Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charg...Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.展开更多
This work extensively investigates the thermal characteristic evolution of lithium-ion batteries under different degradation paths,and the evolution mechanism through multi-angle characterization is revealed.Under dif...This work extensively investigates the thermal characteristic evolution of lithium-ion batteries under different degradation paths,and the evolution mechanism through multi-angle characterization is revealed.Under different degradation paths,the evolution trend of temperature rise rate remains unchanged with respect to depth of discharge during the adiabatic discharge process,albeit to varying degrees of alteration.The temperature rise rate changes significantly with aging during the adiabatic discharge process under low-temperature cycling and high-rate cycling paths.The total heat generation rate,irreversible heat generation rate,and reversible heat generation rate exhibit similar evolution behavior with aging under different degradation paths.The interval range of endothermic process of reversible electrochemical reactions increases and the contribution of irreversible heat to the total heat increases with aging.To further standardize the assessment of different degradation paths on the thermal characteristics,this work introduces the innovative concept of“Ampere-hour temperature rise”.In low-temperature cycling and high-rate cycling paths,the ampere-hour temperature rise increases significantly with aging,particularly accentuated with higher discharge rates.Conversely,in high-temperature cycling and high-temperature storage paths,the ampere-hour temperature rise remains relatively stable during the initial stages of aging,yet undergoes a notable increase in the later stages of aging.The multi-angle characterization reveals distinct thermal evolution behavior under different degradation paths primarily attributed to different behavior changes of severe side reactions,such as lithium plating.The findings provide crucial insights for the safe utilization and management of lithium–ion batteries throughout the whole lifecycle.展开更多
With the significant and widespread application of lithium-ion batteries,there is a growing demand for improved performances of lithium-ion batteries.The intricate degradation throughout the whole lifecycle profoundly...With the significant and widespread application of lithium-ion batteries,there is a growing demand for improved performances of lithium-ion batteries.The intricate degradation throughout the whole lifecycle profoundly impacts the safety,durability,and reliability of lithium-ion batteries.To ensure the long-term,safe,and efficient operation of lithium-ion batteries in various fields,there is a pressing need for enhanced battery intelligence that can withstand extreme events.This work reviews the current status of intelligent battery technology from three perspectives:intelligent response,intelligent sensing,and intelligent management.The intelligent response of battery materials forms the foundation for battery stability,the intelligent sensing of multi-dimensional signals is essential for battery management,and the intelligent management ensures the long-term stable operation of lithium-ion batteries.The critical challenges encountered in the development of intelligent battery technology from each perspective are thoroughly analyzed,and potential solutions are proposed,aiming to facilitate the rapid development of intelligent battery technologies.展开更多
Understanding the thermal safety evolution of lithium-ion batteries during high-temperature usage conditions bears significant implications for enhancing the safety management of aging batteries.This work investigates...Understanding the thermal safety evolution of lithium-ion batteries during high-temperature usage conditions bears significant implications for enhancing the safety management of aging batteries.This work investigates the thermal safety evolution mechanism of lithium-ion batteries during high-temperature aging.Similarities arise in the thermal safety evolution and degradation mechanisms for lithium-ion batteries undergoing cyclic aging and calendar aging.Employing multi-angle characterization analysis,the intricate mechanism governing the thermal safety evolution of lithium-ion batteries during high-temperature aging is clarified.Specifically,lithium plating serves as the pivotal factor contributing to the reduction in the self-heating initial temperature.Additionally,the crystal structure of the cathode induced by the dissolution of transition metals and the reductive gas generated during aging attacking the crystal structure of the cathode lead to a decrease in thermal runaway triggering temperature.Furthermore,the loss of active materials and active lithium during aging contributes to a decline in both the maximum temperature and the maximum temperature rise rate,ultimately indicating a decrease in the thermal hazards of aging batteries.展开更多
