Battery Capacity Degradation Research Articles

Distributed secondary frequency control and state of charge (SoC) balance for droop-controlled battery energy storage systems (BESSs) with a unified SoC relative variation rate

The state of charge (SoC) balance, power sharing, and frequency restoration are common control objectives of battery energy storage systems. However, the SoC balance scheme induced by the power allocation through existing droop controllers can cause the capacity parameters of battery cells to be unequal to the droop coefficient, which is the result of battery capacity degradation. Under this limitation, previous capacity-based droop controllers and the secondary controllers are no longer suitable to address the imprecise power sharing and frequency restoration caused by this problem. Therefore, a power allocation scheme based on the current SoC level ratio is designed to induce a new droop controller and ensure that the SoC simultaneously drops to 0. In order to restore frequency in a distributed manner and obtain the SoC level ratio, a distributed nominal frequency controller, SoC average estimator, and power-sharing controller are designed based on multi-agent systems in both asymptotic and finite-time manners. In the asymptotic scheme, the steady-state performance of the SoC estimator is adjustable, and power sharing and frequency restoration are zero errors. In the finite-time scheme, the influence of parameters on convergence time is well analyzed, and some conservative calculation methods are provided. Several time-domain simulation examples are designed on an improved IEEE-57 bus system to verify the distribution of asymptotic and finite-time schemes.

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.

Revealing Relationship Between In Situ Impedance and Lithium Plating Onset Based on Lithium–Graphite Half-Cells

Lithium plating may occur during charging, especially at high rates or overcharging conditions for lithium-ion batteries (LIBs), which would cause battery capacity degradation and even trigger thermal runaway. Thus, it is essential to detect lithium plating onset during the charging processes. Electrochemical impedance can reveal the dynamic electrode properties of the battery, which is promising for use in battery management systems for the online detection of lithium plating onset. In this article, the impedance at 1 Hz is measured during the over-discharge and fast discharge processes using lithium–graphite half-cells. For half-cells, the variation in graphite electrode potential vs. Li/Li+ during discharging is directly recorded. An equivalent circuit model is proposed and adopted to estimate the real lithium plating reaction overpotential, which is deemed the thermodynamic indicator of lithium plating and is used as validation for the detection of lithium plating onset. Through the auxiliary validation of the estimation of lithium plating overpotential and the shape of incremental capacity curves, the relationship between impedance changes at specific frequency and the lithium plating onset is revealed. The results lay a good foundation for proposing the online diagnostic method of lithium plating onset based on the in situ impedance.

Remaining Service Life Prediction of Lithium-Ion Batteries Based on Randomly Perturbed Traceless Particle Filtering

To address the limitations in the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, stemming from model accuracy, particle degradation, and insufficient diversity in the particle filter (PF) algorithm, this paper proposes a battery RUL prediction method utilizing a randomly perturbed unscented particle filter (RP-UPF) algorithm, based on the constructed battery capacity degradation model. The method utilizes evaluation metrics adjusted R-squared (Radj2) and the Akaike Information Criterion (AIC) to select the battery capacity decline model C5 with a higher goodness of fit. The initial values for constructing the C5 model are obtained using the relevance vector machine (RVM) and nonlinear least squares methods. Based on the constructed battery capacity decline model C5, the RP-UPF algorithm is employed to estimate the posterior parameters and iteratively approach the true battery capacity decline curve, thereby predicting the battery’s RUL. The research results indicate that, using battery B0005 as an example and starting the prediction from the 50th cycle, the RUL prediction results obtained with the RP-UPF algorithm demonstrate reductions in absolute error, relative error, and probability density function (PDF) width of 2%, 2.71%, and 10%, respectively, compared to the PF algorithm. Similar conclusions were drawn for batteries B0006 and B0018. Under the constructed battery capacity degradation model C5, the RP-UPF algorithm shows higher prediction accuracy for battery RUL and a narrower PDF range compared to the PF algorithm. This approach effectively addresses the issue of particle weight degradation in the PF algorithm, providing a more valuable reference for battery RUL prediction.

State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model

The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. Health Indicators (HIs) are first extracted from the battery’s charging curve, and correlation analysis is conducted on seven indirect HIs using Pearson and Spearman coefficients. To reduce dimensionality and eliminate redundancy, Principal Component Analysis (PCA) is applied, with the principal component contributing over 94% used as a fusion HI to represent battery capacity degradation. PSO is then employed to optimize the weights (ε) between the input and hidden layers, as well as the hidden layer bias (u) in the ELM, treating these parameters as particles in the PSO framework. This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. The proposed PSO-ELM model demonstrates superior accuracy in SOH prediction compared with ELM and other methods. Experimental results show that the mean absolute error (MAE) is 0.0034, the mean absolute percentage error (MAPE) is 0.467%, and the root mean square error (RMSE) is 0.0043, providing a valuable reference for battery safety and reliability assessments.

