CNN-LSTM与SWATS进行了优化,用于精确估计离子电池的充电状态,考虑到内部电阻
Zhaowei Zhang1,2, Chen Liu3, Tianyu Li4
1School of Mechanical and Electrical Engineering, Shijiazhuang University, Shijiazhuang, Hebei, 050035, China.
Scientific reports
|August 12, 2025
概括
通过将内部电阻纳入数据驱动模型,对离子电池的精确充电状态 (SOC) 估计得到了改进. 一种新的CNN-LSTM方法与SWATS优化增强了电池性能和电动汽车续航里程预测.
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:
- 精确的充电状态 (SOC) 估计对于电动汽车 (EV) 能源管理和续航里程计算至关重要.
- 数据驱动的方法,特别是循环神经网络,越来越多地用于SOC估计.
- 现有的方法往往忽略了内部阻力,这是影响SOC估计准确性的关键因素.
研究的目的:
- 为准确的离子电池SOC估计开发一个改进的数据驱动模型.
- 通过结合内部电阻测量来解决现有方法的局限性.
- 为了同时优化网络融合速度和性能.
主要方法:
- 使用专用电池测试台生成数据集,包括电流,电压,温度和内部电阻.
- 开发了一个混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型,集成内部阻力数据.
- 一个新的优化算法,将Adam切换到随机梯度下降 (SWATS),用于增强模型训练.
主要成果:
- 与现有的数据驱动方法相比,拟议的模型显示出更高的SOC估计准确性.
- 纳入内部阻力显著提高了SOC估计的稳定性和概括能力.
- SWATS优化方案在平衡融合速度和模型性能方面被证明是有效的.
结论:
- 将内部电阻纳入数据驱动模型对于准确的离子电池SOC估计至关重要.
- 拟议的CNN-LSTM模型与SWATS优化为电动汽车的SOC估计提供了强大而有效的解决方案.
- 这种方法提高了电池管理系统的可靠性,并扩大了电动汽车的实际应用.
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