Battery Charging Process Research Articles

Health-aware battery fast charging technique with zonal battery model using pulsed amplitude-width modulation and multi-step constant current constant voltage

ABSTRACT This article presents an innovative method for rapidly charging batteries while considering health factors. The strategy utilizes a closed-loop control approach based on a mathematical model. Initially, an intricate thermo-electric aging cell model (TEACM) was constructed based on electrochemical aging study. The model’s parameters are continuously updated in real-time by pulling data from the battery pack while it is being charged. The TEACM algorithm analyzes the parameter values and calculates different battery statuses. These estimations are then used to improve the charging process. The charge optimization phase optimizes the precharging and boosts charging processes individually. Several pulse precharging approaches have been analyzed, and by calculating the diffusion coefficient, the most effective precharging method has been identified. The use of multistage constant current technique is utilized to optimize the battery charging process. The optimization is accomplished by implementing the non-linear model predictive controller. Ultimately, the two refined charging methods are combined and sent to the charger. The hypothesis was validated using a cell cycling test bench. The acquired findings were compared with other recognized benchmark charging techniques, demonstrating that the suggested approach charges the cell in half the time compared to the 1C-CCCV charging methodology while also increasing the cycle life by 6.67%.

Atomic-scale probing of ion migration dynamics in Na3Ni2SbO6 cathode for sodium ion batteries

Honeycomb-layered phases like Na3Ni2SbO6 have been extensively researched as high-voltage and high-rate capability cathode materials for sodium-ion batteries. However, our understanding of the structural stability and dynamic reaction mechanisms of Na3Ni2SbO6 cathode during cycling, especially at atomic-scale, remains limited. Here, we track the microstructure evolution during extraction of Na+ ions in Na3Ni2SbO6 cathode at atomic scale in an aberration-corrected transmission electron microscope. The electron beam irradiation that can provide a driving force for the Na+ ion migration, allows us to mimic the battery charge process. By controlling the electron beam dose, we study the structure evolution behavior to obtain insights into understanding the work principle and failure mechanism of Na3Ni2SbO6 cathode under different charge rate conditions. We find that the real-time structural evolution and ion migration pathways of Na3Ni2SbO6 cathode are distinct under different electron beam doses. High-dose irradiation reveals Na ion depletion, surface cracks, and phase transformations, mimicking rapid capacity decay. In contrast, low-dose irradiation shows slower ion migration, ordered Na vacancy formation, and maintaining structural integrity, which more closely resembles the electrochemical process of actual battery. This study provides an atomistic understanding of the structural stability and Na ions deintercalation mechanism in Na3Ni2SbO6 cathodes, offering new insights into optimizing electrode materials.

The Embedded System of Battery Charging with Constant Current System using Microcontroller and IC LM338

The battery has the function of an electrical energy storage system. However, charging the battery is prone to damage. Damage to the battery is often caused by overcharging. The time used during the battery charging process is an important factor that must be considered when charging. thus, efficient battery charging is important and very necessary to avoid damage. An another important aspect of a battery charging method is the current and voltage values supplied during its charging process. The presence of inappropriate values damages and reduce battery life, while the maintenance of a constant current is an important charging process step that extends service life. This research is expected to create a safe and optimal battery charging system with a simple low-cost circuit based on constant current by utilizing the LM388 Integrated Circuit (IC) regulator. This study discusses a 12 V battery charging system with monitored and controlled constant current using an R-Shunt sensor. Voltage monitoring while charging was carried out with voltage sensors using a constant current of 0.1 to 1.3 Ampere. Furthermore, voltages and currents were displayed through a Liquid Crystal Display (LCD) screen controlled with Arduino Uno during battery charging. The measurement results showed that the circuit reached voltages and currents of 14.07 V and 0.19 A respectively for a charge duration of 8 hours.

