Charge Curves Research Articles

A Rapid Depolarization Charge and Discharge Technique for Lithium Ion Batteries

There is a serious diffusion-polarization effect in the working process of lithium-ion batteries, resulting in a large rebound of battery voltage after charge/discharge. The measurement of the open circuit voltage (OCV) of the battery was interfered by the existence of this polarization, while accuracy of battery state of charge estimating based on open circuit voltage and state of charge (SoC) curve is reduced. At the same time, measurement of AC impedance spectrum was interfered by the fluctuating voltage. In this work battery was rapidly depolarized through pulse charge-discharge in discharging procedure. Compared with the standard depolarization method by reducing the discharge rate (0.05C), the discharge test time was reduced by 72.5% and polarization effect was weakened at low SoC. There was no significant difference in results for this charging technology compared with EIS curve tested after conventional charging and reduced test time by 42%.

Влияние варисторного эффекта и контактных явлений на характеристики твердотельных литий-ионных аккумуляторов с полупроводниковыми электродами

The results of measuring the charge-discharge characteristics of solid-state thin-film lithium-ion batteries with a nanocomposite anode based on a-Si(Al) solid solution are presented. The charging characteristics of batteries have a feature in the form of a step on the smooth branch of the charge curve. It is shown that the appearance of the step is associated with the compensation of a-Si(Al) and the change from hole to electron conductivity due to lithiation of the electrode during charging. As a result of lithiation, the electron over-barrier current becomes the main component of the charging current. To maintain a galvanostatic charge mode, the potentiostat increases the voltage by the height of the potential barrier, which leads to the appearance of a step on the charging curve. The impedance of a solid-state thin-film lithium-ion battery of the LiCoO2-LiPON-Si@O@Al electrochemical system was measured in the temperature range from -20°С to 50°С. A structural model of the accumulator is proposed and the parameters of the structural elements of the model are calculated which provide the best fit for experimental Nyquist diagrams. The obtained values of the electrodes’ resistivity are orders of magnitude higher than the results of direct measurements and data from literary sources. According to the IV-characteristics obtained by cyclic voltammetry the high resistance of the electrodes is due to the metal-semiconductor contact and the varistor effect of the electrode material. The results obtained make significant adjustments to the interpretation of the impedance spectra and structural models of solid-state lithium-ion batteries based on semiconductor materials

Chemical capacitance measurements reveal the impact of oxygen vacancies on the charge curve of LiNi0.5Mn1.5O4-δ thin films.

The level of oxygen deficiency δ in high-voltage spinels of the composition LiNi0.5Mn1.5O4-δ (LNMO) significantly influences the thermodynamic and kinetic properties of the material, ultimately affecting the cell performance of the corresponding lithium-ion batteries. This study presents a comprehensive defect chemical analysis of LNMO thin films with oxygen vacancy concentrations of 2.4% and 0.53%, focusing particularly on the oxygen vacancy regime around 4 V versus Li+/Li. A set of electrochemical properties is extracted from impedance measurements as a function of state-of-charge for the full tetrahedral-site regime (3.8 to 4.9 V versus Li+/Li). A defect chemical model (Brouwer diagram) is derived from the data, providing a coherent explanation for all important trends of the electrochemical properties and charge curve. Highly resolved chemical capacitance measurements allow a refining of the defect model for the oxygen vacancy regime, showing that a high level of oxygen deficiency not only impacts the amount of redox active Mn3+/4+, but also promotes the trapping of electrons in proximity to an oxygen vacancy. The resulting stabilisation of Mn3+ thereby mitigates the voltage reduction in the oxygen vacancy regime. These findings offer valuable insights into the complex influence of oxygen deficiency on the performance of lithium-ion batteries based on LNMO.

A Novel Approach for Open-Circuit Voltage and Entropic Heat Coefficient Characterization

We propose a novel approach to evaluate the open circuit potential and the entropic heat coefficient vs. the state of charge curve via charge/discharge tests run at different temperatures. In particular, we drive the battery cell with an extremely small current thus moving along with different state of charges in a quasi-static condition. We present a model to properly define the measurand and we discuss the assumption made to the method application. Finally, we analyse the method accuracy and its reliability through a comparison with other techniques commonly employed in the literature to this purpose.