Aging mechanism of Ni-rich cathode-based lithium-ion batteries: Focusing on upper cut-off voltages

Ni-rich cathode-based lithium-ion batteries are subject to certain limitations in electric vehicle applications due to their cycle life. Therefore, this study aims to investigate the aging mechanism of Ni-rich cathode-based lithium-ion batteries with different upper cut-off voltages, providing ideas for improving their cycle life. The change in the cathode lattice during the charging and discharging process obtained by in-situ XRD shows that the H2-H3 phase transition exacerbates the battery capacity degradation. In addition, the capacity degradation caused by lattice strain induced by the rapid release of anisotropic stress accumulation (ASA) in the H2 region cannot be ignored as well. After lowering the upper cut-off voltage, the surplus lithium in the cathode plays a pivotal role in avoiding the above rapid battery degradation, driven by the advantages of its ability to stabilize lattice structure and reduce the degree of Li/Ni cation mixing. Meanwhile, the low upper cut-off voltage effectively avoids side reactions such as electrolyte decomposition and transition metal dissolution. The above results would contribute to slowing down battery degradation, which would provide strong support for the design of high energy density and long-life lithium-ion batteries.

Co-optimization of virtual power plants and distribution grids: Emphasizing flexible resource aggregation and battery capacity degradation

Coordination between virtual power plants and active distribution networks is crucial as these plants increasingly aggregate distributed resources within the power system. This study introduces a bilevel optimization framework to coordinate the scheduling of multiple virtual power plants and an active distribution network using pricing strategies for energy and reserves. The upper-level optimization minimizes total operating costs by incorporating bidding plans of the active distribution network in various markets, its interactions with multiple virtual power plants, and operational costs. The lower-level optimization maximizes revenue for each virtual power plant, considering both battery capacity degradation costs and operational costs of various resources. To facilitate solutions, this research developed a nonlinear transformation method for modeling capacity degradation. Based on the dispatching strategy from the virtual power plant, this study uses the squared difference between energy consumption of equipment for controllable loads and the strategy as the optimization target to derive control strategies for two equipment types. Results show that the framework effectively integrates dispatch and control strategies without oversimplifying the system model, proving its applicability in various scenarios with diverse resource compositions.

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.

A hybrid battery degradation model combining arrhenius equation and neural network for capacity prediction under time-varying operating conditions

Capacity degradation modeling and remaining useful life (RUL) prediction play a pivotal role in enhancing the safety of battery energy storage systems. Lithium-ion batteries utilized in the systems are subjected to intricate and variable operating conditions, including temperature, charging/discharging rates and depth, which result in time-varying degradation rates. However, most existing models for battery capacity prediction have the limitation of ignoring the quantitative effects of time-varying operating conditions on degradation. Therefore, a hybrid battery capacity degradation model combining Arrhenius equation and lightweight Transformer is constructed. The effects of operating conditions on capacity degradation rates are introduced by an improved Arrhenius degradation equation. The coordinate ascent method embedded with Newton descent is used for estimating the model parameters. Then, the unknown mapping relationships between the degradation statistical features extracted from monitoring data and the degradation factors are captured by the light-weight Transformer. Finally, a novel framework consisting of future capacity and RUL predicting is constructed based on sequential importance resampling (SIR). For the purpose of verification, the cyclic charge-discharge experiments of eight battery cells under two distinct operating profiles are designed and conducted. Compared with other models, the proposed model achieves the best prognostics performance on the tested cells.

Optimization of liquid cooling plate considering coupling effects of heat generation and aging characteristics in power batteries

The heat generation and aging characteristics of power batteries exhibit a strong coupling relationship, and thus designing liquid cooling plates (LCPs) requires considering both aspects to achieve optimal thermal management. In this study, an electric-thermal-aging model (ETAM) correlating internal resistance, capacity, and temperature was developed. Based on this model, the effects of a serpentine LCP on the heat generation and aging characteristics of battery pack under different operating conditions were investigated using a combined simulation of zero-dimensional numerical model and three-dimensional CFD methods. Furthermore, the LCP channel widths in the length and width directions and the number of channels were optimized using orthogonal tests, artificial neural networks, and genetic algorithms, aiming to minimize battery capacity degradation. Results showed that increasing the mass flow rate of the coolant reduced the maximum temperature, temperature difference, capacity loss, and aging inconsistency of the battery pack. Conversely, reducing the coolant inlet temperature could decrease the maximum temperature and capacity loss but increased the temperature difference and aging inconsistency of the battery pack. The orthogonal test results indicated that the number of channels had the greatest impact on the heat generation and aging characteristics of the battery pack, followed by the channel width in the length direction, and the channel width in the width direction had the least impact. After optimization, the maximum temperature of the battery pack decreased by 0.57 %, the maximum temperature difference increased by 1.41 %, and the average capacity degradation decreased by 0.20 %. Moreover, with the increasing mass flow rate, the pressure drop reduction improved from 27.90 % to 33.90 %. This study may provide guidance for the design of LCPs that take battery aging into account.

