In　this　article,　a　model　predictive　control　(MPC)　energy　management　strategy　is　proposed　to　distribute　power　flows　of　proton　exchange　membrane　fuel　cell　(PEMFC)-based　hybrid　power　systems　consisting　of　PEMFC,　battery,　and　waste　heat　recovery　system　such　as　TEG　and　CHP.　To　optimally　meet　the　demand　of　load　power　balancing　as　well　as　protect　PEMFC　from　lifetime　degradation,　a　novel　objective　function　by　considering　fuel　consumption,　state-of-charge　(SOC)　of　battery,　as　well　as　power　slope　and　temperature　of　PEMFC　is　constructed　and　solved　in　the　states　prediction　horizon　within　the　defined　lifetime　constraints　and　SOC　limitations.　In　particular,　temperature　effects　are　newly　introduced　by　adding　a　state-variable　to　the　energy　management　model　and　formulating　a　penalty　function.　Simulations　with　mobility　and　stationary　application　scenarios　are　presented.　In　the　automobile　case,　the　hydrogen　consumption　of　the　constraints　MPC　is　reduced　by　9.98%　compared　with　the　rule-based　strategy,　and　the　same　results　can　be　achieved　in　the　household　application.　A　hardware　in　the　loop　experiment　was　carried　out　to　verify　the　real-time　performance　of　the　MPC　strategy　which　occupied　a　2.21%　average　CPU　load　rate.　The　proposed　MPC　strategy　has　a　promising　fuel　consumption　optimization,　lifetime　extension,　and　real-time　capability.