The　number　of　vehicles　is　rapidly　increasing.　An　effective　control　strategy　for　Hybrid　Electric　Vehicles　(HEVs)　is　important.　In　this　paper,　we　present　a　two-level　control　strategy　that　combines　the　Adaptive-Equivalent　Consumption　Minimization　Strategy　(A-ECMS)　and　the　Adaptive　Dynamic　Programming　(ADP).　At　the　lower　level,　the　A-ECMS　is　used　to　convert　the　consumed　charge　into　Equivalent　Fuel　Consumption　(EFC)　for　every　sample　moment,　and　a　PI　controller　is　used　to　adjust　the　values　of　the　equivalent　factor　of　the　A-ECMS.　At　the　upper　level,　the　ADP　is　used　to　find　the　minimum　of　EFC　corresponding　to　equivalent　factor　for　every　sample　moment,　and　it　maintains　the　State　of　Charge　(SOC)　of　battery　to　charge　and　discharge　smoothly　in　a　high-efficiency　field　for　the　HEV.　As　by　the　ADP,　we　look　into　the　future　and　then　we　can　have　a　better　estimate　for　the　equivalent　factor　than　the　ordinary　A-ECMS.　In　this　way,　we　can　save　energy　as　well　as　calculate　instantaneous　parameters　for　the　control　strategy.　Compared　with　a　typical　rule-based　control　strategy,　the　proposed　control　method　saves　EFC　up　to　10.3%　and　the　stability　of　the　SOC　is　increased　by　more　than　60%,　tested　on　benchmarks.