Fang Pengya, Zhang Han, Zhang Anhao, Chen Gang, Li Jing, Wen Zhenhua, Yin Liping
School of Aero Engine, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China.
Henan Key Laboratory of General Aviation Technology, Zhengzhou University of Aeronautics, Zhengzhou, Henan 450046, China.
ACS Omega. 2025 Jul 17;10(29):31880-31895. doi: 10.1021/acsomega.5c03257. eCollection 2025 Jul 29.
Accurate estimation of lithium-ion batteries' state of health (SOH) is critical for ensuring the efficient, safe, and long-lasting operation of battery systems. To address the challenge of insufficient feature information extraction and SOH estimation under complex operating conditions, a SOH estimation method based on multisource feature extraction and the Sparrow Search Algorithm-Long Short-term Memory Network (SSA-LSTM) is proposed. First, the initial health feature set is constructed by fusing the empirical, statistical and mechanistic dimensions of features. Second, to mitigate the impact of feature redundancy, the features are evaluated and ranked based on both correlation and importance, thereby optimizing the balance between feature quality and quantity. Finally, the obtained optimal feature set is used as input to the SSA-LSTM algorithm, which constructs a SOH estimation algorithm for accurate battery SOH estimation. Experimental results demonstrate that the proposed feature selection method successfully identifies the optimal feature set. Compared with other estimation algorithms, the SSA-LSTM algorithm outperforms in all evaluation indexes, with the maximum root-mean-square error (RMSE) and mean absolute percentage error (MAPE) of the estimation results reaching 0.73% and 0.53%, respectively, across various test cases.
准确估计锂离子电池的健康状态（SOH）对于确保电池系统高效、安全和持久运行至关重要。为应对复杂运行条件下特征信息提取不足和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，通过融合特征的经验、统计和机理维度来构建初始健康特征集。其次，为减轻特征冗余的影响，基于相关性和重要性对特征进行评估和排序，从而优化特征质量和数量之间的平衡。最后，将获得的最优特征集用作SSA-LSTM算法的输入，构建用于准确估计电池SOH的SOH估计算法。实验结果表明，所提出的特征选择方法成功识别出最优特征集。与其他估计算法相比，SSA-LSTM算法在所有评估指标上均表现出色，在各种测试案例中，估计结果的最大均方根误差（RMSE）和平均绝对百分比误差（MAPE）分别达到0.73%和0.53%。
