Charging Method Research Articles

A galvanostatic charging method for quantitative measurements of hydrogen crossover in fuel cell stack

Hydrogen crossover rate has emerged as a key indicator for characterizing the aging process of proton exchange membrane fuel cell. Electrochemical techniques are the most common for quantifying hydrogen crossover. However, for the present, most existing methods are only applicable to the scenario of single fuel cell. There is still a lack of efficient methods for the quantification of hydrogen crossover in stack configuration. In this paper, we establish a galvanostatic charging method with high accuracy and strong operability to measure hydrogen crossover in fuel cell stack for the first time. The fundamentals of this presented method are expounded through comprehensively analyzing the electrochemical processes occurring on fuel cell electrode under galvanostatic conditions. The effectiveness of the galvanostatic charging method is fully validated on a single cell and a multi-cell stack. Furthermore, necessary details for practical implementation of our method are discussed, and a data processing procedure allowing automatic and quick data extraction is correspondingly developed. This new characterization methodology for stack-level fuel cells offers significant advantages of being accurate, high-efficiency and non-invasive, and it will provide a powerful tool for lifespan evaluation, inconsistency analysis and failure diagnosis of fuel cell stacks.

Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model

New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent charging application conditions will cause temperature rise and accelerated capacity decay, which easily bring about safety problems. Aiming at the above-mentioned problems related to the charging process of lithium-ion batteries, this paper proposes an optimization strategy and charging method for lithium-ion batteries based on an improved electric-thermal coupling model. Through the HPPC experiment, the parameter identification of the second-order RC equivalent circuit model was completed, and the electric-thermal coupling model of the lithium battery was established. Taking into account the two factors of charging time and charging temperature rise, the multi-stage charging strategy of the lithium-ion battery is optimized by the particle swarm optimization algorithm. The experimental results show that the multi-stage constant current charging method proposed in this paper not only reduces the maximum temperature during the charging process by an average of 0.83% compared with the maximum temperature of the battery samples charged with the traditional constant current-constant voltage (CC-CV) charging method but also reduces the charging time by an average of 13.87%. Therefore, the proposed optimized charging strategy limits the charging temperature rise to a certain extent on the basis of ensuring fast charging and provides a certain theoretical basis for the thermal management of the battery system and the design and safe charging method of the battery charging system.

Designing and Prototyping of Lithium-Ion Charging System Using Multi-Step Constant Current Method

The need for electrical energy means batteries have a critical role in technological developments in the future. One of the most advanced types of batteries is the lithium-ion battery. The conventional charging system has the disadvantage of taking a relatively long time, so the battery temperature is high. Therefore, a charging method that can shorten the charging time and extend battery life is needed. Some contributions of the paper are the design and prototype of a buck-boost converter for dual-mode lithium-ion battery charging (buck and boost mode) and the implementation of the Multi-Step Constant Current Method (MSCC) algorithm with an optimal charging pattern (OPT) to perform fast charging under voltage, current limit, and temperature monitoring. The test results showed that the proposed charging system prototype has an accuracy of 99.93% for the voltage sensor and 98.86% for the current sensor, whereas the precision of voltage and current sensors are 98.60% and 99.34%, respectively. The proposed method took 45 min to charge the 2-series (2S) and 4-series (4S) batteries. Compared to the CCCV method, the charging time of the MSCC method was 18.18% faster. In terms of battery temperature, MSCC had a lower peak temperature compared to CCCV by 1.5% and 1.25% for 2S and 4S, respectively.

Smart Charging for Electric Car-Sharing Fleets Based on Charging Duration Forecasting and Planning

Electric car-sharing (ECS) is an increasingly popular service in many European cities. The management of an ECS fleet is more complex than its thermal engine counterpart due to the longer ”refueling“ time and the limited autonomy of the vehicles. To ensure adequate autonomy, the ECS provider needs high-capacity charging hubs located in urban areas where available peak power is often limited by the system power rating. Lastly, electric vehicle (EV) charging is typically entrusted to operators who retrieve discharged EVs in the city and connect them to the charging hub. The timing of the whole charging process may strongly differ among the vehicles due to their different states of charge on arrival at the hub. This makes it difficult to plan the charging events and leads to non-optimal exploitation of charging points. This paper provides a smart charging (SC) method that aims to support the ECS operators’ activity by optimizing the charging points’ utilization. The proposed SC promotes charging duration management by differently allocating powers among vehicles as a function of their state of charge and the desired end-of-charge time. The proposed method has been evaluated by considering a real case study. The results showed the ability to decrease charging points downtime by 71.5% on average with better exploitation of the available contracted power and an increase of 18.8% in the average number of EVs processed per day.

Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm

This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (PLoss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes PLoss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

Determination of m ulti‐step constant current based fast charging method for Li‐NMC battery cells using multi‐ swarm particle swarm optimization algorithm

Determination of m <scp>ulti‐step</scp> constant current based fast charging method for <scp>Li‐NMC</scp> battery cells using multi‐ <scp>swarm particle</scp> swarm optimization algorithm

Mobile charging stations for EV charging management in urban areas: A case study in Chattanooga

The advancement of fixed charging stations (FCS) has facilitated electric vehicle (EV) charging and increased EV adoption. FCSs are especially important in dense urban areas, where home charging is not an option. However, FCSs’ construction is limited by budget, fragile power grid infrastructure, and site constraints, among others. Therefore, suitable locations for building FCSs are limited. Even in the presence of FCSs, EV drivers face challenges such as long charging times and inconvenient charging locations. Mobile charging stations (MCS), which allow for scheduling and convenient charging, can be a supplementary charging technology to address these shortcomings and allow rapid expansion of charging infrastructure, assuming the MCS can be scheduled efficiently. This paper develops an optimization framework for the optimal charging management of EVs considering a multi-charger system integrating different types of FCSs and MCSs. Considering the EV driver’s travel trajectory and time value, the proposed framework finds the optimal charging technology and location to minimize the EV user’s overall charging cost and time. The model is implemented in Chattanooga, TN, USA, where the evaluation results demonstrated that MCSs are the optimal charging method for many EV users. Moreover, the results indicated that MCS mitigates the power grid stress caused by EV charging during peak hours.

A four‐coil structure wireless power transfer system with constant current and constant voltage charging: Analysis, design, and experiment

AbstractWireless power transfer (WPT) technology can transmit power through high‐frequency magnetic fields, which is safer and more flexible than conventional conductive charging method. The WPT systems for charging lithium batteries need to meet the charging function of first constant current (CC) and then constant voltage (CV). In addition, to avoid reactive power loss, the input impedance of the system is preferably purely resistive, that is, to meet the zero phase angle (ZPA) condition. In view of the above problems, this paper proposes a four‐coil structure WPT system based on dual‐frequency switching method to realize load‐independent CC and CV charging outputs at two fixed ZPA frequency points, respectively. It is worth noting that there is only one compensation capacitor on the receiving side, which ensures the compactness and portability of the receiving side of the WPT system. An experimental prototype with charging current of 2.2 A in CC mode and charging voltage of 28 V in CV mode is established to verify the correctness and practicality of the proposed four‐coil structure WPT system. The experimental results are basically in good agreement with the theoretical analysis.

A Q-learning based electric vehicle scheduling technique in a distribution system for power loss curtailment

Electric vehicles (EVs) are becoming more common in the distribution network (DN), which burdens the system throughout the charging cycle. However, if EVs are used to assist the utility according to its demands, the scenario turns profitable. In this paper, a new methodology to schedule the EVs and distributed generation (DG) according to the DN system demand is proposed. The proposed method is a hybrid of the intelligent Q-learning algorithm, Loss Sensitivity factor (LSF), and Enhanced Grasshopper Optimization (EGOA) techniques. The main objective of this work is to encourage peak shaving, minimize the power loss and improve the voltage profile of overall DN in presence of EVs. The modelling of EVs is done by considering the most significant parameters such as EV State of Charge (EV SoC), travel itinerary, capacity of EV battery, and charging/discharging levels. Initially the location of EV charging stations and DGs are determined using LSF technique. Further peak shaving of DN considering EVs behaviour is implemented using Q-learning based smart charging method. Later the sizes of DGs are determined using EGOA technique. EGOA is an enhancement in the already existing grasshopper optimization technique that improves the algorithm's exploring capability. The hybrid approach helped in identifying the strong buses for EV charging station and DG placement. Further a peak shave of overall DN demand is achieved using Q-learning technique. Later using EGOA the DG sizing along with optimal EV placement helped in reduction of power loss by around 67% compared to base case. The voltage profile of DN is enhanced with the proposed hybrid approach without using compensation devices.

Providing a Method of Intelligent Charging of Electric Vehicles with Discrete Rates with the Aim of Flattening Load Profile and Reducing Number of Switching Times

Providing a Method of Intelligent Charging of Electric Vehicles with Discrete Rates with the Aim of Flattening Load Profile and Reducing Number of Switching Times

Design of a photovoltaic mppt charge controller using DC-DC zeta converter with a modified three-stage charging method

Nowadays, one of the primary kinds of renewable energies is photovoltaic (PV) systems. In standalone PV systems, the battery charge controller plays an important role in the system efficiency. In the maximum power point tracking (MPPT) charge controller, due to adjusting the voltage level and tracking the maximum power, DC-DC converter and MPPT algorithm are used. ZETA converter, as a DC-DC converter, offers a low output ripple. In the proposed MPPT charge controller, a DC-DC ZETA converter accompanied by a P&amp;amp;O (perturb and observe) algorithm will be used for tracking maximum power. Furthermore, over-charging and gassing are phenomena that may reduce the battery life by increasing the battery's temperature. To overcome this obstacle, the proposed three-stage charging method, which is the battery charging voltage levels (bulk, absorption, and float) are based on the battery temperature. The proposed MPPT charge controller is used for providing lower temperature for the battery when being charged. By comparing the proposed MPPT charge controller with the conventional method, the simulation results can ensure the desired performance of the proposed charge controller.

