An explicit State-of-Charge planning solution for plug-in hybrid electric vehicle based on low-granularity prior-knowledge

The intervention of batteries in hybrid electric vehicles, when paired with an effective Energy Management Strategy (EMS), substantially improves fuel efficiency and reduces emissions in comparison to conventional internal combustion engine vehicles. The evolution of Intelligent Transportation Systems (ITS) has facilitated the possibility of predictive energy management (PEM) predicated on State-of-Charge (SoC) planning. Nevertheless, prevalent methodologies frequently encounter challenges in balancing optimization with real-time applicability. To address these limitations, we have devised an explicit SoC planning method that necessitates sparse traffic prior-knowledge, drawing inspiration from the optimal charge depletion behavior. This innovative method strategically determines the average SoC depletion rate for each anticipated driving road segment by integrating theoretical predictions of optimal depletion rate with experienced constraints. Capitalizing on prior knowledge of sparse traffic velocities and road grades, we have developed a hierarchical PEM framework that seamlessly integrates SoC planning — power split. The results of the simulation experiments reveal that the SoC trajectories and fuel consumption generated by this method are in close approximation to theoretically optimal benchmarks. Furthermore, the computational time of this method is in accordance with the demanding real-time requisites of onboard units even if hundreds of miles. Notably, this approach exhibits an enhanced robustness to predictive discrepancies, ensuring reliability and efficacy in dynamic driving cycles.