Battery Health State Estimation Based on Stacked Autoencoder Deep Learning

随着新能源和储能技术的快速发展，智能电池能量管理系统(BEMS)在电力系统中的应用变得日益重要。然而，现有的电池管理方法大多基于固定规则，缺乏在复杂和动态环境中的灵活响应能力，这限制了电池状态估计和剩余寿命预测的准确性。本文提出了一种基于栈式自编码器(SAE)和通道注意力机制(SENet)的电池健康状态估计方法。通过分析电池的充放电数据，提取关键健康因子，并利用SAE深度学习模型进行特征融合。结合SENet模块自适应调整特征的权重，从而提升模型的预测能力和精度。与传统的基于主成分分析(PCA)和非线性神经网络的方法相比，该方法在提取电池退化特征方面更加高效，并在容量估计和剩余寿命预测中实现了更高的精度和稳定性。此外，该方法具有良好的实时性和鲁棒性，能够有效应对复杂环境的变化。With the rapid development of renewable energy and energy storage technologies, smart Battery Energy Management Systems (BEMS) have become increasingly critical in power systems. However, most existing battery management methods rely on fixed rules, lacking flexible responsiveness in complex and dynamic environments, which limits the accuracy of battery state estimation and remaining useful life prediction. This paper proposes a State of Health (SOH) estimation method for batteries based on a Stacked Autoencoder (SAE) and a channel attention mechanism (SENet). By analyzing battery charge/discharge data, key health indicators are extracted, and a SAE-based deep learning model is employed for feature fusion. Combined with the SENet module, the proposed method adaptively adjusts feature weights to enhance prediction capability and accuracy. Compared with traditional approaches such as Principal Component Analysis (PCA) and nonlinear neural networks, this method demonstrates higher efficiency in extracting battery degradation features and achieves superior precision and stability in capacity estimation and remaining useful life prediction. Additionally, the proposed framework exhibits excellent real-time performance and robustness, enabling effective adaptation to environmental variations in complex scenarios.