TPANet: A novel triple parallel attention network approach for remaining useful life prediction of lithium-ion batteries

Accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) is important for proper equipment operation. Among the numerous existing studies on battery research, data-driven approaches have gained significant attention because they obviate the need for complex chemical and physical modeling of battery processes. However, in most current data-driven methods, the sliding window size values are determined empirically, which affects both the time required for model prediction and the prediction accuracy. To address this issue, this paper proposes a two-stage prediction method for battery capacity aging trajectories. In the first stage, a false nearest neighbor (FNN) technique is utilized to infer the sliding window size of the battery, reducing the error associated with manually selecting the sliding window size. In the second stage, a novel triple parallel attention network (TPANet) based on convolutional neural networks (CNNs) and attention units is developed, which extracts the input battery capacity degradation features through a parallel mechanism. The introduction of attention units in each parallel branch allows the model to enhance the capture of capacity regeneration phenomena as well as long-term dependence on input data. The proposed approach is validated on two publicly available datasets as well as a battery developed by ourselves. Diverse simulation results demonstrate the ability of the proposed approach to accurately predict the RUL of LIBs in a short time with broad generalization capabilities.

Charge and discharge strategies of lithium-ion battery based on electrochemical-mechanical-thermal coupling aging model

The capacity degradation is unfavorable to the electrochemical performance and cycle life of lithium-ion batteries, but the systematic and comprehensive analysis of capacity loss mechanism, and the related improvement measures are still lacking. Considering the aging mechanism of solid electrolyte interphases (SEI) growth, lithium plating, active material loss, and electrolyte oxidation, an electrochemical-mechanical-thermal coupling aging model is developed to investigate the lithium-ion battery capacity degradation. The results show that as the charge and discharge rates increase, all degradation losses of the battery get serious. The loss of positive active material is more sensitive to the discharge rate. The lithium plating loss is more susceptible to the charging rate. The increased charge cut-off voltage and the reduced discharge cut-off voltage both accelerate the battery aging. The charge cut-off voltage plays great roles in the electrolyte oxidation, loss of negative active material, and loss of lithium plating, while the discharge cut-off voltage greatly influences the loss of positive active material. Finally, the battery charging and discharging process is optimized and analyzed to obtain better anti-aging and safety performance. By clarifying the degradation mechanism and proposing effective measures, it is of great benefit to the design and operation of battery management system.

Reduced-order reconstruction of discrete grey forecasting model and its application

Discrete grey forecasting models based on an accumulative operator have been widely used in many practical fields. With the development of grey forecasting models, it is a problem to be solved to further analyze internal mechanisms and unify the structures. This paper aims to reconstruct the model from a perspective of sequence characteristics and simplify the modeling steps under the condition of ensuring the accuracy of the model. First, this paper analyzes dynamic sequence evolution hidden and mines relationship between the structure and original sequence features contained in discrete grey forecasting model. Then, the reconstruction is carried out to prove the equivalence and quantitative relation between reduced-order model and original model. Under order recursive estimation, new parameters are addressed. Finally, theoretical correctness is verified by large-scale numerical simulation. Moreover, the reduced-order model is applied for prediction on the peak of battery incremental capacity and capacity degradation. Results show the effectiveness and superior prediction performance of the reduced-order model, where MAPEs of grey forecasting models have controlled under 4%.

Multi-element synergistic doping enhances high-voltage performance of LiCoO2 via stabilizing internal and surface structures

