For　the　online　energy　optimization　problem　of　plug-in　hybrid　electric　vehicles　(P-HEVs),　this　paper　proposes　a　heuristic　dynamic　programming　(HDP)　based　online　energy　management　strategy,　to　minimize　the　fuel　consumption　of　the　P-HEV.　First　of　all,　considering　the　uncertain　nonlinear　dynamic　process　of　a　vehicle　in　the　actual　traffic　environment,　we　adopt　the　back　propagation　neural　network　(BPNN)　to　construct　the　dynamic　model　of　the　P-HEV.　Then,　on　this　basis,　we　utilize　the　HDP　to　establish　an　energy　management　controller　with　the　aim　of　minimizing　energy　consumption　of　the　P-HEV.　Moreover,　the　energy　management　controller　is　implemented　by　an　online　energy　management　strategy　algorithm.　To　verify　the　effect　of　the　controller,　we　employ　a　practical　route　in　Beijing　road　network　to　simulate　the　BPNN　model　of　the　P-HEV　and　the　proposed　energy　management　strategy.　The　experimental　results　show　several　advantages　of　our　strategy.　First,　compared　to　the　analytic　model,　the　BPNN　model　can　reflect　the　real　dynamic　process　of　the　P-HEV　with　a　higher　precision.　Second,　the　assigned　torques　by　the　strategy　can　effectively　make　the　vehicle　track　the　desired　vehicle-speeds,　and　the　tracking　accuracy　of　the　vehicle-speed　is　higher　than　98%.　Besides,　on　the　premise　of　ensuring　the　real-time　performance,　the　proposed　strategy　can　further　reduce　the　fuel　consumption　and　emissions　of　the　P-HEV　when　compared　with　the　existing　online　energy　management　strategies,　although　its　fuel　consumption　is　more　than　that　of　the　offline　global　optimization　energy　management　strategy　by　4%　approximately.