Capacity Degradation Of Lithium-ion Batteries Research Articles

Impact of low temperature exposure on lithium-ion batteries: A multi-scale study of performance degradation, predictive signals and underlying mechanisms

The rapid global expansion of electric vehicles and energy storage industries necessitates understanding lithium-ion battery performance under unconventional conditions, such as low temperature. This study investigates long-term capacity degradation of lithium-ion batteries after low temperature exposure subjected to various C-rate cycles. Findings reveal that low temperature exposure accelerates capacity degradation, especially with increased C-rates or longer exposure durations. A distinctive sunken region appears on the NMC H-stage peak as cycling progresses after low temperature exposure, indicating NMC particle damage and serving as a predictive signal for identifying low temperature exposure batteries. Electrochemical analyses and invasive detection indicate that low temperature exposure causes NMC particle cracking, leading to dead lithium generation. This dead lithium deposits on electrode surfaces, as well as the overconsumption of electrolyte, inhibiting reactions and reducing capacity. Additionally, dead lithium accumulates on separators, impeding ion transport and further accelerating capacity loss. NMC particle cracks develop faster after low temperature exposure due to stress concentration at internal slits. This stress concentration causes the maximum stress within the NMC particles to exceed tensile stress limits, resulting in rapid crack propagation. The internal cracks become larger with prolonged low temperature exposure, causing greater stress concentration and leading to faster crack propagation. Based on these insights, strategies from existing literature are discussed to mitigate the adverse impacts of low temperature exposure on lithium-ion battery performance and enhance the reliability and longevity of lithium-ion battery in extreme conditions.

Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions

The capacity degradation of lithium-ion batteries occurs both during storage and operational usage. This paper investigates the capacity degradation of lithium-ion batteries during storage (calendar ageing) by analysing the interplay of storage temperature, state-of-charge (SOC), and time. Leveraging the machine learning techniques of Gaussian process regression and extreme gradient boosting (XGBoost), a predictive model is developed to characterize the degradation patterns. The study includes a sensitivity analysis of stress factors to identify their relative impact on degradation. The insights gained from this analysis are utilized to recommend optimal storage conditions, offering practical guidance for enhancing the durability and performance of lithium-ion batteries in real-world applications.

Capacity Degradation Assessment of Lithium-Ion Battery Considering Coupling Effects of Calendar and Cycling Aging

Capacity degradation of lithium-ion batteries largely determines the cost, performance and environmental impact of various products such as renewable energy production systems, portable electronics, and electric vehicles. Degradation assessment is thus of great importance for prognostic and health management of batteries, which ensures a stable production and operation. There are typically two leading sources contributing to the degradation of a lithium-ion battery, namely, cycling aging during charge/discharge cycles and calendar aging during idle states. However, most existing studies on degradation assessment either only consider a single source or ignore the coupling of these two sources, which makes the evaluation inefficient. A common observation in practice is that a battery tends to degrade faster when it experiences charge/discharge cycles continuously for a longer time. To capture this feature, this paper introduces the concept of the cumulative uninterrupted cycling duration (CUCD), which is defined as the consecutive operation duration since a battery ends the idle state. The CUCD can also help to model the coupling between the calendar and cycling aging. This paper then proposes to model the drift rate of cycling aging as a function of the CUCD and the functional form is determined with the monotonic spline. We estimate the model parameters by maximizing the likelihood function. Hypothesis testings toward the significance of the coupling between two aging sources and the monotonicity of the drift rate function under cycling aging are also provided. The effectiveness and the superiority of the model are validated using numerical and real case studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study aims to characterize the degradation of lithium-ion battery simultaneously considering calendar and cycling agings and the coupling effect. Unlike previous research that only supplied experimental results to qualitatively illustrate the existence of the coupling effect between these two aging sources, this study provides procedures to quantitatively test the significance of such an effect. In addition, previous research ignored the coupling effect in the degradation modeling as it is difficult to capture, this study propose the concept of CUCD to characterize it. The CUCD is also introduced as a stress factor for cycling aging, which is based on the fact that a battery will degrade faster when it experiences charge/discharge cycle continuously for a longer time. Hypothesis testing is also provided to illustrate this monotonicity. To deal with another challenge that the expert knowledge of the functional form between cycling aging and CUCD is unavailable except for the monotonicity, a nonparametric method with the shape-restricted spline is adapted. The estimation procedures for the model parameters are also presented.

