A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries

Ma, Jian; Shang, Pengchao; Zou, Xinyu; Ma, Ning; Ding, Yu; Sun, Jianfang; Cheng, Yujie; Tao, Laifa; Lu, Chen; Su, Yuzhuan; Jia, Chong; Jin, Hao; Lin, Yongshou

doi:10.1016/j.apenergy.2020.116167

Cited by 59 publications

(18 citation statements)

References 48 publications

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“…A prediction-based test optimization method was shown in [237] for decreasing cycle test with estimated lifespan for Li-ion batteries. A hybrid transfer-learning method automatically selects historical test data and trained model of other formulations to help construct models of the target batteries.…”

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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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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“…Next, all the individual prediction results are added to obtain the comprehensive SOH prediction from which RUL is obtained. Ma et al (2021) proposed a test optimization method based on prediction. The hybrid transfer learning method can best select the trained predictive model of historical test data and other formulas to help build a model of the target battery.…”

Section: Othersmentioning

confidence: 99%

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

et al. 2021

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Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

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“…The system consists of three modules such as (EHM, ECM, and EDM), and an energy storage module (ESM). The following Equations ( 23) and (24) show finite time interval for the ATHEH system [82]:…”

Section: Autonomous Sensor In Hybrid Electromagnetic Energy Harvesting Systemsmentioning

confidence: 99%

“…They are also heavy, especially when compared with microelectronic devices to which they supply power. A typical battery has a short life span and is thus not practical for devices, which are expected to last more than 10 years [24,25]. In addition, some electronic devices are located in hard-to-reach areas: embedded in people or animals, installed inside structures (bridges, roads, and buildings), placed in satellite systems, etc.…”

Section: Introductionmentioning

confidence: 99%

Review of Power Converter Impact of Electromagnetic Energy Harvesting Circuits and Devices for Autonomous Sensor Applications

et al. 2021

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The demand for power is increasing due to the rapid growth of the population. Therefore, energy harvesting (EH) from ambient sources has become popular. The reduction of power consumption in modern wireless systems provides a basis for the replacement of batteries with the electromagnetic energy harvesting (EMEH) approach. This study presents a general review of the EMEH techniques for autonomous sensor (ATS) applications. Electromagnetic devices show great potential when used to power such ATS technologies or convert mechanical energy to electrical energy. As its power source, this stage harvests ambient energy and features a self-starting and self-powered process without the use of batteries. Therefore, it consumes low power and is highly stable for harvesting energy from the environment with low ambient energy sources. The review highlights EMEH circuits, low power EMEH devices, power electronic converters, and controllers utilized in numerous applications, and described their impacts on energy conservation, benefits, and limitation. This study ultimately aims to suggest a smart, low-voltage electronic circuit for a low-power sensor that harvests electromagnetic energy. This review also focuses on various issues and suggestions of future EMEH for low power autonomous sensors.

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A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries

Cited by 59 publications

References 48 publications

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

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

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

Review of Power Converter Impact of Electromagnetic Energy Harvesting Circuits and Devices for Autonomous Sensor Applications

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