Battery Aging Mechanisms Research Articles

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

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.

A multi-stage lithium-ion battery aging dataset using various experimental design methodologies

This dataset encompasses a comprehensive investigation of combined calendar and cycle aging in commercially available lithium-ion battery cells (Samsung INR21700-50E). A total of 279 cells were subjected to 71 distinct aging conditions across two stages. Stage 1 is based on a non-model-based design of experiments (DoE), including full-factorial and Latin hypercube experimental designs, to determine the degradation behavior. Stage 2 employed model-based parameter individual optimal experimental design (pi-OED) to refine specific dependencies, along with a second non-model-based approach for fair comparison of DoE methodologies. While the primary aim was to validate the benefits of optimal experimental design in lithium-ion battery aging studies, this dataset offers extensive utility for various applications. They include training of machine learning models for battery life prediction, calibrating of physics-based or (semi-)empirical models for battery performance and degradation, and numerous other investigations in battery research. Additionally, the dataset has the potential to uncover hidden dependencies and correlations in battery aging mechanisms that were not evident in previous studies, which often relied on pre-existing assumptions and limited experimental designs.

State of Health Estimation for Lithium-Ion Batteries Based on Transferable Long Short-Term Memory Optimized Using Harris Hawk Algorithm

Accurately estimating the state of health (SOH) of lithium-ion batteries ensures the proper operation of the battery management system (BMS) and promotes the second-life utilization of retired batteries. The challenges of existing lithium-ion battery SOH prediction techniques primarily stem from the different battery aging mechanisms and limited model training data. We propose a novel transferable SOH prediction method based on a neural network optimized by Harris hawk optimization (HHO) to address this challenge. The battery charging data analysis involves selecting health features highly correlated with SOH. The Spearman correlation coefficient assesses the correlation between features and SOH. We first combined the long short-term memory (LSTM) and fully connected (FC) layers to form the base model (LSTM-FC) and then retrained the model using a fine-tuning strategy that freezes the LSTM hidden layers. Additionally, the HHO algorithm optimizes the number of epochs and units in the FC and LSTM hidden layers. The proposed method demonstrates estimation effectiveness using multiple aging data from the NASA, CALCE, and XJTU databases. The experimental results demonstrate that the proposed method can accurately estimate SOH with high precision using low amounts of sample data. The RMSE is less than 0.4%, and the MAE is less than 0.3%.

The aging pattern of lithium-ion batteries based on experiments at different temperatures

Abstract In this paper, we take an 18650-type LiFePO4 battery as the research object to investigate the aging mechanism of Li-ion batteries at different temperatures. The aging tests are conducted at 25°C, 35°C and 45°C. The results show that elevating the operating temperature can improve the cycle life of Li-ion batteries. The aging mechanism was analyzed by incremental capacity and electrochemical impedance spectrum analysis. The following conclusions were obtained: the aging mechanism of lithium-ion batteries mainly includes loss of lithium inventory, loss of active material, and conductivity loss, among which loss of lithium inventory and loss of active material are the main causes of Li-ion battery aging, and the contribution of conductivity loss is the smallest. Further analysis of the causes of battery aging by disassembling and testing the aging electrode sheet SEM and TEM shows that the aging of Li-ion battery is manifested by the rupture of graphite particles, the generation of SEI layer and lithium dendrite, and increasing the temperature can ease the rupture of graphite particles and the generation of lithium dendrite, thus improving the cycle life of the battery.

Advancing Smart Lithium-Ion Batteries: A Review on Multi-Physical Sensing Technologies for Lithium-Ion Batteries

Traditional battery management systems (BMS) encounter significant challenges, including low precision in predicting battery states and complexities in managing batteries, primarily due to the scarcity of collected signals. The advancement towards a “smart battery”, equipped with diverse sensor types, promises to mitigate these issues. This review highlights the latest developments in smart sensing technologies for batteries, encompassing electrical, thermal, mechanical, acoustic, and gas sensors. Specifically, we address how these different signals are perceived and how these varied signals could enhance our comprehension of battery aging, failure, and thermal runaway mechanisms, contributing to the creation of BMS that are safer and more reliable. Moreover, we analyze the limitations and challenges faced by different sensor applications and discuss the advantages and disadvantages of each sensing technology. Conclusively, we present a perspective on overcoming future hurdles in smart battery development, focusing on appropriate sensor design, optimized integration processes, efficient signal transmission, and advanced management systems.

