基于LSTM的离子电池SOH估计,使用数据特征和时空注意力
Gengchen Xu1, Jingyun Xu1,2,3, Yifan Zhu1
1School of Engineering, Huzhou University, Huzhou, P. R. China.
PloS one
|December 26, 2024
概括
电动汽车离子电池的精确健康状况 (SOH) 估计使用一种新的长短期记忆 (LSTM) 方法得到了改进. 这种方法通过考虑电池退化模式和动态变化来提高SOH预测的准确性和稳定性.
科学领域:
- 电池技术 电池技术
- 人工智能的人工智能
- 电动汽车系统 电动汽车系统
背景情况:
- 准确的健康状况 (SOH) 估计对于电动汽车 (EV) 离子电池的性能和可靠性至关重要.
- 传统的长期短期记忆 (LSTM) 模型与电池退化序列的非线性,动态性质作斗争.
研究的目的:
- 为增强离子电池SOH估计开发基于LSTM的先进方法.
- 解决电池退化数据中趋势式非线性和样本对样本的动态变化所带来的挑战.
主要方法:
- 特性工程:将电池降解数据分成趋势和非趋势组件.
- 时空注意力机制:旨在捕捉动态变化并从非趋势数据中提取特征.
- 集成LSTM模型:利用趋势特征,时空特征和SOH估计的实际能力.
主要成果:
- 与传统方法相比,拟议的方法在健康状况 (SOH) 估计方面表现出更高的准确性.
- 提高SOH预测的稳定性,特别是在出现容量再生现象的情况下.
- 通过对不同电池数据集的培训和测试进行验证.
结论:
- 新的LSTM方法有效地捕捉了复杂的电池退化模式,从而改善了SOH估计.
- 数据特征和时空注意力的集成显著提高了电动汽车电池的预测准确性和可靠性.
- 这种方法为确保电动汽车的安全和高效运行提供了一个有前途的解决方案.
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