In　this　article,　an　online　recursive　power　management　strategy　based　on　the　reinforcement　learning　technique　is　proposed　to　achieve　an　optimal　power　distribution　and　excellent　economic　performance　of　a　plug-in　fuel　cell　hybrid　electric　vehicle.　First,　the　optimal　control　algorithm　of　the　power　management　strategy　is　formulated.　Then,　an　online　recursive　algorithm　using　cosine　similarity　and　a　forgetting　factor　to　achieve　the　adaptability　of　the　proposed　strategy　to　various　driving　conditions　is　formulated.　The　parameters　updating　framework　is　triggered　once　the　change　rate　of　cosine　similarity　exceeds　the　threshold　value　of　the　corresponding　driving　cycle.　Some　of　the　important　parameters　such　as　the　learning　rate,　discount　factor,　forgetting　factor,　and　the　change　rate　of　cosine　similarity　are　analyzed　to　optimize　the　online　updating　capability　of　the　strategy.　Finally,　an　experimental　study　is　conducted　to　validate　the　effectiveness　of　the　proposed　strategy.　Both　a　rule-based　strategy　and　an　equivalent　consumption　minimization　strategy　are　implemented　to　establish　the　benchmark　framework　used　to　analyze　the　performance　of　the　proposed　strategy.　The　simulation　and　experimental　results　corroborate　the　effectiveness　and　economic　performance　of　the　proposed　strategy.　A　comparison　with　the　existing　standard　benchmark　strategy　indicates　the　superior　performance　of　the　developed　strategy　reaching　a　remarkable　reduction　in　energy　consumption　at　a　long　driving　distance.