Design and Implementation of an Automated Instrument for Battery Charging Experiments

Accurate and long-term measurements are essential in battery charging research and development, to evaluate and compare different techniques and their effectiveness. In such approaches, a very good control of certain charging parameters is also necessary, during several hours or even days. Thus, it became necessary to obtain an automatic instrument that could ensure the precision and repeatability of the experiments, but also the flexibility regarding the type and size of the b attery. This article presents the design, implementation and preliminary experimentation of an instrument for automation and monitoring of battery charge-discharge cycles. The tool adds exponential current pulses to the battery charging process, especially in the constant current phase. Such pulses are rarely addressed in the literature, although some studies suggest that they have the potential to improve batteries performance.

A machine-learning-based column generation heuristic for electric bus scheduling

Bus scheduling in public transit consists in determining a set of bus schedules to cover a set of timetabled trips at minimum cost. This planning process has evolved recently with the advent of electric buses that introduce constraints related to vehicle autonomy and battery charging process. In particular, column-generation algorithms have regained popularity for solving problems similar to the one considered in this paper, namely, the multi-depot electric vehicle scheduling problem (MDEVSP) with a piecewise linear charging function and capacitated charging stations. To tackle large-scale MDEVSP instances, we design a column generation (CG) heuristic that relies on reduced-sized networks to generate the bus schedules. The reduction is achieved by selecting a priori a subset of the arcs. Multiple selection techniques are studied: some are based on a greedy heuristic and others exploit a supervised learning algorithm relying on a graph neural network. It turns out that combining both selection types yields the best computational results. On 405 artificial instances involving between 568 and 1474 trips and generated from real bus lines in Montreal, the network reduction technique produced an average computational time reduction of 71.6% (compared to the same CG heuristic but without network reduction), while deteriorating solution cost by an average of 2.2%. On 8 larger instances containing more than 2500 trips on average, the proposed solution method also provided an average time saving of 52.5% with an average gap of 4.2% thanks to a transfer learning approach.

Frequency optimization of the AUV wireless charging system for minimum energy dissipation

With the growing demand for ocean exploration, research on docking technology is gaining popularity, resulting in the development of various types of docking systems. In contrast to traditional cable-based systems, this paper introduces a self-powered docking system designed for AUV recovery and recharging. To minimize energy loss in the self-powered system, an analysis of eddy current loss produced by the coils in the seawater is conducted, and a calculation method for determining the equivalent eddy current impedance is proposed. This allows for the transformation of energy loss analysis from the magnetic field to circuit analysis. Then the efficiency of the inductively coupled power transmission system in seawater is analyzed, and a frequency optimization method is proposed to minimize dissipated energy during the AUV battery charging process. Finally, the effectiveness of the frequency optimization method is validated through laboratory experiments. Moreover, the results of the sea trial confirm the feasibility of the optimal wireless charging system operating in a seawater environment.

Proposed Scheme for a Cavity‐Based Quantum Battery

AbstractA scheme for quantum batteries consisting of an atom‐cavity interacting system under a structured reservoir in the non‐Markovian regime is proposed. A multi‐parameter regime for the cavity‐reservoir coupling and reveal how these parameters affect the performance of the quantum battery is investigated. Specifically, how two distinct types of environments impact the charging process of the quantum battery from a memory perspective is examined. The findings indicate that in a memory‐influenced environment, both the stored energy and the extractable work are higher compared to when the cavity interacts with a memory‐less environment. This proposed scheme is simple and may be achievable for realization and implementation.

A novel causative factor of injury: Severe burns related to fires and explosions of lithium-ion batteries of electric motorcycles

Severe burns related to fires and explosions of lithium-ion batteries of electric motorcycles have not been reported to date. We retrospectively studied 419 patients admitted to our burn intensive care unit from January 2016 to December 2021. Of these 419 patients, 26 (22 male, 4 female; median age, 42 years) had burns related to lithium-ion battery fires and explosions, and all of their injury characteristics were similar to those of traditional flame burns. Lithium-ion battery-related burns were the eighth most common cause of burn injuries among all hospitalized patients. The 26 patients comprised 10 unemployed and 16 employed individuals. Twenty-three patients were injured at home during the battery charging process, and three were injured outdoors (one by a fire while the electric motorcycle was stationary and the others two by a fire while riding the motorcycle). The burn sites were distributed over the whole body; the burn area ranged from 10 % to 100 % of the total body surface area, and the burn depth ranged from superficial second-degree burns to third-degree burns. Twenty-three patients had inhalation injuries, and ten underwent prophylactic tracheostomy and intubation. Multiple operations were required for wound repair. Although convenient, lithium-ion electric motorcycles can also cause severe burns. To prevent these injuries, we must increase public safety awareness and education, develop new battery energy storage systems and battery management systems, and ensure the safety of batteries. Consumers should be aware of the potential dangers of lithium-ion batteries and comply with related security measures.

