A novel deep learning framework for state of health estimation of lithium-ion battery

Fan, Yaxiang; Xiao, Fei; Li, Chaoran; Yang, Gang; Tang, Xin

doi:10.1016/j.est.2020.101741

Cited by 240 publications

(65 citation statements)

References 32 publications

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“…The disadvantages are also evident, which mainly focus on the need for a large number of training samples and the complexity of the algorithm, which requires the high computing ability of the system. Fan [106] took an innovative approach to model; a hybrid algorithm by using gate-recursive element convolutional neural network (GRU-CNN) was proposed to analyze and study the charging voltage curves of lithium batteries. The measured data, such as voltage, current, and temperature, are used to estimate SOH online.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

100

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Lithium-ion power batteries have been widely used in transportation due to their advantages of long life, high specific power, and energy. However, the safety problems caused by the inaccurate estimation and prediction of battery health state have attracted wide attention in academic circles. In this paper, the degradation mechanism and main definitions of state of health (SOH) were described by summarizing domestic and foreign literatures. The estimation and prediction methods of lithium-ion power battery SOH were discussed from three aspects: model-based methods, data-driven methods, and fusion technology methods. This review summarizes the advantages and disadvantages of the current mainstream SOH estimation and prediction methods. This paper believes that more innovative feature parameter extraction methods, multi-algorithm coupling, combined with cloud platform and other technologies will be the development trend of SOH estimation and prediction in the future, which provides a reference for health state estimation and prediction of lithium-ion power battery.

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Section: Deep Learning Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Yao

Tang

et al. 2021

WEVJ

100

View full text Add to dashboard Cite

show abstract

“…In data-driven methods, understanding the electrochemical reactions and their modelling is not necessary and so these methods utilize only the degradation patterns present in the data using large data sets. Various methods such as Gaussianprocess based Kalman filter [13]- [15], neural network (NN) [16], [17], fuzzy logic [18], [19], genetic algorithm (GA) [20], support vector machine (SVM) [21], and long short term memory (LSTM) [21], [22], and other data-driven techniques [23]- [25] have been employed for SOH estimation. These methods have high accuracy and are flexible to the changes in SOH.…”

Section: A Literature Reviewmentioning

confidence: 99%

Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

et al. 2022

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Lithium-ion (Li-ion) batteries have increasingly been used in diverse applications. Accurate estimation of the state of health (SOH) of the Li-ion batteries is vital for all stakeholders and critical in various applications such as electric vehicles (EVs). The electrical equivalent circuit (EEC) 2-RC model is often used to model the battery operation but has not been used to capture the degradation of battery cells over time. This paper uses the 2-RC model to capture the degradation of the Li-ion battery. The proposed model is not only time-dependent but also captures the effect of temperature on battery degradation. The proposed approach estimates the SOH accurately and is also considerably flexible for diverse cells of different chemistry. We further generalize an N-RC model approach to evaluate the SOH of the battery. We compare the proposed model (2-RC) with the 1-RC model, and through numerical results, we show that the 2-RC model outperforms 1-RC and reduces the computational cost significantly. Similarly, the 2-RC model outperforms 3-RC and higher-order circuits. We also show that the proposed approach can capture the battery dynamics better for specific smaller orders of the polynomial (associated with Arrhenius equation) when compared with the 1-RC approach with considerably reduced (up to 60%) root mean square error (RMSE). Lastly, the average testing RMSE for 2-RC is 52.4%. INDEX TERMS Lithium-ion battery, State-of-health, Equivalent circuit model, Open circuit voltageThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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“…Fan et al. proposes a hybrid NN, including GRU NN and convolutional NN (CNN), to learn the shared information and time dependencies of charging voltage variation and SOH, and the MAE is restricted within 4.3%, manifesting its effectiveness ( Fan et al., 2020 ). To delineate the uncertainty of battery deterioration and avoid overfitting, Wei et al.…”

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

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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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A novel deep learning framework for state of health estimation of lithium-ion battery

Cited by 240 publications

References 32 publications

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

Dynamic Equivalent Circuit Model to Estimate State-of-Health of Lithium-Ion Batteries

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

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