Battery Aging Research Articles

Reinforcement Learning-Enhanced Adaptive Scheduling of Battery Energy Storage Systems in Energy Markets

Battery Energy Storage Systems (BESSs) play a vital role in modern power grids by optimally dispatching energy according to the price signal. This paper proposes a reinforcement learning-based model that optimizes BESS scheduling with the proposed Q-learning algorithm combined with an epsilon-greedy strategy. The proposed epsilon-greedy strategy-based Q-learning algorithm can efficiently manage energy dispatching under uncertain price signals and multi-day operations without retraining. Simulations are conducted under different scenarios, considering electricity price fluctuations and battery aging conditions. Results show that the proposed algorithm demonstrates enhanced economic returns and adaptability compared to traditional methods, providing a practical solution for intelligent BESS scheduling that supports grid stability and the efficient use of renewable energy.

Mitigating Calendar Aging in Si-NMC Batteries with Advanced Dual-Salt Glyme Electrolytes

Mitigating Calendar Aging in Si-NMC Batteries with Advanced Dual-Salt Glyme Electrolytes

Interpretable Learning of Accelerated Aging in Lithium Metal Batteries.

Lithium metal batteries (LMBs) with high energy density are perceived as the most promising candidates to enable long-endurance electrified transportation. However, rapid capacity decay and safety hazards have impeded the practical application of LMBs, where the entangled complex degradation pattern remains a major challenge for efficient battery design and engineering. Here, we present an interpretable framework to learn the accelerated aging of LMBs with a comprehensive data space containing 79 cells varying considerably in battery chemistries and cell parameters. Leveraging only data from the first 10 cycles, this framework accurately predicts the knee points where aging starts to accelerate. Leaning on the framework's interpretability, we further elucidate the critical role of the last 10%-depth discharging on LMB aging rate and propose a universal descriptor based solely on early cycle electrochemical data for rapid evaluation of electrolytes. The machine learning insights also motivate the design of a dual-cutoff discharge protocol, which effectively extends the cycle life of LMBs by a factor of up to 2.8.

A SOH estimation method of lithium-ion batteries based on partial charging data

Accurate and reliable assessment of lithium-ion battery SOH is critical to improving the safety performance of electric vehicles. In the existing data-driven methods, the models are often complex, the calculation is large and the interpretation ability is not strong, so it is difficult to realize the commercial application. In this paper, a novel SOH estimation method is proposed. Firstly, the incremental capacity (IC) curve is used to extract the aging features from part of the charging data, and a linear battery aging state space equation is established based on the features. The aging state of the battery can be directly observed under the condition of small calculation amount. Second, the aging state of the battery can be tracked and corrected by double Kalman filter to improve estimation accuracy. The performance of the proposed method is verified on CACLE dataset, Oxford dataset and DUT dataset respectively. The estimated errors are all <2.5 %, and the mean RMSE is 0.0066. The results show that the method can be effectively applied to different lithium-ion batteries. In order to further demonstrate the advantages of this estimator, we also compare it with literature methods using the same dataset. In the CACLE data set and Oxford data, the average time of this method is 0.848 s and the average RMSE is 0.0050, which is in a dominant position. The proposed method provides a simple and feasible way to estimate the SOH of electric vehicles.

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.

Review of Cell Level Battery (Calendar and Cycling) Aging Models: Electric Vehicles

Electrochemical battery cells have been a focus of attention due to their numerous advantages in distinct applications recently, such as electric vehicles. A limiting factor for adaptation by the industry is related to the aging of batteries over time. Characteristics of battery aging vary depending on many factors such as battery type, electrochemical reactions, and operation conditions. Aging could be considered in two sections according to its type: calendar and cycling. We examine the stress factors affecting these two types of aging in detail under subheadings and review the battery aging literature with a comprehensive approach. This article presents a review of empirical and semi-empirical modeling techniques and aging studies, focusing on the trends observed between different studies and highlighting the limitations and challenges of the various models.

