The　propelling　torque　of　a　plug-in　hybrid　electric　vehicle　(PHEV)　is　provided　by　the　engine　and　driving　motor.　However,　the　driver　style　has　a　great　influence　on　the　fuel　economy　performance　of　the　PHEV.　To　address　this　issue,　this　study　proposes　a　novel　driving-style-oriented　adaptive　control　strategy　by　using　Particle　Swarm　Optimization　(PSO)　combined　with　Fuzzy　expert　algorithm.　The　first　is　development　of　a　driving　style　recognition　method　by　taking　advantage　of　the　Fuzzy　expert　algorithm.　The　four　input　parameters　of　Fuzzy　expert　system　are　derived　by　Principal　Component　Analysis　based　the　collected　data　of　driving　behavior　real　tests.　Second,　a　novel　driving-style-oriented　adaptive　control　strategy　is　developed　based　the　Equivalent　Consumption　Minimization　Strategy　(ECMS)　integrated　with　adaptive　Equivalent　Factor　(EF).　The　EF　is　updating　according　to　the　corresponding　driving　style　by　using　the　PI-Fuzzy　controller,　and　the　PSO　algorithm　is　applied　to　further　optimize　the　fuzzy　rules　for　tuning　the　PI　factors.　Finally,　in　combination　with　the　above　efforts,　a　novel　drivingstyle-oriented　adaptive　control　strategy　based　PSO-Fuzzy　Expert　Algorithm　has　been　further　established　accordingly.　A　normal　ECMS　with　constant　EF　and　an　adaptive　ECMS　for　neural　network　to　recognize　the　driving　style　is　applied　to　compare　with　the　proposed　strategy.　The　validation　results　indicate　that　the　strategy　significantly　decreasing　the　fuel　consumption　with　the　equivalent　fuel　consumption　is　reduced　by　19.3%,　and　the　proposed　strategy　has　the　best　performance.