A novel adaptive equivalent consumption minimization strategy-based energy management strategy for plug-in hybrid electric vehicles using dynamic traffic information

Adaptive equivalent consumption minimization strategy (AECMS) based energy management strategy (EMS) has superior real-time performance and sub-optimal capability. However, its adaptability to varying initial state of charge (SOC) values is limited. To tackle this issue, this paper proposes a novel AECMS based EMS with an innovative adaptive method for adjusting the equivalent factor for plug-in hybrid electric vehicles (PHEVs). Firstly, the strategy generates a global traffic flow velocity trajectory for the target vehicle by utilizing dynamic traffic flow data, then the corresponding optimal equivalent factor is determined as the initial equivalent factor. Subsequently, a fuzzy adaptive logic controller is developed, taking the average velocity of the current road segment and the ego vehicle’s instantaneous velocity as inputs to dynamically adjust the equivalent factor. Finally, an advanced EMS based on AECMS is established to achieve real-time power distribution among different power components. Simulation results demonstrate that the proposed strategy significantly enhances the energy-saving efficiency and adaptability to varying initial SOC values of PHEVs. Compared with the state-of-the-art AECMS strategy, the proposed method reduces fuel consumption by 6.61%. The real-time optimization capability of the proposed strategy is further validated through hardware-in-the-loop (HIL) platform testing. Moreover, the adaptability and robustness of the proposed EMS are verified under different initial SOC value.