With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction e...With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.展开更多
Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in th...Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.展开更多
We presented a low-precision spectrum for HI Leo,Transiting Exoplanet Survey Satellite data for V523 Cas,and new photometry for both K-type contact binaries.Comparing their light curves on different observing dates,we...We presented a low-precision spectrum for HI Leo,Transiting Exoplanet Survey Satellite data for V523 Cas,and new photometry for both K-type contact binaries.Comparing their light curves on different observing dates,we found small intrinsic variabilities,such as variable amplitudes for HI Leo and the varying heights around the second maxima for V523 Cas.By the Wilson-Devinney Code,we deduced six photometric solutions.The dark spot of V523 Cas may appear on the surface of the more massive component on BJD 2458768,while it disappears on BJD 2458779.Our results indicate that the two binaries are W-type shallow-contact binaries(f≤10%).From the eclipse timing residuals,we found that the orbital periods may continuously increase,accompanied by one to two light-time effects due to additional bodies.The modulated periods and semi-amplitudes are P_(3)=25.8(±1.0)yr and A_(3)=0_·^(d)0066(6)for HI Leo,P_(3)=-14.8(±2.0)yr and A_(3)=0_·^(d)0448(12),P_(4)=18.89(±0.14)yr and A4=0_·^(d)0025(2)for V523 Cas,respectively.The orbital period secularly increases at a rate of dP/dt=2.86(±0.11)×10^(-7)day yr^(-1)for HI Leo and dP/dt=3.45(±0.07)×10^(-8)day yr^(-1)for V523 Cas,which may be attributed to mass transfer from the secondary to the primary.With mass transferring,the shallow-contact binaries,HI Leo and V523 Cas,will evolve into the broken-contact configurations.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
Further applications of electric vehicles(EVs)and energy storage stations are limited because of the thermal sensitivity,volatility,and poor durability of lithium-ion batteries(LIBs),especially given the urgent requir...Further applications of electric vehicles(EVs)and energy storage stations are limited because of the thermal sensitivity,volatility,and poor durability of lithium-ion batteries(LIBs),especially given the urgent requirements for all-climate utilization and fast charging.This study comprehensively reviews the thermal characteristics and management of LIBs in an all-temperature area based on the performance,mechanism,and thermal management strategy levels.展开更多
Electrochemical impedance spectroscopy(EIS)contributes to developing the fault diagnosis tools for fuel cells,which is of great significance in improving service life.The conventional impedance measurement techniques ...Electrochemical impedance spectroscopy(EIS)contributes to developing the fault diagnosis tools for fuel cells,which is of great significance in improving service life.The conventional impedance measurement techniques are limited to linear responses,failing to capture high-order harmonic responses.However,nonlinear electrochemical impedance analysis incorporates additional nonlinear information,enabling the resolution of such responses.This study proposes a novel multi-stage fault diagnosis method based on the nonlinear electrochemical impedance spectrum(NEIS).First,the impact of alternating current excitation amplitude on NEIS is analyzed.Then,a series of experiments are conducted to obtain NEIS data under various fault conditions,encompassing recoverable faults like flooding,drying,starvation,and their mixed faults,spanning different degrees of fault severity.Based on these experiments,both EIS and NEIS datasets are established,and principal component analysis is utilized to extract the main features,thereby reducing the dimensionality of the original data.Finally,a fault diagnosis model is constructed with the support vector machine(SVM)and random forest algorithms,with model hyperparameters optimized by a hybrid genetic particle swarm optimization(HGAPSO)algorithm.The results show that the diagnostic accuracy of NEIS is higher than that of traditional EIS,with the HGAPSO-SVM model achieving a 100%accurate diagnosis under the NEIS dateset and self-defined fault labels.展开更多
基金supported by the National Natural Science Foundation of China(No.U20A20310 and No.52176199)sponsored by the Program of Shanghai Academic/Technology Research Leader(No.22XD1423800)。
文摘Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.
基金This work is supported by the National Natural Science Foundation of China(NSFC,Nos.52176199,and U20A20310)supported by the Program of Shanghai Academic/Technology Research Leader(22XD1423800).
