Lithium-ion Battery Degradation Research Articles

Optimal Configuration and Operation for User-Side Energy Storage Considering Lithium-Ion Battery Degradation

Optimal Configuration and Operation for User-Side Energy Storage Considering Lithium-Ion Battery Degradation

Analysis on cycle degradation of lithium-ion secondary batteries by applying current interruption methods

Analysis on cycle degradation of lithium-ion secondary batteries by applying current interruption methods

A review – exploring the performance degradation mechanisms of LiCoO2 cathodes at high voltage conditions and some optimizing strategies

The factors affecting the capacity degradation of lithium-ion batteries with LiCoO2 as the cathode material at high voltage are discussed, and then doping and surface coating strategies are proposed as corresponding solutions.

An Online Fade Capacity Estimation of Lithium-Ion Battery Using a New Health Indicator Based Only on a Short Period of the Charging Voltage Profile

Currently, the most popular health indicator used to assess the degradation of lithium-ion batteries (LIBs) is the state-of-health (SOH). This indicator is necessary to ensure the safety, degradation management, and good operation of the battery, for example, the correct estimate of the state-of-charge (SOC). In this paper, a new health indicator is proposed as an alternative to the use of the SOH because it has a high correlation and similarity with the SOH and has the advantage that it can be calculated and/or estimated very easily. The new health indicator, named “Degradation Speed Ratio” (DSR) is calculated with variables directly measured (voltage and time), and it is not necessary to spend any time on the total charging cycle, therefore reducing waiting times about 84%. In addition, due to its high correlation with capacity, it is a significant marker of battery end-of-life (EOL). In this study, the obtained DSR and a Gaussian process regression (GPR) model were used to estimate the lost capacity and to compare it with existing models in the literature. The accuracy achieved using the DSR indicator as input is very high. Similarly, the results of a multilayer perceptron neural network (MLPNN) model are shown using the new indicator (DSR) as input to estimate the degradation. The sensitivity and precision of this NN model with unknown data are also very high.

Revealing the critical effect of solid electrolyte interphase on the deposition and detriment of Co(Ⅱ) ions to graphite anode

“Dissolution, migration, and deposition” of transition metal ions (TMIs) result in capacity degradation of lithium-ion batteries (LIBs). Understanding such detrimental mechanism of TMIs is critical to the development of LIBs with long cycle life. In most previous works, TMIs were directly introduced into the electrolyte to investigate such a detrimental mechanism. In these cases, the TMIs are deposited directly on the fresh anode surface. However, in the practical battery system, the TMIs are deposited on the anode covered with solid electrolyte interphase (SEI) film. Whether the pre-presence of SEI film on anode surface influences the deposition and detriment of TMIs is unclear. In this work, the deposition of Co element on graphite anode with and without SEI film were systematically studied. The results clearly show that, in comparison with that of fresh graphite (SEI-free), the presence of SEI film aggravates the deposition of Co ions due to the Li+–Co2+ ion exchange between the SEI and Co2+-containing electrolyte without the driving of the electric field, leading to faster capacity fading of graphite anode. Therefore, how to regulate electrolytes and film-forming additives to design the components of SEI and prevent its exchange with TMIs, is a crucial way to inhibit the deposition and detriment of TMIs on graphite anode.

Evaluation of the influence of high-power charging cycles on the capacity degradation of lithium-ion batteries under various temperatures

High-power-charging (HPC) behavior and extreme ambient temperature not only pose security risks on the operation of lithium-ion batteries but also lead to capacity degradation. Exploring the degradation mechanism under these two conditions is very important for safe and rational use of lithium-ion batteries. To investigate the influence of various charging-current rates on the battery-capacity degradation in a wide temperature range, a cycle-aging test is carried out. Then, the effects of HPC on the capacity degradation at various temperatures are analyzed and discussed using incremental capacity analysis and electrochemical impedance spectroscopy. The analysis results show that a large number of lithium ions accelerate the deintercalation when the HPC cycle rate exceeds 3 C, making the solid electrolyte interphase at the negative surface unstable and vulnerable to destruction, which results in irreversible consumption of active lithium. In addition, the decomposition of electrolyte is significantly promoted when the HPC temperature is more than 30°C, resulting in accelerated consumption of electrode materials and active lithium, which are the main reasons for the capacity degradation of lithium-ion batteries during HPC under various temperatures.

