SOH估计基于多源特征提取和SSA-LSTM算法
Pengya Fang1,2, Han Zhang1, Anhao Zhang3
1School of Aero Engine, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China.
ACS omega
|August 4, 2025
概括
这项研究引入了一种新的方法,用于估计离子电池的健康状况,使用高级功能选择和Sparrow Search Algorithm-Long Short-term Memory Network (SSA-LSTM). 这种方法提高了电池管理系统的准确性和寿命.
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:
- 准确的健康状况 (SOH) 估计对于离子电池的性能和安全至关重要.
- 复杂的运营条件给传统的SOH估计方法带来了挑战.
- 功能提取不足限制了当前电池健康监测的精度.
研究的目的:
- 为离子电池开发一种先进的SOH估计方法.
- 为了改善复杂的操作场景的特征提取和选择.
- 提高电池健康预测的准确性和可靠性.
主要方法:
- 通过融合经验,统计和机械特征,构建了一个多源特征集.
- 使用相关性和重要性排名进行了特征选择,以优化特征质量和数量.
- 一个子搜索算法-长期短期记忆网络 (SSA-LSTM) 用于SOH估计.
主要成果:
- 提出的特征选择方法成功确定了最佳特征集.
- 与其他估计方法相比,SSA-LSTM算法表现出卓越的性能.
- 最大平方根平均误差 (RMSE) 和平均绝对百分比误差 (MAPE) 分别为0.73%和0.53%.
结论:
- 开发的SSA-LSTM方法为离子电池提供了准确可靠的SOH估计.
- 多源特征提取和选择策略有效地应对复杂操作条件下的挑战.
- 这种方法有助于电池系统的高效,安全和长期运行.
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