Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using...Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.展开更多
The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference...The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference between the two states indicated above,this study establishs a co-estimation framework of battery SOC and SOT.This framwork is based on an innovative electrothermal model and adaptive estimation algorithms.The first-order RC electric model and an innovative thermal model are components of the electrothermal model.Specifically,the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional(2-D)thermal resistance network(TRN)submodel for the main battery body,capable of capturing the detailed thermodynamics of large-format Li-ion batteries.Moreover,the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances.Besides,the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter(AUKF)and an adaptive Kalman filter(AKF),which adaptively update the state and noise covariances.Regarding the estimation results,the mean absolute errors(MAEs)of SOC and SOT estimation are controlled within 1%and 0.4°C at two temperatures,indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35°C.展开更多
For efficient and quantitative DNA detection,fluorescence staining is the most often explored approach,which relies on non-covalent binding of dyes with double stranded DNA(dsDNA).Ethidium bromide(EB)is the most class...For efficient and quantitative DNA detection,fluorescence staining is the most often explored approach,which relies on non-covalent binding of dyes with double stranded DNA(dsDNA).Ethidium bromide(EB)is the most classic DNA stain,but suffers from its high carcinogenicity.A series of less toxic alternatives were developed,many of which contain the core structure of the benzothiazole ring.However,the relationship between the structure and the DNA detection performance was not illustrated.Herein,five benzothiazole dyes,namely thiazole orange,SYBR Green I,Pico Green,SYBR Safe,and thioflavine-T,were compared for DNA detection through direct fluorescence and gel electrophoresis,with particular focus on the structure-performance relationship.It turned out that SYBR Green I is currently the best choice for DNA detection.The results in this work may be useful for future DNA-staining dye developments.展开更多
基金supported in part by the National Key Research and Development Program of China(No.2022YFB3305403)Project of basic research funds for central universities(2022CDJDX006)+1 种基金Talent Plan Project of Chongqing(No.cstc2021ycjhbgzxm0295)National Natural Science Foundation of China(No.52111530194)。
文摘Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.
基金National Natural Science Foundation of China(NSFC,Grant No.52107230)Fundamental Research Funds for the Central Universities and the Major State Basic Research Development Program of China。
文摘The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge(SOC)and state-of-temperature(SOT)of Lithium-ion(Li-ion)batteries.Given the influence of cross-interference between the two states indicated above,this study establishs a co-estimation framework of battery SOC and SOT.This framwork is based on an innovative electrothermal model and adaptive estimation algorithms.The first-order RC electric model and an innovative thermal model are components of the electrothermal model.Specifically,the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional(2-D)thermal resistance network(TRN)submodel for the main battery body,capable of capturing the detailed thermodynamics of large-format Li-ion batteries.Moreover,the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances.Besides,the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter(AUKF)and an adaptive Kalman filter(AKF),which adaptively update the state and noise covariances.Regarding the estimation results,the mean absolute errors(MAEs)of SOC and SOT estimation are controlled within 1%and 0.4°C at two temperatures,indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35°C.
基金the Major Scientific and Technological Application Program of Chengdu(No.2019-YF09-0081-SN)the Major Scientific and Technological Special Program of Sichuan Province(No.2018SZDZX0027)the Fundamental Research Funds for Central Universities(No.2018SCUH0075)。
文摘For efficient and quantitative DNA detection,fluorescence staining is the most often explored approach,which relies on non-covalent binding of dyes with double stranded DNA(dsDNA).Ethidium bromide(EB)is the most classic DNA stain,but suffers from its high carcinogenicity.A series of less toxic alternatives were developed,many of which contain the core structure of the benzothiazole ring.However,the relationship between the structure and the DNA detection performance was not illustrated.Herein,five benzothiazole dyes,namely thiazole orange,SYBR Green I,Pico Green,SYBR Safe,and thioflavine-T,were compared for DNA detection through direct fluorescence and gel electrophoresis,with particular focus on the structure-performance relationship.It turned out that SYBR Green I is currently the best choice for DNA detection.The results in this work may be useful for future DNA-staining dye developments.
