-dimensional LIBs and compares the negative electrode potential measured in the vicinity of both the negative and positive tabs. Subsequently, based on the lowest negative electrode potential point, model parameters were obtained at different temperatures and charging rates. This model is further verified under different operating conditions. Finally, a fast-charging strategy without lithium plating is developed in real-time based on the negative electrode potential estimated by the model. The results demonstrate that long-dimensional cells exhibit a lower negative electrode potential on the positive tab side. Across various temperatures and charging rates, the calibrated model achieves a negative electrode potential estimated error within 25 mV, and the estimation error for terminal voltage is within 5 mV. The proposed fast-charging method prevents lithium plating and charges the cell up to 96.8% within an hour. After 100 cycles, the cell experiences a capacity degradation of less than 2%, and the disassembly results indicate that no lithium precipitation has occurred. The methods outlined in this study provide valuable insights for online fast charging of large-dimensional batteries without lithium plating.

A Charging Planning Method for Shared Electric Vehicles with the Collaboration of Mobile and Fixed Facilities

Faced with the charging difficulties of free-floating shared electric vehicles and the high cost of single-demand mobile charging, this paper proposes a cooperative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the system charging total cost minimization model, which searches for the optimal charging scheme and number of mobile charging vehicles. The lower level model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for the mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% when compared to the fixed-only charging scheme, and by 14.9% when compared to the mobile-only charging scheme.

Empowering highway network: Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures, electric vehicles (EVs) have emerged as an effective measure to reduce reliance on fossil fuels, combat climate change, uphold sustainable energy and environmental development, and strive towards carbon peaking and neutrality goals. This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes (DWCLs) and EV charging strategy joint optimization in highway networks. Taking into account established charging resources in highway service areas (HSAs), the nonlinear charging characteristics of EV batteries, and the traffic capacity constraints of DWCLs. The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs. By ensuring that EVs maintain an acceptable state of charge (SoC), the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment, thus reducing the construction cost of wireless charging facilities and user charging expenses. The efficacy and universality of the model are demonstrated using the classical Nguyen–Dupius network as a numerical example and a real-world highway network in Guangdong Province, China. Finally, a sensitivity analysis is conducted to corroborate the stability of the model. The results show that the operating speed of EVs on DWCLs has the largest impact on total cost, while battery capacity has the smallest. This comprehensive study offers vital insights into the strategic deployment of DWCLs, promoting the sustainable and efficient use of EVs in highway networks.

Electric Vehicle Charging Station Power Supply Optimization with V2X Capabilities Based on Mixed-Integer Linear Programming

The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure the effective integration of the charging infrastructure into distribution networks. Given that EV charging costs significantly contribute to the total cost of owning an EV, it is important to hedge against rising electricity prices and ensure affordable charging for the end users. Connecting solar power plants and battery storage to the electric vehicle charging stations (EVCSs) serves as a measure of hedging against potential future electricity price increases but also as an option that can contribute to reducing impact on the distribution network loading. In addition to this, connecting EVCS through grid connections of existing consumers (office/residential buildings, shopping malls, etc.) can reduce grid connection costs for EVCS but also contribute to electricity cost reduction for both EVCS and existing end consumers. Additionally, by integrating advanced charging strategies like the vehicle-to-everything (V2X) approach, the overall charging costs can be reduced even further. This paper focuses on optimizing the power supply and operation of EVCS by considering strategic investments in grid connection, photovoltaic plants, and battery energy storage. The research explores the potential savings derived from reduced energy/charging costs, along with the reduction in peak power expenses for different power supply options. In addition to this, the research explores the effect of different EV charging strategies as well as EVCS grid connection on optimal investments and total system costs. The combined investment and energy management problem is focused on determining the optimal EVCS power supply and operation while minimizing total investment and operation expenditures over the project lifetime. The underlying optimization problems for different supply scenarios are cast as mixed-integer linear programming problems that can be solved efficiently. The results show the influence of different grid connection options and EV charging strategies on the joint operation and costs of EVCS and existing buildings.

Research on Optimum Charging Current Profile with Multi-Stage Constant Current Based on Bio-Inspired Optimization Algorithms for Lithium-Ion Batteries

Compared with the conventional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method offers advantages such as rapid charging speed and high charging efficiency. However, MSCC must find the optimal charging current profile (OCCP) in order to achieve the aforementioned benefits. Hence, in this paper, five bio-inspired optimization algorithms (BIOAs), including particle swarm optimization (PSO), modified PSO (MPSO), grey wolf optimization (GWO), modified GWO (MGWO), and the jellyfish search algorithm (JSA), are applied to solve the problem of searching for the OCCP of the MSCC. The best solution-finding procedure is run on the MATLAB platform developed based on minimizing the objective function of combining charging time (CT) and energy loss (EL) with a proportional weight. Without requiring numerous and time-consuming actual charge-and-discharge experiments, a wide range of searches can be quickly achieved only through the battery equivalent circuit model (ECM) established. The theoretical derivation and correctness are confirmed via the simulation and experimental results, which demonstrate that the OCCPs obtained by using the devised charging strategies possess the shortest CT and the best charging efficiency (CE), and among them, MPSO has the best fitness value (FV). Compared with the traditional CC-CV method, the experimental results show that the maximum improvement rates (IRs) of the studied approaches in terms of six charging performance evaluation indicators (CPEIs), including CT, charging capacity (CHC), CE, charging energy (CWh), average temperature rise (ATR), and FV, are 21.10%, 0.40%, 0.24%, 2.85%, 18.86%, and 68.99%, respectively. Furthermore, according to the comprehensive evaluation with CPEIs, the top three with the best overall performance are the JSA, MPSO, and GWO methods, respectively.