Performance of an automotive reversible CO2 heat pump system during periodic frosting-defrosting cycles

Electric vehicles (EVs) have seen rapid growth in the global market share due to the demand for low-emission commuting. However, EVs face the challenge of range anxiety in cold weather due to the capacity degradation of lithium-ion batteries and the increased demands of cabin and battery heating. Heat pump (HP) technology improves the heating efficiency compared to resistive heating, but in cold and humid weather, frost can grow on the outdoor evaporator and reduce the efficiency of the HP system. In this paper, experiments are carried out to assess the impacts of ambient temperature and relative humidity (RH) on the capacity and efficiency of a transcritical CO2 HP system during the continuous frosting-defrosting cycles under some challenging operating conditions (ambient temperature below 0 °C and RH over 80%). Moreover, the mass of frost accumulated during the frosting period, and that of water retained, drained, and vaporized during the defrosting period are measured and analyzed. The results show that when the ambient temperature decreases from 0 to −10 °C, the peak value of the heating capacity in the first frosting or HP mode period reduces from 4.85 to 3.54 kW, and the coefficient of performance (COPhp) drops from 1.61 to 1.47, but the operating time of the first frosting period significantly increases from 32 to 108 min. Similarly, lowering RH from 90% to 80% at 0 °C also helps to extend the operating time of the frosting period in one frosting-defrosting cycle from 32 to 84 min. In addition, with the current commonly used defrost initiation criterion, which is the air-side pressure drop across the outdoor evaporator increasing by five times, the heating capacity and system efficiency drop by less than 10% at the end of one frosting period. This indicates the potential for a longer operating time of the HP system which could reduce the undesirable impact on the passengers’ comfort during the reversed cycle defrosting.

Collaborative training of deep neural networks for the lithium-ion battery aging prediction with federated learning

Accurate and reliable prediction of the future capacity degradation of lithium-ion batteries is crucial for their application in electric vehicles. Recent publications have highlighted the effectiveness of deep learning, in particular, in generating precise forecasts regarding the aging patterns. However, large quantities of training data covering various aging behaviors are required to train such models effectively. Collecting such a large database centrally is not feasible due to privacy and data communication restrictions of data owners, such as testing facilities or fleet operators. Federated learning provides a solution to this open issue. A framework, which incorporates federated learning into the training of a data-based battery aging model, is presented in this paper. The benefit of federated learning is that even data owners with sensible information can participate in a collaborative model training, since the model training is only conducted locally and all the data remains local and does not have to be disclosed. Thus, more data owners are likely to participate in this collaborative training. This will improve the prediction performance due to the enlarged dataset that can be utilized for the model training. This work shows that the prediction accuracy of the model trained with federated learning is only slightly worse than the prediction results obtained by the ideal case in which all aging data is stored in a central database. A sensitivity analysis is presented to prove the robustness of federated learning even if the datasets between participating data owners are highly imbalanced or exhibit different aging behaviors. Within exemplary scenarios, it is shown that individual data holders can reduce their prediction errors from MAPEmean=7.07% to MAPEmean=0.91% by participating in the proposed federated learning-based framework.

Nonlinear aging knee-point prediction for lithium-ion batteries faced with different application scenarios

The capacity degradation of lithium-ion batteries (LIBs) will accelerate after long-term cycling, showing nonlinear aging features, which not only shortens the long-term life of LIBs, but also seriously endangers their safety. In this paper, by introducing the concept of nonlinear aging degree, a knee-point identification method based on the maximum distance method is established, and the nonlinear aging behavior of LIBs is identified and marked, so as to know whether the nonlinear aging phenomenon has occurred. Furthermore, two knee-point prediction methods have been proposed and compared. The direct knee-point prediction method based on stacked long short-term memory (S-LSTM) neural network and sliding window method is proposed for the scenarios of battery development, early performance evaluation and online application. For scenarios such as echelon utilization and post-safety evaluation, an indirect knee-point prediction method combining capacity prediction and knee-point identification algorithm is proposed. Through multi-dimensional comparison of the two methods, the strengths and weaknesses of their applicable scenarios are analyzed. Our work has guiding significance for finding the ideal replacement opportunity of LIBs in different scenarios, so that the user can be reminded whether to maintain or replace the battery, which greatly reduces the risk of battery safety problems.

Probabilistic State of Health and Remaining Useful Life Prediction for Li-Ion Batteries

