Extreme learning machine model with honey badger algorithm based state-of-charge estimation of lithium-ion battery

Anandhakumar, C.; Sakthivel Murugan, N.S.; Kumaresan, K.

doi:10.1016/j.eswa.2023.121609

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…The ELM's performance was significantly influenced by the training time and the number of neurons in the hidden layer. To address this issue, Anandhakumar [51] and colleagues used an improved optimization algorithm called the Honey Badger Optimization Algorithm (HBA) to select appropriate hidden neurons, resulting in enhanced SOC estimation accuracy. The model achieved an accuracy of 97% in the FUDS drive cycle and 99% in the US06 drive cycle, making it suitable for real-time online estimation.…”

Section: Elm and Wnn Networkmentioning

confidence: 99%

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

Zhao,

Guo,

Chen

2024

WEVJ

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With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain.

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Section: Elm and Wnn Networkmentioning

confidence: 99%