Temperature prediction of battery energy storage plant based on EGA-BiLSTM

Jiang, Ling; Yan, Chunkai; Zhang, Xinsong; Zhou, Bojun; Cheng, Tianyu; Zhao, Jiahao; Gu, Juping

doi:10.1016/j.egyr.2022.02.195

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…Krivik et al used battery electrochemical impedance spectroscopy to predict battery temperature [3]. DIA et al provide a new method in battery temperature estimation, which takes into account the model parameters and uses Kalman filtering to estimate the battery internal temperature [4]. The prediction methods based on gas detection include Fernandes et al using highresolution gas detection devices to monitor harmful gases in real-time [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Wang

Shi

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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New energy electric vehicles play an important role in reducing carbon emissions, reducing fossil energy consumption, and promoting the development of electrified transportation. As an important energy storage and driving source for pure electric vehicles, the safety of power batteries in the charging process has always attracted much attention. During use, the thermal effect of the battery will affect the temperature and electrochemical properties of the battery, greatly affecting the safety and service life of the battery. This article uses the historical data of the real electric bus in operation and selects the real driving data collected on 2 electric buses. A battery temperature prediction method based on CNN-LSTM hybrid nerve in the charging stage of electric bus is proposed. Finally, compared with other models, the results show that the model can effectively predict the short-term battery temperature change in the future.

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Section: Introductionmentioning

confidence: 99%

Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Wang

Shi

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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“…In actual application, the performance of the BiLSTM model is closely tied to the expertise of professionals in the segmentation of time series data. Thus, Jiang and coworkers [26] proposed a method combining the elitist preservation genetic algorithm (EGA) with the BiLSTM model to predict battery temperature. The EGA method is employed to derive an optimized data segmentation strategy, thus enabling the BiLSTM neural network to achieve enhanced prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes

Zhuang,

Ma,

Lam

et al. 2023

Processes

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The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the single model and the hybrid model) have their limitations in dealing with nonlinear and complex temperature data, making it challenging to achieve high-precision prediction results. In order to tackle this issue, this paper introduces a novel two-phase deep learning network for predicting the temperature of wind turbine gearboxes. In the first phase, a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory (BiLSTM) network are separately trained using the same dataset. The two pre-trained networks are combined and fine-tuned to form the 1DCNN-BiLSTM model for the accurate prediction of gearbox temperatures in the second phase. The proposed model was trained and validated by measured datasets from gearboxes from an existing wind farm. The effectiveness of the model presented was showcased through a comparative analysis with five traditional models, and the result has clearly shown that the proposed model has a great improvement in its prediction accuracy.

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Temperature prediction of battery energy storage plant based on EGA-BiLSTM

Cited by 8 publications

References 7 publications

Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Prediction of battery temperature in electric bus charging stage based on CNN-LSTM

Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes

Batteries temperature prediction and thermal management using machine learning: An overview

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