Adjusting coherence length of expanded graphite by self-activation and its electrochemical implication in potassium ion battery

As a cost-effective and well-developed material, graphite is a promising anode material for potassium ion battery due to its high capacity, high tap density, high conductivity and plateau-typed charge curve characteristic. However, graphite suffers from severe capacity fading and poor rate capability. The related research mainstream focuses on electrolyte or binder, aiming at a more robust passivation layer. In contrast, it is not common to transform or modify graphite directly, due to its rigid structure and inert property, which is resistant to gentle chemical treatment. Adjusting coherence length of graphite and its effect on cyclability and rate ability has not been studied yet. Herein, we come up with a strategy of crippling the crystallinity of graphite by strong oxidation first, followed by adjusting coherence length under different pyrolysis temperatures. In the battery test, the expanded graphite pyrolyzed at 750 °C delivers a reversible capacity of 303 mAh/g at a current density of 10 mA/g and 105 mAh/g at a current density of 1000 mA/g. In the long cycling test, a capacity of 160 mAh/g can be maintained after 1000 cycles, with a capacity decay of only 0.02% per cycle. Based on the analysis between coherence length and battery performance, we find that decreasing the coherence length along ab plane contributes to improving rate capability, from both intercalation and pseudo capacitance perspective. Moreover, decreasing the coherence length along c axis contributes to the cyclability.

Recycling spent graphite anodes into a graphite/graphene oxide composite via plasma solution treatment for reuse in lithium-ion batteries

Lithium-ion batteries production increase ultimately leads to toxic waste. Today, recycling of the spent lithium-ion batteries becomes extremely important with much attention paid to recycling of cathode materials. Graphite recovery receives less attention due to low economic benefits, high costs of material cleaning and low cycling stability. In fact, regenerating graphite with low-cost technology is of great importance to solve the problem of recycling spent graphite and environmental contamination. This paper presents a variant of graphite recycling using low-cost plasma solution treatment (less than 28 kWh·kggraphite−1), leading to a capacitive and powerful material. The results were confirmed by cycling of cells based on recycled graphite for 500 cycles at 0.3 A·g−1 with excellent stability and high capacity (392 mAh·g−1), which is greater than the graphite theoretical capacity due to the formation of graphene oxide from the surface graphite layers. The treatment duration does not affect the interplanar distance of the obtained graphites. Long cycling shows a change in the shape of the charge curves with a graphene fraction increase. For all samples the kinetic characteristics were also evaluated: the dependence of the diffusion coefficient, SEI resistance and charge transfer resistance on the potential and the intercalation rate constant.

Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation

Accurate capacity estimation of a lithium-ion battery is crucial to predict its state of health and remaining useful life, determining the reliability and safety of the battery system directly. However, existing data-driven methods usually are based on full battery charging or discharging curves, which are hard to meet in practice because of range anxieties and arbitrary charging behaviors of users. Meanwhile, the lack of sufficient datasets for model training limits the performance of a data-driven model. In this paper, a capacity estimation method for lithium-ion batteries based on an optimized charging voltage section and virtual sample generation is proposed. In the method, characteristics of full and sectional capacity degradation are analyzed to evaluate the feasibility of capacity estimation using a section of charging voltage curve. The extraction of health indicator and optimization of battery voltage range are introduced. A multi-distribution global trend diffusion algorithm is used to generate virtual samples. Based on a long short-term memory neural network, battery capacity estimation model is constructed. Experimental verifications on NASA, CALCE, and Oxford battery datasets are carried out. Results show that the proposed method not only reduces the length of required voltage section for capacity estimation, but also improves the estimation accuracy in comparison to utilization of the full charging curve. Under the operating conditions of NASA B6, CALCE CS36 and Oxford C3, the root mean square error (RMSE) of battery capacity estimation is lower than 1.48%.