Power converter for battery charger of electric vehicle with controllable charging current

Research on electric vehicle chargers primarily focuses on improving charging technology to enhance the convenience, quality, efficiency, and affordability of charging electric vehicles. This paper discusses a different AC-DC power converter topology proposed as a potential candidate for electric vehicle battery charger. Using the proposed converter circuit, the charging process of electric vehicle battery can be simply adjusted to meet the battery requirement, and even for increasing the charging speed while avoiding overcharging of battery. During the charging process, if the voltage of battery approaches to the targeted battery voltage level, the charging current can be reduced gradually to zero. Hence it will prevent the battery from overcharging condition. Moreover, the ability of working at high power factor operation, low distortion of alternating current (AC) input current, and low direct current (DC) output current ripple are other features of the proposed charger circuits. Some test results of the proposed power converter were presented. The test results revealed potential features of the converter for battery charger of electric vehicle with power factor 0.9996, total harmonic distortion (THD) of AC current 2.73%, and low charging current ripple, i.e. 3.91%. Therefore, it minimizes the negative effects caused by ripples of charging current especially for lithium-ion batteries widely used in electric vehicles.

Design of SEPIC Converter for Battery Charging System using ANFIS

Rechargeable batteries are the most widely used medium for storing energy today. One type of rechargeable battery that is widely used is lithium-ion batteries. The large use of lithium-ion batteries in society requires companies to conduct research so that the life time of these batteries can last a long time and charging can take place quickly. Charging system at this time is less efficient in charging lithium batteries where the time needed is still quite long where when lithium batteries are charged with a long time can cause the battery to heat up quickly and can reduce the life time of the battery. To overcome this, a system is needed that can control the battery charger process so that the output voltage and current are constant and battery charging is faster. It is hoped that the SEPIC converter system can help many people who forget to unplug the power supply during the charging process so as to maintain the life time of the battery. Setting the output voltage and current in the DC-DC converter can be done using an Adaptive Neuro Fuzzy Inference System which aims to keep the output of SEPIC stable according to the setting point. In this system, the DC-DC converter used is a SEPIC converter which can increase and decrease the output voltage for battery charging. The battery charging process uses the CC-CV method. In the test, the average error is 0.025% where when the SOC is 60% to 80% the average error is 0.04% and when the SOC is 80% to 95% the average error is 0.0005%.

Design and implementation of microcontroller-based solar charge controller using modified incremental conductance MPPT algorithm

This paper presents the modeling, design, and implementation of a rapid prototyping low-power solar charge controller with maximum power point tracking (MPPT). The implemented circuit consists of a 60 W photovoltaic (PV) module, a buck converter with an MPPT controller, and a 13.5V-48Ah battery. The performance of the solar charge controller is increased by operating the PV module at the maximum power point (MPP) using a modified incremental conductance (IC) MPPT algorithm. While the traditional IC MPPT approach requires a substantial amount of code and algorithmic steps to keep the PV module at the MPP, the proposed IC achieves the same process with a reduced number of lines of code, owing to its optimized algorithmic approach. By adjusting the duty cycle of the generated Pulse Width Modulation (PWM) signal, maximum power transfer from the PV module is attained during the operation of the battery charging process. The simulation model is configured and tested in Matlab/Simulink environment under different solar data (1000 W/m2, 500 W/m2, 800 W/m2) with constant temperature (25 °C). To validate the simulations, experimental studies are conducted using the developed rapid prototype with the 32-bit embedded microcontroller in the laboratory. According to the simulation and laboratory results, the maximum power tracking performance of the traditional method was found to be 95.51%, while the proposed method achieved a ratio of 96.59% with less steady-state oscillation. The average tracking efficiency has increased by 1.13%. The proposed IC tracks the MPP more accurately and provides maximum available power for battery charging at different solar radiations compared to the traditional IC approach. For low-powered electric devices, the proposed system can be used to provide a charging infrastructure solution.

