State of health prediction for li-ion batteries with end-to-end deep learning

Zhu, Chunxiang; Gao, Mingyu; H, Wu; Sun, Changcheng; Zhang, Zhaowei; Bao, Zhengyi

doi:10.1016/j.est.2023.107218

Cited by 16 publications

(1 citation statement)

References 40 publications

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“…Lyu et al [111] estimated the battery SOC based on a GRU model, noting that GRU outperforms LSTM and RNN in network performance and estimation accuracy. Some researchers have further improved the performance of recurrent neural networks by increasing their depth, and used them for studying battery states estimation, such as Stacked Recurrent Neural Network (SRNN) [115][116][117][118], Bidirectional Recurrent Neural Network (Bi-RNN) [119][120][121][122][123], and Graph Neural Network (GNN) [124,125], and have applied them in battery states estimation and achieved high estimation accuracy.…”

Section: ) Rnn 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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