Uneven　temperature　distributions　can　significantly　impact　battery　performance　and　cycle　life.　Industrial　applications,　in　particular,　where　unknown　disturbances　and　limited　sensors　exist,　pose　significant　challenges　for　accurately　estimating　the　battery　temperature　field.　This　work　proposes　a　multi-source　information　fusion　framework　for　capturing　the　spatiotemporal　temperature　dynamics　of　pouch-type　Lithium-ion　batteries.　First,　we　develop　a　system　model　of　the　battery　thermal　process　based　on　physical　insights,　considering　unknown　parameter　deviations　and　unmodeled　dynamics.　Next,　we　use　the　Galerkin-spectral　method　to　expand　the　spatiotemporal　variable　and　reduce　the　original　infinite-dimensional　system　to　a　low-order　model　containing　the　most　important　system　modes.　The　unknown　component　of　the　model　is　then　entirely　stripped　and　integrated　into　a　nonlinear　term.　To　close　the　reality　gap　of　the　physics-based　model,　we　subsequently　develop　an　error　compensation　model　that　learns　the　dynamic　behavior　from　sparse　observations.　Ultimately,　our　framework　design　yields　a　fusion-driven　model　for　reliable　temperature　field　prediction.　Simulations　and　experimental　data　validate　the　superior　performance　and　generalization　capability　of　the　proposed　method.　©　2024　Elsevier　Ltd