Yao Kaihua, Yan Xinyu, Mao Xiling, Li Mengwei, Li Xiao, Lian Ziyu, Han Yuxiang
School of Instrument and Electronics, North University of China, Taiyuan, 030051, China.
School of Instrument and Intelligent Future Technology, North University of China, Taiyuan, 030051, China.
Sci Rep. 2025 May 20;15(1):17468. doi: 10.1038/s41598-025-00482-9.
Accurate estimation of the state of charge (SOC) of lithium-ion batteries (LiBs) proportionally impacts the efficiency of battery management systems (BMS) considering the dynamic and non-linear behavior of LiBs. Changes in the activities of the cathode and anode materials and internal resistance tend to impact the battery capacity. When the battery is operated at high or low temperatures or under the HWFET condition, battery capacity tends to deteriorate drastically. Therefore, high-precision SOC estimation is required to ensure safe and stable operation. In this work, we propose a combined Improved Dung Beetle Optimization (IDBO) and Extreme Learning Machine (ELM) framework for SOC estimation and evaluate the efficiency of the BMS. The novelty of the model stems from the application of the IDBO algorithm, which incorporating Circle chaotic mapping, the Golden sine strategy, and the Levy flight strategy, for hyper-parameter optimization. This effectively resolves the problems of inconsistent performance and instability arising from randomly initialized hidden layer weights and biases in ELM, resulting in enhanced prediction accuracy. The proposed IDBO-ELM method is validated in the context of five parameters, namely, different ambient temperatures, operating conditions, battery materials, initial SOC values, and running time. The experimental results show that the error ranges of both MAE and RMSE of the proposed model for SOC estimation under different conditions are around 1.4%, demonstrating high precision and robustness. The MAE and RMSE of SOC estimation decreased by more than 30%, respectively, compared to those by DBO-ELM. The model provides strong support for the safe and efficient application of LiBs under various practical conditions.
考虑到锂离子电池（LiBs）的动态和非线性行为，准确估计其荷电状态（SOC）对电池管理系统（BMS）的效率有相应影响。阴极和阳极材料的活性以及内阻的变化往往会影响电池容量。当电池在高温或低温下运行或处于HWFET条件下时，电池容量往往会急剧下降。因此，需要高精度的SOC估计来确保安全稳定运行。在这项工作中，我们提出了一种用于SOC估计的改进型蜣螂优化（IDBO）和极限学习机（ELM）相结合的框架，并评估了BMS的效率。该模型的新颖之处在于应用了IDBO算法，该算法结合了圆混沌映射、黄金正弦策略和莱维飞行策略进行超参数优化。这有效地解决了ELM中因随机初始化隐藏层权重和偏差而导致的性能不一致和不稳定问题，从而提高了预测精度。所提出的IDBO - ELM方法在不同环境温度、运行条件、电池材料、初始SOC值和运行时间这五个参数的情况下得到了验证。实验结果表明，所提出模型在不同条件下进行SOC估计时，平均绝对误差（MAE）和均方根误差（RMSE）的误差范围均在1.4%左右，具有高精度和鲁棒性。与DBO - ELM相比，SOC估计的MAE和RMSE分别下降了30%以上。该模型为LiBs在各种实际条件下的安全高效应用提供了有力支持。