Charging a electrochemical double layer supercapacitor exhibiting a distribution of relaxation times

A charging electrochemical double layer supercapacitor can usually be described by a single capacitance and a single resistance in parallel, wherein the latter represents the ohmic losses. Such an ideal behavior may occur if the supercapacitor consists of self-similar porous carbon micro- and nanostructures. However, if the electrochemical double layer supercapacitor consists of a sequence of slices with different relaxation times, a strong deviation from ideal charging curves may occur. Here, it is demonstrated how such charging curves can be interpreted in terms of a distribution of relaxation times. It is found that in the presence of a broad distribution of charge transfer resistances, the voltage initially appears to increase faster than normal during galvanostatic charging. Care should be taken to avoid misinterpretation of the capacitance under such circumstances.

The role of mild and hard anodization regimes of iron oxide nanotubes in the photoelectrochemical performance

• Fe 2 O 3 nanotubes (NTs) prepared by electrochemical anodization for solar water splitting applications. • Two regimes of anodization, Mild and Hard, observed in NTs geometrical features ( D p , D int , W , L ) and anodization curves ( j ( t ) and Q ( t )) • Relation between charge curves Q ( t ), nanotubes length ( L ), and introducing porosity ( P ) as a parametric, established to estimate L by an adapted Classical Faraday’s Law. • Estimated L from adapted Classical Faradaýs Law in excellent agreement with the experimental data from SEM. Self-ordered hematite (α-Fe 2 O 3 ) nanotubes have been intensively studied as the ideal geometrical configuration for photoelectrochemical water splitting. In this work, iron oxide nanotubes were produced by a fast electrochemical anodization at different applied voltages (10 to 100 V) and studied their impact on the photoelectrochemical performance of the photoelectrodes. The anodization voltage highly affects the nanotubes length, pore diameter, interpore distance and porosity that ultimately led to different solar performances. The interplay between oxidation and dissolution rates in the electrochemical process at different voltages led to observe two distinct self-ordering NTs growth regimes: (I) mild-anodization regime from 20-60 V, where both NTs linear growth and geometrical structural ordering are observed; (II) hard-anodization regime from 70-100 V, with non-linear decay of nanotubes thickness combined with a decrease of structural ordering. The highest photocurrent of ∼0.5 mA·cm -2 was obtained for sample anodized at 20 V with a porosity of 30 %. A relation between the anodization charge curves Q ( t ), nanotubes length ( L ) and porosity ( P ) was established to estimate L using Q ( t ) curves. L was found to be in excellent agreement with the experimental data from both scanning electron microscopy (SEM) and anodization curves.

The Development of a Macro Model for a 26650-Type Battery Cell

Abstract The curves for charge and discharge experiments demonstrate specific temperature and current behaviour for the 26650-type battery cell. For that reason, it is extremely important the heat impact on electrochemical reactions and on transfer actions. The base for model parameterization is built by isothermal simulations at different temperatures. In this article, there are presented modifications of thermodynamics and kinetics to accomplish a good connection with the results of the processes concerning charge and discharge curves for different C-rates and various background heat.

Charging Mechanism of Surface Flashover on Polyimide Under Electron Beam Irradiation

The surface flashover along the interface of solid dielectric and vacuum seriously restricts the improvement of the voltage level of vacuum electrical equipment. Surface charging is an important factor in the process of surface flashover, but its mechanism is still unclear. In this article, in situ surface potential measurement is used to measure the surface potential of polyimide radiated by electron beams with different energies, and the charging curves of polyimide at different temperatures are obtained. The experimental results show that the surface potential of polyimide is close to zero after the low-energy electron beam radiation. When the electron beam energy exceeds the critical value, the surface potential is approximately proportional to the electron beam energy. The critical values of different materials are different, such as 1.18 keV for polyimide and 1.86 keV for Teflon. The surface charging characteristic is related to sample thickness, secondary electron yield (SEY), conductivity, permittivity, and so on.