Lithium-ion batteries are widely used in electric vehicles due to their high specific energy value. The continuous charge/discharge cycles pf batteries, determining a change of their State of Health (SoH) and a reduction of their Remaining Useful Life (RUL). The relevant literature describes a variety of methods for the prognostic of battery degradation and RUL estimation, based on electrochemical models or on statistical and artificial intelligence techniques. However, the capacity degradation of lithium-ion batteries depends on many uncertain parameters that are suitable to be estimated and modeled within a probabilistic framework. Therefore, the development of new probabilistic methodologies for the prognostic of lithium-ion batteries can be useful and effective. In this paper, probabilistic models based on time series (AutoRegressive Integrated Moving Average model with eXogenous predictors, (ARIMAX)) and regression approaches (Linear Quantile Regression (LQR), Bootstrap Multiple Linear Regression (B-MLR) and Bayesian Bootstrap Multiple Linear Regression (BB-MLR)) are developed and compared for this purpose. All the considered models are specifically suited up to exploit data coming from Accelerated Degradation Tests (ADTs), and developed under two different approaches (i.e., non-differentiation and differentiation of the target time series) to predict SoH and RUL. Moreover, a dedicated procedure to extract a single, point value from the probabilistic predictions is presented to let the models work also in deterministic scenarios. The performance and the comparison between the proposals and several benchmarks taken from the literature are carried out using data from publicly available databases, confirming the validity and the accuracy of the proposed solutions.

Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM

The remaining useful life (RUL) of a lithium-ion battery is directly related to the safety and reliability of the electric system powered by a lithium-ion battery. Accurate prediction of RUL can ensure timely replacement and maintenance of the batteries of the power supply system, and avoid potential safety hazards in the lithium-ion battery power supply system. In order to solve the problem that the prediction accuracy of the RUL of lithium-ion batteries is reduced due to the local capacity recovery phenomenon in the process of the capacity degradation of lithium-ion batteries, a prediction model based on the combination of the whale optimization algorithm (WOA)-variational mode decomposition (VMD) and short-term memory neural network (LSTM) was proposed. First, WOA was used to optimize the VMD parameters, so that the WOA-VMD could fully decompose the capacity signal of the lithium-ion battery and separate the dual component with global attenuation trend and a series of fluctuating components representing the capacity recovery from the capacity signal of the lithium-ion battery. Then, LSTML was used to predict the dual component and fluctuation components, so that LSTM could avoid the interference of the capacity recovery to the prediction. Finally, the RUL prediction results were obtained by stacking and reconstructing the component prediction results. The experimental results show that WOA-VMD-LSTM can effectively improve the prediction accuracy of the RUL of lithium-ion batteries. The average cycle error was one cycle, the average RMSE was less than 0.69%, and the average MAPE was less than 0.43%.

Fast Modeling of the Capacity Degradation of Lithium-Ion Batteries via a Conditional Temporal Convolutional Encoder–Decoder

Recently, deep neural networks have been successfully applied to battery degradation modeling, and onboard graphic processing units can potentially be used to host these models. However, most existing degradation models rely on recursive operations, which are slow on hardware optimized for vectorized computations. This article proposes a two-encoder, one-decoder conditional temporal convolutional network that uses no recursive operations, thus allowing for the conditional modeling of battery degradation and improving upon the prediction speed of long short-term memory encoder–decoder by a factor of up to 70 for long tests. Datasets containing 115 batteries from multiple sources and 195 000 steps are used to verify the proposed model. The data are augmented by randomly selecting the prediction starting point during training. The proposed model achieves an average prediction error of less than 4% of the maximum available capacity based on large datasets. In addition, prediction results and convolution weight analysis indicate that the model can learn the temporal and conditional dynamics of typical lithium-ion batteries and is robust to the selection of the prediction starting point. We further demonstrate a prediction under hypothetical future conditions and a Monte Carlo prediction under unknown future conditions, and the degradation trends are accurately predicted.

Destructive effects of transitional metal ions on interfacial film of carbon anode for lithium-ion batteries

The dissolution, migration, and deposition of transition metal (TM) ions are the main reasons for the capacity degradation of lithium-ion batteries, which significantly affect their comprehensive electrochemical performance and safety. In this work, the effects of different TM ions (Mn2+, Co2+, and Ni2+) on the electrochemical performance of lithium/mesocarbon microbeads half-cell were compared, and the mechanism was discussed. It is found that Mn2+ ions inhibit the decomposition of lithium salt and promote the decomposition of solvents, resulting in the crack of electrode material and solid electrolyte interface (SEI) film and the increase of capacity degradation. In contrast, Ni2+ ions promote the decomposition of lithium salts to a certain extent, thus inhibiting the excessive damage to the SEI film and electrode material. This study can provide theoretical support and research direction for the design of the electrolytes that can alleviate the damage of TM ions to the anode and the SEI film.

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.

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.

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.

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.

Modeling long-term capacity degradation of lithium-ion batteries

Capacity degradation of lithium-ion batteries under long-term cyclic aging is modeled via a flexible sigmoidal-type regression set-up, where the regression parameters can be interpreted. Different approaches known from the literature are discussed and compared with the new proposal. Statistical procedures, such as parameter estimation, confidence and prediction intervals are presented and applied to real data. The long-term capacity degradation model may be applied in second-life scenarios of batteries. Using some prior information or training data on the complete degradation path, the model can be fitted satisfactorily even if only short-term degradation data is available. The training data may arise from a single battery.

