The　electrical　energy　of　a　plug-in　hybrid　electric　vehicle　(PHEV)　is　provided　by　the　internal　combustion　engine　and　grid　power.　The　fuel　consumption　of　a　PHEV　can　be　minimised　through　optimising　the　operation　at　all-electric　range　(AER).　The　AER　may　vary　with　the　state　of　charge　(SOC),　the　expected　route　characteristic,　traffic　and　the　electrical　energy　available　dominated　by　the　forthcoming　charge　opportunity.　This　research　proposes　an　AER　adaptive　energy　management　strategy　based　on　the　equivalent　consumption　minimisation　strategy　(ECMS)　and　the　forthcoming　energy　consumption　prediction.　The　model　of　the　equivalent　factor　(EF)　is　developed　based　on　the　required　energy　per　unit　distance　(REPD).　The　corresponding　correction　factor　of　EF　is　optimised　with　the　particle　swarm　optimisation　and　developed　as　a　function　of　REPD,　SOC　and　the　AER.　The　artificial　neural　network　is　used　to　predict　REPD　which　is　applied　to　update　the　EF　estimated　model　online.　The　proposed　strategy　is　validated　by　the　numerical　simulation　and　hardware-in-loop　experiment　(HIL).　The　simulation　and　HIL　experiment　results　demonstrate　that　the　proposed　strategy　can　further　improve　the　fuel　economy　of　PHEVs　when　compared　with　the　traditional　ECMS　under　different　driving　cycles.