井冈山大学学报（自然科学版）2025，Vol.46Issue(2)：97-106,10.DOI:10.3969/j.issn.1674-8085.2025.02.012
基于LOF和改进Elman的锂电池健康状态估计
ESTIMATION OF LITHIUM-ION BATTERY HEALTH STATE USING LOF AND ENHANCED ELMAN NETWORK
摘要
Abstract
Accurate estimation of the health state of lithium-ion batteries is essential for enhancing the safety and usability of battery packs,ex-tending their lifespan,and improving energy utilization.To this end,a new method for estimating battery health,based on LOF outlier detection and an enhanced Elman network,is proposed.This method involves extracting four key features from the IC curve-peak,peak position,and slope around the peak-through capacity increment analysis.The LOF algorithm is employed to detect and process outliers in the input data,while the SCA algorithm and Bagging technique via integrated learning are used to refine the Elman network.Validation of the model using the NASA dataset shows that the LOF-SCA-Elman-Bagging model achieves an estimated average root-mean-square error of 1.04％,demonstrating higher accuracy and robustness compared to the traditional methods such as BP,SVM,and GPR.关键词
电池健康状态/容量增量分析/神经网络/局部异常因子/鲁棒性Key words
state of health/incremental capacity analysis/neural network/local outlier factor/robustness分类
信息技术与安全科学引用本文复制引用
曹铭,曾骥,谢世坤,邱嵩,张文祥,付艳恕..基于LOF和改进Elman的锂电池健康状态估计[J].井冈山大学学报（自然科学版）,2025,46(2):97-106,10.基金项目
国家自然科学基金项目(51762034) （51762034）
