多源信息融合背景下新能源汽车动力电池健康状态预测模型优化

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关键词：新能源汽车；动力电池；健康状态预测；多源信息融合 中图分类号：U469.7 收稿日期：2025-12-24 DOI:10.19999/j.cnki.1004-0226.2026.02.014

Optimization of Power Battery Health State Prediction Model for New Energy Vehicles Based on Multi-source Information Fusion

Zhu Guangwei Li Haitao Xinyang Aviation Vocational College,Xinyang 464oo0,China

Abstract:Thispaperfirstreviewsthecurent evaluationstandardsforthehealthstateofnewenergyvehiclepowerbateries,and pointsoutheaccracylimitationsof taditionalsngledatasoureandstaticmodelingmethodsundercomplexworkingconditios.Then thepaperproposesanoptimizationpathforSOHpredictionmodelbasedonmulti-sourceinformationfusion.Byintegratingvoltage, currenttemperature，SOC，istoricaloperatingonditions,andenvironmentaldata，combinedwithdeepleaing，ensemblelearing，andphysics-datahbridmodelingstrategies，powerbaeryealthatepredictionmodelwithigheraccuracytrongrobust ness，andwideradaptabilityisonstructed.Theresearchoncusioncanprovideintellgentdecisionsupprtfornewenergyveiclebat terymanagement systems and promote high-quality industrial development.

Key words:New energy vehicles;Power battry;Health state prediction;Multi-source information fusion

1前言

动力电池作为新能源汽车的核心部件，其健康状态(StateofHealth，SOH)直接关系到整车的安全性、续航能力与运行效率，准确评估与预测动力电池的健康状态已成为提升新能源车辆可靠性与智能化水平的重要课题[1]。（剩余4913字）