A Rolling Convergent Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Vehicles

Energy management strategy (EMS) is regarded as a crucial measure to further leverage the benefits of plug-in hybrid electric vehicles (PHEVs). However, existing EMSs often fail to balance applicability and optimality. As a widely recognized instantaneous optimization strategy, equivalent consumption minimization strategy (ECMS) is considered to be competitive to achieve the desired performance, provided that equivalent factor (EF) is properly tuned. To achieve this goal, a rolling convergent ECMS for energy management of PHEV is proposed in this article. Instead of seeking the instantaneous solution solely from two-dimensional scale like traditional ECMS, this article extends the analysis to three-dimensional global scale. And the analytic expression of the optimal EF initial interval is derived, which can be quantitatively described by the component specifications of PHEV and driving condition information. Subsequently, inheriting the obtained optimal EF initial interval, a rolling convergent method using tabu search is developed for more efficient optimization. Finally, the effectiveness of the proposed strategy is verified by simulation and experiment. The results show that the proposed strategy improves the fuel economy of PHEV by 3.7% and 5.2% over that using the traditional adaptive ECMS under two real-world driving cycles, respectively. And the validity of the optimal EF initial interval is corroborated under several typical driving cycles. Furthermore, the proposed strategy is performed on the test bench, demonstrating its superiority and practicality.