摘 要
随着新能源汽车的快速发展，动力电池作为其核心部件，其健康状态（SOH）评估已成为保障车辆安全运行和延长电池寿命的关键技术问题。针对传统评估方法依赖单一特征且精度不足的问题，本研究提出了一种基于大数据的动力电池健康状态评估方法。通过采集实际工况下的多源数据，构建了包含电压、电流、温度及充放电曲线等多维度特征的数据集，并采用深度学习模型提取复杂特征关系，实现了对电池退化趋势的精准刻画。研究引入了注意力机制优化模型性能，显著提升了小样本条件下的预测准确性。实验结果表明，该方法在不同工况下的平均绝对误差低于3%，相较于传统方法具有更高的鲁棒性和适应性。本研究的主要贡献在于突破了传统方法对先验知识的过度依赖，充分利用大数据优势，为动力电池健康管理提供了新思路，同时为新能源汽车的智能化运维奠定了理论基础。
关键词：动力电池健康状态评估；深度学习；多源数据
Abstract
With the rapid development of new energy vehicles, the assessment of the state of health (SOH) of power batteries, as a core component, has become a critical technical issue for ensuring safe vehicle operation and extending battery life. In response to the limitations of traditional evaluation methods that rely on single features and lack sufficient accuracy, this study proposes a data-driven SOH assessment method based on big data. By collecting multi-source data under real-world operating conditions, a multidimensional dataset was constructed, encompassing voltage, current, temperature, and charge-discharge curves. A deep learning model was then employed to extract complex feature relationships, enabling precise characterization of battery degradation trends. The study further incorporates an attention mechanism to optimize model performance, significantly enhancing prediction accuracy under small-sample conditions. Experimental results demonstrate that the proposed method achieves a mean absolute error of less than 3% across various operating conditions, exhibiting superior robustness and adaptability compared to conventional approaches. The primary contribution of this research lies in overcoming the over-reliance on prior knowledge inherent in traditional methods, fully leveraging the advantages of big data to provide novel insights into power battery health management, and laying a theoretical foundation for the intelligent maintenance of new energy vehicles.
Keywords: State Of Health Assessment For Power Battery；Deep Learning；Multi-Source Data
目 录
引言 1
一、动力电池健康状态评估基础 1
（一）动力电池健康状态定义与意义 1
（二）健康状态评估的关键指标分析 2
（三）大数据在评估中的作用探讨 2
二、数据采集与预处理方法研究 3
（一）数据采集技术与系统设计 3
（二）数据清洗与异常值处理策略 3
（三）数据特征提取与降维方法 3
三、基于大数据的评估模型构建 4
（一）机器学习算法在评估中的应用 4
（二）深度学习模型的设计与优化 4
（三）模型性能评价与对比分析 5
四、实验验证与结果分析 5
（一）实验平台搭建与数据获取 5
（二）不同场景下的评估效果验证 6
（三）结果分析与改进方向 6
结 论 6
致 谢 8
参考文献 9
