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A combined improved dung beetle optimization and extreme learning machine framework for precise SOC estimation.

Kaihua Yao1,2,3, Xinyu Yan4,5,6, Xiling Mao1,2,3

  • 1School of Instrument and Electronics, North University of China, Taiyuan, 030051, China.

Scientific Reports
|May 20, 2025
PubMed
Summary
This summary is machine-generated.

Accurate state of charge (SOC) estimation for lithium-ion batteries (LiBs) is crucial for efficient battery management systems (BMS). This study introduces an Improved Dung Beetle Optimization (IDBO) and Extreme Learning Machine (ELM) framework for precise SOC estimation, enhancing battery safety and performance.

Keywords:
Electric vehiclesExtreme learning machineHigh robustnessImproved dung beetle optimizerLithium-ion batteriesSOC estimation

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Area of Science:

  • Battery Technology
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Accurate state of charge (SOC) estimation is vital for the efficient operation of lithium-ion batteries (LiBs) and their management systems (BMS).
  • Battery performance, including capacity, is significantly affected by factors like temperature, operating conditions, and material degradation.
  • Dynamic and non-linear battery behaviors necessitate high-precision SOC estimation for safe and stable operation.

Purpose of the Study:

  • To develop and evaluate a novel framework combining Improved Dung Beetle Optimization (IDBO) and Extreme Learning Machine (ELM) for enhanced SOC estimation in LiBs.
  • To address the limitations of traditional ELM, such as inconsistent performance due to random weight initialization, by employing IDBO for hyper-parameter optimization.
  • To validate the proposed IDBO-ELM method's robustness and accuracy across diverse conditions, including varying temperatures, operating scenarios, battery materials, initial SOC levels, and durations.

Main Methods:

  • Implementation of an Improved Dung Beetle Optimization (IDBO) algorithm, integrating Circle chaotic mapping, Golden sine strategy, and Levy flight strategy.
  • Application of IDBO for optimizing the hyper-parameters (weights and biases) of the Extreme Learning Machine (ELM) model.
  • Validation of the IDBO-ELM framework for SOC estimation under five distinct parameters: ambient temperature, operating conditions, battery materials, initial SOC, and running time.

Main Results:

  • The proposed IDBO-ELM model achieved high precision and robustness in SOC estimation, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) around 1.4% across various conditions.
  • Significant improvements were observed compared to the DBO-ELM method, with MAE and RMSE decreasing by over 30%.
  • The IDBO-ELM framework demonstrated consistent performance and stability, overcoming ELM's inherent limitations.

Conclusions:

  • The IDBO-ELM framework offers a highly accurate and robust solution for state of charge estimation in lithium-ion batteries.
  • This approach effectively enhances the efficiency and reliability of battery management systems under diverse operational conditions.
  • The study provides strong support for the safe and efficient application of LiBs in practical scenarios through improved SOC prediction.