双相优化深度学习框架为准确,高效和强大的电池SoC估计提供了精确,高效和强大的电池SoC估计
1School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
Scientific reports
|December 5, 2025
概括
精确的电动汽车 (EV) 充电状态 (SoC) 估计至关重要. 一个新的KANBiLSTMAtt深度学习模型以高精度和效率增强了电池管理系统.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 电动汽车 (EV) 的采用需要精确的电荷状态 (SoC) 估计电池健康,续航里程和安全.
- 目前的SoC估计方法在准确性和计算效率方面面临挑战.
研究的目的:
- 介绍KANBiLSTMAtt,一种新的混合深度学习模型,用于强大的离子电池SoC估计.
- 证明模型在处理电池数据中的非线性和长期依赖性方面的能力.
主要方法:
- 结合科尔莫戈罗夫-阿诺德网络 (KAN),双向长期短期记忆 (BiLSTM) 和注意力机制.
- 使用Optuna进行超参数调整和NSGA-II进行多目标优化.
- 使用不同电池化学和温度的LG和CALCE数据集进行验证.
主要成果:
- KANBiLSTMAtt实现了高预测准确度,根平均平方误差 (RMSE) 为0.02%,平均绝对误差 (MAE) 为0.01%,R2为99%.
- 该模型在各种数据集和条件中显示出强大的概括性和稳定性.
- 通过轻量级架构,在90秒内实现高效的融合.
结论:
- KANBiLSTMAtt模型提供了一个可扩展和准确的解决方案,用于对电动汽车的实时SoC估计.
- 这种混合深度学习方法克服了先进电动汽车能源管理传统方法的局限性.
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