融合多源电池数据，动态加权提升寿命预测精度。
Learning to fuse: dynamic integration of multi-source data for accurate battery lifespan prediction
- 用熵权重动态融合多来源电池数据，缓解数据差异影响。
- 模型MAE仅0.0058，RMSE降83.2%，R2达0.9839，优于基线。
- 可解释性强，识别出放电容量和温度是关键老化指标。
精准预测锂离子电池寿命对电动汽车和智能电网的运行可靠性及维护成本控制至关重要。本文提出一种混合学习框架，结合动态多源数据融合与堆叠集成（SE）建模，整合来自美国宇航局（NASA）、先进寿命周期工程中心（CALCE）、MIT-斯坦福-丰田研究院（TRC）及镍钴铝（NCA）体系的异构数据集。采用基于熵的动态加权机制，有效缓解不同数据源间的差异性。SE模型融合岭回归、长短期记忆网络（LSTM）和极端梯度提升（XGBoost），充分捕捉时序依赖与非线性退化特征。在测试中，模型实现均方误差（MAE）0.0058，均方根误差（RMSE）0.0092，决定系数（R²）0.9839，相比基线模型，R²提升46.2%，RMSE降低83.2%。SHAP分析揭示差分放电容量（Qdlin）和测量温度（Temp_m）为关键老化指标。该可扩展、可解释的框架显著提升电池健康状态管理能力，适用于多种储能系统，助力优化维护策略与安全保障。
原文摘要 · Abstract (English)
Accurate prediction of lithium-ion battery lifespan is vital for ensuring operational reliability and reducing maintenance costs in applications like electric vehicles and smart grids. This study presents a hybrid learning framework for precise battery lifespan prediction, integrating dynamic multi-source data fusion with a stacked ensemble (SE) modeling approach. By leveraging heterogeneous datasets from the National Aeronautics and Space Administration (NASA), Center for Advanced Life Cycle Engineering (CALCE), MIT-Stanford-Toyota Research Institute (TRC), and nickel cobalt aluminum (NCA) chemistries, an entropy-based dynamic weighting mechanism mitigates variability across heterogeneous datasets. The SE model combines Ridge regression, long short-term memory (LSTM) networks, and eXtreme Gradient Boosting (XGBoost), effectively capturing temporal dependencies and nonlinear degradation patterns. It achieves a mean absolute error (MAE) of 0.0058, root mean square error (RMSE) of 0.0092, and coefficient of determination (R2) of 0.9839, outperforming established baseline models with a 46.2% improvement in R2 and an 83.2% reduction in RMSE. Shapley additive explanations (SHAP) analysis identifies differential discharge capacity (Qdlin) and temperature of measurement (Temp_m) as critical aging indicators. This scalable, interpretable framework enhances battery health management, supporting optimized maintenance and safety across diverse energy storage systems, thereby contributing to improved battery health management in energy storage systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。