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Green Energy and Intelligent Transportation 2024年第1期3卷 46-54页

作者： Kui Chen Jiali Li Kai Liu Changshan Bai Jiamin Zhu Guoqiang Gao Guangning Wu Salah Laghrouche School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China FEMTO-ST UMR CNRS 6174and FCLABFR CNRS 3539UniversitéBourgogne Franche-ComtéBelfortUTBM90000France

Lithium-ion battery state of health(SOH)estimation is an essential issue in battery management *** order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion battery *** Swarm Optimization algorithm(PSO)is used to automatically adjust and optimize the parameters of ELM to improve estimation ***,collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity *** Grey Relation Analysis(GRA)method to analyze the correlation between battery capacity and five characteristic ***,an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics,and a PSO is introduced to optimize the parameters of the capacity estimation *** proposed method is validated by the degradation experiment of the lithium-ion battery under different *** results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation,and the average absolute percentage error is less than 1%.

关键词： Lithium-ion battery state of health estimation Grey relation analysis method Particle swarm optimization algorithm Extreme learning machine

Transfer learning from synthetic data for open-circuit voltage curve reconstruction and state of health estimation of lithium-ion batteries from partial charging segments

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Energy and AI 2024年第3期17卷 80-97页

作者： Tobias Hofmann Jacob Hamar Bastian Mager Simon Erhard Jan Philipp Schmidt Chair of Systems Engineering for Electrical Energy Storage University of BayreuthWeiherstraße 26Bayreuth95448BavariaGermany BMW Group Petuelring 130Munich80809BavariaGermany

Data-driven models for battery state estimation require extensive experimental training data,which may not be available or suitable for specific tasks like open-circuit voltage(OCV)reconstruction and subsequent state of health(SOH)*** study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory(TCN-LSTM)network trained on synthetic data from an automotive nickel cobalt aluminium oxide(NCA)cell generated through a mechanistic model *** data consists of voltage curves at constant temperature,C-rates between C/30 to 1C,and a SOH-range from 70%to 100%.The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide(NMC)cell training data for higher use *** TL models’performances are compared with models trained solely on experimental data,focusing on different C-rates and voltage *** results demonstrate that the OCV reconstruction mean absolute error(MAE)within the average battery electric vehicle(BEV)home charging window(30%to 85%state of charge(SOC))is less than 22 mV for the first three use cases across all *** SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error(MAPE)below 2.2%for these *** study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets,a lithium iron phosphate(LFP)cell and an entirely artificial,non-existing,cell,showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge,even between different cell chemistries.A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case,where the absence of such comprehensive data hindered the TL process.

关键词： Lithium-ion battery state of health estimation Transfer learning OCV curve Partial charging Synthetic data

Performance simulation method and state of health estimation for lithium-ion batteries based on aging-effect coupling model

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Green Energy and Intelligent Transportation 2023年第3期2卷 16-29页

作者： Deyu Fang Wentao Wu Junfu Li Weizhe Yuan Tao Liu Changsong Dai Zhenbo Wang Ming Zhao School of Automotive Engineering Harbin Institute of TechnologyWeihai 264209ShandongChina School of New Energy Harbin Institute of TechnologyWeihai 264209ShandongChina Guangdong Guanghua Sci-Tech Co. Ltd.Shantou 515000GuangdongChina School of Chemical Engineering and Chemistry Harbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina Zhuhai Zhongli New Energy Sci-Tech Co. Ltd.Zhuhai 519000GuangdongChina

Accurate simulation of characteristics performance and state of health(SOH)estimation for lithium-ion batteries are critical for battery management systems(BMS)in electric *** simplified electrochemical model(SEM)can achieve accurate estimation of battery terminal voltage with less computing *** ensure the applica-bility of life-cycle usage,degradation physics need to be involved in SEM *** work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle(ISP)***,three mechanisms of solid electrolyte interface(SEI)film growth throughout life cycle are analyzed,and an SEI film growth model of lithium-ion battery is built coupled with the ISP ***,a series of identification conditions for individual cells are designed to non-destructively determine model ***,battery aging experiment is designed to validate the battery performance simulation method and SOH estimation *** validation results under different aging rates indicate that this method can accurately es-timate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.

关键词： Improved single particle model Failure physics Characteristics performance simulation state of health estimation

state-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

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Journal of Energy Chemistry 2024年第11期98卷 1-11页

作者： Yvxin He Zhongwei Deng Jue Chen Weihan Li Jingjing Zhou Fei Xiang Xiaosong Hu School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives(ISEA)RWTH Aachen UniversityAachen 52074Germany Center for Ageing Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems(CARL)RWTH Aachen UniversityAachen 52074Germany China Automotive Engineering Research Institute Co.Ltd. Chongqing 401122China

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

关键词： Lithium-ion battery state of health estimation Feature extraction Graph convolutional network Long short-term memory network

Multi‑scale Battery Modeling Method for Fault Diagnosis

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Automotive Innovation 2022年第4期5卷 400-414页

作者： Shichun Yang Hanchao Cheng Mingyue Wang Meng Lyu Xinlei Gao Zhengjie Zhang Rui Cao Shen Li Jiayuan Lin Yang Hua Xiaoyu Yan Xinhua Liu School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Department of Mechanical Engineering Imperial College LondonLondon SW72AZUK National New Energy Vehicle Technology Innovation Center Beijing 102602China

Fault diagnosis is key to enhancing the performance and safety of battery storage ***,it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are *** model-based method has been widely used for degradation mechanism analysis,state estimation,and life prediction of lithium-ion battery systems due to the fast speed and high development *** paper reviews the mainstream modeling approaches used for battery ***,a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is ***,the different modeling approaches are summarized,from microscopic to macroscopic scales,including density functional theory,molecular dynamics,X-ray computed tomography technology,electrochemical model,equivalent circuit model,distributed model and neural network ***,the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application ***,the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.

关键词： Lithium-ion battery Simulation model Fault diagnosis Electrochemical performance state of health estimation