The extraction and prediction of health indicators (HIs) are two vital aspects in remaining useful life (RUL) prediction of lithium-ion batteries (LIBs). Aiming to estimate the RUL precisely, a novel integrated prediction method is proposed for LIBs on the basis of local tangent space alignment (LTSA) feature extraction and adaptive sliding window long short-term memory neural networks (ASW LSTM NN). In the proposed method, the indirect HI is first extracted by LTSA automatically to replace the unmeasurable capacity, and a strong correlation between them is verified by the Spearman correlation coefficient. Following that, with the extracted HI, an adaptive sliding window LSTM is constructed to conduct the RUL estimation of LIBs in routine environment. For the structured neural network, corresponding inputs are dynamically selected by the sliding window, while a varying length window mechanism is devised to update the window data along with the predicting cycle. Hence, the designed predicting method can learn the long-term dependencies by means of the inherent nature of LSTM and simultaneously capture the local fluctuations via the adaptive sliding window. Eventually, extensive experiments are conducted and corresponding results are compared with those obtained by existed approaches. The effectiveness of the integrated prediction method is validated, and our proposed model is proved to be more accurate in predicting the RUL compared with existed approaches.

健康指标（HI）的提取和预测是锂离子电池（LIB）剩余使用寿命（RUL）预测的两个重要方面。为了精确估计 RUL，基于局部切线空间对齐 (LTSA) 特征提取和自适应滑动窗口长短期记忆神经网络 (ASW LSTM NN)，提出了一种新的 LIB 集成预测方法。在所提出的方法中，LTSA首先自动提取间接HI以替换不可测量的容量，并通过Spearman相关系数验证它们之间的强相关性。之后，利用提取的 HI，构建自适应滑动窗口 LSTM 以在常规环境中进行 LIB 的 RUL 估计。对于结构化神经网络，相应的输入由滑动窗口动态选择，同时设计了一个变长窗口机制来随着预测周期更新窗口数据。因此，设计的预测方法可以通过 LSTM 的固有特性学习长期依赖关系，同时通过自适应滑动窗口捕获局部波动。最后，进行了广泛的实验，并将相应的结果与现有方法获得的结果进行了比较。验证了集成预测方法的有效性，并且与现有方法相比，我们提出的模型在预测 RUL 方面更准确。设计的预测方法可以通过 LSTM 的固有特性学习长期依赖关系，同时通过自适应滑动窗口捕获局部波动。最后，进行了广泛的实验，并将相应的结果与现有方法获得的结果进行了比较。验证了集成预测方法的有效性，并且与现有方法相比，我们提出的模型在预测 RUL 方面更准确。设计的预测方法可以通过 LSTM 的固有特性学习长期依赖关系，同时通过自适应滑动窗口捕获局部波动。最后，进行了广泛的实验，并将相应的结果与现有方法获得的结果进行了比较。验证了集成预测方法的有效性，并且与现有方法相比，我们提出的模型在预测 RUL 方面更准确。