The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencin...The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencing aging behaviors and lithium plating within lithium-ion batteries(LIBs).Available lithium-ion plating models often falter in detailed description when integrating the structural intricacies.To address this challenge,this study proposes an innovative hierarchical model that intricately incorporates the layered rolling structure in cells.Notably,our model demonstrates a remarkable capacity to predict the non-uniform distribution of current density and overpotential along the rolling direction of LIBs.Subsequently,we delve into an insightful exploration of the structural factors that influence lithium plating behavior,leveraging the foundation laid by our established model.Furthermore,we easily update the hierarchical model by considering aging factors.This aging model effectively anticipates capacity fatigue and lithium plating tendencies across individual layers of LIBs,all while maintaining computational efficiency.In light of our findings,this model yields novel perspectives on capacity fatigue dynamics and local lithium plating behaviors,offering a substantial advancement compared to existing models.This research paves the way for more efficient and tailored LIB design and operation,with broad implications for energy storage technologies.展开更多
In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,cons...In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,considering the impact of battery aging.Battery aging primarily affects capacity and resistance,becoming more pronounced in the later stages of a battery lifespan.By incorporating aging effects into our fault diagnosis scheme,our proposed approach prevents false or missed alarms for the aged battery cells.The aging effect of battery,capacity fading and resistance growth,are considered unknown parameters.An adaptive observer is employed to design a fault detector,considering unknown parameters in the battery model.The adaptive observers are designed for two different scenarios:In the first scenario,it is presumed that aging effects remain constant over time due to their slow rate of change.Then,it is assumed that aging effects are time-varying.Therefore,the fault detection scheme can detect faults of new battery cells as well as aged cells.Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack,to demonstrate the performance of the proposed approach in more real-world scenarios.The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.展开更多
Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance ...Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH es展开更多
The increasing use of lithium-ion cells in large-scale,long-term applications drives a need for design methods that considers aging and accurate state of health estimation.A common approach is to rely on an empirical ...The increasing use of lithium-ion cells in large-scale,long-term applications drives a need for design methods that considers aging and accurate state of health estimation.A common approach is to rely on an empirical or semiempirical aging model fit to experimental data to estimate the evolution of capacity and power fade.Because aging data are costly to collect,pack designers either use Design of Experiment(DOE)techniques to define a set of efficient tests,or use existing aging data to calibrate aging models.Given the increasing quantity of available aging data,the question arises:how can experimental aging campaigns be quickly compared?However,a methodology for the comparison of sets of aging experiments is not discussed in the literature.As a result,pack designers usually rely on intuition to select between multiple aging studies proposed by DOE techniques or in the literature.This work proposes metrics to quantitatively capture the alignment between a set of aging experiments and a target application.These metrics allow pack designers to quickly compare many sets of aging experiments to evaluate those which have tested conditions relevant to the application.Case studies are presented to illustrate the application of these metrics using aging campaign data from the literature.To validate these metrics,this work examines the relationship between these metric values and aging model validation error for calendar aging data for 18650 NMC battery cells.It is demonstrated that greater metric values correspond to reduced model error for an empirical capacity fade model.展开更多
This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge ra...This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth,operating temperature,and environment conditions,capacities of battery modules decay unevenly and randomly.Based on estimated SOHs of battery modules during battery operation,we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities.A rigorous mathematical analysis of system-level capacity utilization is conducted.It is shown that for large battery strings with uniformly distributed capacities,the average string capacity approaches the minimum,implying an asymptotically near worst-case capacity utility without reorganization.It is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery reconfiguration.An optimal regrouping algorithm is introduced.Analysis methods,simulation examples,and a case study using real-world battery data are presented.展开更多
When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside...When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.展开更多
锂离子电池容量的精确预测有利于提升电池的使用安全和避免电池滥用,受其复杂的内部电化学反应和外部使用条件影响,对其老化后的容量精确预测一直是电池管理系统的难点之一。为实现锂电池全服役周期内容量的高效准确预测,本文提出了一...锂离子电池容量的精确预测有利于提升电池的使用安全和避免电池滥用,受其复杂的内部电化学反应和外部使用条件影响,对其老化后的容量精确预测一直是电池管理系统的难点之一。为实现锂电池全服役周期内容量的高效准确预测,本文提出了一种基于遗传算法优化的Elman神经网络(GA-Elman)电池容量预测模型。首先选择电池不同循环下的放电容量增量、内阻以及温度数据作为有效表征电池老化和容量衰减规律的特征量,其次运用主成分分析算法对特征量进行降维以降低训练量数据维度,然后基于Elman神经网络构建电池容量预测模型,并引入遗传算法优化Elman神经网络的权值和阈值,实现对电池容量的高效精确预测,最后在不同电池上对该模型进行了验证。验证结果表明:与传统Elman神经网络和长短期记忆神经网络(long and short term memory neural network,LSTM NN)预测模型相比,GA-Elman神经网络预测模型有更好的预测精度和更高的运算效率。在不同电池上该模型预测结果的最大平均绝对误差为0.92%,最大均方根误差为1.02%,最小拟合系数为0.9679,表明该模型可以精确预测锂电池衰退过程中的容量并且对不同电池有较强的适应性。展开更多
基金the financial support from The National Key Research and Development Program of China(2022YFB3305402)The National Natural Science Foundation of China(12272072)+1 种基金The Key Project of Chongqing Technology Innovation and Application Development(CSTB2022TIAD-KPX0037)Research Project of the State Key Laboratory of Intel igent Vehicle Safety Technology(NVHSKL-202207)
文摘The accurate representation of lithium plating and aging phenomena has posed a persistent challenge within the battery research community.Empirical evidence underscores the pivotal role of cell structure in influencing aging behaviors and lithium plating within lithium-ion batteries(LIBs).Available lithium-ion plating models often falter in detailed description when integrating the structural intricacies.To address this challenge,this study proposes an innovative hierarchical model that intricately incorporates the layered rolling structure in cells.Notably,our model demonstrates a remarkable capacity to predict the non-uniform distribution of current density and overpotential along the rolling direction of LIBs.Subsequently,we delve into an insightful exploration of the structural factors that influence lithium plating behavior,leveraging the foundation laid by our established model.Furthermore,we easily update the hierarchical model by considering aging factors.This aging model effectively anticipates capacity fatigue and lithium plating tendencies across individual layers of LIBs,all while maintaining computational efficiency.In light of our findings,this model yields novel perspectives on capacity fatigue dynamics and local lithium plating behaviors,offering a substantial advancement compared to existing models.This research paves the way for more efficient and tailored LIB design and operation,with broad implications for energy storage technologies.
