State of health estimation of lithium-ion batteries based on the constant voltage charging curve

Wang, Zengkai; Zeng, Shengkui; Guo, Jianbin; Qin, Taichun

doi:10.1016/j.energy.2018.11.008

Cited by 169 publications

(46 citation statements)

References 38 publications

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“…This approach provides more flexibility for SOH estimation, but the correlation between battery SOH and each selected voltage range is not always the strongest. Therefore, in practical applications, the healthy features extracted from partial charge profiles with high correlations to the battery SOH are the best inputs to the training model for SOH estimation accuracy [32].…”

Section: ) Feature Extractionmentioning

confidence: 99%

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

2021

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Section: ) Feature Extractionmentioning

confidence: 99%

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

2021

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“…According to the comprehensive value calculation of the battery cells and the application of the best priority search algorithm, the hierarchical description is determined in the power state evaluation process. The calculation process of the cell balance degree CF (φi(t)) is shown in Equation (14).…”

Section: Interference Informationmentioning

confidence: 99%

“…Under the influence of many factors such as complex working conditions, cell combination structure and environments [4], there are more interference information in the parameters [12] such as voltage, current and temperature which are measured during the working process in real-time, making the online state evaluation to be quite difficult [13]. The problem of the false alarm rate is still unable to break through [14]. Therefore, the online reliable power state evaluation is the key to contain the safety threat of lithium battery packs [15], which also attracts the international concern.…”

Section: Introductionmentioning

confidence: 99%

A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model

Wang

Stroe

Fernandez

et al. 2020

Journal of Cleaner Production

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The power state evaluation plays a decisive influence on the safety implication of the lithium battery packs, and there is no effective online evaluation method so far due to the imbalance phenomenon among the internal connected battery cells, which cannot be abstained by the advancement of the materials and techniques. A novel power state description method is proposed in this paper by investigating the improved external parameter coupling treatment, in which the mutual relationship description is conducted by the parameter information feature decomposition together with the Bayesian sequential decision algorithm. The complicated power state evaluation model constructing problem of the coupling relationship decomposition is solved by investigating the non-convex optimization under complex working conditions for the lithium battery packs. The evidence combination is realized by introducing the information fusion strategy, according to which the multi criteria decision is realized by using the evidence theory. As can be seen from the experimental results, the voltage difference is within 10 mV in both of the first phase and the second phase, which increases rapidly in the third phase and reaches a maximum of 120mV. Meanwhile, its power state evaluation accuracy is 95.00% and has a good output voltage tracking effect in the complex working conditions. The power state evaluation problem can be solved by the proposed model constructing method, which is suitable for the complex battery cell combination structures and environment influences, realizing the reliable and hierarchical power state evaluation of the lithium battery packs. It provides a safety reference value and the energy management basis for the reliable power supply in the cleaner production of the power lithium battery packs.

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“…In [11], [13], relevant feature vectors are extracted according to the variation characteristics of incremental capacity (IC) curve, and then the GPR is employed to estimate the SOH of the battery. In [26], the constant voltage charging aging factor (CVCAF) is regarded as the measurement index to achieve precise estimation of the battery SOH without full cycle experiment. In [27], a total of 14 features are extracted and analyzed by the grey relational analysis (GRA) and principal component analysis (PCA), and then the relation vector machine (RVM) is leveraged for further prediction.…”

Section: Introductionmentioning

confidence: 99%

State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network

et al. 2019

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This paper proposes a fusion model based on the autoregressive moving average (ARMA) model and Elman neural network (NN) to achieve accurate prediction for the state of health (SOH) of lithiumion batteries. First, the voltage and capacity degradation variation of the battery are acquired through the battery lifecycle data, and the health factor related to the battery aging is selected according to the variation of the voltage profile. Second, the empirical mode decomposition (EMD) is employed to process the capacity degradation data and eliminate the phenomenon of tiny capacity recovery, and multiple data sequences, as well as the related residue, are extracted, then the grey relational analysis (GRA) between sub-sequences and health factor are discussed. Furthermore, the ARMA model and Elman NN model are respectively built by training the subsequent time series data and residue data. Finally, all the individual predictions are combined to generate the estimated SOH sequences. The experimental validation is performed to manifest that the addressed fusion method performs the SOH prediction with satisfactory accuracy, compared with the single ARMA method and Elman NN model. INDEX TERMS Autoregressive moving average (ARMA), Elman neural network (NN), grey relational analysis (GRA), empirical mode decomposition (EMD), state of health (SOH).

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State of health estimation of lithium-ion batteries based on the constant voltage charging curve

Cited by 169 publications

References 38 publications

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model

State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network

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