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Article
Engineering, Civil
Jiaqiang Tian et al.
Summary: In this study, a battery pack inconsistency evaluation method based on an improved GMM and feature fusion approach is proposed. The method accurately estimates battery parameters and quantifies inconsistency using the standard deviation coefficient approach.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Industrial
Mingqiang Lin et al.
Summary: With the widespread use of lithium-ion batteries, battery failures have become a critical concern. Existing studies mainly focus on improving prediction models, while addressing uncertainty issues in the battery degradation process is gaining more attention. In this paper, we propose a new prediction method using neural networks and the hidden Markov model with uncertainty quantification. Experimental results show that the proposed method outperforms in battery health prediction.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Review
Energy & Fuels
Friedrich von Buelow et al.
Summary: The ageing of Lithium-ion batteries depends on their operation during charging, discharging, and rest phases, and can be forecasted to determine the state of health (SOH) of the battery. This SOH forecasting is valuable for fleet managers of battery electric vehicle (BEV) fleets to plan vehicle replacement and optimize operational strategies. However, there are limitations in the applicability and comparability of existing models due to different data sets, metrics, output values, and forecast horizons.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Thermodynamics
Jiaqiang Tian et al.
Summary: This study proposes a state-of-health (SOH) attenuation model considering driving mileage and seasonal temperature for battery health estimation, which is significant for battery pack management and maintenance. The variable forgetting factor recursive least square (VFFRLS) algorithm is used for battery model parameter identification and the extended Kalman-particle filter (EPF) algorithm is proposed for online capacity estimation. The proposed model and algorithm are verified using actual vehicle data over nine months. The experimental results demonstrate the accurate estimation of model parameters and capacity through the proposed algorithm, and the decrease in average capacity of the battery module with total mileage. The compensation of monthly driving mileage and ambient temperature factors effectively improves the accuracy of the SOH model.
Article
Thermodynamics
Ji Wu et al.
Summary: This paper proposes an improved radial basis function neural network (IRBFNN) to accurately estimate the state of health (SOH) of lithium-ion batteries. The IRBFNN can simultaneously fit general trends and local fluctuations, and the maximum estimation errors are within +/- 4%. The results show that the proposed method effectively alleviates the poor estimation performance of traditional neural network-based algorithms in the later stage of battery aging.
Review
Chemistry, Multidisciplinary
Yunhong Che et al.
Summary: This paper provides a comprehensive review of aging mechanisms and health prognostic methods for lithium-ion batteries, and discusses the main challenges and research prospects. The complex relationships between aging mechanisms, modes, factors, and types are summarized. Prognostic methods are categorized based on time scales and objectives, followed by detailed reviews and comparative evaluations. Key challenges are presented and potential solutions are discussed. Future trends and new ideas for battery health prognostics are proposed.
ENERGY & ENVIRONMENTAL SCIENCE
(2023)
Article
Automation & Control Systems
Mao Tan et al.
Summary: With the increase in the number of electric vehicles, battery swapping is seen as promising due to its short waiting time. However, it is challenging to achieve efficient scheduling in a large scale battery swap station due to the uncertainty of the power grid and EV behavior. To address this, a new bi-level scheduling model is proposed, combining deep reinforcement learning for optimal power allocation and MILP subproblems for battery dispatching. Experimental results show excellent performance and cost reduction, benefiting both the battery swap station and the power grid in peak shaving and valley filling.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Energy & Fuels
Mao Tan et al.
Summary: This paper proposes an IES multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor. The method effectively screens multi-level features and removes prediction noise, as well as describes the involvement of different loads in the total load. The integrated model achieves superior performance compared to existing methods, with average prediction accuracies of 97.18% for electricity, cooling, and heat loads, and 97.85% for the total load.
Article
Chemistry, Applied
Xinghua Liu et al.
Summary: Maximizing the utilization of lithium-ion battery capacity is crucial for alleviating range anxiety in electric vehicles. A capacity utilization scheme based on a path planning algorithm is proposed to address the issue of battery pack inconsistency and reduce safety hazards. By using alternating cell discharge and finding the optimal energy path, the proposed scheme improves battery pack consistency and decreases relay loss.
JOURNAL OF ENERGY CHEMISTRY
(2023)
Article
Energy & Fuels
Ruilong Xu et al.
