动力电池健康状态的数据预测模型研究

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中图分类号：U463.633 文献标识码：A 文章编号：1003-8639（2025）11-0015-0

ResearchonDataPredictionModelsforPowerBatteryStateofHealth

Chang Feng

(Zhengzhou University of Science and Technology,Zhengzhou 45oooo, China

【Abstract】The rapid development of new energy vehicles and energy storage industries has imposed higher demandson powerbattery health management.Accurate predictionof Stateof Health has becomeacorechallnge in ensuring system safety and economic viability. Current prediction methods face three major bottlenecks: insuffcient adaptabilitytodynamicoperatingconditions,dificulties incharacterizing micro-level degradationmechanisms,andhigh implementation costs.Therefore,this paper conducts in-depth research onamulti-physics field featurecollaborative extraction mechanism and alightweight deep learning framework.The goal is toestablishanovel prediction paradigm thatcombines real-timeresponsivenesswith industrial-gradeaccuracy,providing a theoretical breakthrough direction for battery lifecycle management.

【Key words】power battery；SOH;data prediction

0 引言

在全球能源结构转型的关键阶段，动力电池健康状态（StateofHealth，SOH）评估的精度瓶颈正深刻制约着新能源汽车与储能系统的规模化发展。（剩余4796字）