PENGARUH RASIO PULLEY TERHADAP BEBAN MAKSIMAL PEMAKAIAN MESIN LISTRIK PORTABEL RAMAH LINGKUNGAN

Abstract -- Population growth can trigger an increase in the need for electrical energy, but this is not balanced with an increase in the supply of electricity, where the installed power capacity is fixed, while the needs of the community continue to increase and various supporting activities. Such conditions encourage the search and study of the use of new energy sources, which are renewable, environmentally friendly, and environmentally friendly which aim to produce electricity without any assistance from BBM and are expected to provide convenience in obtaining electricity. , by utilizing a 12 volt, 75 Ah battery / battery, a 12 volt dc alternator equipped with an inverter with a voltage of 1500 watts to convert DC current to AC, 500 watt ac driving dynamo, pulley with a ratio of 1,333 : 1 inch. The test was carried out with a variation of the pulley ratio of 1,333 : 1 inch, the maximum load material tested was using a 50 watt, 100 watt, 200 watt, 250 watt, 300 watt, 350 watt, 380 watt light bulb. Based on the test data using the maximum load with a pulley ratio of 1,333: 1 inch for 380 watts, the voltage drop can reach 11.4 volts more than the maximum load the portable electric machine does not work optimally. This causes the alternator to not be able to issue an electric voltage of more than 12 volts for the battery charging process, so that the inventor cannot work properly which results in a low inverter output voltage unable to provide electric current to the propulsion motor.

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.

Online capacity estimation of lithium-ion batteries based on convolutional self-attention

ABSTRACT It is of great significance to improve the safety, efficiency, and economy of lithium-ion batteries by improving the capacity estimation accuracy of lithium-ion batteries. In this paper, feature extraction and correlation analysis are carried out on the data of lithium-ion battery charging process, and the voltage curve of constant current charging stage is extracted. The difference characteristics between each cycle are used to describe the battery capacity, and these statistical characteristics are proved to be highly correlated with the battery capacity. Furthermore, an online estimation model of battery capacity based on convolution self-attention is established, and the above characteristics of constant current charging process and the battery capacity of the latest cycle are fused as input vectors of the model to realize online estimation of battery capacity. Finally, an open data set is used to verify the model experiment. The results show that the prediction error of MAE is 0.17% and that of RMSE is 0.22%.

Primary side control strategies for battery charging regulation in wireless power transfer systems for EV applications

AbstractResonant inductive‐based wireless power transfer (WPT) for battery charging has potential applications in electric vehicles (EVs). The EV battery charging process requires the regulation of both charging voltage and current. Duty ratio or frequency control is generally preferred to manage the power flow between the transmitter and receiver coils in the WPT system. In the case of WPT charging, misalignment between the coils and parameter variations are unavoidable issues that result in changes to the output power. Therefore, it is essential to control the power flow to maintain constant current (CC) and constant voltage (CV) modes during battery charging. To address these challenges, various primary‐side control techniques, such as asymmetric clamped mode (ACM), asymmetric duty cycle (ADC), and phase‐shift (PS) fixed frequency control strategies, have been proposed for WPT systems. This paper conducts a comparative analysis of these control methods, considering their output voltage ranges and their ability to maintain zero‐voltage switching (ZVS) for the entire control range. Furthermore, the paper presents a generalized design for reduced‐order small signal modelling, utilizing an extended describing function. The designed controller, based on small signal modelling, will undergo real‐time testing to evaluate its dynamic performance in the series‐series resonant converter.

Modular Design of DC-DC Converters for EV battery fast-charging

Electric vehicles (EV) are emerging as a viable and environment-friendly solution for the daily commute. However, for a broader utilization of the EV, their autonomy range has to be increased. A well dispersed charging network and a fast and efficient battery charger system becomes one important factor for the success of the EV. This paper addresses the challenges of developing such High Power chargers and proposes a modular, easily scalable and efficient implementation of a switch-mode DC-DC converter for performing the constant-current/ constant voltage battery charging process.

