State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series

Liu, Baolei; Xu, Jinli; Xia, Wei

doi:10.1155/2023/8569161

Cited by 12 publications

(5 citation statements)

References 41 publications

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“…The GRU approach was introduced as a simpler alternative to the LSTM. The GRU model has a structure similar to the LSTM but has fewer parameters and, therefore, is computationally more efficient [28]. The GRU method uses two gates, the update and reset gates, to control the flow of information in the network.…”

Section: Proposed Schemementioning

confidence: 99%

Zero-Shot Photovoltaic Power Forecasting Scheme Based on a Deep Learning Model and Correlation Coefficient

Park,

Kim,

Moon

et al. 2023

International Journal of Energy Research

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Accurate photovoltaic (PV) power forecasting is essential for the stable and reliable operation of PV power generation systems. Recently, various deep learning- (DL-) based forecasting models have been proposed for accurate forecasting, but newly built systems cannot benefit from them due to the absence of PV power data. Although zero-shot methods based on single site can be used for PV power forecasting, they suffer from performance degradation problems when the characteristics of the source data and target data are different. To address this issue, we propose a novel zero-shot PV power forecasting scheme that leverages historical data from multiple PV generation systems at different sites. The proposed scheme constructs an individual forecasting model using historical data from each PV generation system. Then, two correlation coefficients are calculated for each forecasting model: one based on the correlation between the input variables of the source data and target data and the other on the correlation between the input variables and output variables of the source data. Lastly, the final forecasting value is calculated as a weighted sum of the predicted values of the constructed forecasting models for the input variables of the target data. In the extensive experiments for diverse DL models for forecasting, correlation coefficient types for weights, and data time intervals, the combination of recurrent neural network, Pearson’s correlation coefficient, and solar-noon time yielded the best prediction performance, with an improvement of up to 34.47% in mean absolute error and up to 15.94% in root mean square error compared to the best single-site zero-shot prediction. In addition, in experiments on PV power data from 9 cities in Korea using this combination, the proposed scheme achieved the best predictive performance in almost all cases and the second-best performance with a very narrow margin only in a few cases.

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Section: Proposed Schemementioning

confidence: 99%

Zero-Shot Photovoltaic Power Forecasting Scheme Based on a Deep Learning Model and Correlation Coefficient

Park,

Kim,

Moon

et al. 2023

International Journal of Energy Research

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“…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%

“…The hybrid model has good robustness, and the RMSE value is 79.68% better than the SVM model. Zhao et al [130] and Liu et al [131] explored a hybrid CNN and GRU model for battery SOH estimation and validated the model by reconstructing feature series samples on the Oxford battery dataset. The average values of RMSE and MAE reached 0.582% and 0.524%, respectively.…”

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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“…Machine learning methods have been widely used to estimate SOH during battery degradation [4]. Guo et al [5], Su et al [6], Lin et al [7], Chen et al [8], and Liu et al [9] respectively studied the performance of SVM, GPR, BPNN, ELM, and CNN-GRU models in battery SOH estimation. The model can fit the relationship between different aging characteristics and SOH and can obtain highprecision results on SOH estimation.…”

Section: Introductionmentioning

confidence: 99%

State of health estimation for lithium battery based on multi-source transfer learning

Jiang,

2024

J. Phys.: Conf. Ser.

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Aiming at the problem that the state of health estimation accuracy decreases when the lithium battery in training and testing sets has different charging conditions, we proposed a solution based on the transfer learning with the multi-source method. This scheme uses the complete aging test data of 16 batteries under different charging conditions as the source domain and extracts aging characteristics respectively to train the basic model. The similarity coefficient was obtained by the similarity evaluation of the first 200 cycles of each source and target battery, and the learning weight of each source battery model was weighted by the similarity coefficient. The experimental results show that the MAE and RMSE of the transfer learning with a multi-source model decreased by 53.63% and 54.97% compared with the single-source estimation model under new charging conditions.

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State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series

Cited by 12 publications

References 41 publications

Zero-Shot Photovoltaic Power Forecasting Scheme Based on a Deep Learning Model and Correlation Coefficient

Zero-Shot Photovoltaic Power Forecasting Scheme Based on a Deep Learning Model and Correlation Coefficient

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

State of health estimation for lithium battery based on multi-source transfer learning

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