Predictability Analysis of Lithium-Ion Battery Remaining Useful Life with Multiscale Entropy

Chen, Yufeng; Bao, Juan; Xiang, Zhengtao; Jian, Wei

doi:10.1109/iccis.2013.281

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…As the cycle number and timescale increase, the multi-scale entropy also increases, which indicates a decrease in RUL predictability. 134 Therefore, using one model to predict battery RUL will lose accuracy as cycle number increases, and a model-switching algorithm is desirable for longterm prediction. Entropy analysis is more commonly used as a feature extraction method in the existing literature.…”

Section: Statistical Approachmentioning

confidence: 99%

Battery Lifetime Prognostics

Lin

et al. 2020

Joule

799

244

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Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.

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Section: Statistical Approachmentioning

confidence: 99%

Battery Lifetime Prognostics

Lin

et al. 2020

Joule

799

244

View full text Add to dashboard Cite

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“…Moreover, the concept of entropy in the field of information theory has been introduced for extracting statistical information from batteries charge/discharge test data. The methods based upon sample entropy and multiscale entropy are proposed to determine the SOH and predict the remaining useful life (RUL) in [25], [26], respectively.…”

Section: Introductionmentioning

confidence: 99%

A Health Monitoring Method Based on Multiple Indicators to Eliminate Influences of Estimation Dispersion for Lithium-Ion Batteries

Tang

Liu

et al. 2019

IEEE Access

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The state-of-health (SOH) is a critical parameter to determine the degradation degree of lithiumion batteries, thus plays an important part in energy management problems of electric vehicles (EVs). Due to the estimation dispersion of health indicators, it is difficult to monitor conveniently and robustly the SOH of on-board batteries. In this paper, a health monitoring method for the application of battery management system (BMS) has been presented to solve this issue. First of all, the necessity of multiple indicators (MIs) is demonstrated through the analysis of estimation dispersion of single indicator (SI), and three health indicators are extracted from actual operating conditions of EVs. After that, the mixed membership function (MMF) according to fuzzy logic theory is applied to integrate the estimation effects of different health indicators. Then, the analytic hierarchy process (AHP) using dispersion information of health indicators is introduced to calculate the weight coefficient of each indicator. Finally, the performance and robustness of the proposed method have been verified by numerous experiments. The results indicate the average estimation errors of five tested batteries are less than 3%, and the influences of estimation dispersion are largely eliminated when compared with the SI-based and other MIs-based methods. INDEX TERMS Li-ion batteries, capacity fading, health monitoring, estimation dispersion, multiple indicators.

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Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

Kim

Lee

2022

Applied Energy

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Predictability Analysis of Lithium-Ion Battery Remaining Useful Life with Multiscale Entropy

Cited by 7 publications

References 10 publications

Battery Lifetime Prognostics

Battery Lifetime Prognostics

A Health Monitoring Method Based on Multiple Indicators to Eliminate Influences of Estimation Dispersion for Lithium-Ion Batteries

Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

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