The abrupt degradation of LiFePO4/graphite battery induced by electrode inhomogeneity

The abrupt degradation of lithium-ion battery (LIB) that seems to be normal in the early hundreds of cycles is a quite common phenomenon. Based on the LiFePO4/graphite battery, a series of methods are used to systematically analyze such a phenomenon that may bring reliability and safety risks to the LIB system. For differential voltage analysis, the shift of the characteristic peak can effectively reflect the lithium loss, which can be an early indicator to warn accelerating decay. The capacity of the cathode and anode can be restored almost completely by reassembling coin cells, proving that the electrode structure degradation is not the key factor for the abrupt failure. The X-ray photoelectron and Raman spectra only denote the different lithiation state rather than significant structural transformation after abrupt decay. The statistical data of coin cells of cathodes and Raman mapping of anodes show that the failed battery has worse compositional homogeneity at either macro or micro scale, which is the most likely mechanism for the abrupt decay. The presented results suggest that the electrode homogeneity is a key factor to make a battery system with robust cycle performance.

Degradation of Lithium-Ion Batteries in an Electric Transport Complex

The article provides an overview and comparative analysis of various types of batteries, including the most modern type—lithium-ion batteries. Currently, lithium-ion batteries (LIB) are widely used in electrical complexes and systems, including as a traction battery for electric vehicles. Increasing the service life of the storage devices used today is an important scientific and technical problem due to their rapid wear and tear and high cost. This article discusses the main approaches and methods for researching the LIB resource. First of all, a detailed analysis of the causes of degradation was carried out and the processes occurring in lithium-ion batteries during charging, discharging, resting and difficult operating conditions were established. Then, the main factors influencing the service life are determined: charging and discharging currents, self-discharge current, temperature, number of cycles, discharge depth, operating range of charge level, etc. when simulating a real motion process. The work considers the battery management systems (BMS) that take into account and compensate for the influence of the factors considered. In the conclusion, the positive and negative characteristics of the presented methods of scientific research of the residual life of LIB are given and recommendations are given for the choice of practical solutions to engineers and designers of batteries. The work also analyzed various operating cycles of electric transport, including heavy forced modes, extreme operating modes (when the amount of discharge and discharge of batteries is greater than the nominal value) and their effect on the degradation of lithium-ion batteries.

Mathematical Models for the Performance Degradation of Lithium-Ion Batteries with Different Status of Charge (SOC) in Long-Term High Temperature Storage

Mathematical models to evaluate and predict the performance degradation of lithium-ion batteries (LIBs) with different status of charge (SOC) in long-term high-temperature storage which are also applicable for setting rational storage conditions (temperature, SOC, and time) of LIBs were established. Parameters including voltage drop (Delta V), reversible capacity (RC) loss, and internal impedance (IMP) increase of LIBs under different temperature (60 °C, 45 °C, and 25°C) are used to allow the model to clarify its function. According to the results obtained from commercial 18650 cylindrical batteries with LiNi0.33Co0.33Mn0.33O2 cathode, the mathematical relationship between Delta V and storage days (x) is fitted into a simple formula: Delta V = m.In(x)-n, and similarly, RC loss = m′.exp (n′.x) and IMP increase = m″.xn″ can also be acquired. In these formulas, m, n, m′, n′, m′′ and n′′ are constants when temperature and SOC are fixed. If only the temperature is fixed, the value of these constants can be derived into a function with SOC (y), respectively, while further plugging the function into the calculation formula of Delta V, RC loss, and IMP increase, respectively, allows the mathematical models to be set up.

Rapid determination of lithium-ion battery degradation: High C-rate LAM and calculated limiting LLI

Herein, incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries, which inevitably exhibit capacity fading caused by multiple mechanisms during charge/discharge cycling. Because battery degradation modes are complex, the simple output of capacity fading does not yield any useful data in that respect. Although IC and DV curves obtained under restricted conditions (<0.1C, 25 °C) were applied in non-invasive analysis for accurate observation of degradation symptoms, a facile, rapid diagnostic approach without intricate, complex calculations is critical in on-board applications. Herein, LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite pouch cells were cycled at 4 and 6C and the degradation characteristics, i.e., loss of active materials (LAM) and loss of lithium inventory (LLI), were parameterized using the IC-DV curves. During the incremental current cycling, the initial steep LAM and LLI slopes underwent gradual transitions to gentle states and revealed the gap between low- and high-current measurements. A quantitative comparison of LAM at high and low C-rate showed that a ICHC revealed the relative amount of available reaction region limited by cell polarization. However, this did not provide a direct relationship for estimating the LAM at a low C-rate. Conversely, the limiting LLI, which is calculated at a C-rate approaching 0, was obtained by extrapolating the LLI through more than two points measured at high C-rate, and therefore, the LLI at 0.1C was accurately determined using rapid cycling.

