Abstract: The real-time optimization method of adaptive equivalent factors was studied，in oder to achieve a SOC balance and reduce fuel consumption and thus further improve the fuel economy of plug-in hybrid electric vehicles． The driving cycle NEDC was taken as an example and was decomposed into different basic driving-cycle block． The relationship curves between the equivalent factors and fuel consumption and SOC variation of each basic driving-cy- cle block were linearly fitted． The real-time optimization problem of equivalent factors was transformed into a simple linear programming problem，and the real-time optimization model of equivalent factors based on linear program- ming was constructed． Then a adaptive equivalent fuel consumption minimization strategy ( A-ECMS) based on li- near programming was proposed． Hardware-in-loop tests show that the real-time optimization method of equivalent factors based on linear programming model can meet the real-time online control requirements of vehicle controller， and it is feasible to apply it in real vehicles． The test and simulation results show that A-ECMS based on linear pro- gramming model can maintain a SOC balance and achieve a fuel economy which is close to the global optimal energy management strategy under different operating conditions． It verifies that the real-time optimization model has prac- tical application value and potential．

Key words: plug-in hybrid electric vehicle, adaptive equivalent fuel consumption minimization strategy, equivalent factor, real-time optimization, linear programming