Cost and fuel-optimal selection of HEV topologies using Particle Swarm Optimization and Dynamic Programming

In this paper, we apply a methodology to select the cost and fuel-optimal topology of a set of topologies for hybrid electric vehicles (HEVs). For each topology, the optimal component sizes are determined by optimizing a weighted sum of fuel consumption and powertrain costs. Vehicle performance constraints are imposed to ensure a minimum level of drivability. The constrained optimization problem is solved using Particle Swarm Optimization (PSO), and deterministic Dynamic Programming (DP) is used to calculate the optimal fuel consumption. The methodology is applied to a torque-assist and a full-parallel HEV with each three degrees of freedom, i.e. the size of the engine, the motor and the battery. Moreover, we consider two driving cycles: the New European Driving Cycle (NEDC) and a driving cycle with considerable elevation changes. We found that in case of the NEDC, the sizing problem can be reduced to one degree of freedom, whereas in case of the other driving cycle, all three degrees of freedom are required. The latter observation is related to the relative importance of the maximum battery capacity compared with the maximum battery power, but also to the size of the electric motor maximizing energy recuperation.