A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm

This paper proposes an improved power management strategy for plug-in hybrid electric vehicles (PHEVs). This strategy consists of a long-term power management approach and a short-term intelligent controller. In the long-term power management, a chaotic improved generalized particle swarm optimization technique (CIGPSO) is used to optimize the motor and diesel engine torques. In order to reduce the computation time, a five-mode rule-based control system is employed, where the CIGPSO estimates the optimal values of the motor and engine torques in a hybrid mode, which manages the power between the motor and engine in accordance with a multi objective cost function. This cost function reduces fuel usage as well as the drawn current from the battery with taking into account the process of the battery aging. Moreover, the CIGPSO is able to obtain the state of charge (SoC) curve of the battery during the charging and discharging of the battery throughout the trip. The short-term controller is designed using an interval type-2 Takagi-Sugeno-Kang fuzzy (IT2TSK) algorithm which depends on human experts to overcome the uncertainties of the diverse driving conditions. Lyapunov stability theory for the online controller is achieved. The proposed strategy reduces the energy consumption compared to other strategies such as generalized PSO and improved multi-objective PSO algorithms. The simulation results are performed using real data for the engine, motor, and battery to demonstrate the flexibility, viability and effectiveness of the proposed approach with comparative results. Index terms—Plug-in hybrid electric vehicle, power management strategy, chaotic improved generalized particle swarm optimization algorithm, rule-based control, interval type-2 Takagi-Sugeno-Kang fuzzy algorithm.

Mass and Charge Transport in Li1-δCoO2 Thin Films-A Complete Set of Properties and Its Defect Chemical Interpretation.

Lithium insertion materials are an essential class of mixed ionic and electronic conductors, and their electrochemical properties depend on the resistive and capacitive interplay of ions and electrons. However, complete sets of the corresponding elementary material parameters, that is, composition-dependent ionic and electronic conductivity, chemical capacitance, and charge-transfer resistance, are rarely reported for lithium-ion battery electrode materials. Moreover, the interpretation of these properties from a defect chemical point of view is not very common. In this work, the impedance of sputtered Li1-δCoO2 thin films is analyzed to extract the fundamental electrochemical properties as a function of state-of-charge (SOC). Within the accessible SOC range, the charge transfer resistance and ionic conductivity vary by more than 1 order of magnitude. The chemical capacitance determined from impedance spectra agrees excellently with the differential capacitance from charge/discharge curves, and, in the dilute regime, even matches the absolute values predicted by defect thermodynamics. The evolution of lithium diffusivity along the charge curve is deconvoluted into the separate contributions of ionic conductivity and chemical capacitance. Finally, we apply the principles of defect chemistry to evaluate the observed trends in terms of lithium activity and point defect concentrations and provide a tentative defect model that is consistent with our results. The consistency of impedance measurements, cycling data, and thermodynamic theory highlights the key role of the chemical capacitance as a powerful material descriptor and emphasizes the relevance of defect chemical concepts for all lithium insertion electrode materials.

Rapid synchronous state-of-health diagnosis of membrane electrode assemblies in fuel cell stacks

Consistency evaluation and degradation diagnosis of membrane electrode assemblies (MEAs) in fuel cell stacks are critical issues for fuel cell lifetime extension and commercialization. This study aims to develop a high-efficiency, high-precision, and synchronous state-of-health (SOH) diagnosis methodology for MEAs at the stack level. The micro-current excitation (MCE) method based on multiple excitations is used to evaluate the MEA inconsistency of a 22-cell commercial stack, involving hydrogen crossover, short-circuit resistance, electrochemical surface area, and double-layer capacitance. High precision is demonstrated by the consistency in the membrane-related parameters and the difference in the electrode-related parameters between the cathode and anode tests. Herein, an efficient single excitation and natural discharging (SEND) analytical method based on the excitation-discharging-combined equivalent circuit model is originally proposed. The accuracy of SEND can be demonstrated by comparing its results with the standard results of the MCE, which fit the baseline y = x with high linearity and identical evaluation of inconsistency. Moreover, the stability of the SEND is proved by its independence from the excitation current, which is reflected by the presence of tiny standard deviations. In addition, the bifurcation feature in the integrated net charge curves of the excitation and natural discharging processes proves the irreversibility of partial oxidation reactions in the high-potential region of excitation. The SEND significantly increases the testing efficiency using only a single excitation. It has promising prospects for the rapid consistency-based MEA screening, degradation evaluation, and recombination of MEAs.

