Charging Algorithm Research Articles

Novel joint data collection and wireless charging algorithm for rechargeable wireless sensor networks

Novel joint data collection and wireless charging algorithm for rechargeable wireless sensor networks

Bi-level programming model of location and charging electric vehicle stations in distribution networks

Electric vehicles (EVs) are currently a focal point in distribution network research due to their potential benefits. Integrating EVs into distribution networks can enhance voltage profile stability. However, unplanned EV connection and charging can lead to increased peak load power consumption, voltage profile irregularities, and heightened network losses. Addressing the placement of charging stations in distribution networks is crucial to prevent voltage drop crises and mitigate other issues. This study proposes a Bi-level particle programming optimization programming approach, utilizing the Particle Swarm Optimization (PSO) algorithm to minimize losses and determine optimal station locations. The algorithm iteratively minimizes losses while considering location and operating costs until a stopping condition is met. Additionally, an EV charging planning algorithm is introduced to manage EV connections without exacerbating peak load or causing severe voltage drops. Simulations conducted on an IEEE 33-bus system using MATLAB analyze the proposed design's impact on power consumption, system losses, and network voltage. Simulations in an IEEE 33-bus system show that the proposed model shifts peak power consumption by about 4.6% to about 11.6% under different EV penetration scenarios. It achieves a reduction in the network losses by up to 0.39%, while the voltage deviations in critical buses are minimized to enhance stability during periods of high demand for EVs. Results demonstrate that the programmed charging algorithm effectively reduces power consumption at peak load, minimizes network losses, and smooths the daily voltage profile.

Narrative Review: Perancangan Sistem Pengisian Cepat untuk Kendaraan Listrik dengan Algoritma Kontrol Baterai

In the future, electric vehicles will become the primary choice for transportation due to their environmentally friendly nature and zero carbon emissions. Electric vehicles use batteries as their main source of energy, which can be recharged by continuously flowing electric current. However, uncontrolled charging can cause overcharge to the battery. Therefore, this study aims to find an efficient strategy to control the voltage using fast charging algorithms for electric vehicles. The method used includes the design of a fast charging circuit, battery assembly, and fast charging testing. The results show that the fast charging circuit uses a buck converter circuit combined with a charger circuit to produce a specification of 48 Volts 6 Amperes. The capacitor and resistor components on the output affect the charging speed, and charging is considered fast charging when it reaches 40% battery capacity within 2 hours of charging.

Optimization and Electrochemical Testing of 3-Electrode Pouch Cell for Insights into Battery Prototyping

When promising new materials are discovered and assessed for their electrochemical performance in batteries or other energy storage devices, their testing is typically performed initially in small coin cells, progressing to the single layer pouch cell (SLP) and on to commercially relevant prototype devices in various cell formats. As this development process moves from laboratory to commercial reality, the findings at coin cell level and SLP are typically used at an early stage to inform on expected performance. It is often the case that some device performance metrics obtained at SLP scale to those of a final prototype or commercial device, with other metrics from the same cell not giving a true indication.In this work, we initially look to answer the question ‘what size does my test cell have to be to inform on final device performance?’ This is done through variation of design and size of Li-ion pouch cells, from coin to SLP, through multilayer pouch (MLP) cells of varied numbers of electrodes, and consideration of the relationship of cell parameters such as capacity, rate, resistance, and lifetime relative to those of final MLP prototype device.A further challenge in the assessment and development of new materials performance and properties as we move from lab to commercially relevant prototype lies in understanding the potentials that individual electrodes experience in larger cell formats. Whilst this is relatively straightforward at the small scale with various methods of constructing coin/ Swagelok test cells with a reference electrode (3-electrode cells), as we move to SLP and MLP, this becomes significantly more challenging. Through the monitoring of individual electrode potentials in the larger format cell, invaluable information can be obtained, for example, for tailoring cell balance for application, helping to develop new algorithms for fast charging and understanding the effects of rate on lifetime.Having considered the necessary cell size to inform on performance of final prototype device, this information has been used in the development of a 3-electrode cell, with the effect of reference electrode size and position, on anode and cathode potential measurement along with cell performance.

