This　study　presents　an　adaptive　energy　management　control　strategy　developed　by　optimally　adjusting　the　equivalent　factor　(EF)　in　real-time　based　on　driving　pattern　recognition　(DPR),　to　guarantee　the　plug-in　hybrid　electric　vehicle　(PHEV)　can　adapt　to　various　driving　cycles　and　different　expected　trip　distances　and　to　further　improve　the　fuel　economy　performance.　First,　the　optimization　model　for　the　EF　with　the　battery　state　of　charge　(SOC)　and　trip　distance　were　developed　based　on　the　equivalent　consumption　minimization　strategy　(ECMS).　Furthermore,　a　methodology　of　extracting　the　globally　optimal　EF　model　from　genetic　algorithm　(GA)　solution　is　proposed　for　the　design　of　the　EF　adaptation　strategy.　The　EF　as　the　function　of　trip　distances　and　SOC　in　various　driving　cycles　is　expressed　in　the　form　of　map　that　can　be　applied　directly　in　the　corresponding　driving　cycle.　Finally,　the　algorithm　of　DPR　based　on　learning　vector　quantization　(LVQ)　is　established　to　identify　the　driving　mode　and　update　the　optimal　EF.　Simulation　and　hardware-in-loop　experiments　are　conducted　on　synthesis　driving　cycles　to　validate　the　proposed　strategy.　The　results　indicate　that　the　optimal　adaption　EF　control　strategy　will　be　able　to　adapt　to　different　expected　trip　distances　and　improve　the　fuel　economy　performance　by　up　to　13.8%　compared　to　the　ECMS　with　constant　EF.