锂离子电池基于放电增量容量特征估计的智能退化监测
Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation
- Amirkabir University of Technology（阿米尔卡比尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对传统IC分析需低电流放电的局限，本文提出基于神经网络的框架，从充电信号直接预测放电IC特征，在53个电池数据集上验证，LSTM模型平衡精度与效率，支持实时BMS集成，实现智能电池退化监测。
AI中文摘要：
准确及时地检测锂离子电池的退化对于确保电动汽车和储能系统等高要求应用中的安全性、可靠性和寿命至关重要。传统的增量容量（IC）分析方法需要低电流循环进行放电测量，限制了其在实时电池管理中的实际应用。本文提出了一种新颖的基于神经网络的框架，直接从充电信号预测放电IC特征，无需低电流放电。该模型在包含53个电池单元、在多种快速充电协议下循环的综合数据集上训练，展现出强大的泛化能力，能有效估计未见电池数据上的退化指标。在评估的多种架构中，LSTM模型在预测准确性和计算效率之间提供了最佳平衡。所提出的方法能够实时集成到电池管理系统（BMS）中，在不干扰正常电池运行的情况下增强退化监测。本文提供了一项将IC分析与实际快速充电场景相结合的研究，标志着向智能化和可扩展的电池健康监测迈出了重要一步。
英文摘要：
Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applications such as electric vehicles and energy storage systems. Traditional incremental capacity (IC) analysis methods require low-current cycling for discharge measurements, limiting their practical use in real-time battery management. This paper proposes a novel neural network-based framework that predicts discharge IC features directly from charging signals, eliminating the need for low-current discharge. Trained on a comprehensive dataset of 53 battery cells cycled under diverse fast-charging protocols, the model demonstrates robust generalization ability, effectively estimating degradation indicators on unseen battery data. Among several architectures evaluated, the LSTM model provides the best balance of prediction accuracy and computational efficiency. The proposed approach enables real-time integration into Battery Management Systems (BMS), enhancing degradation monitoring without disrupting normal battery operation. This paper presents a study to bridge IC analysis with practical, fast-charging scenarios, marking a significant step towards intelligent and scalable battery health monitoring.
