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Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation

Abhijit Kulkarni, Remus Teodorescu

2023enbatterymachine learningstate of healthestimationimpedancee-mobility

Abstract

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A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. This paper proposes a computationally efficient and data-lightweight SoH estimation technique that employs online impedance at four discrete frequencies to derive features for a linear regression approach. The proposed solution circumvents the complexities associated with traditional AI/ML methods that are often unsuitable for low-cost microcontrollers in BMS due to demanding non-linear functions and matrix operations. The accuracy of this method is validated using two experimental datasets, demonstrating a mean absolute error (MAE) of less than 2% across diverse training and testing examples. The findings underscore the method's effectiveness and practicality, making it suitable for real-time implementation in commercial BMS applications.

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Cite This Work

@article{295d099e-7d09-4eb4-a344-2fef94fc88af,
  title={Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation},
  author={Abhijit Kulkarni and Remus Teodorescu},
  year={2023},
  language={en}
}
TY  - JOUR
TI  - Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation
AU  - Abhijit Kulkarni
AU  - Remus Teodorescu
PY  - 2023
LA  - en
ER  -

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