A Review on Advanced Battery Thermal Management Systems for Fast Charging in Electric Vehicles

To protect the environment and reduce dependence on fossil fuels, the world is shifting towards electric vehicles (EVs) as a sustainable solution. The development of fast charging technologies for EVs to reduce charging time and increase operating range is essential to replace traditional internal combustion engine (ICE) vehicles. Lithium-ion batteries (LIBs) are efficient energy storage systems in EVs. However, the efficiency of LIBs depends significantly on their working temperature range. However, the huge amount of heat generated during fast charging increases battery temperature uncontrollably and may lead to thermal runaway, which poses serious hazards during the operation of EVs. In addition, fast charging with high current accelerates battery aging and seriously reduces battery capacity. Therefore, an effective and advanced battery thermal management system (BTMS) is essential to ensure the performance, lifetime, and safety of LIBs, particularly under extreme charging conditions. In this perspective, the current review presents the state-of-the-art thermal management strategies for LIBs during fast charging. The serious thermal problems owing to heat generated during fast charging and its impacts on LIBs are discussed. The core part of this review presents advanced cooling strategies such as indirect liquid cooling, immersion cooling, and hybrid cooling for the thermal management of batteries during fast charging based on recently published research studies in the period of 2019–2024 (5 years). Finally, the key findings and potential directions for next-generation BTMSs toward fast charging are proposed. This review offers an in-depth analysis by providing recommendations and potential solutions to develop reliable and efficient BTMSs for LIBs during fast charging.

Co-Estimating State of Charge and Capacity of Automotive Lithium-Ion Batteries Under Deep Degradation Using Multiple Estimators

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery’s lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery’s lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.

General Machine Learning Approaches for Lithium-Ion Battery Capacity Fade Compared to Empirical Models

Today’s growing demand for lithium-ion batteries across various industrial sectors has introduced a new concern: battery aging. This issue necessitates the development of tools and models that can accurately predict battery aging. This study proposes a general framework for constructing battery aging models using machine learning techniques and compares these models with two existing empirical models, including a commercial one. To build the models, the databases produced by EVERLASTING and Bills et al. were utilized. The aim is to create universally applicable models that can address any battery-aging scenario. In this study, three types of models were developed: a vanilla neural network, a neural network inspired by extreme learning machines, and an encoder coupled with a neural network. The inputs for these models are derived from established knowledge in battery science, allowing the models to capture aging effects across different use cases. The models were trained on cells subjected to specific aging conditions and they were tested on other cells from the same database that experienced different aging conditions. The results obtained during the test for the vanilla neural network showed an RMSE of 1.3% on the Bills et al. test data and an RMSE of 2.7% on the EVERLASTING data, demonstrating similar or superior performance compared to the empirical models and proving the ability of the models to capture battery aging.

On safety of swelled commercial lithium-ion batteries: A study on aging, swelling, and abuse tests

Lithium-ion battery technology has advanced significantly, making these power sources essential for portable electronic devices such as smartphones. In 2023, global smartphone shipments reached nearly 1.2 billion units, underscoring the widespread reliance on these batteries. However, as batteries age, they may swell and potentially pose explosion risks. To investigate the safety of swollen batteries, this study conducts accelerated aging and swelling tests on lithium-ion batteries from five leading brands, which together represent over half of the global smartphone market share. The research involves a series of comprehensive tests, including Accelerated Rate Calorimeters (ARC) test, mechanical, electrical, and thermal abuse tests in accordance with Chinese national standards, as well as gas composition and theoretical flammability analyses on both new and swollen batteries. The findings indicate that swollen batteries generally exhibit safer behavior under floating charging conditions, and both new and swollen batteries pass the abuse tests within the standard framework. This study suggests that the safety of swollen lithium-ion batteries cannot be categorically labeled as dangerous or safe and should be assessed within the context of specific environments.

Remaining useful life prediction of lithium-ion batteries based on DBO[sbnd]CNN-DSformer

In order to improve the accuracy of predicting RUL of lithium-ion batteries, a lithium-ion battery RUL prediction method based on the DBOCNN-DSformer model is proposed. Firstly, the health characteristics of the battery are extracted and the local information of health features is mined using CNN. DSformer is utilized for global information, local information, and variable correlation learning of battery aging characteristics. The DBO is used to optimize the super-parameters of the CNN-DSformer model and build the DBOCNN-DSformer model. Finally, the battery aging data set was used for verification. The results show that DBOCNN-DSformer, which sets different prediction starting points, can extract sequence information from input data and establish long-term dependencies between sequences. The maximum average MRE error in the NASA data set was 0.05, the maximum average MAE was 0.018, and the maximum average AE error was within 5. The maximum average MRE error of the CALCE data set was 0.37, the maximum average MAE was 0.014, and the maximum average AE prediction error was within 10. Compared with LSTM, RNN, and Transformer models, it was found that DBOCNN-DSformer showed high prediction accuracy and good robustness.

Insights into the Deterioration Mechanism of Charging Ability during Calendar Aging and Cycling Aging of High-Voltage Co-Poor NCM Cathode-Graphite Full Battery.