With the escalating commercialization of 5 G technology and the constant emergence of novel electronic devices, the demand for energy density of LiCoO2 cathode material has been on a consistent rise. Boosting the upper cut-off voltage is an essential approach for enhancing the energy density. While the high-voltage environment facilitates adverse side reactions like irreversible phase transition and electrolyte degradation, thereby accelerating battery capacity degradation. Herein, a straightforward and efficient high-temperature solid-phase strategy is utilized and the elements Ba, Mg, Ga, and Ti are selected for trace doping. Our findings reveal positive synergistic effects among these elements. Mg and Ga significantly suppress the arising of oxygen ligand holes. The distinct distribution of Mg and Ba balances and improves the electronic conductivity inside the elementary particles. Additionally, a triple control system of Ba, Mg, and Ga highlights the important role of Ti. Advanced characterization methods confirm that this approach reduces the particle radius and enhances rapid charging and discharging capabilities of the cathode material. What's more, it effectively suppresses harmful phase transitions and side reactions, maintaining the structure stability. Within the range of 3–4.6 V, the discharge specific capacity reaches 188 mAh/g after 100 cycles, with a capacity retention of 86 %. Furthermore, it demonstrates outstanding rate performance, achieving a specific capacity of 104 mAh/g at 10 C. This improved electrochemical performance underscores the benefits of the synergistic strategy utilizing Ba, Mg, Ga, and Ti, providing a novel approach for developing high-performance LiCoO2 materials.

Unsupervised feature extraction for lithium-ion battery electrochemical impedance spectroscopy and capacity estimation using deep learning method

In this paper, we propose an unsupervised feature extraction technique using a variational autoencoder and generative adversarial network (VAEGAN) that automatically extracts meaningful latent variables from the electrochemical impedance spectra (EIS) of lithium-ion (Li-ion) batteries. These variables accurately reflect the degree of Li-ion battery degradation and are closely related to its capacity. By analyzing the latent variables, it was found that the network can learn the effect of Li-ion battery capacity degradation on the EIS. The extracted latent variables are then used as input features for Gaussian Process Regression (GPR) to estimate the capacity of Li-ion batteries under uneven usage and with unknown historical data. The results show that the capacity prediction method proposed in this paper can significantly reduce prediction errors compared to the current state-of-the-art methods. The method not only provides a more reliable capacity estimation but is also robust to highly noisy EIS data, making it more suitable for practical application scenarios.

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.

A Novel Model for Battery Optimal Sizing in Microgrid Planning Considering Battery Capacity Degradation Process and Thermal Impact

A Novel Model for Battery Optimal Sizing in Microgrid Planning Considering Battery Capacity Degradation Process and Thermal Impact

Comprehensive testing technology for new energy vehicle power batteries based on improved particle swarm optimization

As the new energy industry continues to progress, the health management of power batteries has become the key to ensuring the performance and safety of automobiles. Therefore, accurately predicting battery capacity decline is particularly important. A battery capacity degradation prediction model combining unscented particle filtering, particle swarm optimization, and SVR is constructed. It optimizes regression parameters through the introduced optimization strategy. Unscented particle filtering is used to improve particle swarm optimization and battery detection model. The study tested four various models of lithium-ion batteries. The model predicted a mean square error of 0.0011 for battery 5, 0.0007 for battery 6, 0.0022 for battery 7, and 0.0013 for battery 18. In the prediction of different battery types, the mean square error of the NIMH battery was reduced by 0.0008 compared with the particle swarm optimization-support vector regression algorithm, and by 0.0005 compared with the unscented particle filtering-regression vector regression algorithm. The mean square error of lithium-iron phosphate battery was reduced by 0.0008 and 0.0004 respectively compared with comparison models. The mean square error value of lithium titanate battery was reduced by 0.0007 and 0.0003 respectively in the research model compared with comparison models. It improves the prediction accuracy in lithium-ion batteries. Its application in battery health management can provide important technical support for improving battery performance and extending service cycles. The proposed method can be used for battery monitoring and management of power grid energy storage system. By accurately predicting the capacity decline of battery, the operation strategy of energy storage system can be optimized to ensure the efficient operation and long life of the system. The battery management system can be used for drones and aviation equipment to predict battery health and capacity decline in real time, ensuring the safety and reliability of flight missions.

Comprehensive study of the aging knee and second-life potential of the Nissan Leaf e+ batteries

Aging knee may induce rapid degradation of lithium-ion batteries (LIBs) and pose safety risks. It is crucial to study the battery aging knee and find solutions to address it. In this light, this study focuses on the comprehensive aging characterizations of the Nissan Leaf third generation (Gen3) LIBs to elucidate their aging trajectories. Twenty-four battery cells were divided into six groups and cycled at different working conditions, i.e., voltage, current, and temperatures. The test result shows that the batteries tend to reach the aging knee around 75 % state of health (SOH). Then, we improved the working conditions after the knee point to mimic second-life operation. Key findings show that the aging knee can be stopped by improving the working conditions. The battery capacity degradation slowed down to about 4 % per 1000 cycles, projecting a second-life potential of 4000–6000 cycles between 75 % and 50 % SOH. Post-mortem analysis indicated that negative electrode failure is the cause of aging knee and gas generation. The impact of working conditions on the battery aging is analyzed. Based on the analysis, we propose strategies for both the first and second life operations of LIBs to improve battery life and avoid the aging knee.

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries