Efficient Thermal Electric Skipping Strategy applied to the Control of Series/Parallel Hybrid Powertrain 2020-01-1193
The optimal control of hybrid powertrains represents one of the most challenging tasks for the compliance with the legislation concerning CO2 and pollutant emission of vehicles. Most common off-line optimization strategies (Pontryagin minimum principle – PMP – or dynamic programming) allow to identify the optimal control along a predefined driving mission at the expense of a quite relevant computational effort. On-line strategies, suitable for on-vehicle implementation, involve a certain performance degradation depending on their degree of simplification and computational effort.
In this work, a simplified control strategy is presented, where the conventional power-split logics, typical of the above-mentioned strategies, is here replaced with an alternative utilization of the thermal and electric units for the vehicle driving (Efficient Thermal Electric Skipping Strategy - ETESS). The choice between the units is realized at each time and is based on the comparison between the effective fuel rate of the thermal engine and an equivalent fuel rate related to the electrical power consumption. The equivalent fuel rate in a pure electric driving is associated to a combination of brake specific fuel consumption of the thermal engine, and electro-mechanical efficiencies along the driveline.
The ETESS is applied for the simulation of segment C hybrid vehicle, equipped with a thermal engine and two electric units (motor and generator). The methodology is tested along regulatory driving cycles (WLTP, Artemis) and RDE, with different powertrain variants. Numerical results underline that the proposed approach performs very close to most common control strategies (consumed fuel per kilometer higher than PMP of about 1% on average). The main advantage is a reduced computational effort (decrease of 99% on average). The ETESS is straightforwardly adapted for an on-line implementation, through the introduction of an adaptative factor, preserving the computational effort and the fuel economy.
Vincenzo De Bellis, Enrica Malfi, Daniela Tufano, Fabio Bozza