Article(id=1321491691316563993, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491612593664576, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.1.225, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1678723200000, receivedDateStr=2023-03-14, revisedDate=1687622400000, revisedDateStr=2023-06-25, acceptedDate=1692979200000, acceptedDateStr=2023-08-26, onlineDate=1792997771338, onlineDateStr=2026-10-26, pubDate=1769702400000, pubDateStr=2026-01-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792997771338, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792997771338, creator=13701087609, updateTime=1792997771338, updator=13701087609, issue=Issue{id=1321491612593664576, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='1', pageStart='1', pageEnd='346', issueExtLink='null', onlineDate='null', pubDate='1769702400000', pubDateStr='2026-01-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792997752568, creator='13701087609', updateTime=1792998003208, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321492663950487647, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491612593664576, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321492663950487648, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1321491612593664576, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=225, endPage=232, ext={EN=ArticleExt(id=1321491691509501978, articleId=1321491691316563993, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=State-of-health Estimation for Lithium-ion Battery Based on Hunter-prey Optimizer Algorithm and Long Short-term Memory Neural Network, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

The state-of-health (SOH) of batteries is an important indicator for evaluating their performance. Aimed at the difficulty in accurately estimating the SOH of batteries, a novel SOH estimation method for lithium-ion batteries was proposed by combining the hunter-prey optimizer (HPO) algorithm and long short-term memory (LSTM) neural network. Through the analysis of influences of current and temperature on the incremental capacity (IC) curve, the peak value of the highest peak in the IC curve and the corresponding voltage, temperature and current were introduced as the model input, and the dynamic parameters of LSTM neural network were adjusted using the HPO algorithm. Finally, the battery charging and discharging data under the condition of peak cutting and valley filling in the environment of energy storage were used for experimental verification. Results indicate that the SOH estimation method for lithium-ion batteries based on HPO-LSTM neural network has higher estimation accuracy compared with the traditional LSTM network methods, and it also has good robustness under different network training amounts.

, authors=Tinghua WANG, Bo YAN, Jingyun WU, Darui HE, authorsList=Tinghua WANG, Bo YAN, Jingyun WU, Darui HE, authorCompany=null, correspAuthors=Tinghua WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321491691853434907, articleId=1321491691316563993, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=基于HPO-LSTM网络的锂电池健康状态估计, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

电池健康状态SOH（state-of-health）是评价电池性能的重要指标。针对电池健康状态难以准确估算的问题，提出猎人猎物优化HPO（hunter-prey optimizer）算法和长短期记忆LSTM（long short-term memory）神经网络相结合的锂电池健康状态估计方法。通过分析电流、温度对容量增量IC（incremental capacity）曲线的影响，引入IC曲线中最高峰的峰值及其对应的电压、温度、电流作为模型输入，利用HPO算法对LSTM网络进行动态调参，最后采用储能环境下削峰填谷工况的电池充放电数据进行实验验证。结果表明：基于HPO-LSTM网络的锂电池健康状态估计方法相较传统的LSTM网络方法具有更高的估算精度，在不同网络训练量下具有较好的鲁棒性。

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鄢博（1994—　），女，硕士。研究方向：电网储能及安全技术。E-mail：511612029@qq.com。

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鄢博（1994—　），女，硕士。研究方向：电网储能及安全技术。E-mail：511612029@qq.com。

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吴静云（1989—　），女，博士，高级工程师。研究方向：电网储能及安全技术。E-mail：673112739@qq.com。

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吴静云（1989—　），女，博士，高级工程师。研究方向：电网储能及安全技术。E-mail：673112739@qq.com。

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何大瑞（1991—　），男，硕士。研究方向：电力系统及其自动化。E-mail：hdr2908@163.com。

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何大瑞（1991—　），男，硕士。研究方向：电力系统及其自动化。E-mail：hdr2908@163.com。

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