Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation

Chen, Liaogehao; Zhang, Yong; Zheng, Ying; Li, Xiangshun; Zheng, Xiujuan

doi:10.1016/j.neucom.2020.07.081

Cited by 90 publications

(24 citation statements)

References 34 publications

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“…The data can be smoothed using empirical mode decomposition techniques. Reference [53] uses the battery capacity and the discharge voltage difference at equal time intervals as the battery indirect HI, and uses the phase space reconstruction method to reconstruct the time series of the extracted HI. Additionally, the ensemble empirical mode decomposition method is used to preprocess the reconstructed time series data of the two HIs to avoid the influence of data noise.…”

Section: Indirect Hi Based On Discharge 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 Discharge Processmentioning

confidence: 99%

Review on the Selection of Health Indicator for Lithium Ion Batteries

Zhou

Zheng

2022

Machines

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“…The Hurst exponent was estimated using the fly optimization algorithm. Chen, Liaogehao, et al [259] investigated the RUL prediction of LIBs using hybrid data-driven method employment based on error compensation (EC) and Support vector regression (SVR). They implemented the idea of EC by combining the predictions of RUL prediction with phase-space reconstruction SVR (PSR-SVR) and forecast error.…”

Section: ) Relevance Vector Machinementioning

confidence: 99%

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

et al. 2021

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Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.INDEX TERMS Battery datasets, battery data repository, remaining useful life (RUL), battery management, li-ion battery, RUL prediction methods.

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“…Currently, the methods of RUL prediction have been divided into three main categories: the model‐based methods, the data‐driven methods, and the hybrid model methods 9‐12 . The model‐based methods are introduced to analyze the capacity fading of lithium‐ion batteries by establishing an equivalent model.…”

Section: Introductionmentioning

confidence: 99%

A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

Wei

Wang

et al. 2022

Intl J of Energy Research

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Summary To accurately predict the remaining useful life (RUL) of lithium‐ion battery, this paper presents a novel machine‐learning method using gated recurrent unit (GRU) with error compensation (EC). Moreover, the dropout Monte Carlo technology is selected for reliable RUL prediction with uncertainty quantification. First, the equal charging voltage time is established and the variational mode decomposition is introduced to reduce the influence of capacity regeneration phenomenon. Then, the phase space reconstruction with C‐C technology is adopted to obtain the optimal input sequence, and the GRU with EC is established for RUL prediction. Finally, the probability distribution and 95% confidence interval are obtained based on dropout Monte Carlo technology. Compared with the existing methods, the proposed method can not only obtain a higher accuracy with a mean absolute error below 1.55% but also achieve reliable RUL prediction with probability distribution.

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Remaining useful life prediction of lithium-ion battery with optimal input sequence selection and error compensation

Cited by 90 publications

References 34 publications

Review on the Selection of Health Indicator for Lithium Ion Batteries

Review on the Selection of Health Indicator for Lithium Ion Batteries

A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management

A data‐driven approach with error compensation and uncertainty quantification for remaining useful life prediction of lithium‐ion battery

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