To　improve　fuel　economy　and　reduce　missions　of　the　plug-in　hybrid　electric　vehicles　(PHEV)　in　charge-sustaining　mode,　the　fuzzy　controller　taking　the　demand　torque,　the　battery　state　of　charge　(SOC),　and　the　motor　speed　as　inputs　and　the　engine　torque　as　the　output　was　designed.　The　open　trapezoids　at　the　left　and　right　sides　and　the　triangle　at　the　center　were　selected　as　the　shapes　to　describe　the　membership　function　of　fuzzy　set.　In　order　to　facilitate　the　realization　of　fuzzy　control,　the　parameters　were　quantized　to　the　interval　of　[0,　1].　According　to　the　different　working　modes　of　PHEV,　the　fuzzy　subsets　of　the　input　and　output　variables　were　determined.　The　membership　function　and　control　rules　were　coded,　and　the　particle　swarm　optimization　(PSO)　were　used　to　optimize　the　value　of　the　membership　function　and　control　rules.　Under　the　premise　of　ensuring　the　dynamic　power,　taking　the　most　stable　battery　SOC　and　maximum　improvement　of　fuel　economy　and　emission　performance　as　the　goals,　the　weight　coefficient　was　introduced　to　establish　the　CO,　NOx　and　HC　emission　indices　and　the　value　function　of　the　equivalent　fuel　consumption　of　the　vehicle,　and　a　multi-objective　fuzzy　control　strategy　based　on　the　particle　swarm　optimization　(PSO-fuzzy)　was　formed.　Integrating　theoretical　modeling　and　experimental　data　modeling　method,　the　numerical　model　of　key　components　of　the　PHEV　power　transmission　system,　the　vehicle　dynamics　model　and　the　driver　model　were　built　based　on　MATLAB/Simulink.　The　combined　driving　cycle　of　typical　Chinese　city　and　highway　driving　cycles　was　used　for　verification.　The　results　show　that　compared　with　the　fuzzy　control　strategy　without　optimizing　by　PSO,　the　battery　SOC　operation　is　more　stable;　the　torque　between　the　engine　and　the　motor　is　more　reasonably　distributed,　and　the　engine　operating　points　are　distributed　in　more　efficient　area　while　at　the　same　time　the　most　operating　points　of　motor　are　working　in　high　efficient　area;　fuel　consumption　for　one　hundred　kilometers　decreases　by　12.4%,　CO　emissions　decrease　by　2.7%,　NOx　emissions　decrease　by　4.4%,　and　HC　emissions　decrease　by　4.3%.　©　2016,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.