Summary: This paper proposes a hybrid battery health prediction method that combines Transformer and online correction models. It accurately predicts the battery health by establishing a nonlinear relationship between measured data and capacity decline. By considering multi-scale health features and reducing feature dimensions, this method achieves optimal prediction performance for batteries under different aging conditions.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Zhiqi Zhang et al.
Summary: This article proposes a method for estimating the health status of lithium-ion batteries based on the GBDT model, utilizing machine learning techniques to accurately evaluate battery health through the selection of effective feature combinations.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Engineering, Industrial
Yujie Wang et al.
Summary: This paper discusses the application of digital twin technology in the field of battery management, presents a networked architecture of cloud-side-end collaboration, and establishes a digital twin model of the battery for refined and safety management.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Chemistry, Physical
Mingqiang Lin et al.
Summary: This paper proposes a multi-feature-based multi-model fusion method for estimating the state-of-health (SOH) of lithium-ion batteries. By extracting features from different sources and using multi-model fusion, the accuracy of SOH estimation is improved.
JOURNAL OF POWER SOURCES
(2022)
Article
Automation & Control Systems
Yizhao Gao et al.
Summary: This article presents a scheme using a simplified reduced-order electrochemical model and dual nonlinear filters for the reliable co-estimations of cell state-of-charge (SOC) and state-of-health (SOH). By accessing unmeasurable physical variables such as surface and bulk solid-phase concentration, the feasibility and performance of SOC estimator are revealed. Aging factors including loss of lithium ions, loss of active materials, and resistance increment are identified to improve the precision of SOC estimation for aged cells.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Review
Engineering, Electrical & Electronic
Wiljan Vermeer et al.
Summary: Battery aging is a critical problem in battery research that limits the power and energy capacity during the battery's life. This article reviews empirical and semiempirical modeling techniques and aging studies, highlighting the limitations and challenges of different models. The study finds that stress factors are often oversimplified and their correlations are not taken into account. The knowledge provided in this article can be used to evaluate the limitations of aging models and improve their accuracy for various applications.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Energy & Fuels
Pengliang Qin et al.
Summary: This paper introduces a new method for extracting aging features and a gradient boosting-based data-driven method, which can effectively improve the prediction accuracy of data-driven algorithms. The designed online incremental learning strategy can reduce learning time significantly.
JOURNAL OF ENERGY STORAGE
(2022)
Letter
Automation & Control Systems
Yujie Wang et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Energy & Fuels
Mei Zhang et al.
Summary: This paper proposes an improved Douglas-Peucker feature extraction algorithm and utilizes the LAOS-XGboost model to predict the State of Health (SOH) of batteries. The experimental results show that the proposed method achieves high accuracy and robustness in predicting the SOH of different batteries and the same battery.
Article
Energy & Fuels
Kaiquan Li et al.
Summary: This paper compares three typical neural network models for accurate estimation of battery state of health (SOH) and applies empirical mode decomposition and Pearson correlation coefficient to the research. The results indicate that LSTM and bidirectional LSTM have higher performance in battery SOH estimation.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Chunyu Wang et al.
Summary: Accurate estimation of battery SOC and SOH is crucial for battery management systems. This paper proposes an advanced fusion estimation method considering temperature and aging effects, achieving precise estimation through parameter identification and adaptive filtering.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Kaiquan Li et al.
Summary: This paper compares three typical neural networks and variants on accuracy and robustness, extracts effective health indicators from measurement data using empirical mode decomposition, and applies the Pearson correlation coefficient to select features. Several neural network models are built and optimized using the novel hyperband optimization method. The experimental results show that LSTM and bidirectional LSTM have higher charge-discharge conditions insensitivity and precision in battery SOH estimation.
JOURNAL OF ENERGY STORAGE
(2022)
Review
Energy & Fuels
Xin Sui et al.
Summary: This paper systematically reviews the five most studied types of machine learning algorithms for battery state of health estimation, comparing their advantages and applicability. Support vector machine and artificial neural network algorithms are still research hotspots, while deep learning shows great potential in estimating battery SOH under complex aging conditions with big data. Ensemble learning provides an emerging alternative in balancing data size and accuracy.
Article
Automation & Control Systems
Guangzhong Dong et al.
