数据驱动下的用户行为特征标签与电池容量衰减诱因分析与容量预测

User Behavior Feature Labels and Analysis of Battery Capacity Degradation Triggers and Capacity Prediction under Data-Driven Conditions

摘要: 本文通过数据驱动方法，研究了电动汽车用户行为特征与电池容量衰减之间的关系。通过对不同行为进行标签化与分析，揭示了快充、深度充电、高温充电等特定行为显著加速了电池容量的衰减。文章采用横向与纵向分析相结合的方法，识别和评估了用户行为的累积效应。为了进一步提升分析的深度和预测的准确性，本文结合了统计学模型和多层感知器(MLP)神经网络模型，对于捕捉用户行为与电池容量衰减之间的微妙联系尤为有效。通过训练MLP模型，本文能够学习用户行为特征与电池容量衰减之间的复杂映射关系，从而构建一个基于行为特征的容量衰减预测模型，为电池管理系统提供优化方向。

Abstract: This paper investigates the relationship between electric vehicle user behavior characteristics and battery capacity degradation through a data-driven approach. By labeling and analyzing various behaviors, the study reveals that specific actions, such as fast charging, deep charging, and high-temperature charging, significantly accelerate battery capacity degradation. A combination of cross-sectional and longitudinal analysis is employed to identify and evaluate the cumulative effects of user behaviors. To enhance the depth of analysis and improve prediction accuracy, the study integrates statistical models with a multilayer perceptron (MLP) neural network, which proves particularly effective in capturing the subtle links between user behavior and battery capacity degradation. By training the MLP model, we can learn the complex mapping relationship between user behavior characteristics and battery capacity degradation, leading to the development of a behavior-based capacity degradation prediction model. This model provides optimization directions for battery management systems.