Parameter sensitivity analysis of a multi-physics coupling aging model of lithium-ion batteries

A high-precision lithium-ion battery simulation model is the basis to ensure the reliability of battery management system (BMS), and obtaining the values of model parameters accurately and efficiently is the premise of its online application. In this work, the thermal and stress fields are coupled on the electrochemical model, and the various aging mechanisms under the influence of multi-physics coupling are considered. A multi-physics coupling aging model which can simulate the internal processes of battery is established. Based on up to 10,500 simulation cases, the sensitivities to the simulation results of terminal voltage and battery temperature of 42 model parameters are analyzed, including parameters related to multi-physics field and battery aging mechanisms. The model parameters are divided into three sensitivity levels: high, medium and low. The innovative aspects of this work include the extensive number of parameters studied, the numerous simulation cases, research objects related to multi-physics coupling and battery aging, and the concurrent parameter sensitivity analysis to both voltage and temperature simulation results. This work plays an important guiding role in the accurate and efficient identification of model parameters, and is of great significance for online application of model.

The Study of High-Energy Lithium Polymer Cells for Second-Life Applications

The environmental impact of lithium-ion batteries (LiBs) depends directly on their useful lifetime. Doubling the useful lifetime cuts the environmental impact per functional unit in half. However, if the cell is insufficient for one application it can still be sufficient for another. The beneficial operational value of LiBs between various applications presents a prospective solution to environmental sustainability. However, the capacity and power of the LiBs tend to degrade over time, which is a major concern for its application. The investigation and identification of the several key battery aging mechanisms are essential to its second life application. This paper is focused on the experimental analysis performed on XALT 31 Ah high-energy lithium polymer cells with NMC 433 as active electrode material. Characterization and cycling tests were performed on new cells and the data obtained was compared to equal cells cycled 7 years ago. The tests were carried out with the same temperatures (25 and 45 degrees Celsius) for both new and old batteries, respectively. The internal resistance diminishes after 100 cycles for both new and old batteries at 25oC. It does however decrease more for the new batteries, than the old ones. The decrease in resistance can be due to an increase in capacity and it is normal to see in the BoL of a cell. In addition, the temperature during cycling is assumed to be higher than the storage temperature, which also can decrease internal resistance. Since both the new and old batteries behave the same way, it is argued that this is the natural development of resistance in this cell type. When cycled at 45oC, the cell resistance increased after 100 cycles. Even though resistance diminishes with increasing temperature. Other ageing mechanisms such as SEI growth and lithium plating are activated by higher temperatures, resulting in an increase in internal resistance at 45 degrees. For the new and old batteries, the dQdV curves at the BoL are very similar after 100 cycles, it is difficult to know if the trend would continue as the cells age further. Based on this study findings, new approaches can be developed for more accurate and reliable prediction on the aging degradation of high energy lithium batteries.

Understanding the lithium-ion battery's aging mechanisms of mesophase graphite negative electrodes with/without amorphous titanium(IV) oxide nanocoatings by atomic layer deposition

Graphite is still the mainstream anode materials among others in commercial lithium battery market. The solid electrolyte interphase (SEI) film formed by the reaction of graphite surface and electrolyte, which are significantly influenced the cell aging behaviors, which some innate aging mechanisms of graphite remained to be clarified. In this work, amorphous titanium(IV) oxide (A-TiO2) nanocoatings with different thicknesses (1–11 nm) are uniformly grown on mesophase graphite (MPG) negative electrodes by atomic layer deposition (ALD) to compare the material characteristics and electrochemical performance of the assembled cell samples. The cycling performance of the A-TiO2/MPG negative electrodes are significantly improved with the increase of the A-TiO2 nanocoating thickness that the cell capacities (capacity retention rates) are about 172 (52.2 %), 240 (72.6 %), 270 (79.4 %), 277 (82.6 %), 298 (89.7 %), 315 (92.0 %) and 321 mAhg−1 (93.6 %) for MPG electrodes with the A-TiO2 thicknesses of 0, 1, 3, 5, 7, 9 and 11 nm, respectively after the 230th cycle test. The improvement in cell cycle life is attributed to the A-TiO2 nanocoatings as protective layers that isolate the ion diffusions which are from the degraded electrolyte compounds into graphite and protect the graphite from having further reactions with electrolyte.

