Article
Computer Science, Artificial Intelligence
Wei Guo, Mao He
Summary: Predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of battery-powered systems. This paper proposes an integrated RUL prediction method based on optimal relevance vectors (RVs) and a modified degradation model (MDM) with the Hausdorff distance (HD), which improves the accuracy of long-term predictions.
APPLIED SOFT COMPUTING
(2022)
Article
Thermodynamics
Fang Yao, Wenxuan He, Youxi Wu, Fei Ding, Defang Meng
Summary: This paper proposes a hybrid prediction model PSO-ELM-RVM for accurately predicting the remaining useful life (RUL) of lithium-ion batteries. The model integrates particle swarm optimization (PSO), an extreme learning machine (ELM), and relevance vector machine (RVM) to provide prediction results with uncertainty expression. Experimental results validate the effectiveness of the proposed model.
Article
Energy & Fuels
Hailin Feng, Dandan Song
Summary: In this paper, a new health indicator (HI) is proposed to predict the remaining useful life (RUL) of lithium-ion batteries from the discharge surface temperature, which is convenient for real-time measurement and online estimation. The results show that the new HI is effective for degradation modeling, with a RUL prediction error of less than 5 cycles for 5#, 6# and 7# batteries.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Thermodynamics
Zewang Chen, Na Shi, Yufan Ji, Mu Niu, Youren Wang
Summary: This paper proposes a hybrid algorithm combining BLS with RVM for predicting the remaining useful life of lithium-ion batteries. Experimental results show higher prediction accuracy and stronger long-term prediction capabilities for the proposed algorithm.
Article
Energy & Fuels
Jing-Song Qiu, Yong-Cun Fan, Shun-Li Wang, Xiao Yang, Jia-Lu Qiao, Dong-Lei Liu
Summary: An improved multi-kernel relevance vector machine model is proposed for predicting the remaining useful life of lithium-ion batteries, utilizing aging features extracted through gray relation analysis and kernel function combination coefficients determined by an improved gray wolf constrained optimization algorithm. The model shows higher prediction accuracy and more robust long-term prediction capability compared to other models.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Chemistry, Multidisciplinary
Xinyan Liu, Hong-Jie Peng, Bo-Quan Li, Xiang Chen, Zheng Li, Jia-Qi Huang, Qiang Zhang
Summary: This study presents an interpretable hybrid machine learning framework to tackle the chemical challenges in complex pattern batteries. The framework exhibits excellent performance in prediction and physical understanding, and identifies a new performance indicator that sheds light on battery design and optimization. Its versatility and adaptability make this approach applicable to other energy storage systems.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Engineering, Mechanical
Junchuan Shi, Alexis Rivera, Dazhong Wu
Summary: This paper proposes a physics-informed machine learning method for accurate modeling and prediction of the remaining useful life (RUL) of Lithium-ion batteries. The method considers the impact of battery health and operating conditions on battery aging and combines a calendar and cycle aging model with an LSTM layer for modeling and prediction. Experimental results demonstrate that the proposed method can accurately model and predict the degradation behavior and RUL of Lithium-ion batteries under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Jianguo Wang, Shude Zhang, Chenyu Li, Lifeng Wu, Yingzhou Wang
Summary: This article proposes a novel hybrid method to enhance the prediction precision and robustness of lithium-ion battery remaining useful life. By utilizing improved complete ensemble empirical mode decomposition and a special-designed interpolation reconstruction mechanism, the battery capacity degradation series is decomposed into a trend subseries and fluctuation subseries. Weighted least square support vector machine and long short-term memory network are then used to predict these subseries. Experimental results demonstrate that the proposed method achieves higher prediction accuracy and robustness compared to other models.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Energy & Fuels
Yeong-Hwa Chang, Yu-Chen Hsieh, Yu-Hsiang Chai, Hung-Wei Lin
Summary: This paper aims to establish a predictive model for battery lifetime using data analysis. The procedure of model establishment is illustrated in detail, including the data pre-processing, modeling, and prediction. The characteristics of lithium-ion batteries are introduced. In this study, data analysis is performed with MATLAB, and the open-source battery data are provided by NASA. The addressed models include the decision tree, nonlinear autoregression, recurrent neural network, and long short-term memory network. In the part of model training, the root-mean-square error, integral of the squared error, and integral of the absolute error are considered for the cost functions. Based on the defined health indicator, the remaining useful life of lithium-ion batteries can be predicted. The confidence interval can be used to describe the level of confidence for each prediction. According to the test results, the long short-term memory network provides the best performance among all addressed models.
