A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression

Pang, Xiaoqiong; Liu, Xiaoyan; Jia, Jianfang; Wen, Jinyu; Shi, Yuanhao; Zeng, Jianchao; Zhao, Zhen

doi:10.1016/j.microrel.2021.114405

Cited by 71 publications

(18 citation statements)

References 42 publications

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“…The NASA battery data set is used for verification, and the results show that the estimation error of SOH is less than 2.5%. The reference [72] extracts the peak of the IC curve and the area under the peak as health indicators, and proposes a new method for RUL prediction of lithium-ion batteries that fuses incremental capacity analysis and Gaussian regression process. that fuses incremental capacity analysis and Gaussian regression process.…”

Section: Indirect Hi Based On Charging Processmentioning

confidence: 99%

Review on the Selection of Health Indicator for Lithium Ion Batteries

Zhou

Zheng

2022

Machines

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Scientifically and accurately predicting the state of health (SOH) and remaining useful life (RUL) of batteries is the key technology of automotive battery management systems. The selection of the health indicator (HI) that characterizes battery aging affects the accuracy of the prediction model construction, which in turn affects the accuracy of SOH and RUL estimation. Therefore, this paper analyzes the current status of HI selection for lithium-ion batteries by systematically reviewing the existing literature on the selection of HIs. According to the relationship between HI and battery aging, battery HI can be divided into two categories: direct HI and indirect HI. The capacity and internal resistance of the battery can directly represent the aging degree of the battery and are the direct HIs of the battery. Indirect HIs refer to characteristic parameters extracted from battery charge and discharge data that can characterize the degree of battery aging. This paper analyzes and summarizes the advantages and disadvantages of various HIs and indirect HIs commonly used in current research, providing useful support and reference for future researchers in selecting HIs to characterize battery aging. Finally, in view of the capacity regeneration phenomenon in the aging process of the battery, the selection direction of future HI is proposed.

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Section: Indirect Hi Based On Charging Processmentioning

confidence: 99%

Review on the Selection of Health Indicator for Lithium Ion Batteries

Zhou

Zheng

2022

Machines

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“…The set θ can be determined by maximize likelihood method, which aims to maximize the log-likelihood functions shown in Eq. ( 10) [26], where n is the dimension of the input x.…”

Section: Gaussian Process Regression Techniquementioning

confidence: 99%

“…It can explain the uncertainty of prediction results in the form of probability. Therefore, GPR is suitable for modeling the dynamic and nonlinear tool wear evolution [25,26,27]. Moreover, it is recognized that the cutting parameters are not always fixed in actual production.…”

Section: Introductionmentioning

confidence: 99%

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

Qiang¹,

Shi²,

Liu³

et al. 2022

Preprint

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Tool wear is inevitable in actual manufacturing, especially in extreme processing conditions for milling difficult-to-cut materials. With the degradation of cutting performance, the undesirable tool status could trigger instability in machining process that causes negative effect on machined surface and dimension accuracy of the precision components. Therefore, tool condition monitoring for machining process is imperative. With distinctive characteristics on easy acquisition and non-interference in machining process, the power consumption of machine tool has been proved to be a reliable indicator for reflecting the changes of cutting process in industry. Thus, the present study proposes a novel method for monitoring tool condition using net cutting power consumption. The result indicates that the developed approach performs better than traditional approach which uses original total power to predict tool wear. This can be attributed to the changes of net cutting power consumption is more relevant with tool wear evolution. Furthermore, predicting tool wear under variable cutting parameters is achieved using Gaussian process regression, because it has good generalization performance and also explains the uncertainty of prediction results in the form of probability. This study reveals that low-cost sensor like power meter, can be used as an important supplement to monitor tool condition in the industry, and also provides a research basis for predicting tool wear under different cutting conditions.

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“…14,15 Data-driven models build capacity degradation models by profiling the battery's measurable data. Commonly used data-driven methods include: artificial neural networks (ANN), 16 support vector machines (SVM), 17 long short-term memory (LSTM) network, 18 relevance vector machines (RVM), 19,20 Gaussian process regression (GPR), 21 etc. The advantage of this approach is that it does not require complex expression formulas for the physical-chemical mechanism of the battery and avoids the disadvantages of model-based methods.…”

Section: Introductionmentioning

confidence: 99%

Research on the remaining useful life prediction method of lithium‐ion batteries based on aging feature extraction and multi‐kernel relevance vector machine optimization model

Qiu

Fan

Wang

et al. 2022

Intl J of Energy Research

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Summary Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models.

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A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression

Cited by 71 publications

References 42 publications

Review on the Selection of Health Indicator for Lithium Ion Batteries

Review on the Selection of Health Indicator for Lithium Ion Batteries

Application of cutting power in tool condition monitoring and wear prediction based on Gaussian process regression under variable cutting parameters

Research on the remaining useful life prediction method of lithium‐ion batteries based on aging feature extraction and multi‐kernel relevance vector machine optimization model

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