Comparison of battery electromotive-force measurement and modelling approaches

In this paper, different approaches for obtaining a battery Electromotive-Force (EMF) model, also referred to as Open-Circuit Voltage, are compared by experimentally measuring them and by subsequently applying different post-processing strategies, thus resulting in different EMF model realisations. The considered methods include GITT, interpolation of charge and discharge curves and extrapolation to zero current. The experiments are performed for two cells, namely a Lithium Nickel–Manganese–Cobalt (NMC) and a Lithium iron-Phosphate (LFP) cell. The accuracy of the EMF models is compared visually, as well as quantitatively in terms of voltage prediction accuracy using a linear parameter-varying overpotential model and SoC estimation accuracy using an extended Kalman filter. The results show that different methods excel at different performance indicators, for instance most accurate or least experiment time, but overall extrapolation to zero current has the best all-round performance.

Solvent control of the nucleation-induced voltage hysteresis in Li-rich LiFePO4 materials

Phase transformations during the reversible ion insertion into metal-ion battery materials have been extensively studied due to the importance of understanding the mechanisms of (de)lithiation and controlling the battery performance. In this work, we demonstrate that the electrolyte, which is typically neglected in the studies of phase transformation mechanisms, plays an essential role in governing the kinetics of nucleation. We compare nucleation rates for Li-rich LiFePO4 materials in carbonate and aqueous electrolytes and show that the nucleation rate is an order of magnitude higher in aqueous electrolyte. Consequently, this leads to a decrease in the hysteresis of the charge and discharge curves in an aqueous medium by 30%, i.e., a significant increase in the energy efficiency of cells with an aqueous electrolyte. We provide a detailed characterization of the lithium-ion intercalation kinetics and formulate assumptions about the possible reasons for the solvent dependence of the nucleation kinetics.

State‐of‐health estimation for lithium‐ion batteries based on partial charging segment and stacking model fusion

AbstractState‐of‐health (SOH) estimation is essential for evaluating the aging process of lithium‐ion batteries, which can effectively guarantee the steady application of the battery system. Most existing prediction approaches apply a single model or a single feature to achieve SOH estimation based on the entire charging curve. In this paper, a multifeature‐based stacked ensemble learning framework is proposed for SOH prediction using partial charging curves. Firstly, combined with the range of state‐of‐charge (SOC) commonly used in the actual operation of vehicles, the charging segment is effectively intercepted through the mapping correlation between the SOC and the terminal voltage. Then, five relevant features characterizing the battery health status are extracted from multiple data, such as temperature, voltage, and incremental capacity profiles. Finally, a two‐level stacking ensemble framework is developed to fuse several individual estimation methods for higher SOH accuracy. To validate the performance of the proposed method, the Oxford University data set and the NASA data set are deployed for comparison experiments, and the results reveal the superior precision and robustness of the developed model in SOH estimation.

(Digital Presentation) Prediction of an in-Plane Anomalous Current Using Numerical Simulation and Machine Learning