文摘This work extensively investigates the thermal characteristic evolution of lithium-ion batteries under different degradation paths,and the evolution mechanism through multi-angle characterization is revealed.Under different degradation paths,the evolution trend of temperature rise rate remains unchanged with respect to depth of discharge during the adiabatic discharge process,albeit to varying degrees of alteration.The temperature rise rate changes significantly with aging during the adiabatic discharge process under low-temperature cycling and high-rate cycling paths.The total heat generation rate,irreversible heat generation rate,and reversible heat generation rate exhibit similar evolution behavior with aging under different degradation paths.The interval range of endothermic process of reversible electrochemical reactions increases and the contribution of irreversible heat to the total heat increases with aging.To further standardize the assessment of different degradation paths on the thermal characteristics,this work introduces the innovative concept of“Ampere-hour temperature rise”.In low-temperature cycling and high-rate cycling paths,the ampere-hour temperature rise increases significantly with aging,particularly accentuated with higher discharge rates.Conversely,in high-temperature cycling and high-temperature storage paths,the ampere-hour temperature rise remains relatively stable during the initial stages of aging,yet undergoes a notable increase in the later stages of aging.The multi-angle characterization reveals distinct thermal evolution behavior under different degradation paths primarily attributed to different behavior changes of severe side reactions,such as lithium plating.The findings provide crucial insights for the safe utilization and management of lithium–ion batteries throughout the whole lifecycle.
基金supported by the National Natural Science Foundation of China (NSFC,Nos.52176199,and U20A20310)supported by the Program of Shanghai Academic/Technology Research Leader (22XD1423800)。
文摘With the significant and widespread application of lithium-ion batteries,there is a growing demand for improved performances of lithium-ion batteries.The intricate degradation throughout the whole lifecycle profoundly impacts the safety,durability,and reliability of lithium-ion batteries.To ensure the long-term,safe,and efficient operation of lithium-ion batteries in various fields,there is a pressing need for enhanced battery intelligence that can withstand extreme events.This work reviews the current status of intelligent battery technology from three perspectives:intelligent response,intelligent sensing,and intelligent management.The intelligent response of battery materials forms the foundation for battery stability,the intelligent sensing of multi-dimensional signals is essential for battery management,and the intelligent management ensures the long-term stable operation of lithium-ion batteries.The critical challenges encountered in the development of intelligent battery technology from each perspective are thoroughly analyzed,and potential solutions are proposed,aiming to facilitate the rapid development of intelligent battery technologies.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.52176199,and U20A20310)supported by the Program of Shanghai Academic/Technology Research Leader(22XD1423800)。
文摘Understanding the thermal safety evolution of lithium-ion batteries during high-temperature usage conditions bears significant implications for enhancing the safety management of aging batteries.This work investigates the thermal safety evolution mechanism of lithium-ion batteries during high-temperature aging.Similarities arise in the thermal safety evolution and degradation mechanisms for lithium-ion batteries undergoing cyclic aging and calendar aging.Employing multi-angle characterization analysis,the intricate mechanism governing the thermal safety evolution of lithium-ion batteries during high-temperature aging is clarified.Specifically,lithium plating serves as the pivotal factor contributing to the reduction in the self-heating initial temperature.Additionally,the crystal structure of the cathode induced by the dissolution of transition metals and the reductive gas generated during aging attacking the crystal structure of the cathode lead to a decrease in thermal runaway triggering temperature.Furthermore,the loss of active materials and active lithium during aging contributes to a decline in both the maximum temperature and the maximum temperature rise rate,ultimately indicating a decrease in the thermal hazards of aging batteries.
基金supported by the financial support from the National Key Research and Development Program of China(2022YFB3807200)the Fundamental Research Funds for the Central Universities(2242022K330047)+3 种基金the dual creative talents from Jiangsu Province(JSSCBS20210152,JSSCBS20210100)the National Natural Science Foundation of Jiangsu Province(BK20221456,BK20200375)the Natural Science Foundation of China with(22109021)the Research Fund Program of Guangdong Provincial Key Lab of Green Chemical Product Technology(6802008024)。
文摘With the assistance of artificial intelligence,advanced health prognosis technique plays a critical role in the lithium-ion(Li-ion) batteries management system.However,conventional data-driven early aging prediction exhibited dramatic drawbacks,i.e.,volatile capacity nonlinear fading trajectories create obstacles to the accurate multistep ahead prediction due to the complex working conditions of batteries.Herein,a novel deep learning model is proposed to achieve a universal and accurate Li-ion battery aging prognosis.Two battery datasets with various electrode types and cycling conditions are developed to validate the proposed approaches.Knee-point probability(KPP),extracted from the capacity loss curve,is first proposed to detect knee points and improve state-of-health(SOH) predictive accuracy,especially during periods of rapid capacity decline.Using one-cycle data of partial raw voltage as the model input,the SOH and KPP can be simultaneously predicted at multistep ahead,whereas the conventional method showed worse accuracy.Furthermore,to explore the underlying characteristics among various degradation tendencies,an online model update strategy is developed by leveraging the adversarial adaptationinduced transfer learning technique.This work gains new sights into the comprehensive Li-ion battery management and prognosis framework through decomposing capacity degradation trajectories and adversarial learning on the unlabeled samples.
