State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications

Terala, Pranaya K.; Ogundana, Ayodeji S.; Foo, Simon Y.; Amarasinghe, Migara Y.; Zang, Huanyu

doi:10.3390/mi13091397

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…Chen developed a fault detection and classification algorithm for transmission lines based on unsupervised feature learning and convolutional sparse autoencoders [21]. Liu formulated a CNN-LSTM model for pattern recognition of four types of defects derived from XFDTD simulation software [22].…”

Section: Introductionmentioning

confidence: 99%

Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU

Zhang,

Chen,

Zhang

et al. 2024

IET Generation Trans & Dist

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Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors.

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Section: Introductionmentioning

confidence: 99%

Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU

Zhang,

Chen,

Zhang

et al. 2024

IET Generation Trans & Dist

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“…However, caution must be taken to avoid domain mismatch and larger datasets are often required to fine tune. Terala et al proposed a novel high performance Neural Network (NN) SOC estimation approach using Stacked Encoder-Decoder Bi-Directional Long Short Term Memory (LSTM) [16]. However, they also suffer from fluctuating-SOC [17] effects frequently seen in deep learning applications.…”

Section: Introductionmentioning

confidence: 99%

“…The Bayes probabilistic based methods combine cell model with Bayes inference algorithm to eliminate noise uncertainties. However, its accuracy heavily depends on the accuracy of the model developed [16]. Data driven algorithms are preferable to model-based filtering algorithms because they do not require complex model design and parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Battery State of Charge Estimation With an Ensemble Algorithm Using Central Difference Kalman Filter (CDKF) and Non-Linear Autoregressive With Exogenous Input (NARX)

Ogundana,

Terala,

Amarasinghe

et al. 2024

IEEE Access

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Conventional model-based probabilistic inference methods require increasingly complex models to improve Electric Vehicle (EV) battery State of Charge (SOC) estimation. Deep learning methods gained popularity in recent years with their model free estimations. However, practical constraints such as insufficient training data, model complexity for real time implementation, and generalization on new dataset hinder performance reliability. Another major practical drawback of the data driven deep learning approach is its poor convergence from an unfamiliar initial error state as training dataset does not adequately accommodate these practical error scenarios. This paper proposes an ensemble method that uses a weighted estimate of the Central Difference Kalman Filter (CDKF) and Nonlinear Autoregressive with Exogenous Input (NARX) to accurately estimate SOC in the early stages of degradation. We employ a parallel ensemble estimation method that reduces the estimation bias, improves generalization, accuracy, robustness, and reliability of the estimator. We propose a pre-estimated voting weight to combine the ensemble algorithm and employ the CDKF covariance dependent method as the optimum approach for initializing the ensemble system to achieve a robust convergence performance. The state converges on an average of 136 time-steps when initialized halfway from the true state. The average Mean Absolute Error (MAE) performance of the ensemble method is about 0.5 % with an average training data of about 31,041 time-steps. The model was validated using conventional drive cycle data and was shown to outperform its individual ensemble members and gated Recurrent Neural Networks (RNN) such as Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and the Bidirectional LSTM (BiLSTM).

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“…For example, an adaptive back propagation neural network was introduced to improve the SOC estimation accuracy of an unscented Kalman filtering algorithm [ 15 ]. A recurrent convolutional neural network (RCNN) was employed for SOC prediction of lithium-ion batteries [ 16 ], and a stacked encoder–decoder bi-directional long short-term memory (LSTM) was used to estimate SOC for the electric vehicle and hybrid electric vehicle [ 17 ]. Generally, the key factor for the success of AI model application is sufficient or even complete training samples for obtaining a high classification accuracies in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

Lin

Tu³

et al. 2023

Sensors

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The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.

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State of Charge Estimation of Lithium-Ion Batteries Using Stacked Encoder–Decoder Bi-Directional LSTM for EV and HEV Applications

Cited by 12 publications

References 39 publications

Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU

Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU

Electric Vehicle Battery State of Charge Estimation With an Ensemble Algorithm Using Central Difference Kalman Filter (CDKF) and Non-Linear Autoregressive With Exogenous Input (NARX)

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries

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