Battery Degradation Mechanisms Research Articles

Progressive degradation behavior and mechanism of lithium-ion batteries subjected to minor deformation damage

Minor deformation damage poses a concealed threat to battery performance and safety. This study delves into the progressive degradation behavior and mechanisms of lithium-ion batteries under minor deformation damage induced by out-of-plane compression. The effects of varying initial states of charge and loading speed on battery degradation are also analyzed. It has been observed that a deformation damage degree as low as 3.1 % can cause a significant transient capacity reduction up to 6.3 %. More transient capacity reduction can occur at higher initial state of charges and loading speed. Additionally, the investigation reveals that the capacity decay rate between the normal cell and cells experiencing deformation damage degree of <4.7 % is almost similar, with a maximum difference of merely 1.887 mAh/cycle and a minimum difference of 0.001 mAh/cycle, both observed over 3000 cycles. Non-destructive and destructive analysis methods are used to investigate degradation behaviors of the cells experiencing minor deformation dagame. It is found that such progressive degradation is primarily driven by the loss of active lithium ions, followed by the loss of cathode and anode active materials. It is anticipated that the findings will offer theoretical underpinnings for advancements in battery damage assessment, early failure prediction, and facilitation of loss adjustment and compensation by insurance agencies.

Nondestructive diagnosis technique for LiFePO4/graphite cells based on the internal resistance curve analysis

To explain the degradation mechanism of lithium-ion batteries, a nondestructive diagnosis technology to estimate the degree of degradation of the cell components is required. A technology that satisfies that requirement is widely used to analyze the open-circuit voltage (OCV) curve of a cell and estimate the positive-electrode and negative-electrode capacities and the amount of deactivated lithium. However, it has been difficult to estimate the degradation of electrodes composed of active materials with flat electrode potentials, such as lithium iron phosphate (LFP). The novel nondestructive method developed in this study can estimate the degradation state of battery components using LFP by analyzing the “internal resistance curve” of the battery. This method is used to determine the degradation parameters that explain both the measured OCV curve and the measured internal resistance curves of the cell by comparing the internal resistance curves of the cells with those of the positive and negative electrodes. The results of calendar life tests of lithium-ion batteries using LFP at elevated temperature and high voltage were analyzed by using this method, and the results of the analysis show that the degradation of the LFP positive electrode was progressing.

Unraveling the Degradation Mechanisms of Lithium-Ion Batteries

Lithium-Ion Batteries (LIBs) usually present several degradation processes, which include their complex Solid-Electrolyte Interphase (SEI) formation process, which can result in mechanical, thermal, and chemical failures. The SEI layer is a protective layer that forms on the anode surface. The SEI layer allows the movement of lithium ions while blocking electrons, which is necessary to prevent short circuits in the battery and ensure safe operation. However, the SEI formation mechanisms reduce battery capacity and power as they consume electrolyte species, resulting in irreversible material loss. Furthermore, it is important to understand the degradation reactions of the LIBs used in Electric Vehicles (EVs), aiming to establish the battery lifespan, predict and minimise material losses, and establish an adequate time for replacement. Moreover, LIBs applied in EVs suffer from two main categories of degradation, which are, specifically, calendar degradation and cycling degradation. There are several studies about battery degradation available in the literature, including different degradation phenomena, but the degradation mechanisms of large-format LIBs have rarely been investigated. Therefore, this review aims to present a systematic review of the existing literature about LIB degradation, providing insight into the complex parameters that affect battery degradation mechanisms. Furthermore, this review has investigated the influence of time, C-rate, depth of discharge, working voltage window, thermal and mechanical stresses, and side reactions in the degradation of LIBs.