Design and Validation of Lifetime Prediction Model for Lithium-Thiocarbonyl Chloride Batteries Based on Accelerated Aging Experiments

The purpose of this study is to establish a life prediction model of lithium-thiocarbonyl chloride batteries by semi-empirical method. In the experiment, accelerated life tests on several groups of batteries at different temperatures were conducted. After a period of operation in the range from 25 °C to 74 °C, it was found that the higher the temperature, the less the remaining capacity of the batteries. According to the study of the battery aging mechanism, the characteristic parameter impedance of an aging battery would change exponentially with the increase of storage time and environmental temperature. The established life prediction model showed that the change in battery impedance spectrum had a good law, which made it possible to predict the state of charge (SOC) of the battery according to the local change characteristics of the electrochemical impedance spectroscopy (EIS) spectrum. The experimental data were compared with the fitted prediction curve, and the maximum deviation of the prediction was only 4.1036%, which indicated that the constructed model had high accuracy.

State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network

Accurate state of health estimation of lithium-ion batteries is essential to enhance the reliability and safety of a battery system. However, the estimation accuracy based on a data-driven model is degraded by one health feature and incorrect hyper-parameters selection. This paper develops a battery state of health estimation method based on multi-health features extraction and an improved long short-term memory neural network. To accurately describe the aging mechanism of batteries, health features are extracted from battery data, such as time features, energy features, and incremental capacity features. The correlation between multi-health features and state of health is evaluated by the grey relational analysis. Aiming at the problem that the hyper-parameters of an neural network model are difficult to select, an improved quantum particle swarm optimization algorithm is developed to correctly obtain the hyper-parameters. The experimental results show that the mean absolute error, mean absolute percentage error, and root mean square error of this method are all within 1%, which is much lower than other methods, with high state of health estimation accuracy and robustness.

(Invited) A Framework for in-Line Analysis and Quantification of Battery Aging Mechanisms Using Electrochemical Signatures Under Fast-Cycling Conditions

Rapid, in-situ Li-ion battery state-of-health quantification is challenging. Li-ion battery aging modes can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. In literature, Li-ion battery aging modes are typically lumped into categories such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. Importantly, as a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation. The present research describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. Once developed, the model can be used to quickly identify the battery aged state, without relying on slow reference performance tests. The framework first develops a physically based Li-ion battery model that captures aging effects (i.e., LLI and LAM). Once the aging model is calibrated to known battery performance at aged states, the model is used to generate synthetic voltage responses under a variety of aged conditions. These synthetic responses are then used to train a machine-learning model. In the end, the machine-learning model is used, in conjunction with experimentally measured voltage profiles, to identify cycle-by-cycle battery state-of-health variables of real cells in operando. The framework is used to identify battery state-of-health for two different electrode loadings and for cells charged a variety of fast-rate conditions (i.e., 1C, 4C, 6C, and 9C). Figure 1

