Abstract
The powertrain efficiency of plug-in hybrid electric vehicles (PHEV) can be increased by effectively using the engine along the electric motor to gradually discharge the battery throughout a driving cycle. This sets the requirement of the optimal shaping of the battery state-of-charge (SoC) reference trajectory. The paper deals with the online synthesis of the optimal SoC reference trajectory, which inherently includes adaptive features in relation to the prediction of upcoming driving cycle features such as the trip distance, the road grade profile, the mean vehicle velocity and the mean demanded power. The method performs iteratively, starting from an offline-synthesized SoC reference trajectory obtained based on dynamic programming (DP) control variable optimization results. The overall PHEV control strategy incorporating the proposed online SoC reference trajectory synthesis method is verified against the DP benchmark and different offline synthesis methods. For this purpose, a model of a PHEV-type city bus is used and simulated over a wide range of driving cycles and conditions including varying road grade and low-emission zones (LEZ).
Highlights
Plug-in hybrid electric vehicles (PHEV) bridge the gap between conventional and fully electric vehicles in terms of leveraging investment costs, driving range, efficiency, emissions, and infrastructure requirements [1]
A PHEV can operate in a charge depleting (CD) mode until its battery is discharged to a prescribed lower limit value, after which a charge sustaining (CS) mode is activated to sustain the battery state-of-charge (SoC) and extend the driving range
This paper proposes an online, computationally efficient, heuristics-based SoC reference trajectory synthesis method for a general class of driving cycles including the presence of low-emission zones (LEZ) and varying road grades
Summary
Plug-in hybrid electric vehicles (PHEV) bridge the gap between conventional and fully electric vehicles in terms of leveraging investment costs, driving range, efficiency, emissions, and infrastructure requirements [1]. As such, they are considered as an effective intermediate solution towards fully electrified road transportation. The PHEVs are characterized by a complex powertrain structure consisting of several power sources, including an internal combustion engine, one or more electric machines, and a battery. It is essential to develop an optimal PHEV control strategy that properly coordinates the different power sources in various operating modes. The implementation of this mode requires the planning of a proper SoC reference trajectory, which is either commanded to an explicit SoC controller or set as a constraint when solving an online optimal control problem [3]
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