A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism

Yang, Kuo; Tang, Yugui; Zhang, Shujing; Zhang, Zhen

doi:10.1016/j.energy.2022.123233

Cited by 86 publications

(18 citation statements)

References 38 publications

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“…Some of these stochastic parameters and sub-systems, particularly in the physical layer, are chronologically dependent, and interactive correlations exist [81,82]. The time-sequence, auto-correlation, and cross-correlation of DERs and loads in the physical layer of the SMG might affect the developed analytical reliability evaluation model, considering various uncertainties [83,84]. The recorded and measured data for stochastic parameters, considering their time-sequence correlation, should be used to generate the state matrices [85].…”

Section: Headermentioning

confidence: 99%

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

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The reliability of smart micro‐grids (SMGs), as a cyber‐physical system (CPS), might be influenced by cyber failures and information transmission faults. Several Monte Carlo simulation (MCS)‐based approaches have been reported to assess the reliability of SMGs and smart grids. On the other hand, analytical reliability assessment methods have been presented in some research works, while the cyber system has not been concerned. However, the literature shows a research gap in developing an accurate and fast reliability evaluation method for SMGs based on the unavailability of cyber elements and information transmission faults. This article tries to fill the discussed gap by adding the analytical modelling of cyber‐physical interdependencies and information transmission faults to available analytical methods, focusing on physical uncertainties. Comparing the proposed model with existing MCS‐based and analytical reliability evaluation methods illustrates the advantages of this research. Test results show that less than 5.7% expected energy not supplied (EENS) error occurs by the proposed method, which would be much faster than MCS‐based ones. Moreover, the sensitivity analyses highlight the impacts of the cyber network topologies on the cyber‐physical interdependencies.

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Section: Headermentioning

confidence: 99%

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Aslani

Hashemi‐Dezaki

Ketabi

2022

IET Renewable Power Gen

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“…The authors of studies [70][71][72] all used GRU as the neural network for model training; the dataset is the INR 18650-20R and A123 18650 lithium battery from the CALCE dataset [41] with inputs of voltage, current, and temperature, respectively; and the RMS error obtained from the test dataset was not significantly different. Kuo et al [73] tested a 18650 Li-ion battery cell and used GRU with an encoderdecoder structure, in which the input vector is current, voltage, and temperature; further, they compared with LSTM, GRU, and sequence-to-sequence structure, the result shows that the MAE of their proposed neural network is lower than other methods at three different drive cycles and temperatures.…”

Section: Recurrent Type -Grumentioning

confidence: 99%

Deep Learning in State of Charge Estimation for Li-ion Battery: A Review

Zhong¹,

Zhang²,

Xu³

et al. 2022

Preprint

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As one of the critical state parameters of the battery management system, lithium battery state of charge (SOC) can provide an essential reference for battery safety management, charge/discharge control, and energy management of electric vehicles. To analyze the application of deep learning in electric vehicle power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, structural characteristics, advantages and disadvantages of lithium battery SOC estimation in deep learning method. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Secondly, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of four types of deep learning methods, were concluded using the structure of neural network used for training as the classification criterion. Finally, the challenges and future development directions of lithium battery SOC estimation in deep learning method were explained.

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“…The attention mechanism (AM) is a resource allocation mechanism that can highlight the impact of more important information by assigning different weights to the input features so that features containing important information do not disappear as the step size increases. 26,27 Qin et al 27 proposed that combining the model with the RNN network makes it simpler for the model to learn long-term interdependencies in the sequence, which improves the model's prediction accuracy. To make full use of the measurable parameter data of lithium-ion batteries and improve the estimation accuracy and effectiveness of traditional models, this paper combines the advantages of CNN, bi-directional long short-term memory (BiLSTM), and AM, and designs an attention-based CNN-BiLSTM network model to estimate and evaluate SOH and RUL of the lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Yang

Liu

et al. 2022

Journal of Algorithms & Computational Technology

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The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries describe the current aging degree of the batteries from different perspectives, and accurate and efficient battery health estimation is essential for their safe use. To improve the effectiveness and accuracy of the batteries’ health assessment models, this paper proposes a new method for SOH and RUL estimation of lithium-ion batteries. Convolutional neural networks (CNNs), bi-directional long short-term memory (BiLSTM), and attention mechanism (AM) to build a hybrid network model for capacity estimation of lithium-ion batteries, and further calculate the SOH and RUL estimation results. By using Center for Advanced Life Cycle Engineering (CALCE) lithium-ion battery capacity degradation data, we extracted the battery health indicator (HI) and verified the reasonableness of HI selection by using Gray Relational Analysis (GRA) and compared it with other network models to calculate the prediction accuracy by various evaluation indexes. The experimental results show that the method has higher estimation accuracy while avoiding the construction of complex battery mechanism degradation models and is highly generalized.

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A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism

Cited by 86 publications

References 38 publications

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Analytical reliability evaluation method of smart micro‐grids considering the cyber failures and information transmission system faults

Deep Learning in State of Charge Estimation for Li-ion Battery: A Review

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

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