Optimal Selection of Equivalence Factors for ECMS in Mild Hybrid Electric Vehicles

The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more ef- ficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Min- imization Strategy). Finding the best control parameters (equiv- alence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equiv- alence factors, for charging and discharging, by applying ge- netic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.