Summary: Battery prognostics and health management are crucial for lithium-ion batteries in electric vehicles. This article presents a probabilistic method for battery degradation modeling and health prognosis based on dynamic Bayesian network (DBN). Experimental tests show that the proposed methods can provide accurate and reliable battery state-of-health estimation and remaining useful life prediction.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Hailin Feng et al.
Summary: This paper proposes a novel GPR-based method for predicting the SOH and RUL of Li-ion batteries, utilizing feature extraction and establishment of prediction models. The results show that the proposed model outperforms other models in terms of prediction accuracy.
JOURNAL OF POWER ELECTRONICS
(2021)
Article
Chemistry, Physical
Kodjo S. R. Mawonou et al.
Summary: This paper investigates the critical health assessment of Lithium-ion batteries and proposes two new aging indicators to improve current solutions, as well as introducing a data-driven battery aging prediction method using the random forest algorithm. The study shows that these methods can generate reliable health status evaluation and high-precision aging prediction.
JOURNAL OF POWER SOURCES
(2021)
Article
Computer Science, Artificial Intelligence
Ugur Yayan et al.
Summary: The transition to non-fossil fuels presents challenges in battery technologies, particularly in the efficiency of Li-ion batteries for electric vehicles (EVs). Despite improvements, capacity degradation with aging remains a key issue in the industry.
APPLIED ARTIFICIAL INTELLIGENCE
(2021)
Article
Thermodynamics
Jiaqiang Tian et al.
Summary: This work proposed an aging mode identification method based on open-circuit voltage matching analysis, established the open-circuit voltage model of the full cell, and developed a non-destructive aging mechanism identification method to quantify the loss of lithium inventory and active materials of electrodes. The developed models and methods showed high accuracy in predicting remaining useful life and short-term state of health.
Article
Chemistry, Physical
Yue Zhang et al.
Summary: Rechargeable tellurium (Te)-based batteries are promising energy storage devices with high volumetric energy density, but face challenges in terms of electrochemistry and overall performance. Research focuses on understanding the role of Te structure, carbon host chemistry, electrolytes, and addressing issues like Te pulverization and parasitic effects to achieve reversible Te phase transitions. Additional studies are conducted on novel metal-Te based batteries to compare performance data and explore potential research directions for the future.
ENERGY STORAGE MATERIALS
(2021)
Article
Electrochemistry
Marc D. Berliner et al.
Summary: Porous electrode theory is commonly used to model battery behavior, but most effective parameters are not practically identifiable from cycling data in lithium-ion batteries. The only identifiable parameter from C/10 discharge data is the effective solid diffusion coefficient. Additional experiments are required to uniquely determine the full set of parameters.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2021)
Article
Engineering, Electrical & Electronic
Xiaosong Hu et al.
Summary: This article conducts a comprehensive study on data-driven State of Health (SOH) estimation methods for lithium-ion batteries. A new classification for health indicators is proposed, and a combination of fusion-based selection method and Gaussian process regression (GPR) shows superior estimation performance.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2021)
Article
Chemistry, Physical
Xiaoyu Li et al.
JOURNAL OF POWER SOURCES
(2020)
Article
Thermodynamics
Rui Pan et al.
Review
Green & Sustainable Science & Technology
Yujie Wang et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2020)
Article
Energy & Fuels
Yaxiang Fan et al.
JOURNAL OF ENERGY STORAGE
(2020)
Article
Chemistry, Physical
Peiyao Guo et al.
JOURNAL OF POWER SOURCES
(2019)
Article
Engineering, Electrical & Electronic
Xuning Feng et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Thermodynamics
Fangfang Yang et al.
Article
Thermodynamics
Chang Liu et al.
Article
Energy & Fuels
J. Li et al.
Article
Chemistry, Physical
Duo Yang et al.
JOURNAL OF POWER SOURCES
(2018)
Article
Engineering, Electrical & Electronic
Fangfang Yang et al.
MICROELECTRONICS RELIABILITY
(2017)
Article
Electrochemistry
Rui Pan et al.
ELECTROCHIMICA ACTA
(2017)
Article
Energy & Fuels
Meru A. Patil et al.
Article
Automation & Control Systems
Datong Liu et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2015)
Article
Computer Science, Interdisciplinary Applications
Nina Golyandina et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2014)
Article
Chemistry, Physical
Wei He et al.
JOURNAL OF POWER SOURCES
(2011)