A Smart Charging Method for Optimum Electric Vehicles Integration in the Distribution System in Presence of Demand Response Program

A Smart Charging Method for Optimum Electric Vehicles Integration in the Distribution System in Presence of Demand Response Program

An EDA-based method for solving electric vehicle charging scheduling problem under limited power and maximum imbalance constraints

Electronic vehicles (EVs) are receiving increasing attention to addressing global warming challenges since fossil fuel is replaced with fuel cell technology. Hence, new challenges arise as demands have increased for using EVs. One of these challenges is the long waiting time of charging EVs spent in queues, especially during peak hours. So, in this study, we aim to propose an efficient method for the electric vehicle charging scheduling problem (EVCSP), which an actual charging station inspires. The most important constraint in this problem is balancing power consumption between charging lines, leading to a limited number of devices that can be charged simultaneously. Also, in this problem, EVs may have interrelationships with each other during the scheduling procedure. So, the estimation of distribution algorithm (EDA) as a competent method in handling the possible relations among decision variables is applied in our proposed hybrid EDA-based solving method. Our proposed method comprises two EDAs, a Markov network-based EDA and a Mallows model-based EDA. It achieves an appropriate schedule and charging line assignment simultaneously while minimizing the total tardiness considering problem constraints. We compared our method with a constraint programming (CP) model and the state-of-art meta-heuristic methods in terms of the objective function value by simulation on a benchmark dataset. Results from the experimental study show significant improvement in solving the introduced EVCSPs.

A Multifunctional, Non-Constant Current Charger Based on a Dual-Switching, Bidirectional Flyback Converter

This study implements a multifunctional charger based on a dual-switching, bidirectional flyback converter (DSBFC). The proposed charger adopts seven charging methods, including the incremental-current charging, constant-voltage (CV) charging, constant-current (CC) charging, pulse-current (PC) charging, triangular-current (TC) charging, sinusoidal-current (SC) charging, and positive/negative pulse-current (Reflex) charging methods. The charging process of a lithium-ion battery is divided into three stages: an initial term, a mid-term, and a final term. In the initial term, the incremental-current charging method is used in the initial term of charging for a soft start and to inhibit an increase in temperature. In the mid-term, five charging methods, including CC, PC, TC, SC, and Reflex-current charging, are used for charging. The CV charging method prevents overcharging the lithium-ion battery in the final term. Based on our experimental results, this study compares the four charging methods (PC, TC, SC, and Reflex-current) with the CC charging method to verify their improvement of the charging speed and increase in temperature in the mid-term. The charging speeds increased by 14.38%, 14.04%, 16.36%, and 27.27%, respectively, and the rise in temperature decreased by 37.8%, 40.5%, 48.6%, and 13.51%, respectively; all performed better than the CC charging method. Finally, users can adjust the charging method of the proposed DSBFC according to the needs of batteries so as to achieve excellent performance.

Micro-Patterning of Electret Charge Distribution by Selective Liquid-Solid Contact Electrification

Contact electrification (CE) at interfaces has been documented for over 2600 years, but its mechanism remains ambiguous and is not fully understood, especially for liquid-solid cases. For the first time, we proposed an electret micro-patterning charging method using flowing tap water. The selective oxygen plasma treatment restrained the involvement of the charge-transfer process in liquid-solid contact electrification and obtained an NPU-shaped 3D charge profile. The electret surface potential is up to −650V and able to maintain 82% of its initial potential after 45 days, demonstrating excellent charge stability. The water charging method can be further used for droplet-based electricity power generation and water-drop self-powered sensing applications. This work demonstrates the feasibility and effectiveness of the innovatively proposed electret water charging method and advances a novel application in the energy harvesting field. [2021-0263]

Modelling and Control of SEPIC and BIDC Converter Based off-Board EV Battery Charger using PV Array

Recent years have seen a dramatic increase in the usage of renewable energy sources in a variety of industries, including the automotive sector where it plays a role in charging the batteries of electric vehicles (EVs). Here, we present a method for lightweight EVs that uses a solar energy power conversion with SEPIC and bidirectional interleaved DC-DC converter (BIDC) to charge their batteries from the grid instead of on-board. The suggested technology can recharge the electric vehicle's battery throughout daylight and nighttime hours. The EV battery and the backup battery both get charged during peak sunlight hours, and the backup battery helps charge the EV battery when the sun isn't out. In order to test the effectiveness of the proposed charging method, a simulation was run in the MATLAB software using the Simulink application.

