基于长期短期内存优化的离子电池的能量状态估计 适应性Cubature卡尔曼波器
Enguang Hou1,2, Heyan Song1, Zhen Wang1
1School of Rail Transportation, Shandong Jiao Tong University, Jinan, China.
这项研究通过提高能量状态 (SOE) 估计准确度来提高离子电池 (LIB) 的安全性. 新的LSTM优化ACKF方法的错误率低于0.90%,优于传统方法.
科学领域:
- 电池技术 电池技术
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 精确的能量状态 (SOE) 估计对于离子电池 (LIB) 的安全性和可靠性至关重要.
- 人工智能和计算能力的进步使大数据方法能够用于复杂的电池管理.
- 现有的国有企业估计方法可能缺乏足够的准确性和适应性.
研究的目的:
- 为LIBs开发一种改进的国有企业估计方法,以提高准确性和适应性.
- 集成长期短期内存 (LSTM) 网络与自适应立方卡尔曼波器 (ACKF) 进行性能优化.
- 通过模拟和与现有技术的比较来验证拟议方法的有效性.
主要方法:
- 适应性立体卡尔曼波器 (ACKF) 具有代噪声方程更新,以提高适应性.
- 纳入高级等效模型,以提高国有企业估计的准确性.
- 长短期内存 (LSTM) 网络以优化欧米内部电阻 (OIR) 和实际能量 (AE) 估计.
- 对OIR和AE估计进行代ACKF更新,以进一步提高适应能力.
主要成果:
- 通过LSTM优化的ACKF方法,在各种SOE级别 (100%,65%,30%) 中,能源估计状态误差低于0.90%.
- 在国有企业估计准确性方面,拟议的方法明显优于独立的ACKF方法.
- 无论能量的初始状态如何,都观察到一致的精度,突出显示了强度.
结论:
- 用LSTM优化的ACKF方法显著提高了LIBs中SOE估计的准确性和适应性.
- 这种改进的估计为确保电池的安全性和寿命提供了更可靠的数据.
- 该研究验证了将深度学习与电池管理系统的先进过技术相结合的潜力.
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