Attention-based CNN-BiLSTM for  SOH and RUL estimation of  lithium-ion batteries

Zhu, Zhenyu; Yang, Qing; Liu, Xin; Gao, De-Xin

doi:10.1177/17483026221130598

Cited by 16 publications

(2 citation statements)

References 38 publications

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“…Thus, for the model's training, data were used to vary the capacity concerning the number of discharge cycles, the load/unloading voltage regarding the time, and the charging/unloading current in relation to the time. The results show that the model achieves a mean battery capacity estimation error of 1.5% [22].…”

Section: Literature Reviewmentioning

confidence: 95%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

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Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.

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Section: Literature Reviewmentioning

confidence: 95%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

View full text Add to dashboard Cite

show abstract

“…In recent years, hybrid models have attracted the attention of researchers. Some of them have put forward the comprehensive use of hybrid model to estimate the battery state and achieved good results [39][40][41]48,[129][130][131][132][133][134][135][136][137][138][139][140][141][142]. For example, Song et al [39] tried to build a hybrid model CNN-LSTM to estimate the battery SOC by using the feature extraction capability of CNN and the time series prediction capability of RNN, extracted advanced spatial features from the original data through CNN, captured the nonlinear relationship between SOC and measurable data such as current, voltage and temperature through LSTM, and obtained better performance than the LSTM or CNN single model.…”

Section: Hybrid Modelmentioning

confidence: 99%

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Zhao

Zhou

et al. 2023

WEVJ

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Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods.

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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

Xue,

Qiu,

Peng

et al. 2024

Advanced Science

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The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi‐sources, heterogeneity, high‐dimensionality, and small‐sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high‐entropy alloys, and other fields with similar data challenges.

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Attention-based CNN-BiLSTM for SOH and RUL estimation of lithium-ion batteries

Cited by 16 publications

References 38 publications

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

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