文摘In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,considering the impact of battery aging.Battery aging primarily affects capacity and resistance,becoming more pronounced in the later stages of a battery lifespan.By incorporating aging effects into our fault diagnosis scheme,our proposed approach prevents false or missed alarms for the aged battery cells.The aging effect of battery,capacity fading and resistance growth,are considered unknown parameters.An adaptive observer is employed to design a fault detector,considering unknown parameters in the battery model.The adaptive observers are designed for two different scenarios:In the first scenario,it is presumed that aging effects remain constant over time due to their slow rate of change.Then,it is assumed that aging effects are time-varying.Therefore,the fault detection scheme can detect faults of new battery cells as well as aged cells.Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack,to demonstrate the performance of the proposed approach in more real-world scenarios.The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.
基金supported by the National Natural Science Foundation of China (No.62173281,52377217,U23A20651)Sichuan Science and Technology Program (No.24NSFSC0024,23ZDYF0734,23NSFSC1436)+2 种基金Dazhou City School Cooperation Project (No.DZXQHZ006)Technopole Talent Summit Project (No.KJCRCFH08)Robert Gordon University。
文摘Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH es
基金supported by The Center for Automotive Research at The Ohio State University and the Department of Mechanical and Aerospace Engineering at The Ohio State University.
文摘The increasing use of lithium-ion cells in large-scale,long-term applications drives a need for design methods that considers aging and accurate state of health estimation.A common approach is to rely on an empirical or semiempirical aging model fit to experimental data to estimate the evolution of capacity and power fade.Because aging data are costly to collect,pack designers either use Design of Experiment(DOE)techniques to define a set of efficient tests,or use existing aging data to calibrate aging models.Given the increasing quantity of available aging data,the question arises:how can experimental aging campaigns be quickly compared?However,a methodology for the comparison of sets of aging experiments is not discussed in the literature.As a result,pack designers usually rely on intuition to select between multiple aging studies proposed by DOE techniques or in the literature.This work proposes metrics to quantitatively capture the alignment between a set of aging experiments and a target application.These metrics allow pack designers to quickly compare many sets of aging experiments to evaluate those which have tested conditions relevant to the application.Case studies are presented to illustrate the application of these metrics using aging campaign data from the literature.To validate these metrics,this work examines the relationship between these metric values and aging model validation error for calendar aging data for 18650 NMC battery cells.It is demonstrated that greater metric values correspond to reduced model error for an empirical capacity fade model.
基金supported in part by the Army Research Office(W911NF-19-1-0176).
文摘This paper analyzes the system-level state of health(SOH)and its dependence on the SOHs of its component battery modules.Due to stochastic natures of battery aging processes and their dependence on charge/discharge rate and depth,operating temperature,and environment conditions,capacities of battery modules decay unevenly and randomly.Based on estimated SOHs of battery modules during battery operation,we analyze how the SOH of the entire system deteriorates when battery modules age and become increasingly diverse in their capacities.A rigorous mathematical analysis of system-level capacity utilization is conducted.It is shown that for large battery strings with uniformly distributed capacities,the average string capacity approaches the minimum,implying an asymptotically near worst-case capacity utility without reorganization.It is demonstrated that the overall battery usable capacities can be more efficiently utilized to achieve extended operational ranges by using battery reconfiguration.An optimal regrouping algorithm is introduced.Analysis methods,simulation examples,and a case study using real-world battery data are presented.
文摘When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.
文摘锂离子电池容量的精确预测有利于提升电池的使用安全和避免电池滥用,受其复杂的内部电化学反应和外部使用条件影响,对其老化后的容量精确预测一直是电池管理系统的难点之一。为实现锂电池全服役周期内容量的高效准确预测,本文提出了一种基于遗传算法优化的Elman神经网络(GA-Elman)电池容量预测模型。首先选择电池不同循环下的放电容量增量、内阻以及温度数据作为有效表征电池老化和容量衰减规律的特征量,其次运用主成分分析算法对特征量进行降维以降低训练量数据维度,然后基于Elman神经网络构建电池容量预测模型,并引入遗传算法优化Elman神经网络的权值和阈值,实现对电池容量的高效精确预测,最后在不同电池上对该模型进行了验证。验证结果表明:与传统Elman神经网络和长短期记忆神经网络(long and short term memory neural network,LSTM NN)预测模型相比,GA-Elman神经网络预测模型有更好的预测精度和更高的运算效率。在不同电池上该模型预测结果的最大平均绝对误差为0.92%,最大均方根误差为1.02%,最小拟合系数为0.9679,表明该模型可以精确预测锂电池衰退过程中的容量并且对不同电池有较强的适应性。
