Time/space　separation-based　modeling　methods　have　been　widely　researched　for　estimating　lithium-ion　battery　(LIB)　thermal　dynamics.　However,　these　methods　have　been　developed　in　an　offline　environment　and　may　not　perform　well　in　real-time　application　since　the　battery　systems　in　electric　vehicles　(EVs)　are　usually　subject　to　external　disturbances.　Furthermore,　the　onboard　measurements　of　temperature　are　often　corrupted　by　significant　error.　To　address　these　problems,　we　present　a　reduced　model-based　observer　design　for　online　temperature　distribution　estimation　in　LIBs.　First,　an　extreme　learning　machine　(ELM)-based　offline　spatiotemporal　model　is　constructed　to　approximate　the　thermal　dynamics　of　LIB.　Second,　an　adaptive　reduced　order　observer　is　designed　based　on　the　offline　model　developed　in　the　previous　step.　The　offline　model　is　then　updated　with　the　estimation　results　of　the　observer.　As　the　performance　of　the　estimator　is　highly　related　to　the　placement　of　sensors,　a　genetic　algorithm　(GA)-based　integrated　optimization　strategy　is　also　developed　to　determine　the　optimal　sensor　location　for　online　estimation.　Finally,　the　whole　temperature　distribution　is　estimated　in　real　time　using　the　observer,　the　measured　voltage,　current　and　the　limited　available　temperature　data.　Two　experiments　on　different　batteries　with　different　input　currents　verify　the　effectiveness　of　this　developed　model.