Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries

Mamo, Tadele; Wang, Fu‐Kwun

doi:10.1109/access.2020.2995656

Cited by 39 publications

(31 citation statements)

References 35 publications

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“…By applying the dropout during both training and inference, it is possible to analyze it as if predictions from many different networks have been made, that is, a Monte Carlo sample from the space of all possible networks. Hence, it is entitled Monte Carlo Dropout (MCD) [ 32 ]. Therefore, a distribution of predictions is gathered and a measure of uncertainty of the model can be evaluated.…”

Section: Methodsmentioning

confidence: 99%

Static Attitude Determination Using Convolutional Neural Networks

Santos

Seman

Bezerra

et al. 2021

Sensors

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The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).

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Section: Methodsmentioning

confidence: 99%

Static Attitude Determination Using Convolutional Neural Networks

Santos

Seman

Bezerra

et al. 2021

Sensors

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show abstract

“…An area of emphasis that can be identified in much SoH literature is battery prognostics and time series forecasting of the SoH based on neural network approaches as part of efforts to develop more robust battery management systems [34,35], which has been demonstrated to be a high-accuracy technique. Examples of the time series forecasting approach include works such as [36,37], focusing on online state of charge prediction, and [38][39][40] for predictive SoH models, with a focus on estimating the remaining useful life (RUL) of the battery system. However, in the context of offline battery screening, time series forecasting approaches are limited in the fact that predictions are made in the context of the history of the long-term battery degradation, which makes it inapplicable to end-of-life estimation of the current battery SoH.…”

Section: Model-free State Of Health Estimationmentioning

confidence: 99%

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Rastegarpanah

Hathaway

2021

Energies

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The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.

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“…4. The attention layer takes the weights computed from LSTM layers and computes a new time step as expressed in ( 10) [40]. In the attention mechanism, the weights of LSTM layer are adjusted which are directly responsible for the score.…”

Section: Proposed Attention-lstm Modelmentioning

confidence: 99%

Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network

Shifat

Hur

2020

IEEE Access

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Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take place in a motor. Stator related faults are among the most commonly occurring faults in BLDC motor. With an initial raise in local heating, a fault in the stator can largely reduce motor efficiency and account for the entire system breakdown. In this study, we present a deep learning-based approach to estimate the remaining useful life (RUL) of BLDC motor affected by different stator related faults. To analyze the motor health degradation, we have investigated two types of stator faults namely inter-turn fault (ITF) and winding short-circuit fault (WSC). A generator was coupled with the motor and using an average value rectifier (AVR), generator's output voltage was monitored for the entire lifecycle. A proven neural network for effective sequence modeling, recurrent neural network (RNN) is selected to train the voltage degradation data. For a better estimation of nonlinear trends, long-short term memory (LSTM) with attention mechanism is chosen to make predictions of the motor RUL for both types of faults. The main concern that encourages authors of this paper is the proposed method can be used for the real-time condition monitoring and health state estimation of BLDC motors. Also, the proposed AVR-LSTM method is not affected by environmental influences, making it suitable for diverse operating conditions.

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Long Short-Term Memory With Attention Mechanism for State of Charge Estimation of Lithium-Ion Batteries

Cited by 39 publications

References 35 publications

Static Attitude Determination Using Convolutional Neural Networks

Static Attitude Determination Using Convolutional Neural Networks

Rapid Model-Free State of Health Estimation for End-of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network

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