A Multistage Current Charging Method for Energy Storage Device of Microgrid Considering Energy Consumption and Capacity of Lithium Battery

Modular multilevel converter battery energy storage systems (MMC-BESSs) have become an important device for the energy storage of grid-connected microgrids. The efficiency of the power transmission of MMC-BESSs has become a new research hotspot. This paper outlines a multi-stage charging method to minimize energy consumption and maximize the capacity of MMC-BESSs. Firstly, based on condition monitoring and data collection, the functional relationship between the internal resistance/capacity and other states of lithium batteries is established. Since the energy consumption of the battery is related to internal resistance, current, and time, the energy consumption calculation expression of the battery pack is established, and the objective function is designed to optimize energy consumption and capacity in order to determine the charging current curve of each stage. Compared with the constant current charging method, the proposed multistage current charging method for an MMC-BESS decreases energy consumption by 4.3% and increases the capacity of 5 SOC intervals by 1.56%.

Performance investigation of a grid-connected system integrated photovoltaic, battery storage and electric vehicles: A case study for gymnasium building

It has been a pressing challenge for human beings to reduce the energy consumption of buildings and decrease carbon emission. The application of photovoltaic (PV) system in buildings is identified as a renewable energy and carbon reduction approach. As a kind of typical building, the gymnasium usually has a large non-occupation roof area, which shows great application potential to cover the building demand by available photovoltaic source. This paper presents the performance of a grid-connected system integrated photovoltaic, battery storage, and electric vehicles (EV) applied in gymnasium buildings. The main building demand of gymnasium buildings can be easily covered by adopting an appropriate size of PV installation and battery capacity. The load cover ratio can achieve 0.95 when PV generation equals the energy consumption of the building and the battery with 2.7 times capacity of average daily building load is equipped. The proposed charging strategy for EVs can significantly decrease the peak power of the system compared with the constant power charging method. The peak grid power of the system adopted with the proposed charging strategy can be decreased by 51.7% of the rated load power on a typical day when PV generation is 1.4 times the energy consumption of the building and the battery capacity is 66% of the average daily building load. The results of this study show a reference for the configuration and operation scheme of PV system and electric vehicles in gymnasium buildings for renewable energy development and zero-carbon activity.

A Constant Current Wireless Power Transfer Scheme with Asymmetric Loosely Coupled Transformer for Electric Forklift

Due to the numerous advantages such as being convenient, safe, and contactless, wireless power transfer (WPT) is becoming the mainstream charging method for electric vehicles. This paper presents a constant current WPT system with asymmetric loosely coupled transformer for electric forklifts using lead-acid batteries. First, based on the Neumann formula, this paper analyzes the mutual inductance of the coaxial rectangular coil, and designs an asymmetric loosely coupled transformer based on the practical application requirements, which makes the secondary side light and miniaturized. Second, the WPT system is analyzed in terms of the requirements of constant current charging, and the dual-LCL compensation is proposed according to the output current and power requirements. The transfer characteristics and anti-interference capability of the topology are analyzed. The constant current output feature of the system under the condition of variable load is demonstrated. After that, a dual-active bridge secondary-side independent control strategy is proposed, the phase shift angle is adjusted to ensure constant charging current and high efficiency of the system. Finally, a wireless charging experimental platform is established in accordance with the proposed asymmetric loosely coupled transformer and WPT system. The system can achieve 45 A constant current output and 3 kW output power with 91.2% transmission efficiency.

Combined Approach for Power Loss Minimization in Distribution Networks in the Presence of Gridable Electric Vehicles and Dispersed Generation

Increased electric vehicles (EVs) penetration into the distribution network burdens the system during the charging process. But the same turns out to be advantageous if the EVs are used to support the utility based on the requirement. In this work, an attempt is made to efficiently schedule both the grid-to-vehicle and vehicle-to-grid operational modes of EVs with the objective of reducing power loss in the distribution system in presence of distributed generation (DG). The EVs are modeled by considering the dominating factors, such as EV State of Charge (SoC), trip conditions, EV battery capacity, charging/discharging levels. An improvement to the existing grasshopper optimization technique is also suggested in this article, which enhances the exploring capability of the metaheuristic algorithm. The presented methodology is a combination of smart charging method, voltage stability index, and enhanced grasshopper optimization algorithm (EGOA), which decides the size of DGs to be placed in the system. The proposed hybrid approach tries to schedule both the EVs and DGs so as to achieve reduced power loss and improved voltage profile. In order to test the robustness of the proposed strategy, various analyses considering uncertainty in EVs and load conditions are performed.