概率hysteresis因子预测用于含硅石墨负极的电动汽车电池
Probabilistic Hysteresis Factor Prediction for Electric Vehicle Batteries with Graphite Anodes Containing Silicon
AI总结：
本文提出数据驱动的概率hysteresis因子预测方法，针对含硅石墨负极电池，通过数据标准化框架和统计学习模型提升SoC估计精度。
摘要：
含硅石墨基负极的电池具有更高的能量密度和改进的充电性能，但引入了显著的电压滞后效应，使荷电状态（SoC）估计尤为困难。现有方法依赖于详尽的高保真测试或专注于传统石墨基锂离子电池，未考虑不确定性量化或计算约束。本文提出了一种数据驱动的概率滞后因子预测方法，特别针对含硅石墨负极电池的应用。提出了一种数据标准化框架，以标准化异构驾驶周期在不同运行条件下的数据。统计学习和深度学习模型用于在考虑不确定性的同时评估预测滞后因子的性能，同时考虑计算效率。进行了大量实验，评估最佳模型配置在未见过的车辆模型中的泛化能力，通过重新训练、零样本预测、微调和联合训练进行重新训练。通过解决SoC估计中的关键挑战，本研究促进了先进电池技术的采用。摘要页面可在：https://runyao-yu.github.io/Porsche_Hysteresis_Factor_Prediction/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
