相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Review
Energy & Fuels
Alan G. Li et al.
Summary: This article introduces different methods for characterizing the degradation of lithium-ion batteries at different levels and emphasizes the role of machine learning in improving accuracy and speeding up computation time. By surveying the latest advancements in degradation research, we find that the existing techniques lay the foundation for a unified machine learning method, which can characterize degradation at multiple levels and effectively manage lithium-ion systems.
Article
Green & Sustainable Science & Technology
Mario A. Tovar Rosas et al.
Summary: In this study, accurate charging and discharging schedules for two ideal Battery Energy Storage Systems (BESS) powered by solar PV and wind energy are proposed based on predictions using a hybrid CNN-LSTM neural network. The integration of the BESS with the proposed itineraries effectively mitigates peaks in the electric demand profile.
Article
Energy & Fuels
Qi Zhang et al.
Summary: In this paper, a series of electrochemical impedance models were developed for quantitative analysis of internal processes in lithium-ion batteries. The relationship between model parameters and impedance was analyzed, and the proposed model was validated through comparison with measured data, showing high simulation accuracy and adaptability.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Automation & Control Systems
Kailong Liu et al.
Summary: This paper provides a systematic review of recent advancements in electrochemical model development and parameterization for battery management systems. It summarizes and analyzes classic pseudo-two-dimensional models and related model order reduction methodologies, as well as enhanced models considering cell internal inhomogeneity. The paper also discusses parameter identification techniques and solutions for optimizing the parameterization procedure, and highlights current research gaps and challenges.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Thermodynamics
Yan Ma et al.
Summary: This paper proposes a novel method for state of health (SOH) estimation of electric vehicles (EVs) based on improved long short-term memory (LSTM) and health indicators (HIs) extraction from charging-discharging process. By selecting external characteristic parameters related to voltage, current, and temperature as HIs and using correlation analysis and information elimination methods, the method accurately estimates the battery capacity. Additionally, the proposed method utilizes differential evolution grey wolf optimizer (DEGWO) for hyperparameters optimization, improving the performance of the model. Experimental results using battery datasets from NASA and MIT demonstrate the high accuracy and fitting performance of the proposed method for different types of batteries.
Review
Energy & Fuels
G. Vennam et al.
Summary: This article reviews the internal and external degradation mechanisms of Lithium-ion batteries, introduces the corresponding mathematical models and software algorithms, and discusses the application of battery management systems in health state estimation and future research directions.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Seyedmehdi Hosseininasab et al.
Summary: In this paper, a novel coestimation scheme based on a fractional-order battery model is proposed, which has the advantages of high accuracy and low computational cost in estimating battery internal resistance and capacity fade.
JOURNAL OF ENERGY STORAGE
(2022)
Review
Energy & Fuels
Xin Lai et al.
Summary: This study reviews the framework and methods of life cycle assessment (LCA) and evaluates the entire lifespan of lithium-ion batteries (LIBs). The results show that battery production significantly impacts the environment and resources, while battery materials recycling and remanufacturing have considerable environmental and economic values. Moreover, greening of electricity is critical to reducing carbon emissions during the battery life cycle.
Article
Energy & Fuels
Kirandeep Kaur et al.
Summary: By utilizing data-driven modeling with measurable battery signals, this study introduced the use of deep neural networks for battery capacity estimation. The results demonstrate that the LSTM model outperforms others in accuracy, and battery temperature has a relatively minor impact on capacity estimation.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Electrochemistry
Guangzhong Dong et al.
Summary: This study developed a physics-based aging model to describe the degradation dynamics of lithium-ion batteries, which accurately simulates capacity fade. The model considers the formation and growth of the solid-electrolyte-interphase layer, as well as crack propagation due to stress generated by volume expansion of electrode particles.
ELECTROCHIMICA ACTA
(2021)
Article
Chemistry, Physical
Fojin Zhou et al.
Summary: This study reveals the influence mechanism of transition metal deposition on the performance of lithium-ion batteries through proposing a comprehensive capacity degradation model. Experimental results show that Li plating significantly reduces the lithium-ion concentration, leading to capacity attenuation; while the growth of SEI alone has little effect on the capacity, and Mn dissolution accelerates SEI layer growth, significantly affecting battery capacity.
JOURNAL OF POWER SOURCES
(2021)
Article
Energy & Fuels
Fang Liu et al.
Summary: This study proposed an SOH estimation framework that can automatically correct errors caused by battery consistency, providing accurate estimation of battery health status during electric vehicle charging and discharging. By introducing an equivalent circuit based on the AR model, the complexity of the method was reduced while maintaining estimation accuracy. Comparing with traditional external feature relationship methods, this framework achieves better practicality and higher estimation accuracy in estimating lithium-ion battery SOH during discharge.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Chemistry, Physical
Prashant Gargh et al.
Summary: The study shows that lithium plating damages the solid electrolyte interface but reforms during relaxation, causing impedance changes in batteries. Additionally, the measured capacity loss is linearly correlated with impedance changes, suggesting impedance monitoring can be used for predicting the health status of Li-ion batteries.
JOURNAL OF POWER SOURCES
(2021)
Article
Energy & Fuels
Xianqiang Li et al.
Summary: The study investigated the degradation behavior of large-format prismatic lithium-ion batteries, revealing a two-stage degradation pattern where the second stage may be caused by loss of active materials and loss of lithium inventory. Mechanical force was identified as one of the factors leading to rollover failure in the batteries.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Chemistry, Physical
Jinpeng Tian et al.
Summary: The proposed method in this paper utilizes offline OCV test results to estimate aging diagnosis of lithium ion batteries at an electrode level, achieving fast diagnosis. The estimated aging parameters are close to the results obtained by offline tests, enabling reconstruction of OCV-Q curves for battery capacity estimation with high accuracy. The influence of voltage ranges on estimation results is also discussed in the study.
ENERGY STORAGE MATERIALS
(2021)
Article
Energy & Fuels
Xiaogang Wu et al.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2020)
Article
Energy & Fuels
Fang Liu et al.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2020)
Article
Green & Sustainable Science & Technology
Rui Xiong et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2020)
Article
Energy & Fuels
F. Cadini et al.
Article
Energy & Fuels
Aravinda R. Mandli et al.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2019)
Article
Chemistry, Physical
Tiansi Wang et al.
JOURNAL OF POWER SOURCES
(2016)
Article
Chemistry, Physical
Xuebing Han et al.
JOURNAL OF POWER SOURCES
(2014)
Article
Chemistry, Physical
Matthieu Dubarry et al.
JOURNAL OF POWER SOURCES
(2012)
Article
Electrochemistry
M. Safari et al.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2011)
