基于多样性增强集成学习的电池健康状态评估

为了提高多样性、小样本数据条件下电池健康状态的评估精度，基于集成学习理论提出了一种多样性增强的Stacking集成学习回归算法。该算法核心思想是通过基于动态时间规整的K-均值聚类算法构建多样性数据，然后采用Stacking集成学习回归算法学习数据的多样性特征，获得更佳的模型精度，并增强模型对多样性数据的泛化能力。Stacking集成学习回归算法由多个基学习器和一个输出学习器构成，首先通过多个基学习器获得初步结果，然后通过输出学习器对初级结果进行进一步学习获得最终结果。最后采用美国国家宇航局的公开电池数据集验证了算法的有效性。

In order to improve the evaluation accuracy of battery SOH under the conditions of diversity and small sample data, a diversity-enhanced Stacking integrated learning regression algorithm is proposed based on integrated learning theory. The core idea of the algorithm is to build diversity data through K-means clustering algorithm based on dynamic time warping, and then use Stacking integrated learning regression algorithm to learn the diversity characteristics of the data, obtain better model accuracy, and enhance the model to diversity data Generalization ability. Stacking integrated learning regression algorithm is composed of multiple base learners and an output learner. First, preliminary results are obtained through multiple base learners, and then the primary results are further studied through the output learners to obtain the final results. Finally, the public battery data set of NASA was used to verify the effectiveness of the algorithm.