Energy　management　and　eco-driving　are　crucial　for　the　energy　saving　of　range-extender　electric　vehicles　(REEVs)　in　urban　traffic　environments.　In　this　work,　the　collaborative　energy　management　and　vehicle　speed　optimization　strategy　for　REEVs　supported　by　vehicle-to-everything　(V2X)　is　proposed,　where　the　two　problems　are　coupled　and　a　multi-input-multi-output　(MIMO)　reinforcement　learning　algorithm　is　proposed　to　simultaneously　solve　the　two　problems　to　obtain　a　cooperative　strategy　to　achieve　the　optimal　strategy　when　crossing　signalized　intersections.　Also,　a　multi-objective　reward　function　is　designed　by　considering　fuel　economy　and　driving　safety.　The　collaboration　strategy　results　in　improved　fuel　efficiency,　battery　wear,　smoother　speed　profile　for　comfort,　and　engine　operation　near　optimal　BSFC　for　lower　fuel　consumption.　©　2025　IEEE.