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Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability

Author

Listed:
  • Mo, Daijiang
  • Wang, Shunli
  • Fan, Yongcun
  • Takyi-Aninakwa, Paul
  • Zhang, Mengyun
  • Wang, Yangtao
  • Fernandez, Carlos

Abstract

Accurately estimating the state of health (SOH) of lithium batteries is a critical and challenging task in battery management systems. Data-driven models are widely used for SOH estimation but still suffer from the difficulty of balancing speed, accuracy, and adaptability. Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning kernel function is designed to complement the polynomial kernel function and form a joint function. This joint function is then introduced into a single implicit-layer extreme learning machine, which achieves fast speed and strong adaptive capability. To enhance the algorithmic parameter search capability, the optimal Latin hypercube idea, and the Osprey algorithm's global exploration strategy are introduced, which effectively improves the algorithm's global search capability. Additionally, it successfully regulated the positional update through the design of the logarithmic weighting factor, which improved the local search and convergence capabilities of the algorithm. The experiment validates the effectiveness and rationality of the proposed model for advancing battery management system applications.

Suggested Citation

  • Mo, Daijiang & Wang, Shunli & Fan, Yongcun & Takyi-Aninakwa, Paul & Zhang, Mengyun & Wang, Yangtao & Fernandez, Carlos, 2024. "Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024976
    DOI: 10.1016/j.energy.2024.132723
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    References listed on IDEAS

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