测试技术学报2025，Vol.39Issue(3)：313-321,329,10.DOI:10.62756/csjs.1671-7449.2025039
基于改进模态分解混合模型的锂电池剩余容量预测
Hybrid Model for Lithium-Ion Battery Remaining Capacity Prediction Based on Improved Variational Mode Decomposition
摘要
Abstract
With the widespread application of lithium-ion batteries in daily life,the development of effi-cient and accurate battery state-of-charge prediction technologies is of great significance for enhancing user experience and ensuring stable device operation.An efficient hybrid prediction model that utilizes the spar-row bird optimization algorithm(SBOA)to optimize variational mode decomposition(VMD)in combina-tion with Gaussian process regression(GPR)and gated recurrent units(GRU)is proposed.High-precision predictions of lithium-ion battery state-of-charge are achieved by this model.Comparisons with traditional GRU and VMD-GRU models reveal that the proposed model can swiftly and effectively cap-ture battery degradation trends.The mean absolute error(MAE)and root mean square error(RMSE)of the proposed method are 0.19%and 0.31%,respectively,demonstrating higher prediction accuracy and generalization capability.关键词
蛇鹭优化算法/变分模态分解/高斯过程回归/门控循环单元/剩余容量预测Key words
secretary bird optimization algorithm(SBOA)/variational mode decomposition/Gaussian process regression/gated recurrent unit/remaining capacity prediction分类
动力与电气工程引用本文复制引用
宁弘扬,惠周利,冯娜娜,杨明..基于改进模态分解混合模型的锂电池剩余容量预测[J].测试技术学报,2025,39(3):313-321,329,10.基金项目
国家自然科学基金资助项目(12272356) （12272356）
山西省基础研究计划资助项目(202203021211088) （202203021211088）
