Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery

Zhao, Xuebing; Xuan, Dongji; Zhao, Kaiye; Li, Zhen-Zhe

doi:10.1016/j.est.2020.101789

Cited by 56 publications

(19 citation statements)

References 31 publications

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“…The construction of Elman NN is shown in Figure 10 , and a one-step delay mechanism is imbedded in the hidden layer to promote the global stability and time-varying ability of Elman NN ( Zhao et al., 2020 ). The unit of input layer accounts for only the signal transmission, whereas the output layer takes charge of weighting ( Li et al., 2019a ).…”

Section: Machine-learning-based Soh Predictionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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Section: Machine-learning-based Soh Predictionmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

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“…Furthermore, Guo et al studied the effects of the key parameters of neural network functions on the battery SOC estimation results [17]. Zhao et al used the ant colony optimization algorithm to optimize the neural network to improve the estimation accuracy [18]. Feng et al divided hidden layers into separate modules and employed a neural network to estimate the battery SOC [19].…”

Section: Introductionmentioning

confidence: 99%

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

et al. 2022

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The traditional battery state of charge (SOC) estimation method, which is based on neural networks, directly uses terminal voltage and terminal current as the input data. Although it is convenient to implement, it produces a large estimation error when the current and voltage change drastically. To solve this problem, a new method, which uses a composite multiscale wavelet transform, is proposed to estimate the battery SOC. In the proposed method, a wavelet transform is applied to the input data, and this process obtains the approximate coefficients and detail coefficients of the input data at different scales. A neural network then uses these coefficients as inputs to estimate the SOC. The experimental results show that the proposed method can improve the accuracy of the battery SOC estimation without changing the neural network structure or algorithm.

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“…This method can solve the particle degeneracy phenomenon of the particle filter by optimizing the importance of the probability density function. Chen et al [30] estimated the state of health of lithium-ion batteries based on the fusion of the autoregressive moving average model and the Elman neural network [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries

Huang

Wang

et al. 2021

Processes

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The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample.

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Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery

Cited by 56 publications

References 31 publications

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform

A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries

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