Article
Computer Science, Artificial Intelligence
Liming Deng, Wenjing Shen, Hongfei Wang, Shuqiang Wang
Summary: This paper introduces a novel empirical model for predicting the remaining useful life of lithium-ion batteries by modeling both global and local degradation processes. The model outperforms state-of-the-art methods in capturing degradation and regeneration phenomena.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Energy & Fuels
Xianmeng Meng, Cuicui Cai, Yueqin Wang, Qijian Wang, Linglong Tan
Summary: A new method for RUL prediction of Lithium-ion batteries is proposed in this paper, which combines noise reduction, optimization algorithm and regression analysis. The method shows excellent performance in experiments and outperforms existing methods.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Ivan Sanz-Gorrachategui, Pablo Pastor-Flores, Milutin Pajovic, Ye Wang, Philip Orlik, Carlos Bernal-Ruiz, Antonio Bono-Nuez, Jesus Sergio Artal-Sevil
Summary: This article explores the estimation of battery remaining useful life (RUL) and proposes health indicators and effective estimation algorithms. The algorithms show satisfactory results on a recent dataset from Toyota Research Institute.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
Sadiqa Jafari, Yung-Cheol Byun, Seokjun Ko
Summary: The study introduces a hybrid model utilizing machine learning techniques to accurately estimate the remaining useful life and energy of lithium-ion batteries, providing valuable insights for evaluating battery health and deterioration over time.
Article
Engineering, Multidisciplinary
Antonio Bracale, Pasquale De Falco, Luigi Pio Di Noia, Renato Rizzo
Summary: In this paper, probabilistic models based on time series and regression methods are developed and compared for predicting the remaining useful life of lithium-ion batteries. The models are developed using data from accelerated degradation tests and different approaches are used for predicting battery health and lifespan. The results demonstrate the accuracy and effectiveness of the proposed models.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Dawei Pan, Hengfeng Li, Shaojun Wang
Summary: This research proposes a new model that combines LSTM based on transfer learning and particle filter (PF) model to solve the RUL prediction problem of lithium-ion batteries. The experimental results demonstrate that the method has high accuracy and application potential for RUL prediction under different working conditions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Review
Engineering, Aerospace
Siyu Zhang, Lei Su, Jiefei Gu, Ke LI, Lang Zhou, Michael Pecht
Summary: In practical mechanical fault detection and diagnosis, transfer learning combined with deep learning can improve the performance of the target task while reducing the demand for large-scale supervised data and high computation power. However, direct transfer may lead to a significant reduction in detection performance due to domain differences. Domain adaptation strategies can address this issue by transferring distribution information from the source domain to the target domain. This survey reviews various current domain adaptation strategies combined with deep learning and analyzes their principles, advantages, and disadvantages, as well as their application in fault diagnosis.
CHINESE JOURNAL OF AERONAUTICS
(2023)
Article
Chemistry, Multidisciplinary
Dong Wang, Le Dong, Guoying Gu
Summary: Lattice metamaterials constructed by curved microstructures exhibit large stretchability and are promising in soft electronics and soft robotics. Fractal structures are particularly efficient in improving stretchability as it shows multiple-order uncurling. However, the development of fractal metamaterials is hindered by hierarchical structures and large deformations. In this study, a design framework combining experiments, hierarchical theoretical models, and finite element simulations is developed to program the mechanical behaviors of fractal metamaterials. For 3D printing, a digital design tool is developed to visualize the structure and automatically generate the manufacturing representations. Results show that large stretchability, bionic stress-strain curve matching, and imperfection insensitivity can be programmed by tuning the geometric parameters. An integrated device of an electromyogram sensor embedded in an imperfection-insensitive fractal metamaterial that matches the J-shaped stress-strain curve of human skin is demonstrated. Light-emitting diode devices based on fractal metamaterial with shape reconfiguration are also presented. This study paves a new way to realize multifunctional soft devices using fractal metamaterials.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Engineering, Multidisciplinary
Yanqing Deng, Bingchang Hou, Changqing Shen, Dong Wang
Summary: Machine condition monitoring aims to evaluate machine health conditions by analyzing machine vibration signals. This paper proposes a statistical learning modeling based method to construct an interpretable health indicator with an inherent statistical threshold. The proposed method can effectively detect early machine faults and provide degradation assessment trends.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Energy & Fuels
Xiangxin An, Guojin Si, Tangbin Xia, Dong Wang, Ershun Pan, Lifeng Xi
Summary: In this research, a complex maintenance planning and production scheduling problem for serial-parallel manufacturing systems under time-of-use tariffs is studied. An energy-efficient two-stage maintenance strategy is developed, which aims to minimize the total electricity cost and tardiness cost. The strategy includes preventive maintenance planning in the first stage and a mixed-integer programming model for scheduling with maintenance actions in the second stage. The results demonstrate the effectiveness of this strategy in reducing electricity costs and ensuring system productivity, providing guidance for industrial enterprises.
