An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit

Li, Chaoran; Xiao, Fei; Fan, Yaxiang

doi:10.3390/en12091592

Cited by 126 publications

(58 citation statements)

References 37 publications

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“…A more recent work presented in [41], introduced a stacked BiLSTM model and compared its results with three previous publications [13], [38], [42] as the same dataset was used [40] in all four cases. The BiLSTM showed better accuracy than the other methods when the comparison was done at different temperatures, 0 • C, 10 • C, and 25 • C. Each model in [41] was trained five times, and the average result was used as the final number for comparison, although it is not clear if the other authors in [13], [38], [42] have used training repetition, hence there is difficulty in cross-comparison among publications even though the same dataset was used. The final structure found to be optimal by the authors in [41] was composed of two stacked BiLSTMs each with 64 hidden neurons, which is equivalent to four unidirectional LSTM stacked layers and over 130,000 learnable parameters, the sum of all the weights and biases in the structure.…”

Section: ) Gated Rnns Applied To Soc Estimationmentioning

confidence: 99%

“…As an example, the performance similarity of the GRU and LSTM for solving speech recognition problems [39] suggests that performance similarities would also result in SOC estimation despite the obvious application differences. This assumption was initially confirmed in [42] where a GRU had been applied to perform SOC estimation using the same dataset [40] already used by the LSTM in [38]. In [46], a GRU was applied to estimate the SOC of an NMC and LFP at seven different temperatures, ranging from 0 • C to 50 • C. Only the FUDS, DST, and CC drive cycles were used to generate the dataset.…”

Section: ) Gated Rnns Applied To Soc Estimationmentioning

confidence: 99%

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Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

et al. 2020

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The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the states of electrified vehicle batteries. Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power capabilities. This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial basis functions (RBF), and Hamming networks. Comparisons between methods are shown in terms of data quality, inputs and outputs, test conditions, battery types, and stated accuracy to give readers a bigger picture view of the ML landscape for SOC and SOH estimation. Additionally, to provide insight into how to best approach with the comparison of different neural network structures, an FNN and long short-term memory (LSTM) RNN are trained fifty times each for 3000 epochs. The error is somewhat different for each training repetition due to the random initial values of the trainable parameters, demonstrating that it is important to train networks multiple times to achieve the best result. Furthermore, it is recommended that when performing a comparison among estimation techniques such as those presented in this review paper, the compared networks should have a similar number of learnable parameters and be trained and tested with identical data. Otherwise, it is difficult to make a general conclusion regarding the quality of a given estimation technique. INDEX TERMS Machine learning, artificial intelligence, deep learning, battery management systems (BMS), electric vehicles, state of charge, state of health.

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Section: ) Gated Rnns Applied To Soc Estimationmentioning

confidence: 99%

Section: ) Gated Rnns Applied To Soc Estimationmentioning

confidence: 99%

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

et al. 2020

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“…Step 4. Second layer filtering: Conduct KF-based filtering using Equations (18)- (21), and produce a further corrected SOC estimate, that is, the output SOC.…”

Section: Lithium-ion Batterymentioning

confidence: 99%

“…Besides, the machine learning algorithms are devised based on various mechanisms such as artificial neural networks [17], fuzzy logic inference [18], and support vector regression (SVR) [19]. These algorithms require a large amount of training data to establish the nonlinear relationship between the input to the battery and the output from the battery [20,21]. The performance of these algorithms is highly dependent on the quantity and quality of the training data, which in turn restricts the applicability and accuracy of these methods.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

et al. 2019

Electronics

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Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.

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“…These methods do not require detailed information on the battery system, also known as the "black box" model. Chaoran Li et al designed a SOC estimation method based on the recurrent neural network (RNN) [12]. Observable variables, such as voltage, current, and temperature, are directly mapped to SOC estimates.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model

Qiu

Tao

et al. 2019

Applied Sciences

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Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.

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An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit

Cited by 126 publications

References 37 publications

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model

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