Highly Safe and Fast Charing Method Based on Electrochemical Thermal Life Model for Lithium Ion Battery

Long charging time is one of the technical barriers that should be overcome for the wide acceptance of electric vehicles (EVs) in the market. The charging time can be simply reduced by an increased charging current that adversely reduces lifespan and deteriorates the safety of batteries. Therefore, the design of an appropriate charging protocol is a challenging issue. We have developed a fast charging method based on a reduced order electrochemical life model (ROM) considering side reactions and lithium plating. Among different fast charging profiles, the negative pulse (NP) charging method is selected. The pulse discharging current between high charging currents can promote lithium stripping so that lithium ions can be recovered from the plated lithium. The fast charging currents are optimized using a nonlinear model predictive control (NMPC) algorithm. The objectives are set to reduce the charging time with minimized degradation speed. Multiple constraints are considered in NMPC such as state of charge, cutoff voltage, side reaction rate, anode potential, and ion concentrations. The proposed fast charging algorithm is tested in real-time using a Battery-In-the-Loop (BIL) system. The results have shown that the charging time can be reduced while degradation rate can be maintained compared with the normal charging method. Reference [1] Yin Y., Bi Y., Hu Y., Choe, S. Y. (2021). Optimal Fast Charging Method for a Large-Format Lithium-Ion Battery Based on Nonlinear Model Predictive Control and Reduced Order Electrochemical Model. Journal of The Electrochemical Society, 167, 160559 [2] Yin Y., and Choe, S. Y. (2020). Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control. Applied Energy, 271, 115232.[3] Song M., Choi M., and Choe, S. Y. (2023). Start-Up Charging Strategy for a Large-Format NMCA/Graphite Pouch Cell from Subzero Temperature Using an Electrochemical, Thermal, and Mechanical Life Model. Journal of The Electrochemical Society, 170, 060533[4] Song M., and Choe, S. Y. (2019). Fast and safe charging method suppressing side reaction and lithium deposition reaction in lithium ion battery. Journal of Power Sources, 436, 226835

Practical implementation and field test evaluation of a decentralized grid-friendly charging algorithm for electric vehicles

Practical implementation and field test evaluation of a decentralized grid-friendly charging algorithm for electric vehicles

Design and analysis of a high-efficiency bi-directional DAB converter for EV charging

The escalating demand for electric vehicles (EVs) arises from apprehensions regarding fossil fuel depletion and the ecological repercussions of conventional combustion engines, propelling the transition towards environmentally sustainable alternatives. EVs are conventionally comprised of a powertrain, battery management system, and communication infrastructure, with the battery playing a pivotal role. Achieving an efficient EV battery charger necessitates the implementation of a proficient charging algorithm and a high-power converter capable of adeptly regulating battery parameters. Among the array of available options, a bi-directional DC/DC converter is often employed for this purpose. This study predominantly focuses on the Bi-Directional Dual Active Bridge Converter with Single-Phase Shift Control. It is renowned for attaining a broad voltage range through transformer turn ratio adjustments. Its controllable parameters, such as phase shift ratio and duty cycle, bolster its versatility. Moreover, due to its input–output isolation and minimal passive elements, this converter exhibits superior efficiency due to its soft-switching capabilities. The analytical framework encompasses steady-state and dynamic assessments, small-signal modeling, and system transfer function analysis, all of which contribute to formulating an optimized controller design. Additionally, the study conducts simulations of various battery charging techniques, including constant current (CC), constant voltage (CV), and CCCV mode, employing a 1.5 kW Dual Active Bridge DAB-based charger through MATLAB/SIMULINK. Furthermore, voltage mode control simulations for a 5 kW DAB converter augment the study's insights into effective EV charging strategies.

An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%.

