Plug‐in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero‐emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A‐ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine‐tuned for the Toyota Prius plug‐in hybrid powertrain and their performances are evaluated by using a high‐fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A‐ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A‐ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.

No abstract

Plug-in hybrid electric vehicle (PHEV) development seems to be essential for a sustainable transportation system along with electric vehicles. An appropriate power management strategy for a PHEV determines how to blend the engine and the battery power in such a way that leads to significant fuel economy improvement and environmental footprint reduction. To evaluate and validate the controls design, software and hardware-in-the-loop (SIL/HIL) simulations are useful approaches, especially at the early stages of controls design. To conduct SIL/HIL tests, an accurate and relatively fast mathematical model of the real powertrain is required which solely contains the essential dynamics of the plant. In this paper, a physics-based model of a power-split plug-in powertrain is developed and implemented using MapleSim software. This model contains a chemistry-based lithium-ion battery pack, which can distinguish it from other models used in the literature, since the performance of a PHEV greatly depends on its battery. The symbolic computation power of MapleSim makes the model very suitable for real-time SIL/HIL tests.

Model Predictive Control (MPC) can be an interesting concept for designing a power management strategy for hybrid electric vehicles according to its capability of online optimization by receiving current information from the powertrain and handling hard constraints on such problems. In this article a power management strategy for a power split plug-in hybrid electric vehicle is proposed using the concept of MPC to evaluate the effectiveness of this method on minimizing the fuel consumption of those vehicles. Also the results are compared with Dynamic Programming.

No abstract