Article
Engineering, Mechanical
Bingchang Hou, Dong Wang, Tangbin Xia, Zhike Peng, Kwok-Leung Tsui
Summary: This paper proposes a new decomposition approach called difference mode decomposition (DMD) to adaptively decompose a mixed signal into concerned components (CC), reference components, and noise. The proposed DMD relies on convex optimization and Fourier transform, and its decomposition is mathematically justified and composes physical interpretations. Analyses of simulated and real-world bearing and gear vibration signals are used to verify the effectiveness and superiority of the proposed DMD over existing adaptive mode decomposition algorithms.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Bingchang Hou, Xiao Feng, Jin-Zhen Kong, Zhike Peng, Kwok-Leung Tsui, Dong Wang
Summary: This paper proposes a new method called optimized weights spectrum autocorrelation (OWSAC) to identify fault characteristic frequencies (FCFs) and their harmonics for rotating machine fault diagnosis. The OWSAC method does not require any fault signature extraction methods for signal preprocessing, and effectively eliminates the influence of interference spectral lines and noise spectral lines by introducing optimized weights spectrum and adaptive threshold method.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Editorial Material
Engineering, Mechanical
Chao Hu, Kai Goebel, David Howey, Zhike Peng, Dong Wang, Peng Wang, Byeng D. Youn
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Dong Wang, Bingchang Hou, Tongtong Yan, Changqing Shen, Zhike Peng
Summary: This article discusses the construction of a physically interpretable prototypical neural network and its correlation with physically interpretable fault features to support machine health conditions. It emphasizes the importance of physically interpretable weights in machine condition monitoring.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Qi Li, Liang Chen, Lin Kong, Dong Wang, Min Xia, Changqing Shen
Summary: This paper introduces a new cross-domain augmentation (CDA) method to achieve fault diagnosis under unseen working conditions. Through adversarial training on multi-source domains and the augmented domain, the method enables learning generalized and augmented features, facilitating the generalization ability of the model. Extensive experiment studies show that the method can successfully solve the cross-domain diagnosis problem under unseen working conditions.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Thermodynamics
Simin Peng, Yunxiang Sun, Dandan Liu, Quanqing Yu, Jiarong Kan, Michael Pecht
Summary: This paper proposes a battery state of health estimation method based on multi-health features extraction and an improved long short-term memory neural network. The correlation between multi-health features and state of health is evaluated by the grey relational analysis. Improved quantum particle swarm optimization algorithm is used to obtain the hyper-parameters. The experimental results show high estimation accuracy and robustness of the proposed method.
Article
Automation & Control Systems
Xiao Feng, Dong Wang, Bingchang Hou, Tongtong Yan
Summary: Federated Learning (FL) is a technique that addresses the problems of data silos and data privacy by enabling learning of a global model while keeping personal data stored locally. However, the statistical heterogeneity challenge and model interpretability have not been well addressed. This paper proposes an interpretable FL framework for machine condition monitoring and fault diagnosis, using a local client model to identify fault characteristic frequencies and their harmonics. The effectiveness of the method is demonstrated through experiments with bearing run-to-failure datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Dazhi Wang, Sihan Wang, Deshan Kong, Jiaxing Wang, Wenhui Li, Michael Pecht
Summary: The study aims to predict the electromagnetic field and output performance of permanent magnet eddy current devices (PMECDs) using a proposed physics-informed sparse neural network (PISNN). A unified physical model is defined for different PMECDs, solving a parameterized magnetic quasi-static problem. The model integrates soft and hard constraint modules, with physical equations, into the objective function and utilizes stochastic gradient descent for training. Results demonstrate accurate and efficient predictions of the EM field distribution and output torque, showcasing the potential for transfer learning.
