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SOH Estimation Based on Multisource Feature Extraction and SSA-LSTM Algorithm
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DOI：10.1021/acsomega.5c03257.png)
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摘要
En 中文
锂离子电池的健康状态（SOH）的准确估计对于确保电池系统的高效、安全和长寿命运行至关重要。为解决复杂工况下特征信息提取不足及SOH估计的挑战，提出了一种基于多源特征提取和麻雀搜索算法-长短期记忆网络（SSA-LSTM）的SOH估计方法。首先，通过融合经验、统计和机理三个维度的特征构建初始健康特征集。其次，为降低特征冗余的影响，基于相关性和重要性对特征进行评估和排序，从而优化特征质量与数量的平衡。最后，将获取的最优特征集作为SSA-LSTM算法的输入，构建用于准确电池SOH估计的算法。实验结果表明，所提出的特征选择方法成功识别了最优特征集。与其他估计算法相比，SSA-LSTM算法在所有评价指标上均表现更优，其估计结果的最高均方根误差（RMSE）和平均绝对百分比误差（MAPE）分别达到0.73%和0.53%，这一结果在各类测试案例中均得到验证。
Keyword:
lithium-ion batteries
state of health (SOH)
multisource feature extraction
Sparrow Search Algorithm
LSTM network
期刊
IF:
4.3
论文数:
3.4W
被引数:
9.8W
机构
引用论文
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