A Novel Remaining Useful Life Prediction Method for Capacity Diving Lithium-Ion Batteries

Gao, Kaidi; Xu, Jingyun; Li, Zuxin; Cai, Zhiduan; Jiang, Dongming; Zeng, Aigang

doi:10.1021/acsomega.2c03043

“…1 that the degradation trajectories exhibit obvious two-phase hybrid deteriorating features with evident inflection points. Specifically, the LIBs' capacity decreases slowly in the first phase with a linear trend, and after about 500 cycles, the power starts to dive rapidly and show a nonlinear trend, which was disclosed by [41]. Furthermore, it is clear from Fig.…”

Section: A Motivation and Modeling Descriptionmentioning

confidence: 81%

“…However, most of the current researches only focus on the two-phase linear model, which may not be accurate in some complex LIBs applications. It is encountered in practice that some LIBs with two-phase degradation patterns exhibit a slow and stable linear trend in the first degradation phase while a fluctuating nonlinear trend in the subsequent degradation phase [40][41][42]. From a practical perspective, the reason for this phenomenon is that the internal active material of LIBs will gradually lose during cycling, whereas the internal resistance increases slowly [43].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction for Two-Phase Hybrid Deteriorating Lithium-Ion Batteries Using Wiener Process

Cui,

Lu,

Han

2024

IEEE Access

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Owing to operating condition switching and internal degradation mechanisms, the degradation processes of some lithium-ion batteries (LIBs) exhibit non-monotone and two-phase patterns, which are composed of a linear first phase and a nonlinear second phase. The existing Gamma process and Inverse Gaussian process methods are limited to modeling the monotone degradation data. Besides, traditional singlephase nonlinear models and two-phase linear models are insufficient to describe such a degradation process effectively. Therefore, degradation modeling and remaining useful life (RUL) prediction of the hybrid deteriorating LIBs is still a compelling practical issue. In this paper, a two-phase hybrid degradation model with a linear first phase and a nonlinear second phase is formulated based on the widely used Wiener processbased model. Taking into account the random effects caused by the unit heterogeneity and the uncertainty of the degradation state at the changing point, we obtain the analytical solutions of the lifetime estimation and RUL prediction under the concept of the first passage time (FPT). In addition, to conduct model parameter identification, the expectation maximization (EM) algorithm in conjunction with a profile log-likelihood function method are utilized for offline parameter estimation. Subsequently, the Bayesian rule is adopted to conduct the online parameter updating. Finally, the numerical and practical experiments are provided for verification and show that the proposed method could achieve high estimation accuracy for the RUL prediction of the two-phase hybrid deteriorating LIBs.INDEX TERMS Lithium-ion batteries, RUL prediction, two-phase degradation, unit-to-unit variability, Wiener process.

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“…There is great interest in describing, understanding, and modeling battery degradation. Numerous reviews in the literature discuss battery diagnosis, SOH prediction [128,129], SOX estimation [130,131], RUL estimation [132][133][134][135][136][137][138][139][140][141][142], battery charging, and fault prognostic methods. However, most of the models described in the literature are not chemical-agnostic and extrapolation from cell to pack level.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

A Comprehensive Review of EV Lithium-Ion Battery Degradation

Júnior¹,

Sanseverino²,

Gallo³

et al. 2023

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Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

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“…There is great interest in describing, understanding, and modeling battery degradation. Numerous reviews in the literature discuss battery diagnosis, SOH prediction [203,204], SOX estimation [205,206], RUL estimation [207][208][209][210][211][212][213][214][215][216][217], battery charging, and fault prognostic methods. However, most models described in the literature are not chemical-agnostic and extrapolating from cell to pack level.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

A Comprehensive Review of EV Lithium-Ion Battery Degradation

Rufino Júnior,

Sanseverino,

Gallo

et al. 2023

Preprint

0

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Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

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A Novel Remaining Useful Life Prediction Method for Capacity Diving Lithium-Ion Batteries

Cited by 11 publications

References 39 publications

Remaining Useful Life Prediction for Two-Phase Hybrid Deteriorating Lithium-Ion Batteries Using Wiener Process

Remaining Useful Life Prediction for Two-Phase Hybrid Deteriorating Lithium-Ion Batteries Using Wiener Process

A Comprehensive Review of EV Lithium-Ion Battery Degradation

A Comprehensive Review of EV Lithium-Ion Battery Degradation

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