| 引用本文: | 孙立明,余 涛.考虑噪声影响的电动汽车动力电池SOH和RUL联合估计[J].电力系统保护与控制,2025,53(23):49-62.[点击复制] |
| SUN Liming,YU Tao.Joint estimation of SOH and RUL for electric vehicle power batteries considering noise effects[J].Power System Protection and Control,2025,53(23):49-62[点击复制] |
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| 摘要: |
| 电动汽车动力电池状态的监测和估计对于提高其安全、稳定运行有着重要意义。为解决电池健康状态(state of health, SOH)和剩余使用寿命(remaining useful life, RUL)估计中存在的健康特征难以提取以及模型受噪声影响程度大等问题，考虑数据噪声对于估计模型的影响，提出了一种基于ICEEMDAN-DO-GRU的强鲁棒性估计模型实现对电动汽车动力电池SOH和RUL的准确估计。首先，利用一种基于改进的自适应噪声完备集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN)方法对NASA公开的电池数据集进行信号分解以提取用于模型估计的健康特征。其次，使用蒲公英优化器(dandelion optimizer, DO)对门控循环单元(gated recurrent unit, GRU)的隐藏层神经元数目和初始学习率进行优化以提高GRU的性能。最后，研究了所提模型在不同噪声影响下的有效性，并将DO-GRU的结果与其他4种典型的神经网络进行对比分析。实验结果表明，所提ICEEMDAN-DO-GRU模型在SOH和RUL估计方面有较高的准确性和较强的鲁棒性。 |
| 关键词: 动力电池 健康状态估计 剩余寿命估计 ICEEMDAN 蒲公英优化器 门控循环单元 |
| DOI：10.19783/j.cnki.pspc.250101 |
| 投稿时间：2025-01-24修订日期：2025-05-15 |
| 基金项目:国家自然科学基金企业创新发展联合基金项目资助(U24B6010) |
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| Joint estimation of SOH and RUL for electric vehicle power batteries considering noise effects |
| SUN Liming,YU Tao |
| (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China) |
| Abstract: |
| Monitoring and estimating the state of electric vehicle power batteries are of great significance for ensuring their safe and stable operation. To address the challenges in estimating the battery state of health (SOH) and remaining useful life (RUL), specifically the difficulty in extracting health-related features and the strong sensitivity of models to noise, a highly robust estimation model based on ICEEMDAN-DO-GRU is proposed, fully accounting for the impact of data noise on model performance. First, the ICEEMDAN decomposition method is used to perform signal decomposition on NASA’s publicly available battery dataset to extract health features for model estimation. Second, the dandelion optimizer (DO) is used to optimize the number of hidden layer neurons and the initial learning rate of the gated recurrent unit (GRU), thereby improving its performance. Finally, the effectiveness of the proposed model under different noise conditions is studied, and the DO-GRU results are comprehensively compared with four other typical neural networks. The experimental results prove that the proposed ICEEMDAN-DO-GRU model achieves high accuracy and strong robustness in both SOH and RUL estimation. |
| Key words: power battery state of health estimation remaining useful life estimation ICEEMDAN dandelion optimizer gated recurrent unit |