基金financially supported by the National Natural Science Foundation of China(NSFC,U20A20310,52107230,52176199,52102470)the support of the research project Model2Life(03XP0334),funded by the German Federal Ministry of Education and Research(BMBF)。
文摘Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.
基金supported by the National Natural Science Foundation of China(Grant No.11873003)。
文摘We presented a low-precision spectrum for HI Leo,Transiting Exoplanet Survey Satellite data for V523 Cas,and new photometry for both K-type contact binaries.Comparing their light curves on different observing dates,we found small intrinsic variabilities,such as variable amplitudes for HI Leo and the varying heights around the second maxima for V523 Cas.By the Wilson-Devinney Code,we deduced six photometric solutions.The dark spot of V523 Cas may appear on the surface of the more massive component on BJD 2458768,while it disappears on BJD 2458779.Our results indicate that the two binaries are W-type shallow-contact binaries(f≤10%).From the eclipse timing residuals,we found that the orbital periods may continuously increase,accompanied by one to two light-time effects due to additional bodies.The modulated periods and semi-amplitudes are P_(3)=25.8(±1.0)yr and A_(3)=0_·^(d)0066(6)for HI Leo,P_(3)=-14.8(±2.0)yr and A_(3)=0_·^(d)0448(12),P_(4)=18.89(±0.14)yr and A4=0_·^(d)0025(2)for V523 Cas,respectively.The orbital period secularly increases at a rate of dP/dt=2.86(±0.11)×10^(-7)day yr^(-1)for HI Leo and dP/dt=3.45(±0.07)×10^(-8)day yr^(-1)for V523 Cas,which may be attributed to mass transfer from the secondary to the primary.With mass transferring,the shallow-contact binaries,HI Leo and V523 Cas,will evolve into the broken-contact configurations.
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supported by National Natural Science Foundation of China(NSFC)(nos.U20A20310,52176199,and 52076121)sponsored by Program of Shanghai Academic/Technology Research Leader(22XD1423800).
文摘Further applications of electric vehicles(EVs)and energy storage stations are limited because of the thermal sensitivity,volatility,and poor durability of lithium-ion batteries(LIBs),especially given the urgent requirements for all-climate utilization and fast charging.This study comprehensively reviews the thermal characteristics and management of LIBs in an all-temperature area based on the performance,mechanism,and thermal management strategy levels.
基金supported by National Key Research and Development Program of China(Funding Number:2019YFB1504605)。
文摘Electrochemical impedance spectroscopy(EIS)contributes to developing the fault diagnosis tools for fuel cells,which is of great significance in improving service life.The conventional impedance measurement techniques are limited to linear responses,failing to capture high-order harmonic responses.However,nonlinear electrochemical impedance analysis incorporates additional nonlinear information,enabling the resolution of such responses.This study proposes a novel multi-stage fault diagnosis method based on the nonlinear electrochemical impedance spectrum(NEIS).First,the impact of alternating current excitation amplitude on NEIS is analyzed.Then,a series of experiments are conducted to obtain NEIS data under various fault conditions,encompassing recoverable faults like flooding,drying,starvation,and their mixed faults,spanning different degrees of fault severity.Based on these experiments,both EIS and NEIS datasets are established,and principal component analysis is utilized to extract the main features,thereby reducing the dimensionality of the original data.Finally,a fault diagnosis model is constructed with the support vector machine(SVM)and random forest algorithms,with model hyperparameters optimized by a hybrid genetic particle swarm optimization(HGAPSO)algorithm.The results show that the diagnostic accuracy of NEIS is higher than that of traditional EIS,with the HGAPSO-SVM model achieving a 100%accurate diagnosis under the NEIS dateset and self-defined fault labels.