Modeling degradation of lithium-ion batteries considering cell-to-cell variations

Battery packs exhibit both intrinsic cell-to-cell variations and spatio-temporal cell-to-cell differences in temperature and other stress factors, shaping the evolution of the degradation paths of the cells. To account for these variations and differences in degradation or cell spreading, we propose a statistical approach for modeling the degradation of lithium-ion batteries that utilizes a 3-parameter non-homogeneous Gamma process. This approach predicts the capacity fade or time-to-failure for any battery architecture and adjusts the distributions of the fitted degradation data of the cells with acceleration factors. At the pack level, cells are modeled with compositions of Gamma-distributed variables for configurations in parallel and series. Actual values of the capacity fade or time-to-failure at different thermal conditions are compared with predicted values, showing relative errors in the range 1 – 12%. We also present a methodology for estimating the minimum number of cells required for modeling the evolution of the spreading and degradation paths by analyzing the effect of the sample size on estimating the degradation for different sets of cells. This sampling strategy is particularly useful for reducing the computational cost of running simulations required for designing battery packs, battery management systems and battery thermal management systems.

Differentiating Degradation Characteristics in Lithium-Ion Cells

Lithium-ion batteries are prevalent in everyday usage and ensuring they are used efficiently is of paramount concern. The degradation of lithium-ion batteries under cycling can affect various components in the cells. Understanding the degradation characteristics of the cell can aid in ensuring longer lifetime through optimized usage. Long-term automated EIS data collection has been used as a non-destructive tool to track the impedance changes within the cells over time due to repeated cycling. Separating the different degradation factors contributed by the graphite and NMC electrodes in a non-invasive way is still a challenge. In this work, EIS measurements of half-cells dissembled from full cells are shown to provide further insight into cell degradation characteristics. By using this methodology to differentiate the degradation at different electrodes, new materials can be easily screened for their viability in next generation batteries. The methodology also helps estimate the degradation changes with cycling and provides useful information for further understanding the degradation mechanism.

Remaining useful life prediction for lithium-ion battery based on the particle filter considering temperature effect

Temperature would affect the degradation process of lithium-ion battery. Therefore, considering the influence of temperature, this paper proposes method to predict the Remaining useful life (RUL) of the lithium-ion battery based on Arrhenius and double exponential model. And update the parameter by particle filter. Firstly, we establish a capacity degradation model with considering the influence of temperature, which is based on Arrhenius model and double exponential model. And then, in order to obtain the initial value of the parameters, we process the fitted the lithium-ion battery degradation data. Next, we use the particle filter (PF) algorithm to update the model parameters to realize the capacity estimation and the RUL prediction. Finally, according the experiment, we prove that the accuracy of the method proposed in this paper is better than that the method without considering the influence of temperature change. The result shows that the lithium-ion battery capacity degradation model established in this paper has great potential in the RUL prediction of the lithium-ion battery.

Multimodal Analysis of Low Cobalt Cathode Degradation in Lithium-Ion Batteries

The further development of the lithium-ion battery (LIB) for transportation applications requires increased energy density paired with the capability for fast charging. This development needs to keep pace with the increasing market share projection for electric vehicles.Next generation electrodes that can increase the cell energy density and capacity, have been studied and developed throughout the past years by changing active materials and microstructural geometries. These new materials present a range of challenges covering areas of performance, reliability, and sustainability. Among these challenges, battery degradation limits the performance and reliability of current LIBs. Furthermore, degradation may hinder advancement of batteries that support development of a more sustainable and equitable global energy infrastructure.The following study will connect performance degradation with morphological and chemical changes within higher capacity low cobalt LIB cathodes’ microstructure while cycled under different cut-off voltage and operating temperatures. To correlate these factors with the electrode degradation a combined set of electrochemical, and ex situ imaging characterization techniques are applied. The applied techniques include, scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray absorption near edge structure (XANES) imaging.Degradation of low cobalt cathodes was assessed under different cut-off voltage and operating temperatures. Higher cycling temperature was found to accelerate degradation related to rock-salt phase formation, electrolyte decomposition and cathode microstructural particle breakage. Degradation of battery performance reveals evolution of both active materials and the supporting phases of the cathodes. Further understanding of electrode degradation mechanisms will enable design of better LIBs to advance cleaner energy storage technologies to reduce our dependence on fossil fuel.

