电化学（中英文）2026，Vol.32Issue(8)：25-36,12.DOI:10.61558/2993-074X.3616
基于电化学性能数据驱动的神经网络预测海水电解液金属空气电池放电终止时间
Neural Network Driven by Electrochemical Performance Data for Predicting the Discharge Termination Time of Seawater Electrolyte-Based Metal-Air Batteries
摘要
Abstract
Seawater electrolyte-based metal-air batteries exhibit great promise for marine energy supply systems.However,con-ventional statistical analysis methods,though applicable to seawater metal-air battery lifetime prediction,have inherent limitations of insufficient prediction accuracy and large error.Herein,a deep time-series regression framework based on InceptionTime and incorporating prior-biased attention pooling is proposed to construct a nonlinear mapping between electrochemical performance sequences and the discharge termination time of catalysts.Specifically,chronoamperometric profiles are employed to extract long-term stability features,while prior knowledge derived from linear sweep voltamme-try is introduced to strengthen the attention weighting over critical potential regions.Under a nested leave-one-catalyst-out cross-validation framework with the one-standard-error rule for epoch selection,the model demonstrates high consistency across various aggregation strategies on a small-sample test set containing different air cathode catalysts,yielding a coeffi-cient of determination over 0.90.These findings suggest that integrating multiscale temporal features with electrochemical prior knowledge through an attention-driven regression framework can improve the prediction accuracy and reduce the prediction error of discharge termination time in the current dataset,thereby providing a preliminary data-driven approach for catalyst evaluation in seawater metal-air batteries.关键词
海水金属空气电池/电化学性能/催化剂失效时间预测/时间序列回归Key words
Seawater metal-air batteries/Electrochemical performance/Catalyst failure time prediction/Time series regression引用本文复制引用
申鹏鹏,潘奕池,刘育荣,张露丹,牛宁,王冠军,杨德坤,田新龙,饶鹏..基于电化学性能数据驱动的神经网络预测海水电解液金属空气电池放电终止时间[J].电化学（中英文）,2026,32(8):25-36,12.基金项目
This work was supported by the National Natural Science Foundation of China(52274297,52561041),the Hainan Province Science and Technology Special Fund(ZDYF2025GXJS004,ZDYF2026SHFZ048),the Start-up Research Foundation of Hainan University(KYQD(ZR)23169). （52274297,52561041）