IEEE MAGNETICS LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Yu Zhou, Lin Gao, Dong Wang, Wenhui Wu, Zhiqiang Zhou, Tingqun Ye
Summary: In this study, an improved localized feature selection method based on multiobjective binary particle swarm optimization was proposed to address fault diagnosis by utilizing the local distribution of data without the need for balancing strategies.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Meimei Zheng, Zhiyun Su, Dong Wang, Ershun Pan
Summary: This paper investigates the joint optimization of maintenance and spare part ordering from multiple suppliers for systems consisting of multiple components. A model is established through a Markov decision process, and a value iteration algorithm and a hybrid deep reinforcement learning algorithm (HDRL) are designed to solve the model. Numerical experiments validate the effectiveness of the HDRL algorithm.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Electrical & Electronic
Shilong Sun, Haodong Huang, Tengyi Peng, Dong Wang
Summary: This study proposes an improved data privacy diagnostic framework for multiple same types of machinery components. The framework enriches data by integrating various data sources and improves data protection efficiency by utilizing different local diagnosis models, enabling the interaction of data information. Experimental results demonstrate the flexibility and reliability of the proposed method in industrial scenarios.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Chemistry, Physical
Tianyu Chen, Zhibin Lu, Guangjin Zeng, Yongmin Xie, Jie Xiao, Zhifeng Xu
Summary: The study introduces a high-performance LSGM electrolyte-supported tubular DC-SOFC stack for portable applications, which shows great potential in developing into high-performing, efficient, and environmentally friendly portable power sources for distributed applications.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Wenbin Tong, Yili Chen, Shijie Gong, Shaokun Zhu, Jie Tian, Jiaqian Qin, Wenyong Chen, Shuanghong Chen
Summary: In this study, a three-dimensional porous NiO interface layer with enhanced anode dynamics is fabricated, forming a Schottky contact with the zinc substrate, allowing rapid and uniform zinc plating both inside and below the interface layer. The resulting NiO@Zn exhibits exceptional stability and high capacity retention.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Yafeng Bai, Kaidi Li, Liying Wang, Yang Gao, Xuesong Li, Xijia Yang, Wei Lu
Summary: In this study, a flexible zinc ion supercapacitor with gel electrolytes, porous alpha-MnO2@reduced graphene oxide cathode, and activated carbon/carbon cloth anode was developed. The device exhibits excellent electrochemical performance and stability, even at low temperatures, with a high cycle retention rate after 5000 cycles.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Anmol Jnawali, Matt D. R. Kok, Francesco Iacoviello, Daniel J. L. Brett, Paul R. Shearing
Summary: This article presents the results of a systematic study on the electrochemical performance and mechanical changes in two types of commercial batteries with different anode chemistry. The study reveals that the swelling of anode layers in batteries with silicon-based components causes deformations in the jelly roll structure, but the presence of a small percentage of silicon does not significantly impact the cycling performance of the cells within the relevant state-of-health range for electric vehicles (EVs). The research suggests that there is room for improving the cell capacities by increasing the silicon loading in composite anodes to meet the increasing demands on EVs.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Yohandys A. Zulueta, My Phuong Pham-Ho, Minh Tho Nguyen
Summary: Advanced atomistic simulations were used to study ion transport in the Na- and K-doped lithium disilicate Li2Si2O5. The results showed that Na and K doping significantly enhanced Li ion diffusion and conduction in the material.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Zongying Han, Hui Dong, Yanru Yang, Hao Yu, Zhibin Yang
Summary: An efficient phase inversion-impregnation approach is developed to fabricate BaO-decorated Ni8 mol% YSZ anode-supported tubular solid oxide fuel cells (SOFCs) with anti-coking properties. BaO nanoislands are successfully introduced inside the Ni-YSZ anode, leading to higher peak power densities and improved stability in methane fuel. Density functional theory calculations suggest that the loading of BaO nanoislands facilitates carbon elimination by capturing and dissociating H2O molecules to generate OH.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Suresh Mamidi, Dan Na, Baeksang Yoon, Henu Sharma, Anil D. Pathak, Kisor Kumar Sahu, Dae Young Lee, Cheul-Ro Lee, Inseok Seo
