A real-time multi-objective optimization method in energy efficiency for plug-in hybrid electric vehicles considering dynamic electrochemical characteristics of battery and driving conditions

The dynamic performance of power batteries and complex driving conditions significantly impact the energy efficiency of plug-in hybrid electric vehicles (PHEVs). To further enhance the energy utilization of PHEVs, this paper proposes a real-time multi-objective optimization method based on the adaptive equivalent consumption minimization strategy (ECMS) considering the dynamic characteristics of the battery and various driving conditions. First, a second-order RC battery model is established and calibrated based on the experimental data. To characterize the dynamic battery performance and facilitate the real-time adjustment of constraints within the energy management strategy (EMS), the state of charge (SOC) estimation method is developed and the instantaneous and long-term state of power is estimated by considering multiple constraints such as SOC, maximum current and voltage. Subsequently, a data-driven model for recognizing driving conditions is introduced to achieve the adaptive adjustment of equivalent factors. Moreover, to mitigate the impact of the EMS on battery life, a model for battery life degradation is developed and integrated into the optimization. The results indicate that the proposed method reduces battery life degradation by 15.8 % compared with the optimized rule-based strategy and has only a 1.7 % cost differential compared with the offline-optimized ECMS. Furthermore, the hardware-in-the-loop experiment demonstrates the practical applicability of the proposed method.