State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Wei, Meng; Min, Yong; Li, Jia Bo; Wang, Qiao; Xu, Xinxin

doi:10.1109/access.2020.3031340

Cited by 73 publications

(19 citation statements)

References 36 publications

(45 reference statements)

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“…Among all DL architectures compared in the study, the proposed transformer model achieved the lowest RMSE of 1.1075%, 1.3139% and 1.1914% and MAE of 0.4441%, 0.5680% and 0.6502% on the test drive cycles outperforming even the recurrent models which has been widely used for SOC estimation as shown in Table 2 . We also note that the convolutional models such as the Resnet 40 and the Inception Time 51 also outperformed the conventional GRU 41 and LSTM 52 model. The baseline Transformer model that is not trained with the proposed training framework scores poorly along with the feedforward DNN.…”

Section: Resultsmentioning

confidence: 77%

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Hannan

How

Lipu

et al. 2021

Sci Rep

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Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

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Section: Resultsmentioning

confidence: 77%

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Hannan

How

Lipu

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Among all DL architectures compared in the study, the proposed transformer model achieved the lowest RMSE of 1.1075%, 1.3139% and 1.1914% and MAE of 0.4441%, 0.5680% and 0.6502% on the test drive cycles outperforming even the recurrent models which has been widely used for SOC estimation as shown in Table 2. We also note that the convolutional models such as the Resnet 41 and the Inception Time 40 also outperformed the conventional GRU 42 and LSTM 43 model. The baseline Transformer model that is not trained with the proposed training framework scores poorly along with the feedforward DNN.…”

Section: Resultsmentioning

confidence: 79%

Towards Accurate State of Charge Estimation for Lithium-ion Batteries using Self-supervised Transformer Model: A Deep Learning Approach

Hannan

How

Lipu

et al. 2021

Preprint

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Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning-enabled transformer model achieves the lowest root-mean-square-error (RMSE) of 1.2% and a mean-absolute-error (MAE) of 0.7% on the test dataset at various ambient temperatures. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

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“…The hyperparameters we need to consider here are the time step length T and the hidden state size m of the LSTM layer. For the proposed model, we set T as varying among [5,10,15] and m as varying among [16,32,64,128]. The average RMSE results for the 5-fold CV are tabulated in Table 3.…”

Section: Performance With Different Hyperparametersmentioning

confidence: 99%

“…This approach was implemented by [9], which used standard driving cycles (SDC) as input of the simulation model. The second method consists of predicting the SOC through Machine Learning (ML) techniques using the battery data collected while driving the electric vehicle [10]; for example, using information from the battery itself (voltage, current, temperature) and ML algorithms to predict the future SOC.…”

Section: Introductionmentioning

confidence: 99%

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

Hong¹,

Hwang²,

Kim³

et al. 2021

Applied Sciences

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An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

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State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks

Cited by 73 publications

References 36 publications

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Towards Accurate State of Charge Estimation for Lithium-ion Batteries using Self-supervised Transformer Model: A Deep Learning Approach

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

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