A Review of Capacity Fade Mechanism and Promotion Strategies for Lithium Iron Phosphate Batteries

Commercialized lithium iron phosphate (LiFePO4) batteries have become mainstream energy storage batteries due to their incomparable advantages in safety, stability, and low cost. However, LiFePO4 (LFP) batteries still have the problems of capacity decline, poor low-temperature performance, etc. The problems are mainly caused by the following reasons: (1) the irreversible phase transition of LiFePO4; (2) the formation of the cathode–electrolyte interface (CEI) layer; (3) the dissolution of the iron elements; (4) the oxidative decomposition of the electrolyte; (5) the repeated growth and thickening of the solid–electrolyte interface (SEI) film on the anode electrode; (6) the structural deterioration of graphite anodes; (7) the growth of lithium dendrites. In order to eliminate the problems, methods such as the modification, doping, and coating of cathode materials, electrolyte design, and anode coating have been studied to effectively improve the electrochemical performance of LFP batteries. This review briefly describes the working principle of the LFP battery, the crystal structure of the LFP cathode material, and its electrochemical performance as a cathode. The performance degradation mechanism of LFP batteries is summarized in three aspects—cathode material, anode material, and electrolyte—and the research status of LFP material modification and electrolyte design is emphatically discussed. Finally, the challenges and future development of LFP batteries are prospected.

Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system.

Unveiling the Degradation Mechanism of Sodium Ion Batteries Based on Na4Fe3(PO4)2P2O7 Cathode and Hard Carbon Anode Suggests Anode Particle Size Reduction for Cycling Stability

AbstractTo improve the cycle life of sodium‐ion batteries, it is essential to understand the microscopic processes that lead to cell degradation. The mismatched response time of anode and cathode has profound but poorly understood impact on cycle life. In this work, we combine electrochemical and materials characterization along with electrochemical modeling to investigate the root cause of degradation in sodium‐ion full cells made from Na4Fe3(PO4)2P2O7 (NFPP) cathodes and hard carbon (HC) anode. Our results pinpoint to the slow diffusion of Na in HC as the main cause of diffusional polarization that leads to cathode experiencing high local potentials and ultimately to active material loss over cycling. We demonstrate that by reducing the anode particle size, the diffusional timescales in anode can be matched with that of cathode to improve both extractable capacity as well as cycle life. These observations shed light on non‐intuitive and intricate ways in which cathode and anode can interact with each other to cause degradation in Na‐ion batteries and how microscopic understanding of these cause and effects can help design long lasting batteries.

Investigating explainable transfer learning for battery lifetime prediction under state transitions

Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.

Coordination of dissolved transition metals in pristine battery electrolyte solutions determined by NMR and EPR spectroscopy.

The solvation of dissolved transition metal ions in lithium-ion battery electrolytes is not well-characterised experimentally, although it is important for battery degradation mechanisms governed by metal dissolution, deposition, and reactivity in solution. This work identifies the coordinating species in the Mn2+ and Ni2+ solvation spheres in LiPF6/LiTFSI-carbonate electrolyte solutions by examining the electron-nuclear spin interactions, which are probed by pulsed EPR and paramagnetic NMR spectroscopy. These techniques investigate solvation in frozen electrolytes and in the liquid state at ambient temperature, respectively, also probing the bound states and dynamics of the complexes involving the ions. Mn2+ and Ni2+ are shown to primarily coordinate to ethylene carbonate (EC) in the first coordination sphere, while PF6- is found primarily in the second coordination sphere, although a degree of contact ion pairing does appear to occur, particularly in electrolytes with low EC concentrations. NMR results suggest that Mn2+ coordinates more strongly to PF6- than to TFSI-, while the opposite is true for Ni2+. This work provides a framework to experimentally determine the coordination spheres of paramagnetic metals in battery electrolyte solutions.

Research on EIS characterization and internal morphological changes of LIBs during degradation process

Electric vehicles (EVs) are increasingly being used today, but the safety of their battery system remains an issue. Therefore, a clear understanding of the degradation mechanism of EV batteries has become a pressing issue. In this study, the EIS characterization and internal morphological changes are investigated during degradation process of 18,650 LIBs. The research results show that the EIS value of LIBs changes to varying degrees with battery degradation, but this change is not only due to the ageing of LIB materials, but also related to the LIB’s state of charge. In lithium-ion battery EIS, the ohmic impedance is mainly influenced by the SOH, while the transfer impedance and extended impedance are influenced by the SOC of the battery. In addition, as the aging process progresses, the particles of the anode and cathode materials of the LIB gradually become smaller and more fragmented, the decomposition on the surface of the negative electrode becomes more significant, which is mainly due to the fact that the decomposition of the graphite negative electrode is more pronounced, which evidenced by scanning electron microscopy (SEM) images. These new finds will help further improve the design and management of LIBs.

