Computation-light AI models for Robust Battery Capacity Estimation based on Electrochemical Impedance Spectroscopy

Zhansheng Ning, Prasanth Venugopal, Thiago Batista Soeiro, Gert Rietveld

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
166 Downloads (Pure)

Abstract

Electrochemical impedance spectroscopy (EIS) holds great promise for assessing battery degradation. Nevertheless, many present methods for extracting battery aging features from EIS data are unsuitable for cells that have very different aging behaviors. Another challenge is that the time complexity of machine learning (ML) models based on full-frequency EIS significantly increases. To tackle these issues, this article investigates two feature extraction methods together with suitable regression algorithms. First, manual feature extraction methods are studied that involve feature extraction via feature correlation and consistency analysis, enhancing the robustness of battery capacity estimation within Gaussian process regression (GPR) and support vector machine (SVM) algorithms. On the other hand, automatic feature extraction methods extract latent features using convolutional neural networks (CNNs) as inputs to the model. Best results are achieved with the CNN-based methods that achieve robust capacity estimation with 2.88% accuracy for different battery cells at varying temperatures while remaining insensitive to EIS measurement uncertainty. This is a factor of 2-3 more accurate than the existing published approaches on the same dataset. By combining the advantages of manual and automatic feature extraction approaches, a reduced EIS dataset is used to train a lightweight CNN model, resulting in an inference time of less than 21.48 ms on STM32L476RG, whereas the existing approaches take up to 50 ms for achieving a similar accuracy.

Original languageEnglish
Pages (from-to)3146-3158
Number of pages13
JournalIEEE Transactions on Transportation Electrification
Volume11
Issue number1
Early online date29 Jul 2024
DOIs
Publication statusPublished - Feb 2025

Keywords

  • Accuracy
  • Batteries
  • Battery charge measurement
  • Capacity Estimation
  • Computation-light ML
  • EIS
  • Estimation
  • Feature extraction
  • Feature Extraction
  • Li-ion Battery
  • Robustness
  • State of charge
  • Temperature measurement

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