Adaptive Real-Time Optimal Control For Energy Management Strategy Of Extended Range Electric Vehicle

The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programmingbased global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13?23 times higher than that of the shooting method.