Accurate　prediction　of　the　State　of　Health　for　lithium-ion　batteries　is　critical　to　ensure　the　operational　safety　and　prolong　the　service　life　of　energy　storage　systems.　However,　conventional　purely　data-driven　models　and　physics-informed　neural　network　often　suffer　from　limited　accuracy　and　poor　generalization　when　dealing　with　battery　samples　exhibiting　diverse　electrochemical　characteristics.　To　address　these　challenges,　this　paper　proposes　a　Physics-Informed　Neural　Network　integrated　with　Transformer　for　state　of　health　prediction　(PI-TNet).　The　framework　introduces　a　Convolutional　Data　Processor　to　extract　multi-dimensional　features　of　electrochemical　processes,　while　incorporating　the　Verhulst　model　as　learnable　parameters　within　a　Vision　Transformer　architecture.　This　hybrid　design　enables　simultaneous　capture　of　long-term　temporal　dependencies　in　sequential　cycling　data　and　precise　modeling　of　battery　degradation　mechanisms.　Extensive　cross-dataset　validation　using　both　NASA　and　CALCE　battery　datasets　demonstrates　that　PI-TNet　significantly　outperforms　existing　methods　in　handling　long-sequence　prediction　tasks　while　achieving　better　accuracy　and　generalization　performance.　©　2025　The　Author(s)