Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping  

作　　者：Shuquan Wang Feng Gao Zhan Ma Hao Tian Yusen Zhang 

机构地区：[1]School of Control Science and Engineering,Shandong University,Jinan 250061,Shandong,China [2]Key Lab of Power System Intelligent Dispatch and Control of Ministry of Education,Shandong University,Jinan 250061,Shandong,China

出　　处：《Journal of Energy Chemistry》2025年第1期565-577,共13页能源化学(英文版)

基　　金：National Natural Science Foundation of China (Grant No. 52225705)。

摘　　要：As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and extraction.Tested on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and reliability.Its robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

关 键 词：Lithium-ion battery Battery prognostics Fusion model Convolutional neural network Transfer learning 

分 类 号：TM9[电气工程—电力电子与电力传动]