Even normally shipped batteries may contain disturbing factors such as very small amounts of foreign matter or structural inhomogeneity, which can cause serious accidents such as thermal runaway or explosions. Since dismantling and identifying the cause is time-consuming and costly, nondestructive techniques for identifying the cause are required. A method for detecting abnormal current distribution by magnetic field inverse analysis has been proposed as one such method, but it is difficult to directly elucidate the factors that cause the abnormal current distribution itself. In this study, we first focused on the effect of different separator structures and analyzed the in-plane directional current distribution using machine learning in order to construct a non-destructive model for estimating the causes.A one-dimensional model based on porous electrode theory [1] was used. Constant current charging, homogeneous electrode layer structure, uniform Li concentration in active material particles, and isothermal conditions were assumed. The electrode layer was assumed to be a porous body consisting of active material, electrolyte, and auxiliary materials (conductivity assistant and binder). Using the previously reported structural information and physical properties [2], [3], Case 1 was a coin cell using LiNi1/3Co1/3Mn1/3O2 as the cathode active material, and Case 2 was a laminated cell using LiCoO2 as the cathode active material. The anode active material was graphite and the electrolyte was 1.0 M LiPF6 solution in EC・DEC solvent for both Case 1 and 2. The degree of flexure of the cathode and anode was determined using the formulas from previous studies [4]. Cross-sectional images of the actual biaxially oriented separator were obtained using a focused ion beam scanning electron microscope, and the three-dimensional simulated structure was reproduced based on these images (Sep. A). Similarly, a nonwoven fabric structure (Sep. B) and a foam structure (Sep. C) were also reproduced.Since the collector foils are connected in-plane, the potentials at both ends of the cell must be uniform. Under this constraint, the ion current distribution can be estimated depending on the in-plane structure distribution of the separator. In this study, the correlation between the local porosity (ε) of the separator and the local ion current (denoted as C-rate) was determined using a nonlinear regression method called Support Vector Regression (SVR) [5]. 100 points were taken randomly from 0.4~1.0 and 5~10 for ε and C-rate, respectively, and used as explanatory variables. Each charging curve was compared with the charging curve at the in-plane mean value of (ε, C) = (0.545, 7.5), and the RMSE (root mean square error) of the voltage was calculated as the objective variable.The in-plane reaction distribution was obtained from the voltage error predicted from ε and C-rate by SVR and the porosity distribution of the separator. The mesh size was 40 nm/pixel. For each separator, the difference between high and low reaction profiles was more clearly observed in Case 2. The reaction overvoltage distribution at a certain point in Case 2 was acquired, and it was confirmed that the reaction was concentrated near the separator of the negative electrode. This also made it possible to estimate the Li deposition point, which is the starting point of degradation.Using machine learning to analyze the in-plane current distribution, it was found that the current distribution differs depending on the electrode conditions even for the same separator. We will use this technology to develop a technique for identifying factors based on the current distribution obtained by nondestructive measurement.

Eco-friendly aerosol multicoated silicon anodes in lithium-ion batteries

• An eco-friendly aerosol-assisted chemical deposition (AACD) has been established. • A silicon anode is fabricated by the AACD and is used in lithium-ion batteries. • The features of charge curves are understood through univariate linear regression. Silicon is a promising alternative anode material in lithium-ion batteries as it has a higher theoretical specific capacity than the commercial graphite-based anodes. Within this work, a new multicoated composite material was fabricated through a simple, non-vacuum, and economical aerosol-assisted chemical deposition. This eco-friendly preparation method was developed for the first time with the sodium alginate for a nano-silicon composite. Such a multicoated composite generates excellent electrochemical performance. Our results suggest that the anode delivers a specific capacity of ∼760 mAh g −1 at 0.36 A g −1 after 300 discharge/charge cycles.

State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression

State-of-Health (SOH) estimation is crucial for the safety and reliability of battery-based applications. Data-driven methods have shown their promising potential in battery SOH estimation, yet creating a high-performance model with a compact structure is still a grand challenge. This paper focuses on constructing the elastic feature to formulate auto-configurable Gaussian Process Regression (GPR) to address this issue. To eliminate the impacts of the kernels on GPR, an evolutionary framework is designed to organize the kernel configuration. Meanwhile, a hierarchical feature construction strategy reduces the complexity of the extracted feature according to the geometry of the charging curve. Experiments on three battery datasets demonstrate the effectiveness of the proposed method, demonstrating the practical value of the proposed method for the battery management system (BMS) to construct feature more feasible, and to provide the optimal kernel configuration automatically.