Multi-Parameter Optimization Method for Remaining Useful Life Prediction of Lithium-Ion Batteries

Li, Bing; Gao, Xiaoyu; Li, Pengcheng; Liu, Zhen

doi:10.1109/access.2020.3011625

Cited by 13 publications

(6 citation statements)

References 29 publications

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“…There are 127,372 sets of original data with 25 dimensions in each group. For the convenience of the model training, the data were extracted every 1 h, and the data of 7 dimensions, namely stack voltage, current, inlet temperature of hydrogen and air, outlet pressure of hydrogen, flow rate of cooling water and inlet hygrometry, were used based on our previously proposed multi-parameter optimization theory [30].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…In this paper, the 95% and 96% of the initial voltage are regarded as the judgment standard of the battery's end of life (EOL) [30].…”

Section: Evaluation Indexmentioning

confidence: 99%

“…where x i is the actual value of the original data, and xi is the predicted value. In this paper, the 95% and 96% of the initial voltage are regarded as the judgment standard of the battery's end of life (EOL) [30].…”

Section: Evaluation Indexmentioning

confidence: 99%

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A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

et al. 2022

Applied Sciences

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The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate.

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Section: Data Preprocessingmentioning

confidence: 99%

“…In this paper, the 95% and 96% of the initial voltage are regarded as the judgment standard of the battery's end of life (EOL) [30].…”

Section: Evaluation Indexmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

et al. 2022

Applied Sciences

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“…This process is dependent on improved particle swarm optimization (PSO), information entropy, and moving average filter (MAF) for multi-parameter optimization. This method gives a great estimation accuracy under the both low and high temperature condition and use less training data [236].…”

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 uncertainty of the RUL prediction can be managed by adjusting the model noise through short-term prediction and correction loops. Long et al (2020) proposed a Li-ion battery RUL estimation method based on improved particle swarm optimization (PSO) and the technique for order preference by similarity to ideal solution (TOPSIS). Use the moving average filter (MAF) to perform battery raw data filtering to obtain a smooth battery life decline curve.…”

Section: Improved Particle Filtering Algorithmsmentioning

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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Multi-Parameter Optimization Method for Remaining Useful Life Prediction of Lithium-Ion Batteries

Cited by 13 publications

References 29 publications

A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

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

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