河北水利电力学院学报2026，Vol.36Issue(3)：35-41,7.DOI:10.16046/j.cnki.issn2096-5680.2026.03.006
基于CNN-BiLSTM的锂电池健康状态估计
Prediction of Lithium Batteries SOH Based on CNN-BiLSTM
摘要
Abstract
The State of Health(SO H)of lithium-ion batteries is influenced by multiple coupled fac-tors,making it difficult to measure directly.To address this issue and accurately capture the degradation trend and key inflection points,this study constructs a high-precision prediction model.We propose a CNN-BiLSTM model that integrates a Convolutional Neural Network(CNN)and a Bidirectional Long Short-Term Memory network(BiLSTM).The CNN is employed to extract local spatial features from op-erational data,including voltage,current,temperature,and impedance,while the BiLSTM captures bidi-rectional temporal dependencies.Based on the ARC-FY08 Q4 standard dataset from the NASA Ames Re-search Center,we compare the performance of five models:LSTM,BiLSTM,CNN,CNN-LSTM,and CNN-BiLSTM.On the test set,the CNN-BiLSTM model exhibits significantly higher prediction accuracy than the other four models;its predicted curve closely matches the true SOH variation trend,and the rec-ognition accuracy for key degradation stages exceeds 90%.By combining the strengths of local feature ex-traction and bidirectional temporal modeling,the CNN-BiLSTM model can effectively predict lithium-ion battery SOH variations,providing reliable support for battery health management and lifespan assess-ment.关键词
锂电池/数据驱动/健康状态(SO H)/CNN-BiLSTMKey words
lithium battery/data-driven/SOH/CNN-BiLSTM分类
信息技术与安全科学引用本文复制引用
高波,周家睦,董子阳,王兵锐,杜瑶..基于CNN-BiLSTM的锂电池健康状态估计[J].河北水利电力学院学报,2026,36(3):35-41,7.基金项目
河北省沧州市科技计划项目(23244101042) （23244101042）
河北省教育厅科学研究项目资助(ZC2023092) （ZC2023092）
国家级大学生创新创业项目(202510085002) （202510085002）