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

The impact of integrated circuits’ performance in low-temperature environments on charging efficiency of chargers

Abstract This paper explores the current research status of chargers and lithium-ion batteries in low-temperature environments. Based on this, it provides a detailed discussion and analysis of charger charging conditions, lithium-ion battery performance analysis, the correlation between low-temperature environments and charging, and optimization design strategies for charging in low-temperature environments. The study reveals that low-temperature environments distort charger current harmonics, disrupt the power supply capacity of the grid, and reduce fast charging conversion efficiency. The paper elucidates the lithium-ion battery lithiation mechanism, lithiation detection methods, and countermeasures for lithiation issues in low temperatures. Besides highlighting the various adverse effects of lithiation on low-temperature charging, it also points out the shortcomings of detection methods and proposes appropriate strategies to inhibit lithiation. Addressing charging issues in low-temperature environments, the paper introduces charging optimization design strategies involving temperature-modulated current charging and genetic algorithm optimization. This research offers theoretical guidance and technical support for the design and optimization of chargers and lithium-ion batteries in low-temperature environments, further advancing the development and application of low-temperature charging technology.

A Comprehensive Review on Smart Electromobility Charging Infrastructure

This study thoroughly analyses Smart Electromobility Charging Infrastructure (SECI), exploring its multifaceted dimensions and advancements. Delving into the intricate landscape of SECI, the study critically evaluates existing technologies, integration methodologies, and emerging trends. Through a systematic examination of literature and empirical studies, the article elucidates the evolving ecosystem of smart charging solutions, considering aspects including advancements in charging protocols. Additionally, the review highlights challenges and prospects in the SECI domain, providing insightful information for scholars, practitioners, and policymakers involved in the dynamic field of electromobility. Technical potentials, including functionalities and integration with the smart grid, have been thoroughly reviewed. An analysis is conducted on the effects of intelligent charging on power distribution systems and strategies to lessen these effects. This study also examines the development of intelligent charging algorithms, optimisation methods, and security analysis. This paper, therefore, contributes to fostering a more thorough comprehension of the current state and future trajectories of Smart Electromobility Charging Infrastructure.

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

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

EcoCharge: Wireless Power Hub for Electric Vehicles

This research paper presents the creation of the "EcoCharge," a cutting-edge wireless charging station designed exclusively for electric cars (EVs). By offering a smooth and eco-friendly charging experience, the system seeks to transform the infrastructure for EV charging. The suggested approach, which makes use of state-of-the-art technology, allows for simple charging in the absence of bulky cables or physical connectors. EcoCharge incorporates cutting-edge wireless power transfer techniques, such resonant or inductive coupling, to guarantee effective energy transmission with a minimum of losses. It also has smart features that optimize charging processes and guarantee compatibility with different EV models and battery sizes. By using dynamic charging algorithms, power management may be made more effective, increasing charging efficiency and cutting down on charging times. The system also places a high priority on sustainability, reducing its negative effects on the environment by using eco-friendly materials and energy-efficient components. Index Terms: Wireless charging station, Electric vehicles (EVs), EcoCharge, Inductive coupling, Charging efficiency.

A fast charge algorithm for Li-ion battery for electric vehicles

The renewable solar energy industry and electric vehicle industry are today seeking for fast battery pack recharging methods to achieve higher performances, and fast energy recovery for energy storage systems (ESS) and for electric vehicles. The charge rate of batteries impacts directly the temperature which in turn impacts the capacity fade, thus it should be kept low to prevent the cells from warming up. This not only limits the charging rate but also puts us on a trade-off, a long lifetime or a fast recharge. In this study, we tried to achieve fast charging using a new charging method that combine two charging methods, without much deterring the capacity of the battery, in order to be able to maintain a long battery lifetime. Charging time of around 82 min was achieved for a 1.8 Ah battery. We compared our findings with the literature with known charging profiles.

A $$\varGamma $$-Convergence Result and An Off-the-Grid Charge Algorithm for Curve Reconstruction in Inverse Problems

A $$\varGamma $$-Convergence Result and An Off-the-Grid Charge Algorithm for Curve Reconstruction in Inverse Problems

Design and Development of MPPT Solar Charge Controller for Efficiency Enhancement in Solar PV System

