A multi-model feature fusion model for lithium-ion battery state of health prediction. (10th December 2022)

• Record Type: Journal Article
• Authors: Yao, Xing-Yan; Chen, Guolin; Hu, Liyue; Pecht, Michael
• Is Part Of: Journal of energy storage. Volume 56:Part B(2022)
• Publication Date: 2022-12-10
• Subjects: Lithium-ion batteries (LIBs) -- State of health (SOH) -- Graph neural network (GNN) -- Graph sample and aggregate (GraphSAGE) -- Multi-model -- Feature fusion; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2022.106051
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: State of health (SOH) prediction is key to battery health management and safety. Health indicators (HIs) are effective and feasible to predict battery SOH. The existing approaches according to HIs focused on single-source features of HIs such as voltage, current or temperature by a single model to predict SOH. The accuracy and robustness of these approaches can still be improved especially for the lack of battery datasets in applications. Multi-sources HIs can enrich the diversity of features and supply complementary information. In addition, multi-model fusion for multi-sources features can improve the robustness of prediction results. In this paper, a multi-model feature fusion based on multi-source features is proposed to improve the effectiveness and robustness of battery SOH prediction. 27 HIs are firstly extracted from multi-sources signals of the charge-discharge process, and the HIs are divided into three classes by the Pearson correlation coefficient. Subsequently, three feature vectors for the classified HIs are obtained individually by three different deep learning models according to HIs' characteristics. Finally, the feature space is fused from the three feature vectors to predict SOH by the fully connected network (FCN). The effectiveness of the proposed method is verified on the MIT dataset. Results show that the MSE and the MAPE of the proposed method achieve 0.0007 % and 0.2106 %, respectively. Compared with the results of single models and different HIs subsets, the proposed method realizes high accuracy and robustness of SOH prediction. Highlights: A novel multi-source feature fusion model for battery SOH prediction is proposed. Three classes of features are obtained for battery SOH according to the correlation coefficient. The features are fused by using CNN, LSTM and GraphSAGE. The results are compared with single models and different HI subsets.