Adversarial learning for robust battery thermal runaway prognostic of electric vehicles

The thermal runaway risk in batteries is a critical issue affecting their safety. Although considerable research is dedicated towards the thermal runaway prognostic of batteries, a common challenge remains that how to reduce the false positive rate to enable practical real world application. To address this problem, this paper proposes a robust thermal runaway prognostic method for Li-ion batteries using a generative adversarial network. Specifically, the model utilizes the charging curves of batteries as features during training to reduce the randomness of sparse data and enhance the robustness of the model. A resampling algorithm is proposed to realize the standardized conversion of the charging curve to retain the complete charging information. By employing a one-class generative adversarial network, the method generates a detection model that provides a reference normal curve for the original battery charging process, detecting potential anomalies. Real accident data of electric vehicles that experienced thermal runaway and subsequent fires is used to verify the performance of the model. Experimental results show that the proposed method can identify all abnormal cells with a false positive rate of 1.75% before thermal runaway occurs, reducing 7.54% to 31.18% false positive rate compared to existing methods.

Variational quantum algorithm for ergotropy estimation in quantum many-body batteries

Quantum batteries are predicted to have the potential to outperform their classical counterparts and are therefore an important element in the development of quantum technologies. Of particular interest is the role of correlations in many-body quantum batteries and how these can affect the maximal work extraction, quantified by the ergotropy. In this paper we simulate the charging process and work extraction of many-body quantum batteries on noisy intermediate-scale quantum devices and devise the variational quantum ergotropy (VQErgo) algorithm, which finds the optimal unitary operation that maximizes work extraction from the battery. We test VQErgo by calculating the ergotropy of a many-body quantum battery undergoing transverse field Ising dynamics following a sudden quench. We investigate the battery for different system sizes and charging times and analyze the minimum number of ansatz circuit repetitions needed for the variational optimization using both ideal and noisy simulators. We also discuss how the growth of long-range correlations can hamper the accuracy of VQErgo in larger systems, requiring increased repetitions of the ansatz circuit to reduce error. Finally, we optimize part of the VQErgo algorithm and calculate the ergotropy on one of IBM's quantum devices. Published by the American Physical Society 2024

Perspective study on charge time measurement of long-term stored lithium-ion batteries used in electric-powered aircraft assessed and modelled by specific growth model with diffusion process backup

Electrical energy sources and batteries are currently in great demand. The demand is growing, and their use is on the rise in a variety of applications. In contrast, there are limited both production/manufacturing options and an effort to ensure more environmentally friendly acquisition, operation and use. In this article, we study, test, and model the charging process of Li-ION batteries. We study a set of long-term stored Li-ION batteries and compare the data and results with a set of new Li-ION batteries. The charging data are obtained experimentally by long-term testing, and the volume (of the data) is very large and statistically very representative. Modelling of the Li-ION battery charging process is based on specific growth model forms. Furthermore, the charging process is simulated using special forms of the diffusion process, specifically the Geometric Ornstein-Uhlenbeck process, which seems to be the most suitable for this purpose. The research is carried out with the aim to obtain the first passage time (FPT), i.e. the charge time of the battery. The first passage time is further studied with respect to the suitability of its expression using parametric probability distributions. We also acquired the numerical statistics of the charging time FPT as well as the respective parametric distribution. The FPT results obtained for long-term stored and new Li-ION batteries were compared using well-known and lesser-known statistical tests, such as the Kullback-Leibler divergence.

A Variational Problem for the Power Factor in Capacitance Charging Process

In this study, the solution of a variational problem that allows determining the input voltage form that provides the maximum power factor of the battery charging process in the RLC circuit and the change character of the charging current for constant R, L, C and charging time has been examined. The defined problem is obtained and solved in the form of a linear integro-differential equation. A quadratic voltage criterion is proposed to analyze the convergence of the power factor to its maximum value for various modes of charging the battery.