Maximum power point tracking charge controller simulation is done in in proteus 8 professional software and validate using development of hardware model of MPPT solar charge controller for battery. Solar panel does not generate enough voltage all time.Panel can generate 12V to 21V according to solar radiation and environmental condition. For charging of 12V battery minimum required voltage is 14.6V. So, the Maximum power point tracking controller will give extra voltage into usable current, so battery can charge in short period.Development of MPPT Solar Charge Controller is done using ardunionano controller board, ACS 712 current sensor, mosfet driver IR2102, dc-dc buck converter using mosfet, inductor, capacitor and wifi module for data collection, LCD display,LED indication for battery status of charging. Battery charge using three state charging say buck, absorption and float mode. Performance analysis done using simulation model using LCD display status, where first column of display shows PV voltage, PV current and PV power. Second column of display reads battery voltage, status of battery. Third column reads pwm duty cycle in percentage and load status. Voltage level is shown by LED. Yellow LED indicates a fully charged battery, green indicates a regular voltage, and red indicates a low voltage. Performance study was done and the model was validated using a hardware model. All parameters and functionalities for measured solar panel voltage, current, power, battery voltage, charger state, SOC, PWM duty cycle, and load status are shown in real-time on the hardware display. Three-step battery charging algorithms are utilized, specifically the buck stage, absorption stage, and float stage. The battery peak charges to 90% SOC and the battery current is almost steady during the buck stage. In the buck stage, the battery voltage rises to a peak of 14.4V. Absorption occurs in the second phase when the battery's voltage remains constant and its current decreases. Battery current will be very low in the final phase, almost like trickle current.

Energy Management Strategies of Grid Connected Renewable Source for EV Charging Station

The growing popularity of electric vehicles (EVs) has made the need for effective charging infrastructure necessary. Combining renewable energy sources with EV charging stations offers a viable way to lessen transportation's negative environmental effects and cut back on fossil fuel use. This study looks into different grid-connected renewable energy source energy management techniques for EV charging stations. This study aims to optimize energy utilization, lower operating costs, and improve the sustainability of EV charging infrastructure by utilizing renewable energy generation, such as solar and wind power, in conjunction with intelligent charging algorithms. The study examines the body of research, evaluates various energy-management strategies, and suggests creative solutions to the problems arising from incorporating renewable energy sources into EV charging stations. The report also addresses the possible advantages, practical issues, and financial effects of putting these strategies into practice. This research advances our understanding of energy management systems for grid-connected renewable sources in the context of EV charging infrastructure through thorough analysis and simulation studies.

Smart Vehicle to Grid Energy Management Algorithm for Electric Vehicle Supported by Photovoltaic Energy Sources

The paper demonstrates grid improvements with results considering the integration of smart charging algorithms of electric vehicles (EVs) and photovoltaic (PV) energy sources to effectively manage energy flow. It aims to minimize energy costs by leveraging the bidirectional energy transfer capability of EVs and optimizing their interaction with the grid and PV sources. The proposed algorithm takes into account various parameters such as transformer load ratios, solar power generation, and the state of charge of vehicle batteries. The algorithms are implemented by acquiring one year’s real-time data of a predetermined transformer in a neighborhood from the electricity distribution company. The number of EV is estimated for the years 2025 and 2030. Buildings with energy management systems (BEMS), which charge EV with a controlled charging algorithm, provide renewable energy support with PV and reduce the transformer load during peak hours with the V2G algorithm. BEMS is not applied in buildings without EV or PV. Simulation results demonstrate that as the number of EVs increases, renewable energy source support and controlled charging algorithm alone are not sufficient. However, it demonstrate that energy costs and grid stress are successfully minimized by using the V2G algorithm in addition to controlled charging algorithm and PV.

Hybrid Charging Station for Authentic Electric Vehicle

Electric vehicles powered by batteries are becoming increasingly popular around the world. Several causes are driving this trend, including the need to reduce air and noise pollution and reliance on fossil fuels. To have a better understanding of how batteries behave in various situations. It is vital to be aware of certain battery performance factors in certain circumstances. A battery management system includes a battery fuel gauge, an optimal charging algorithm, and circuitry for cell and thermal balance. It estimates essential states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power decline, and remaining useful life, using three non-invasive measures from the battery: voltage, current, and temperature. This paper reviews several papers published regarding EV charging types, methods, BMS, state of charge

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method.