State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method

State of health (SOH) and remaining useful life (RUL) prediction are crucial for battery management systems (BMS). However, accurate SOH and RUL prediction still need to be improved due to the complicated battery aging mechanism. This work combines incremental capacity analysis (ICA) and differential voltage analysis (DVA) based on the second-order RC model with an improved Bidirectional Gated Recurrent Unit (BiGRU) to develop SOH and RUL prediction framework. Firstly, the voltage is reconstructed through the second-order RC model to obtain the incremental capacity (IC) and differential voltage (DV) curves to avoid the influence of measurement noise and the complex parameter adjustment process in the filtering method on the IC and DV curves. Then, a new set of battery aging features are extracted from the reshaped IC and DV curves to improve SOH and RUL prediction accuracy and robustness. Next, the BiGRU method with attention mechanism (BiGRU-AM) is used to build the prediction models for battery aging features, SOH, and RUL. To reduce the impact of the capacity regeneration phenomenon, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is used to decompose the SOH prediction results, and the decomposed residual is used as the input to improve the prediction accuracy of RUL. The uncertainty of RUL prediction results is analyzed by Monte Carlo (MC) simulation. Finally, the proposed method is verified by experimental battery data from Center for Advanced Life Cycle Engineering (CALCE) and Sandia National Laboratory. Experimental results show that the voltage reconstruction results based on the second-order RC model are applied to ICA and DVA analysis, effectively avoiding the influence of noise. The RMSE of voltage reconstruction is within 0.0006, and the Pearson correlation coefficient between the four aging features extracted from the reconstructed IC/DV curve and SOH is above 0.9. Moreover, this method has good robustness to the cell inconsistency, temperature uncertainty, and a satisfied generalization ability to different battery chemistries, which the maximum RUL predicted AE of CALCE and Sandia battery is within 10 and 5, respectively.

Remaining useful life prediction of lithium-ion battery based on chaotic particle swarm optimization and particle filter

The remaining useful life (RUL) prediction of lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. Due to the complexity of the battery aging mechanism, the accurate prediction of RUL by traditional methods is difficult to guarantee. To improve the prediction performance of the particle filter (PF), an improved particle filter based on the chaotic particle swarm optimization algorithm (CPSO-PF) is presented. Then it is applied to predict the RUL of lithium-ion batteries. First, for a better posterior estimate in the PF, CPSO is used to drive the prior distribution of the particles toward a high likelihood probability to obtain a better-proposed distribution, which helps overcome the problem of degeneracy and impoverishment of particles. Then, Three models were employed to track the degradation trajectory of the batteries, including PF、the extended Kalman particle filter (EKPF), and CPSO-PF. Finally, the RUL of lithium-ion batteries was predicted with the three models. The experimental results demonstrate that CPSO-PF has higher prediction accuracy and strong robustness.

An SVM-Based Health Classifier for Offline Li-Ion Batteries by Using EIS Technology

This paper presents an offline testing framework and simulation to measure the aging situation of Li-ion batteries within the Battery Management System (BMS) or laddering use for maintenance activities. It presents the use case of Electrochemical Impedance Spectroscopy (EIS) as a non-destructive inspection method to detect battery states. Multiple cycles (charge and discharge) were done to gain EIS results in different conditions like temperature. Results were captured and digitalised through a suitable circuit model and mathematical methods for fitting. The State of Health (SOH) values were calibrated, and data were reshaped as vectors and then used as input for Support Vector Machine (SVM). These data were then used to create a machine learning model and analyse the aging mechanism of lithium-ion batteries. The machine learning model is established, and the decision boundaries are visualised in 2D graphs. The accuracy of these machine learning models can reach 80% in the test cases, and good fitting in lifetime tracking. The framework allows more reliable SOH estimation in electric vehicles and more efficient maintenance or laddering operations.

Data-Driven Battery Aging Mechanism Analysis and Degradation Pathway Prediction

Capacity decline is the focus of traditional battery health estimation as it is a significant external manifestation of battery aging. However, it is difficult to depict the internal aging information in depth. To achieve the goal of deeper online diagnosis and accurate prediction of battery aging, this paper proposes a data-driven battery aging mechanism analysis and degradation pathway prediction approach. Firstly, a non-destructive aging mechanism analysis method based on the open-circuit voltage model is proposed, where the internal aging modes are quantified through the marine predator algorithm. Secondly, through the design of multi-factor and multi-level orthogonal aging experiments, the dominant aging modes and critical aging factors affecting the battery capacity decay at different life phases are determined using statistical analysis methods. Thirdly, a data-driven multi-factor coupled battery aging mechanism prediction model is developed. Specifically, the Transformer network is designed to establish nonlinear relationships between factors and aging modes, and the regression-based data enhancement is performed to enhance the model generalization capability. To enhance the adaptability to variations in aging conditions, the model outputs are set to the increments of the aging modes. Finally, the experimental results verify that the proposed approach can achieve satisfactory performances under different aging conditions.

