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 60 publications

(22 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Machine-learning-based Soh Predictionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…The purpose of the model-based method [14] is to gradually converge the output parameters (such as voltage) of the model to the target value during the recursive process and then obtain an estimated value of the battery state parameters. The model-based method requires an accurate battery model to correctly reflect the battery characteristics and they estimate battery SOC using typical regression algorithms of modern control theory, including least squares [15], filters [16,17], neural networks [18], fuzzy control algorithm [19], sliding mode observer [20] and other methods. Meanwhile, charge imbalance is a very common problem in multi-battery SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

A Fast Lithium-Ion Battery Impedance and SOC Estimation Method Based on Two-Stage PI Observer

Chen

Huo

Yang

2021

WEVJ

View full text Add to dashboard Cite

Due to the complex changes in battery state, the accurate and fast estimation of battery state of charge (SOC) is still a great challenge. Here, a fast estimation method of battery impedance and SOC based on a multi-level PI observer is proposed. The observer model reflects the change of the battery state characteristics through the dynamic impedance, and then the system compensation factor is added to the observer to dynamically adjust the parameters of the battery model. The effectiveness of the algorithm is verified by the compound dynamic stress test (DST) experiment. The results show that the introduction of the compensation factor enables the system to tolerate a certain degree of impedance fluctuation and capacity attenuation and the maximum SOC estimation error can be kept within 2%.

show abstract

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

Cited by 60 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 Fast Lithium-Ion Battery Impedance and SOC Estimation Method Based on Two-Stage PI Observer

Contact Info

Product

Resources

About