Plug-in Hybrid Electric Vehicle Energy Management System using Particle Swarm Optimization

Plug-in Hybrid Electric Vehicles (PHEVs) are the new generation of automobiles that can run not only on the energy from gasoline but also that from an electric outlet stored in a battery pack. Hence, these vehicles can significantly reduce the consumption of gasoline by taking advantage of cheaper renewable and non renewable sources of energies available at the domestic electric outlet. Thus PHEVs can contribute significantly in reducing the overall green house gas emissions from automobiles. In this paper a simplified powertrain of power split PHEV is modeled. The main objective of the study is to increase the fuel economy of the PHEV. To achieve this goal, a gradient free optimization algorithm, namely “Particle Swarm Optimization (PSO)” technique, has been implemented using the aforementioned simplified model. An optimization problem is formulated with Equivalent Fuel Consumption Minimization (EFCM) as the main objective function along with some constraints to be satisfied. This problem is then solved using the PSO algorithm and the optimal energy management algorithms are finally run in Argonne National Lab’s simulation software PSAT. The simulation results are then compared with PSAT’s default control strategy which indicate significant improvements in fuel economy with the PSO optimized algorithms.