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A polynomial scale transformation and improved Wiener process for a novel lithium-ion battery performance degradation model: remaining useful life performance

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Abstract

This study contributes to propose a novel lithium-ion battery performance degradation model based on improved Wiener process. The aging of lithium-ion batteries brings potential hazards to the power system of electric vehicles, so the health status of lithium-ion batteries needs to be evaluated. First, a polynomial scale transformation model is established to scale the cycle number to transform the nonlinear Wiener process into linear Wiener process, and model parameters are estimated by the maximum likelihood functions. Second, a performance degradation model based on the improved Wiener process is constructed to estimate the remaining useful life (RUL) performance, in which the cumulative loss reaching the failure threshold is taken as the failure criterion. Finally, the proposed RUL estimation method is tested using data provided by NASA. The test results proved that the estimation errors of proposed model were controlled within 15%. The RUL estimation method proposed in this study provides a new way for the reliability evaluation of lithium-ion batteries and guarantees the safe operation of electric vehicle power system.

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Abbreviations

RUL:

Remaining useful life

EVs:

Electric vehicles

SOH:

State of health

MA:

Moving average

AR:

Autoregressive

LSTM:

Long short-term memory

SVM:

Support vector machine

BP:

Back propagation

R2:

Coefficient of determination

RMSE :

Root mean square error

u 0 :

Initial value

U(t):

Standard normal distribution

μ * :

Degradation rate after scale transformation

m :

The number of cycles

z i :

The actual value of cumulative capacity loss

z i * :

The fitting value of cumulative capacity loss

\(\bar{z}\) :

The average value of cumulative capacity loss

z 0 :

The cumulative capacity loss at the initial time

μ*:

The degradation rate of cumulative capacity loss

m*:

The number of cycles

σ*:

The cumulative capacity diffusion speed

L :

Failure threshold

μ :

Degradation rate

C(t):

Standard Brownian motion

f :

Probability density function

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Authors and Affiliations

  1. College of Engineering, Hebei Normal University, Shijiazhuang, 050024, China

    Chao Fu, Qing Lv & Xiancong Wu

  2. Institute of Innovation and Circular Economy, Asia University, Taichung, Taiwan

    Ming-Lang Tseng

  3. Department of Business Administration, Asia University, Taichung, Taiwan

    Ming-Lang Tseng

  4. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Ming-Lang Tseng

  5. Adam Smith Business School, University of Glasgow, Glasgow, UK

    Ming K. Lim

Authors
  1. Chao Fu
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  2. Qing Lv
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  3. Ming-Lang Tseng
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  4. Xiancong Wu
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  5. Ming K. Lim
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Contributions

CF: conceptualize, original version and finalized the final version; QL: conceptualize, original version and finalized the final version; M-LT: conceptualize, original version and finalized the final version; M-LT: conceptualize, original version and finalized the final version; XW: conceptualize, original version and finalized the final version; MKL: conceptualize, original version and finalized the final version.

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Correspondence to Ming-Lang Tseng.

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Appendix

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Table 5 Third-order scale transformation model
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5.

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Fu, C., Lv, Q., Tseng, ML. et al. A polynomial scale transformation and improved Wiener process for a novel lithium-ion battery performance degradation model: remaining useful life performance. J Ambient Intell Human Comput 15, 187–196 (2024). https://doi.org/10.1007/s12652-022-03883-0

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