Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model

Abstract

The state of health (SOH) of lithium-ion (Li+) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential regression (MER) model to determine SOH of the Li+ batteries. By determining the changepoint boundaries using the characteristic values such as voltage transition rate (VTR), temperature transition rate (TTR), and charge capacities of the Li+ battery at the changepoint timestamps, we determined the parametric values of the biphasic MER. The characteristic transition rate values, which depend on the transition probabilities of the rolling standard deviations of the measured voltage and temperature, were later utilized with the matching charge capacities to model various training–testing dataset combinations. This helped to estimate the SOH of the battery at different life-cycle phases. This study also developed a technique for real-time estimation of the remaining useful life of the battery by using the MER model parameters, VTR, and TTR which were previously unseen parametric values of the Li+ battery. The result obtained from the proposed model indicates that our technique will be effective for online SOH estimation of Li+ batteries.

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Correspondence to Chinedu I. Ossai.

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Ossai, C.I., Egwutuoha, I.P. Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model. Neural Comput & Applic 33, 1193–1206 (2021). https://doi.org/10.1007/s00521-020-05031-1

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Keywords

  • Anomaly detection
  • Lithium-ion battery
  • Levenberg–Marquardt algorithm
  • Multiphase exponential regression
  • State of health
  • Voltage
  • Temperature