An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features

Accurate state of health (SOH) estimation of lithium-ion batteries is essential for quality control and improving safety and efficiency of electric vehicles and energy storage systems. Due to inadequate consideration of different categories of measurement data and stages of battery working in constructing degradation features, current SOH estimation methods are limited in accuracy and fail to facilitate development of specific battery management strategies. To address these issues, this paper proposes a novel SOH estimation method for lithium-ion batteries based on multi-category and multi-stage (MC-MS) features and an input optimization interpretable multi-variable long short-term memory (IO-IMV-LSTM) model. First, a MC-MS degradation feature construction scheme is presented based on working principles and degradation mechanism of lithium-ion batteries. Then, an improved interpretable estimation model called IO-IMV-LSTM is introduced, which uses feature importance to identify the most effective features and optimize model input. Finally, influence of battery measurement data from different categories and working stages on SOH estimation results is analyzed, and the findings can be used to optimize battery utilization and maintenance strategies. A public dataset together with degradation dataset of our testbed is used for validation experiments. Results show that the MC-MS features are superior to existing features and the performance of the IO-IMV-LSTM model is significantly improved through input optimization. The proposed method showcases remarkable improvements and advantages compared with state-of-art methods. Specifically, the proposed SOH estimation method achieves satisfactory performance on four evaluation indicators: mean absolute error, mean absolute percentage error, root mean square error, and R-square, with average values of 0.20%, 0.22%, 0.24%, and 99.34%, respectively.

Implementing Reversible Swelling into the Numerical Model of a Lithium-Ion Pouch Cell for Short Circuit Prediction

Mechanical simulation models have become crucial for understanding Li-ion battery failure and degradation mechanisms. However, existing safety assessment models lack the implementation of SOC-dependent thickness variations referred to as reversible swelling. Reversible swelling affects the applied preload force on a constrained pouch cell, potentially impacting its safety. To investigate this, a finite element RVE model was developed in LS-Dyna. Two swelling models, simplified homogenous expansion (HE) and locally resolved expansion (LE), were implemented along with a reference basis model (BM) without expansion. Six different stress- or strain-based short circuit criteria were calibrated with abuse test simulations at different SOCs and preload forces. Short circuit prognosis improved on average by 0.8% and 0.7% for the LE and HE model compared to the BM, with minimum principal stress being the most suitable criterion. The LE model exhibited a softer mechanical response than the HE model or BM, accounting for the pouch cell surface unevenness at small indentations. This study demonstrated the feasibility and usefulness of implementing an expansion model in a commercial FE solver for improved short circuit predictions. An expansion model is crucial for simulating aged battery cells with significant geometry changes strongly affecting the preload force of a constrained battery cell.

Review on degradation mechanism and health state estimation methods of lithium-ion batteries

Review on degradation mechanism and health state estimation methods of lithium-ion batteries

Degradation diagnosis of lithium-ion batteries considering internal gas evolution

Degradation diagnosis is essential for the safety of lithium-ion batteries. However, there lacks a method to provide detailed information about battery degradation mechanisms comprehensively. As an important product of electrolyte decomposition, internal gas evolution is rarely involved in degradation diagnosis. In this paper, internal gas evolution is evaluated based on X-ray computed tomography (CT), and used for degradation diagnosis by combination with internal resistances. Internal resistances are identified based on the equivalent circuit model (ECM) to describe different degradation mechanisms. Region of interest (ROI) volume of the tomogram, which reflects internal gas volume, is extracted to describe electrolyte decomposition using an X-ray CT. The proposed method is verified based on accelerated aging experiments and evaluation experiments of two batteries at different C-rates, whose results show that the tested batteries have different primary degradation mechanisms due to different degradation paths. It is recommended that the replacement of lithium-ion batteries adhere to the criteria of internal resistance and ROI volume change rates. The proposed method considers internal gas evolution and thus improves the effect of the degradation diagnosis in terms of degradation mechanisms.

Interpretable Battery Cycle Life Range Prediction Using Early Cell Degradation Data

Battery cycle life prediction using early degradation data has many potential applications throughout the battery product life cycle. For that reason, various data-driven methods have been proposed for point prediction of battery cycle life with minimum knowledge of the battery degradation mechanisms. However, managing the rapidly increasing amounts of batteries at end-of-life with lower economic and technical risk requires prediction of cycle life with quantified uncertainty, which is still lacking. The interpretability (i.e., the reason for high prediction accuracy) of these advanced data-driven methods is also worthy of investigation. Here, a physics-informed Quantile Regression Forest (QRF) model, having the advantage of not assuming any specific distribution of cycle life, is introduced to make cycle life range prediction with uncertainty quantified as the width of the prediction interval, in addition to point predictions with high accuracy. The hyperparameters of the QRF model are optimized with a proposed alpha-logistic-weighted criterion so that the coverage probabilities associated with the prediction intervals are calibrated. The interpretability of the final QRF model is explored with two global model-agnostic methods, namely permutation importance and partial dependence plot.

A hierarchical control with thermal and electrical synergies on battery cycling ageing and energy flexibility in a multi-energy sharing network

Electrochemical batteries are essential for renewable sharing and sustainability transformation, whereas battery degradation will adversely affect energy flexibility and renewable energy penetration. Numerous studies have explored methods to decelerate battery degradation from the perspective of electrode materials, electrolytes, temperature control, and so on. However, in integrated multi-energy systems, few studies explored the combined operation of integrated thermal storages and smart battery charging/discharging methods to decelerate battery degradation, in accordance with the nonlinear degradation mechanism of batteries. In this study, renewable electricity (RE) recharging strategy and depth of discharge (DoD)-based control strategy were developed to decelerate the cycling ageing and prolong the service lifetime of both lead-acid and Li-ion batteries. The underlying mechanism of the excess RE recharging strategy on integrated thermal storage is to reduce the total number of cycles of batteries for service provision to buildings. The mechanism of the DoD-based control strategy is to achieve the minimum cycling ageing rate by dynamically searching for the minimum gradient along the battery degradation curve through the active switch between the charging and discharging processes, according to the battery state of charge and dynamic power. Research results show that the proposed excess RE-hot water storage tank recharging strategy and the DoD-based control strategy effectively decelerate the battery degradation, increasing the annual equivalent relative capacity of the battery from 94.21% to 95.29% and 94.73%, respectively. Furthermore, the proposed synergistic operation between thermal and electrical storages will improve the annual equivalent relative capacity from 94.21% to 95.46%. From the economic perspective, the synergistic operation can significantly reduce the total cost of the system from 7.96 × 105 US$ to 7.78 × 105 US$ by 2.26% for Li-ion batteries application and from 1.05 × 106 US$ to 8.47 × 105 US$ by 19.33% for lead-acid batteries application. Research results can provide frontier guidelines with cutting-edge technologies for energy saving, decarbonization, and carbon neutrality transformation from perspectives of dynamic controls and synergistic operations on thermal/electrical storage.

Data-driven state-of-health estimation for lithium-ion battery based on aging features

Reliable state-of-health (SOH) estimation is crucial to the safe operation of lithium-ion battery. Data-driven SOH estimation becomes a hot research topic with the booming of high-performance machine learning algorithms. The effectiveness of a data-driven approach will be enhanced significantly if the input features are extracted properly. In order to improve the SOH estimation accuracy, the features highly associated with battery degradation should be utilized in the data-driven model. In this paper, an aging feature extraction method based on electrochemical model (EM) is proposed to account for the battery degradation mechanisms. The aging features of EM such as charge transfer resistance, solid phase diffusion coefficient and electrode volume fraction are defined as internal health features (IHFs) for SOH estimation. Moreover, external health features (EHFs) are directly extracted from the voltage and temperature curves that split into multiple stages. Then, IHFs and multi-stage EHFs are selected appropriately for SOH estimation in offline and online application scenarios. Finally, two well-known machine learning algorithms are employed to construct data-driven SOH estimation model using IHFs and EHFs. Experimental data are used to prove that the proposed method can effectively improve the accuracy of SOH estimation under different application scenarios and battery charge–discharge modes.