State estimation and aging mechanism of 2nd life lithium-ion batteries: Non-destructive and postmortem combined analysis

Lithium-ion batteries used in electric vehicles (EVs) are expected to reach end-of-life (EoL) after 10–15 years of operation. Consequently, it is urgent to devise repurposing strategies, which may range from recycling to a 2nd life in stationary energy storage applications. In this paper, aiming the 2nd life repurposing of the batteries, the state estimation and aging mechanisms of retired 32,650-type LiFePO4/graphite batteries were studied. The aging state of batteries was evaluated in terms of their capacity, resistance and self-discharge rate. Test results show that there is no strong correlation between battery capacity decay and resistance increase. To identify and quantify the aging mechanisms of the 2nd life batteries after being cycled at different rates (0.5C, 1C and 2C), non-destructive analyses (incremental capacity/differential voltage curves, modified equivalent circuit model, and electrochemical impedance spectroscopy) are combined with postmortem analysis. The results reveal that the aging of the batteries cycled at 0.5C is primarily due to the loss of active material (LAM), while the loss of lithium inventory (LLI) is rather insignificant. For the batteries cycled at 1C and 2C, in addition to LAM, LLI is also severe, eventually leading to an increase in resistance of the battery, which causes a rapid decline of the battery capacity. The results also indicate the non-destructive methods are reliable to identify ongoing aging mechanisms.

Effect of aging temperature on thermal stability of lithium-ion batteries: Part A – High-temperature aging

Aging and thermal runaway are two significant reasons why lithium-ion batteries are struggling to become more widely available. Aging at different temperatures causes differences in the aging mechanism and thermal runaway behaviour of lithium-ion batteries. In this paper, four sets of commercial lithium-ion batteries are aged at 25 °C, 40 °C, 60 °C and 80 °C respectively for 100 cycles. Then the morphology and composition of the electrodes and separators are analysed in order to reveal the mechanism of changes in electrical performance and thermal stability due to aging at different temperatures. The differences in the decomposition products of the solid electrolyte intermediate (SEI) layer are an important factor in inducing changes in thermal runaway behaviour. At 60 °C, the accumulation of SEI decomposition products results in thicker SEI layers and shorter thermal runaway times. At 80 °C, the SEI decomposition products are heavily transformed into particles with a loose structure, generating a large amount of gas in the process, which further leads to the rupture of the aluminium-plastic film and the evaporation of the electrolyte, with a longer duration of thermal runaway and a lower maximum temperature.

Online Battery Protective Energy Management for Energy-Transportation Nexus

Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids (MGs). However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile MG states. This article develops a novel online battery anti-aging energy management method for energy-transportation nexus by using a novel deep reinforcement learning (DRL) framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modeling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modeled as multiobjective learning by using a DRL algorithm. The training objective is to maximize renewable penetration while reducing MG power fluctuations and vehicle battery aging costs. The developed energy-transportation nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a MG in the U.K. This article provides an efficient and economical tool for MG power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.

An efficient regrouping method of retired lithium-ion iron phosphate batteries based on incremental capacity curve feature extraction for echelon utilization

Due to the long service life of lithium-ion iron phosphate (LFP) batteries, retired LFP batteries from electric vehicles are suitable for echelon utilization. Sorting and regrouping should be carried out in advance to ensure the performance of retired LFP batteries. Effective methods are often time consuming and expensive. The incremental capacity (IC) curve, derived from the capacity-voltage curve, is often employed to analyze the aging mechanism of batteries. Compared with the existing methods, it is relatively economical and can reflect the batteries' long-term consistency of the capacity loss. Therefore, a sorting and regrouping method based on the IC curve is proposed and applied to 320 retired LFP batteries. The peaks of the IC curve were extracted as the features and used for sorting. In the regrouping, the K-means++ algorithm was applied for clustering, and then the t-test was used to further improve the regrouping consistency. The mean squared error (MSE) of each group generally decreased by more than 60 % from the previously 0.04 MSE level. Finally, the validation experiment showed that short-term consistency was improved by more than 5 times compared with randomly grouped batteries, and the capacity gap of the same groups of batteries was less than 3 times that of the different groups.