Optimal Energy Management Control of a Parallel Plug-in Hybrid Electric Vehicle in the Presence of Low Emission Zones 2019-01-1215
In order to reduce air and noise pollution in urban environments stemming from personal and public transport, Low Emission Zones (LEZ) are being introduced in many cities worldwide. There are various LEZ implementations which usually restrict access to road vehicles driven by fossil fuels based on their emission levels. Electrified road vehicles are preferred modes of transportation in LEZ because they are cleaner and quieter. Mid-term solution to fully electrified vehicles are hybrid electric vehicles (HEV) and their plug-in counterparts (PHEV), which successfully tackle the current drawbacks of fully electric vehicles (EV) such as high price of battery pack, limited driving range, relatively slow charging, and lack of charging infrastructure. Although PHEVs are already capable of traversing LEZ in the electric-only driving model, provided that the battery state-of-charge (SoC) is high enough, there is a potential to further improve their effectiveness when dealing with a LEZ by appropriately modifying the energy management control strategy.
This paper deals with design of a LEZ-anticipating control strategy for a PHEV given in a P2 parallel powertrain configuration. A control-oriented backward-looking model of the PHEV powertrain is used as a design basis, where the powertrain relations are represented by a set of kinematic equations, while the fuel consumption and electric energy losses are modelled by means of simple lookup tables. The core control strategy is based on combining a rule-based (RB) controller (including an explicit SoC controller) and equivalent consumption minimization strategy (ECMS), and it is superimposed by generating an optimal SoC reference trajectory aimed at enabling pure electrical driving through forthcoming LEZs and minimizing overall fuel consumption. The optimal SoC reference trajectory is derived from the globally optimal SoC trajectories obtained by using dynamic programming (DP) optimization. The proposed control strategy is verified by computer simulations against the DP-based globally optimal benchmark for several LEZ scenarios and various driving cycles.
Jure Soldo, Branimir Skugor, Josko Deur