Heat Generation and Degradation Mechanism of Lithium-Ion Batteries during High-Temperature Aging.

High-temperature aging has a serious impact on the safety and performance of lithium-ion batteries. This work comprehensively investigates the evolution of heat generation characteristics upon discharging and electrochemical performance and the degradation mechanism during high-temperature aging. Post-mortem characterization analysis revealed that lithium plating is the main degradation mechanism. The occurrence of side reactions leads to cell capacity fading and electrochemical performance degradation. The DC resistance and AC impedance increase significantly, and the severe internal polarization makes the incremental capacity curve shift to lower voltage. In the early aging stage, the cell degrades slightly, and the temperature rise rate has not changed significantly upon discharging. The cell capacity plays a leading role, whose degradation makes the temperature rise decrease. With the aging deepening, the severe cell degradation makes the temperature rise rate increase significantly. Even if the capacity fading, the temperature rise still increases significantly compared to the fresh state. Furthermore, irreversible heat and reversible heat increase significantly with the aging deepening and current rate increasing.

State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning.

Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.

A Review of the Impact of Battery Degradation on Energy Management Systems with a Special Emphasis on Electric Vehicles

The increasing popularity of electric vehicles (EVs) has been attributed to their low-carbon and environmentally friendly attributes. Extensive research has been undertaken in view of the depletion of fossil fuels, changes in climatic conditions due to air pollution, and the goal of developing EVs capable of matching or exceeding the performance of today’s internal combustion engines (ICEs). The transition from ICE vehicles to EVs can reduce greenhouse gases significantly over a vehicle’s lifetime. Across the different types of EVs, the widespread usage of batteries is due to their high power density and steady output voltage, making them an excellent energy storage device (ESD). The current downsides of battery-powered electric vehicles include long recharge times, the impact of additional strain on the grid, poor societal acceptance due to high initial costs, and a lack of adequate charging infrastructure. Even more problematic is their short driving range when compared to standard ICE and fuel cell EVs. Battery degradation occurs when the capacity of a battery degrades, resulting in a reduction in travel range. This review article includes a description of battery degradation, degradation mechanisms, and types of degradation. A detailed investigation of the methods used to address and reduce battery degeneration is presented. Finally, some future orientation in terms of EV research is offered as vital guidance for academic and industrial partners.

Study on the Capacity Degradation Mechanism and Capacity Predication of Lithium-Ion Battery Under Different Vibration Conditions in Six Degrees-of-Freedom

Abstract In order to study the degradation mechanism of lithium-ion batteries subjected to vibration aging in actual use and also to achieve capacity estimation and prediction, the following work has been done: First, the road spectra of two commonly seen domestic roads in China are collected in the field and modeled on a six degrees-of-freedom motion platform as the vibration working conditions of the batteries. Second, aging cycle experiments were conducted on batteries with different placement directions (X-axis direction, Y-axis direction, and Z-axis direction) under two vibration conditions, and the effects of experimental conditions on the decline results were analyzed; third, quantification of battery decline patterns to analyze the main causes of battery capacity decline; and then, through further analysis of the two vibration conditions on the lithium battery by in-situ and ex-situ methods as its internal mechanisms. Finally, the quantified results were input into the generative adversarial networks and long-term and short-term memory network prediction model to predict the capacity, and the errors of 20 predictions are as follows: the average values are 2.8561% for Group X, 2.7997% for Group Y, 3.0182% for Group Z, and 2.9478% for Group N, which meet the requirements of battery management system estimation. This paper provides a basis for the study of aging mechanism and capacity estimation of lithium-ion batteries under vibration aging conditions, which helps manufacturers to package batteries more rationally to extend battery life and develop battery management system (BMS)-related strategies.