Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrat...Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.展开更多
Lithium-ion batteries(LIBs)are susceptible to mechanical failures that can occur at various scales,including particle,electrode and overall cell levels.These failures are influenced by a combination of multi-physical ...Lithium-ion batteries(LIBs)are susceptible to mechanical failures that can occur at various scales,including particle,electrode and overall cell levels.These failures are influenced by a combination of multi-physical fields of electrochemical,mechanical and thermal factors,making them complex and multi-physical in nature.The consequences of these mechanical failures on battery performance,lifetime and safety vary depending on the specific type of failure.However,the complex nature of mechanical degradation in batteries often involves interrelated processes,in which different failure mechanisms interact and evolve.Despite extensive research efforts,the detailed mechanisms behind these failures still require further clarification.To bridge this knowledge gap,this review systematically investigates three key aspects:multiscale mechanical failures;their implications for performance,lifetime and safety;and the interconnections between the different types and scales of the mechanical failures.By adopting a multiscale and multidisciplinary perspective,fragmented ideas from current research are integrated into a comprehensive framework,providing a deeper understanding of the mechanical behaviors and interactions within LIBs.We highlight the main characteristics of mechanical failures in LIBs and present valuable insights and prospects in four key areas of theories,materials,designs and applications,for improving the performance,lifetime and safety of LIBs by addressing current challenges in the field.As a valuable resource,this review may serve as a bridge for researchers from diverse disciplines,facilitating their understanding of mechanical failures in LIBs and encouraging further advancements in the field.展开更多
Au-Ag gradient alloy nanoparticles were directly synthesized in a microreaction system with their surface plasmon resonance been facilely adjusted.The surface plasmon resonance wavelength was red-shifted through incre...Au-Ag gradient alloy nanoparticles were directly synthesized in a microreaction system with their surface plasmon resonance been facilely adjusted.The surface plasmon resonance wavelength was red-shifted through increasing the raw ratio of Au^(3+):Ag^(+),decreasing the synthesis temperature or the residence time.A linear relationship was found between the surface plasmon resonance wavelength and the synthesis temperature,or the residence time.The range of surface plasmon resonance wavelength of monodispersed Au-Ag gradient alloy could be extended to 548 nm generated on the enrichment of Au as outer layer.It provided a suitable way to prepare Au-Ag gradient alloy NPs with longer surface plasmon resonance wavelength than 520 nm(Au)at low temperature.展开更多
基金supported by the National Natural Science Foundation of China(52375144 and 52205153)the Shanghai Pujiang Programme(23PJD019)the Shanghai Gaofeng Project for University Academic Program Development。
文摘Accurate state of health(SOH)estimation is a cornerstone for ensuring the safety,performance and longevity of lithium-ion batteries,especially in electric vehicle(EV)applications.While numerous studies have demonstrated the significant advantages of data-driven methods in SOH estimation,most rely on laboratory-standardized test data.This raises concerns about the generalization and robustness of the models under real-world operating conditions,where batteries undergo irregular driving patterns,incomplete charging cycles,and unpredictable environments.Notably,real-world EV data reflects the coupling between battery aging characteristics and actual operating conditions,providing an unprecedented perspective for developing SOH estimation models.This review provides a comprehensive and systematic overview of data-driven SOH estimation using real-world data,a topic that has received increasing attention but lacks a consolidated research framework.The paper begins by reviewing the established SOH estimation methodologies and points out the specific challenges arising from the transition to real-world data.It then probes practical issues across the pipeline:data pre-processing for anomalies,solutions for the lack of labels,feature extraction from complex operating data,machine learning model construction,and performance evaluation across various system deployments.Key insights are presented on how to handle noisy,unlabeled,and heterogeneous data using robust modeling strategies.Moreover,a valuable extension focusing on applying the advancements to battery reuse and recycling is discussed,with the goal of developing a whole lifecycle health diagnosis framework.The paper concludes with promising prospects,encompassing open-source standardized dataset establishment,weakly supervised learning,physics-reinforced modeling,real-world deployment,and advanced sensing technology,emphasizing that real-world data makes the transition of data-driven methods from theoretical validation to industrial deployment promising.This paper aims to assist researchers and practitioners in navigating the complexities of real-world SOH estimation,accelerating the collaborative innovation and industrial adoption in battery health management.
基金support from the China Scholarship Council,the National Natural Science Foundation of China(52375144,52375145 and 52205153)the China Postdoctoral Science Foundation(2022M721138 and 2023T160216)+1 种基金Shanghai Pujiang Programme(23PJD019)the East China University of Science and Technology,and the University of Strathclyde during the course of this work.
文摘Lithium-ion batteries(LIBs)are susceptible to mechanical failures that can occur at various scales,including particle,electrode and overall cell levels.These failures are influenced by a combination of multi-physical fields of electrochemical,mechanical and thermal factors,making them complex and multi-physical in nature.The consequences of these mechanical failures on battery performance,lifetime and safety vary depending on the specific type of failure.However,the complex nature of mechanical degradation in batteries often involves interrelated processes,in which different failure mechanisms interact and evolve.Despite extensive research efforts,the detailed mechanisms behind these failures still require further clarification.To bridge this knowledge gap,this review systematically investigates three key aspects:multiscale mechanical failures;their implications for performance,lifetime and safety;and the interconnections between the different types and scales of the mechanical failures.By adopting a multiscale and multidisciplinary perspective,fragmented ideas from current research are integrated into a comprehensive framework,providing a deeper understanding of the mechanical behaviors and interactions within LIBs.We highlight the main characteristics of mechanical failures in LIBs and present valuable insights and prospects in four key areas of theories,materials,designs and applications,for improving the performance,lifetime and safety of LIBs by addressing current challenges in the field.As a valuable resource,this review may serve as a bridge for researchers from diverse disciplines,facilitating their understanding of mechanical failures in LIBs and encouraging further advancements in the field.
基金supports from the Fundamental Research Funds for National Nature Science Foundation of China(51172072)the Central Universities(WJ0913001)+1 种基金the Focus of Scientific and Technological Research Projects(109063)the State Key Laboratory of Chemical Engineering at ECUST(SKL-ChE-08C09).
文摘Au-Ag gradient alloy nanoparticles were directly synthesized in a microreaction system with their surface plasmon resonance been facilely adjusted.The surface plasmon resonance wavelength was red-shifted through increasing the raw ratio of Au^(3+):Ag^(+),decreasing the synthesis temperature or the residence time.A linear relationship was found between the surface plasmon resonance wavelength and the synthesis temperature,or the residence time.The range of surface plasmon resonance wavelength of monodispersed Au-Ag gradient alloy could be extended to 548 nm generated on the enrichment of Au as outer layer.It provided a suitable way to prepare Au-Ag gradient alloy NPs with longer surface plasmon resonance wavelength than 520 nm(Au)at low temperature.
