针对硅碳负极电池的电压滞环，提出概率化预测方法。
Probabilistic Hysteresis Factor Prediction for Electric Vehicle Batteries with Graphite Anodes Containing Silicon
- 构建数据统一框架，处理不同工况下的驾驶循环差异。
- 融合统计与深度学习，实现带不确定性评估的滞环因子预测。
- 模型在未见车型上表现稳定，适合实际车载应用。
采用硅-石墨负极的电池虽能提升能量密度和充电性能，但其显著的电压滞环特性使电池状态（SoC）估计变得尤为困难。现有滞环建模方法多依赖高保真测试或仅针对传统石墨基锂电池，缺乏对不确定性的量化及计算效率考量。本文提出一种数据驱动的概率化滞环因子预测方法，专用于硅-石墨负极电池。设计数据统一框架，标准化不同运行条件下的异构驾驶循环；结合统计学习与深度学习模型，在考虑计算效率的前提下评估滞环因子预测精度及不确定性。通过重训练、零样本预测、微调和联合训练等多种方式，全面验证最优模型配置在未见车辆型号上的泛化能力。该研究解决了SoC估计中的关键挑战，推动先进电池技术的实际应用。更多信息可访问：https://runyao-yu.github.io/Porsche_Hysteresis_Factor_Prediction/
原文摘要 · Abstract (English)
Batteries with silicon-graphite-based anodes, which offer higher energy density and improved charging performance, introduce pronounced voltage hysteresis, making state-of-charge (SoC) estimation particularly challenging. Existing approaches to modeling hysteresis rely on exhaustive high-fidelity tests or focus on conventional graphite-based lithium-ion batteries, without considering uncertainty quantification or computational constraints. This work introduces a data-driven approach for probabilistic hysteresis factor prediction, with a particular emphasis on applications involving silicon-graphite anode-based batteries. A data harmonization framework is proposed to standardize heterogeneous driving cycles across varying operating conditions. Statistical learning and deep learning models are applied to assess performance in predicting the hysteresis factor with uncertainties while considering computational efficiency. Extensive experiments are conducted to evaluate the generalizability of the optimal model configuration in unseen vehicle models through retraining, zero-shot prediction, fine-tuning, and joint training. By addressing key challenges in SoC estimation, this research facilitates the adoption of advanced battery technologies. A summary page is available at: https://runyao-yu.github.io/Porsche_Hysteresis_Factor_Prediction/
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