A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

Ren, Lei; Dong, Jiabao; Wang, Xiaokang; Meng, Zihao; Li, Zhao; Deen, M. Jamal

doi:10.1109/tii.2020.3008223

Cited by 408 publications

(122 citation statements)

References 26 publications

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“…Due to its excellent performance, it is used in the SOH estimation and prediction of lithium-ion batteries. For example, in literature [107,108], the LSTM algorithm is adopted to establish a performance degradation model of lithium-ion batteries, and the health degradation degree and remaining service life of the batteries are accurately predicted and estimated through calculation.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

102

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Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

102

View full text Add to dashboard Cite

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“…In contrast to the conventional LSTM method which equalizes output section accompanied by input section like one-to-one configuration, the paper supports many-toone configuration to be adaptable towards different input categories along with considerably decrease the amount of variables for finer generalization. In 2020, Auto-CNNLSTM method for RUL estimation was suggested in [239]. The model is developed depending on deep convolution neural network (CNN) and LSTM to mine deeper information in finite data.…”

Section: ) Artificial Neural Network (Ann)mentioning

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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“…The authors of [129] use the PHM dataset generated under highspeed dry milling operation with a three-flute tungsten [101], [127], [128], [106], [117] , [149], [ [129], [159], [160], [161], [158], [157] [129]. Few more publically available datasets like the milling machine tool wear dataset of NUAA_Ideahouse [163], "System-level Manufacturing and Automation Research Testbed" (SMART) at the University of Michigan [164] can be used for the RUL prediction in the future.…”

Section: B the 2010 Phm Data Challenge Data Set For Cnc Millingmentioning

confidence: 99%

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

et al. 2021

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An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

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A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

Cited by 408 publications

References 26 publications

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

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

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

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