Development of a Control Strategy for Complex Light-Duty Diesel-Hybrid Powertrains

Hybrid Electric Vehicles (HEVs) represent a powerful technology to save fuel and reduce CO2 emissions, through the synergic use of a conventional internal combustion engine and one or more electric machines. However their performance strongly depends on the control strategy that shares the power demand among the engine and the electric motors at each time instant, with the objective of minimizing a pre-defined cost function over an entire driving cycle, and satisfying, at the same time, any additional constraints. The aim of this work is therefore the definition of a methodology to develop, through numerical simulation, a sub-optimal hybrid powertrain controller: starting from the problem definition, the ideal performance for a case study hybrid architecture was analyzed through a global optimization algorithm in order to point out information which can be used to define new control laws. Coupling these information with an approach based on the instantaneous minimization of a cost function, a sub-optimal energy management system was then developed trying to merge the strength of global optimization algorithm with the low computational requirements of heuristic strategies. Finally, the powertrain controller previously developed was implemented in a detailed vehicle model and tested through numerical simulations over different driving cycles in order to compare its performance with the upper bound set by the results achieved by the global optimization algorithm.