Lithium-ion battery (LIB) has gained significant recognition for the power cell market owing to its impressive energy density and appealing cost benefit. Among various cathodes, a high-voltage cobalt-poor lithium nickel manganese cobalt oxide cathode (Co-poor NCM cathode) has been considered as a promising strategy to enhance its energy density. Despite these advantages, high-voltage Co-poor NCM cathode-graphite full battery usually suffers from poor rate performance. However, fast charging has been a key indicator for widespread application of power batteries. Although many efforts have been made to improve the charging performance of fresh batteries, few works investigate the charging ability during calendar aging and cycling aging of high-voltage Co-poor NCM cathode-graphite full battery. In this work, we found that the charging ability becomes worse during calendar aging and cycling aging. Results showed that the increasing charge transfer resistance from the cathode is the major obstacle to achieving fast charging during the aging process. To address the problem, high-voltage Al2O3-coated Co-poor NCM cathode successfully prepared via a simple atomic layer deposition (ALD) method has been developed to reduce the decay of charging performance during calendar aging and cycling aging. Al2O3-coated NCM cathode can effectively reduce the growth rate of the resistance of cathode, which is benefiting from the conversion of Al2O3 into LiAlO2 with high ionic conductivity and the restriction formation of rock salt phase. Benefiting from the decreased charge transfer resistance of the NCM cathode, the mismatch of the lithium-ion reaction kinetics is well alleviated, thus effectively reducing the polarization under fast charging. As a result, Al2O3-coated NCM cathode-graphite full battery shows the slow deterioration of charging performance during the aging process. This work provides a promising strategy for constructing fast-charging batteries during calendar aging and cycling aging.

Short‐Term Tests, Long‐Term Predictions – Accelerating Ageing Characterisation of Lithium‐Ion Batteries

AbstractFor the battery industry, quick determination of the ageing behaviour of lithium‐ion batteries is important both for the evaluation of existing designs as well as for R&amp;D on future technologies. However, the target battery lifetime is 8–10 years, which implies low ageing rates that lead to an unacceptably long ageing test duration under real operation conditions. Therefore, ageing characterisation tests need to be accelerated to obtain ageing patterns in a period ranging from a few weeks to a few months. Known strategies, such as increasing the severity of stress factors, for example, temperature, current, and taking measurements with particularly high precision, need care in application to achieve meaningful results. We observe that this challenge does not receive enough attention in typical ageing studies. Therefore, this review introduces the definition and challenge of accelerated ageing along existing methods to accelerate the characterisation of battery ageing and lifetime modelling. We systematically discuss approaches along the existing literature. In this context, several test conditions and feasible acceleration strategies are highlighted, and the underlying modelling and statistical perspective is provided. This makes the review valuable for all who set up ageing tests, interpret ageing data, or rely on ageing data to predict battery lifetime.

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.

Vehicle-to-grid as a competitive alternative to energy storage in a renewable-dominant power system: An integrated approach considering both electric vehicle drivers' willingness and effectiveness

Vehicle-to-grid (V2G) technology, which enables bidirectional power flow between electric vehicles (EVs) and power grids, is a possible solution for integrating EVs and renewable energy (RE) into the power system. While EV drivers are indispensable components for the V2G applications, the extant power system studies have underexamined the willingness of EV drivers to participate in V2G. In addition, few studies investigate whether the small-scale and time-varying V2G supply can replace utility-level energy storage. Given this background, this study combined discrete choice experiments with energy storage capacity expansion planning (ES-CEP) to simulate time-varying V2G participation and effectiveness. The choice experiments identified drivers’ preferences for V2G, which was used to simulate V2G participation under different compensation schemes. Time-varying V2G participation was integrated into ES-CEP for renewable-dominant power system to assess the potential of V2G to replace ES at the utility scale. The results showed that most drivers were unlikely to engage in V2G, although this reluctance decreased during the night. Higher guaranteed state-of-charge, accessible V2G facilities, and monetary enrollment compensation encouraged the participation. The ES-CEP results showed that, even with a limited V2G (4.5–15.3 % of EV drivers), V2G significantly reduced the optimal ES capacity (36.5–45.6 % for power and 39.6–60.6 % for energy), shaved peak netload by 18.5 %, and saved the total cost by 3.35 %, even after accounting for V2G enrollment compensation and battery degradation. EVs that charged during the daytime and participated in V2G at night were the most utilized, resulting in 0.92 % of annual battery aging, but generated a profit of 636,749 KRW (553 USD). The findings of this study underscore the importance of a suitable compensation scheme for V2G and supplementary measures for incentivizing EV charging during off-peak hours, as these elements are essential for the successful implementation of V2G.

Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell

Battery degradation is critical to the cost-effectiveness and usability of battery-powered products. Aging studies help to better understand and model degradation and to optimize the operating strategy. Nevertheless, there are only a few comprehensive and freely available aging datasets for these applications. To our knowledge, the dataset1 presented in the following is one of the largest published to date. It contains over 3 billion data points from 228 commercial NMC/C+SiO lithium-ion cells aged for more than a year under a wide range of operating conditions. We investigate calendar and cyclic aging and also apply different driving cycles to cells. The dataset1 includes result data (such as the remaining usable capacity or impedance measured in check-ups) and raw data (i.e., measurement logs with two-second resolution). The data can be used in a wide range of applications, for example, to model battery degradation, gain insight into lithium plating, optimize operating strategies, or test battery impedance or state estimation algorithms using machine learning or Kalman filtering.

Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion (MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and extraction. Tested on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and reliability. Its robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

Distribution of relaxation times-based analysis of aging mechanisms and prediction of heating domain for alternating current pulse self-heating lithium-ion batteries

Lithium-ion batteries (LIBs)-based electric vehicles (EVs) often encounter challenges such as the reduction in endurance mileage and the increase in charging time at low temperatures. To address these issues, the alternating current (AC) pulse self-heating in LIBs emerges as a pivotal method to overcome performance limitations of EVs at low temperatures. However, employing AC pulsing with high amplitude and low frequency can potentially lead to battery degradation. In this study, the state transitions and aging causes of LIBs during the self-heating are meticulously examined using the distribution of relaxation times (DRT) method. The results reveal that at an AC frequency of 50 Hz or higher, even with amplitudes up to 12 C, there is no degradation for the battery used in this study. For aged LIBs, the significant rise in impedance of solid electrolyte interphase (SEI) due to lithium plating, is the main factor driving battery aging during AC self-heating. Subsequently, an electrochemical-thermal coupling (ETC) model grounded in the degradation mechanism has been developed and experimentally validated to accurately predict the optimal heating domain for self-heating. This approach holds significant implications for the rapid selection of self-heating parameters for facilitating efficient battery performance enhancement.

Enhancing data-driven-based state of health estimation for diverse battery applications through effective feature construction

Data-driven-based State of Health (SOH) estimation methods are popular for their ability to avoid considering the battery's complex aging mechanisms. However, aging features extracted from specific voltage intervals and the SOH estimation models established under specific conditions may no longer be applicable to other battery applications. This work first reveals the necessary conditions for constructing effective aging features in a partial voltage interval, and specifically proposes an applicable method for determining the voltage interval of constructing aging features based on the incremental capacity curve and the voltage curve's shape. The proposed method can be adapted to different battery applications and is suitable for partial charging conditions. Given the different aging patterns present in different battery types and applications, a feature self-scaling -based online learning strategy is developed to reduce the model's reliance on aging datasets and enable it to adapt to real-time battery aging patterns. The proposed methods are validated through both an actual aging experiment and three publicly available aging datasets, showcasing their effectiveness in extending the applicability of the proposed SOH estimation methods for diverse battery applications.

Protocol for Quantifying All Electrolyte Compositions in Aged Lithium‐ion Batteries

AbstractThe aging of lithium‐ion batteries (LIBs) typically accompanies the degradation of electrolyte, but the relationship between them remains unclear. Therefore, quantifying residual electrolyte in batteries at different states of health (SOH) is a crucial issue. Here, we have developed a comprehensive characterization method to quantitatively analyze the electrolyte salts, solvents, and additives in commercial pouch cell, achieving quantification of all electrolyte compositions with high accuracy. Compared to the reported external standard method used in gas chromatography‐mass spectrometry (GC‐MS), we developed an internal standard method, which offers higher accuracy and reliability, with the maximum error decreased from 9.54 % to 3.48 %. Moreover, the quantitative accuracy of the calibration curves remains unchanged after 2 months. Multi‐instruments analysis is also utilized for the extraction and quantitative analysis of electrolyte in practical battery systems, achieving less than 5 % quantification error for all compositions. With our proposed method, it becomes possible to determine the absolute amounts of all electrolyte compositions, rather than obtaining limited information such as concentration or relative content. It is believed that this protocol of quantifying electrolyte compositions in commercial cells could serve as a baseline for further studies to reveal the relationship between electrolyte degradation and battery aging.