An enhanced copula-based method for data-driven prognostics considering insufficient training units

Data-driven based prognostics typically requires sufficient run-to-failure training units in order to learn degradation characteristics of engineering components or products without the need of understanding the physics-based degradation mechanisms. With insufficient training units, however, the model form learned from the training units may be inaccurate, which could result in large remaining useful life (RUL) prediction errors for the actual test units. A recently proposed copula-based sampling method does not assume any degradation model form, but builds a set of statistical correlation models for the RUL prediction, which shows high RUL prediction accuracy with sufficient training units. This paper proposes an enhanced copula-based method to address the instability issue of the method when dealing with insufficient training units. In particular, the sampling part for the RUL prediction in the original time domain is replaced by an analytical formulation in the standard uniform space with multiple copulas so that the stability issue can be addressed. Furthermore, a simplified version of the RUL prediction is proposed with extremely high efficiency. Effectiveness of the enhanced method is demonstrated by two case studies: one for resistance degradation of lead-acid batteries and another for capacity degradation of lithium-ion batteries.

A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries

Coulombic efficiency, as an important battery parameter, is highly related to the loss of lithium inventory, which is the dominant aging factor for lithium-ion batteries. In this paper, a semi-empirical model is derived from this relationship to capture the capacity degradation of lithium-ion batteries. The coulombic efficiency-based model effectively captures the convex degradation trend of lithium iron phosphate batteries and presents better fitting performance than the existing square-root-of-time model. To evaluate the proposed model, a battery cycle life experiment was designed, in which the subjects were continuously cycled under a federal urban driving schedule to simulate real-life battery usage. To perform online battery health estimation and prognostics, a particle filtering framework incorporating the proposed model was constructed to update the model parameters regularly with periodically measured data. Remaining useful life of the battery was then predicted by extrapolating the models with renewed parameters. The experimental results indicated that the proposed prognostic method can provide higher prediction accuracy than the existing square-root-of-time model.

A Method for Interval Prediction of Satellite Battery State of Health Based on Sample Entropy

Satellites need batteries to provide energy when operating in shadow regions, and lithium-ion batteries have become the batteries of choice for most satellites due to their high energy density, low self-discharge rate, and long cycle life. When a satellite battery is working in outer space, its capacity will gradually decrease as the number of cycles increases, and a certain degree of capacity recovery will occur. Due to the excellent mapping relationship between the discharge cutoff voltage and the capacity degradation of lithium-ion batteries and the fact that the sample entropy (SampEn) can sensitively capture local fluctuations, such as the recovery effect during lithium-ion battery capacity degradation, a method for interval prediction of the satellite battery state of health (SOH) based on SampEn was proposed. This method adopts a neural network model based on lower upper bound estimation (LUBE). The method uses the discharge cutoff voltage and the discharge voltage SampEn as the inputs and the battery SOH as the output for the neural network model. To improve the prediction interval coverage and reduce the prediction interval width, especially considering that the lower bound of the interval prediction often determines whether the satellite battery output power reaches the warning threshold, a modified comprehensive indicator function, the coverage width-based criterion (CWC), was constructed. Additionally, based on the nondifferentiability of this indicator function, a simulated annealing algorithm was used to optimize the neural network; at the same time, the optimal values of the interval coverage and interval width were taken into account, resulting in the lower bound of the prediction interval being closer to the actual value. Finally, test data from a NASA #18 battery were used to validate, analyze and verify the interval prediction algorithm proposed in this paper. The results were compared with those obtained from a support vector machine (SVM)-based interval prediction method.

Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network

Lithium-ion batteries play critical roles in many electronic devices. It is necessary to develop a reliable and accurate remaining useful life (RUL) prediction approach to provide timely maintenance or replacement of battery systems. A novel RUL prediction approach based on Long Short-term Memory (LSTM) Recurrent Neural Network (RNN) is proposed in this paper. LSTM is able to capture long-term dependencies and model sequential data among the capacity degradation of lithium-ion batteries. The advantages of our proposed method include: 1) obtaining high prediction accuracy without accurate physics-based model or expertise and 2) decreasing the cumulation errors by multi-step ahead prediction each time, while traditional RUL method predicts one-step ahead once and then uses the current estimated value to predict next one, which causes cumulation errors increased. The Center for Advanced Life Cycle Engineering (CALCE) battery datasets are used to demonstrate the effectiveness of the proposed method. The results show that, compared with echo state networks (ESN), the proposed method has higher accuracy, more stable and reliable performance for lithium-ion batteries RUL prediction.