Summary: Li-CO2 batteries, which utilize CO2 and have a high energy density, are hindered in practical applications due to slow kinetics and safety hazards. This study introduces a stable and highly conductive ceramic-based solid electrolyte and a metal-organic framework catalyst to improve the safety and performance of Li-CO2 batteries. The optimized Li-CO2 cell shows outstanding specific capacity and cycle life, and the post-cycling analysis reveals the degradation mechanism of the electrodes. First-principles calculations based on density functional theory are also performed to understand the interactions between the catalyst and the host electrode. This research demonstrates the potential of MOF cathode catalyst for stable operation in Li-CO2 batteries.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Ganghua Xiang, Zhihuan Qiu, Huilong Fei, Zhigang Liu, Shuangfeng Yin, Yuen Wu
Summary: In this study, a CeFeOx-supported Pt single atoms and subnanometric clusters catalyst was developed, which exhibits enhanced catalytic activity and stability for the preferential oxidation of CO in H2-rich stream through synergistic effect.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Dimitrios Chatzogiannakis, Marcus Fehse, Maria Angeles Cabanero, Natalia Romano, Ashley Black, Damien Saurel, M. Rosa Palacin, Montse Casas-Cabanas
Summary: By coupling electrochemical testing to operando synchrotron based X-ray absorption and powder diffraction experiments, blended positive electrodes consisting of LiMn2O4 spinel (LMO) and layered LiNi0.5Mn0.3Co0.2O2 (NMC) were studied to understand their redox mechanism. It was found that blending NMC with LMO can enhance energy density at high rates, with the blend containing 25% LMO showing the best performance. Testing with a special electrochemical setup revealed that the effective current load on each blend component can vary significantly from the nominal rate and also changes with SoC. Operando studies allowed monitoring of the oxidation state evolution and changes in crystal structure, in line with the expected behavior of individual components considering their electrochemical current loads.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Chiara Cementon, Daniel Dewar, Thrinathreddy Ramireddy, Michael Brennan, Alexey M. Glushenkov
Summary: This Perspective discusses the specific power and power density of lithium-ion capacitors, highlighting the fact that their power characteristics are often underestimated. Through analysis, it is found that lithium-ion capacitors can usually achieve power densities superior to electrochemical supercapacitors, making them excellent alternatives to supercapacitors.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Weihao Wang, Hao Yu, Li Ma, Youquan Zhang, Yuejiao Chen, Libao Chen, Guichao Kuang, Liangjun Zhou, Weifeng Wei
Summary: This study achieved an improved electrolyte with excellent low-temperature and high-voltage performance by regulating the Li+ solvation structure and highly concentrating it. The electrolyte exhibited outstanding oxidation potential and high ionic conductivity under low temperature and high voltage conditions, providing a promising approach for the practical application of high-voltage LIBs.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Martin Bures, Dan Gotz, Jiri Charvat, Milos Svoboda, Jaromir Pocedic, Juraj Kosek, Alexandr Zubov, Petr Mazur
Summary: Vanadium redox flow battery is a promising energy storage solution with long-term durability, non-flammability, and high overall efficiency. Researchers have developed a mathematical model to simulate the charge-discharge cycling of the battery, and found that hydraulic connection of electrolyte tanks is the most effective strategy to reduce capacity losses, achieving a 69% reduction.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
M. Rodriguez-Gomez, J. Campo, A. Orera, F. de La Fuente, J. Valenciano, H. Fricke, D. S. Hussey, Y. Chen, D. Yu, K. An, A. Larrea
Summary: In this study, we analysed the operando performance of industrial lead cells using neutron diffraction experiments. The experiments revealed the evolution of different phases in the positive electrode, showed significant inhomogeneity of phase distribution inside the electrode, and estimated the energy efficiency of the cells.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Jiawei Liu, Chenpeng Wang, Yue Yao, Hao Ye, Yinglong Liu, Yingli Liu, Xiaoru Xu, Zhicong Chen, Huazheng Yang, Gang Wu, Libin Lei, Chao Wang, Bo Liang
Summary: The study focuses on utilizing double conductive Ni-pads as anode collectors in micro-tubular solid oxide fuel cells. The simulation results show excellent performance and stability of DCNPs, and also highlight the potential applications in various fields.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Yang Wang, Kangjie Zhou, Lang Cui, Jiabing Mei, Shengnan Li, Le Li, Wei Fan, Longsheng Zhang, Tianxi Liu
Summary: This study presents a polyimide sandwiched separator (s-PIF) for improving the cycling stability of Li-metal batteries. The s-PIF separator exhibits superior mechanical property, electrolyte adsorption/retention and ion conductivity, and enables dendrite-free Li plating/stripping process.
JOURNAL OF POWER SOURCES
(2024)