Fast Simulations of Lithium-Ion Battery Degradation

We propose an algorithm to speed up physics-based battery lifetime simulations by one to two orders of magnitude compared to the state-of-the-art. This algorithm makes use of the difference between the “fast” timescale of battery cycling and the “slow” timescale of battery degradation by adaptively selecting and simulating representative cycles, and hence requires fewer cycle simulations to simulate the entire lifetime. This enables interactions with the simulations on a human timescale [1], and therefore opens the possibility for much faster and more accurate model development, testing, and comparison with experimental data.Physics-based electrochemical models, based on porous electrode theory [2], are very useful for understanding battery degradation, and hence predicting lifetime, since they can directly simulate a wide range of degradation mechanisms at various scales. Many modeling studies investigate the degradation for a single cycle, and there have been significant advances in making simulations of individual cycles as efficient as possible [3,4,5]. However, relatively few simulations of the entire battery life have been conducted. This may be due to the complexity of these models, both in terms of implementation effort and computational time. Previous work has addressed the implementation challenges [6], and this work addresses the computational time.Our proposed multiple scale algorithm simulates degradation over the entire lifetime of a battery in just a few seconds. The algorithm is implemented in the open-source battery modeling package PyBaMM [6] and thus can easily be used to simulate any (current or future) aging model implemented there. Alternatively, it can easily be incorporated in other simulation frameworks. We demonstrate the algorithm in a few case studies, including degradation mechanisms such as SEI formation, lithium plating, and loss of active material. In each case we achieve one to two order of magnitude speed-ups compared to the baseline. Since the speed-up combines multiplicatively with speed-ups in simulation of a single cycle, the entire lifetime of the battery can be simulated in just a few seconds.

Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries

Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life.

An efficient and independent modeling method for lithium-ion battery degradation

An efficient and independent modeling method for lithium-ion battery degradation

Laboratory facility development for studying the heavy charge and discharge modes effect on the degradation of lithium-ion batteries

The article is devoted to the laboratory facility development for studying the lithium-ion batteries (LIB) characteristics in severe operating conditions, typical of electric vehicles. The electrical schemes of charging and discharging modules for 18650 batteries are shown. A feature of the modules is the ability to set any form of current (change the amplitude and frequency of the signal, change the average value, change the direction of current flow). The operating principle of the control program is described, which allows independent testing of three batteries using a single microcontroller. A feature of the modules is the ability to set any form of current (change the amplitude and frequency of the signal, change the average value, change the direction of current flow). The operating principle of the control program is described, which allows independent testing of three batteries using a single microcontroller. The created laboratory facility is presented, which provides the possibility of simultaneous testing of up to 30 batteries with different test programs. An example of the laboratory facility operation in the form of a current, voltage and temperature oscillogram fragment of the experiments is given.

유연 리튬 폴리머 이차전지의 기계적 변형에 따른 성능 및 안전성 평가

Purpose: Flexible lithium-ion batteries have been used as energy sources for wearable and flexible devices. However, their reliability in mechanical stress environments must be guaranteed for them to be stably used in these applications. In this study, the effect of mechanical fatigue on the performance and safety of a lithium polymer battery, which is a flexible lithium-ion battery, was investigated.<BR>Methods: Mechanical fatigue tests were conducted under bending, torsion, and wrinkling (i.e., a combination of bending and torsion) modes at various fatigue rates and strains. The capacity degradation of these fatigued flexible lithium-ion batteries was evaluated using the charge–discharge test. Their safety was assessed using various environmental and electrical misuse tests in accordance with existing standards.<BR>Results: The capacity degradation of all fatigued batteries was greater than that of the undeformed battery. In particular, the torsion and wrinkling stress had a greater effect on capacity degradation. The environmental and electrical misuse tests revealed that leakage, explosion, and ignition phenomena were not caused by mechanical fatigue.<BR>Conclusion: Mechanical fatigue stress can be concluded to be the main factor affecting the performance degradation of flexible lithium-ion batteries. Furthermore, further studies are required to determine the root causes of this significant performance degradation.

A method for capacity prediction of lithium-ion batteries under small sample conditions

Accurate life prediction of lithium-ion battery is very important for the safe operation of battery system. At present, the data-driven life prediction method is an effective method. However, it is difficult to obtain full life cycle data of long-life lithium batteries, which leads to low accuracy of prediction results. In addition, the degradation of lithium-ion batteries has different trends in different stages, the commonly used methods are insufficient to describe global time variables which make it difficult to adapt to changes in different stages of lithium-ion battery capacity degradation. To solve the above problems, the paper proposes a deep adaptive continuous time-varying cascade network based on extreme learning machines (CTC-ELM) under the condition of small samples. First, a virtual sample generation method based on multi-population differential evolution is proposed, which uses multi-distribution overall trend diffusion technology to adaptively determine the virtual sample range, and combines with the improved differential evolution algorithm to achieve small sample data amplification. Then, a new prediction network with CTC-ELM is constructed. Finally, it is verified on different data sets. Experiments show that the method proposed can effectively expand the sample set of lithium-ion batteries and achieve high accuracy in the estimation of lithium-ion battery capacity.