A method for estimating the aging state of lithium‐ion batteries based on a multi‐linear integrated model

Sun, Hanlei; Yang, Dongfang; Wang, Licheng; Wang, Kai

doi:10.1002/er.8709

Cited by 55 publications

(20 citation statements)

References 39 publications

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“…High and low temperatures can significantly impact the lifetime of LIB, as well as the SOH estimation [55]. Therefore, the SSA-Elman model is further verified at high temperature and low temperature.…”

Section: High-temperature and Low-temperature Validationmentioning

confidence: 89%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

114

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The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to system safety and economic development. This paper proposes a SOH estimation method based on the SSA-Elman model for the first time. To improve the correlation rates between features and battery capacity, a method combining median absolute deviation filtering and Savitzky–Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB, five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model, the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition, a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally, the performance of the proposed model is verified based on NASA dataset, and the outputs of the Elman, LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH, with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition, RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

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Section: High-temperature and Low-temperature Validationmentioning

confidence: 89%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

114

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“…The advantage is that the convergence speed is fast and independent adaptive learning rates are designed for different parameters by calculating the first‐ and second‐moment estimates of the gradient. The parameter update method is as follows 48–50

m_{t} = β_{1} m_{t - 1} + (1 - β_{1}) g_{t},

n_{t} = β_{2} n_{t - 1} + (1 - β_{2}) g_{t}^{2} .

m_{t}

is the first‐order moment estimation of the gradient,

n_{t}

is the second‐order moment estimation of the gradient,

β_{1}

and

β_{2}

are hyperparameters.…”

Section: The Proposed Modelmentioning

confidence: 99%

A state‐of‐health estimation method considering capacity recovery of lithium batteries

Guo

Zhu³

et al. 2022

Intl J of Energy Research

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At present, the rapid development of new energy sources makes lithium-ion batteries (LIBs) widely used, but LIBs will inevitably age during using. State of health (SOH) is a direct reflection to the aging of LIBs, so it is necessary to estimate the SOH. During the aging process of the LIBs, the phenomenon of capacity recovery will occur if the battery is standing for too long. Existing SOH estimation methods based on neural network do not propose countermeasures for the phenomenon, but in fact, capacity recovery is inevitable and it has a great impact on SOH estimation. According to this vacancy, this paper proposes a SOH estimation method based on double bi-directional long shortterm memory (DBiLSTM) model, which can accurately estimate recovered capacity and improve accuracy of SOH estimation. First, the capacity of LIB is decomposed at multiple scales using wavelet analysis, and the smooth and fluctuating components are obtained. Then six features are proposed based on the changes in the battery after aging. The proposed features are decomposed into new features suitable for the two components. Finally, the smooth component and the fluctuation component are estimated synchronously, and the estimated results are reconstructed to obtain the final estimated SOH. The method proposed in this paper is verified in the NASA dataset and compared with the bi-directional long short-term memory (BiLSTM) model. Comparing with the direct estimation by BiLSTM, the root mean square error (RMSE) is reduced by at least 0.0084 and the mean absolute percentage error (MAPE) is reduced by at least 0.52% when the battery capacity fluctuates greatly. The experimental results show that the proposed method can significantly improve the accuracy of SOH estimation when the capacity fluctuates greatly.Abbreviations: BiLSTM, bi-directional long short-term memory; CC, constant current; CNN, convolution neural network; CV, constant voltage; DBiLSTM, double bi-directional long short-term memory; FN, Nth feature; FN HF , the high frequency components of the Nth feature; FN LF , the low frequency component of the Nth feature; LIB, lithium-ion battery; LSTM, long short-term memory; MAPE, mean absolute percentage error; MLP, multi-layer perceptron; PCoE, prognostics center of excellence; RMSE, root mean square error; SOH, state of health; SVR, support vector regression.

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“…In order to accurately evaluate the electrical properties of the manufactured EI-TENG, the prepared EI-TENG (12 cm Â 4 cm) was placed on a reciprocating device for testing, and the measured properties are shown in For practical application, TENG must be able to work for a long time, so its long-term output stability is very important. [48][49][50] Under the condition of constant experimental conditions (the ambient humidity is 30%, the reciprocating frequency is 3 Hz, and the maximum triboelectric layer spacing is 6 mm), EI-TENG has been tested for 5000 output cycles. Test results are shown in Figure 3c, and the voltage and current output of EI-TENG have not decreased significantly in 5000 cycle tests, which proves that EI-TENG has good durability and output stability.…”

Section: Electrical Performance Of Ei-tengmentioning

confidence: 99%

Electrodeless Nanogenerator for Dust Recover

et al. 2022

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Triboelectric nanogenerator (TENG) has the advantages of low cost, low weight, simple structure, and high efficiency. It is a promising low‐frequency mechanical energy capture technology, which shows great potential in dealing with energy and environmental crisis and promoting new electronic products. TENGs have demonstrated irreplaceable design freedom over traditional electronic devices, including its ability to work independently of electrodes. In fact, the displacement current is an unreal current, it can work independently without electrodes. Moreover, due to the lack of “shielding effect” of the electrode, the surface potential can be larger than devices worked with electrodes. Here, based on TENG's high‐voltage characteristic without electrode, the application of dust removal is explored, and a kind of electrodeless polyethylene triboelectric blackboard eraser was demonstrated as an example, which can effectively restrain the flying of chalk dust (limit the dust diffusion within 0.648 m). The electrostatic charge decayed blackboard eraser can be recharged by the electrodeless TENG without requirement of electrodes establishment. In fact, contact electrification may happen frequently in natural environment, and further design may be achieved without the limitation of electrodes.

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A method for estimating the aging state of lithium‐ion batteries based on a multi‐linear integrated model

Cited by 55 publications

References 39 publications

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

A state‐of‐health estimation method considering capacity recovery of lithium batteries

Electrodeless Nanogenerator for Dust Recover

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