Thermal safety boundary of lithium-ion battery at different state of charge

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.

Thermal characteristic evolution of lithium-ion batteries during the whole lifecycle

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.

Multi-level intelligence empowering lithium-ion batteries

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.

Research on the impact of high-temperature aging on the thermal safety of lithium-ion batteries

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.

Nonlinear health evaluation for lithium-ion battery within full-lifespan

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.

Co-estimation of state-of-charge and state-of-temperature for large-format lithium-ion batteries based on a novel electrothermal model

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.

Pt‑Based Intermetallic Compound Catalysts for the Oxygen Reduction Reaction:Structural Control at the Atomic Scale to Achieve a Win–Win Situation Between Catalytic Activity and Stability

The development of ordered Pt-based intermetallic compounds is an effective way to optimize the electronic characteristics of Pt and its disordered alloys,inhibit the loss of transition metal elements,and prepare fuel cell catalysts with high activity and long-term durability for the oxygen reduction reaction(ORR).This paper reviews the structure–activity characteristics,research advances,problems,and improvements in Pt-based intermetallic compound fuel cell catalysts for the ORR.First,the structural characteristics and performance advantages of Pt-based intermetallic compounds are analyzed and explained.Second,starting with 3d transition metals such as Fe,Co,and Ni,whose research achievements are common,the preparation process and properties of Pt-based intermetallic compound catalysts for the ORR are introduced in detail according to element types.Third,in view of preparation problems,improvements in the preparation processes of Pt-based intermetallic compounds are also summarized in regard to four aspects:coating to control the crystal size,doping to promote ordering transformation,constructing a“Pt skin”to improve performance,and anchoring and confinement to enhance the interaction between the crystal and support.Finally,by analyzing the research status of Pt-based intermetallic compound catalysts for the ORR,prospective research directions are suggested.

All-temperature area battery application mechanism,performance,and

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.

Fault Diagnosis of Proton Exchange Membrane Fuel Cell Based on Nonlinear Impedance Spectrum

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.