Optimization of Powertrain Energy Management for Range Extended Electric Vehicle Using Modified Particle Swarm Algorithm

Abstract

Abstract Energy and Power Management Strategies play vital role in improving efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used is the given system. A mathematical model for hybrid powertrain of Range Extended Electric Vehicle (REEV) has been developed in this study and is further optimized to reduce the level of fuel consumption and NOx emissions individually by optimizing the control strategy of Power Management System (PMS). A modified Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the optimum PMS strategy. In performing these optimizations, the control signal consisting of genset speed and power for a full 2-hour cycle was used as the controllable decision parameters input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours giving slightly more than 2 minutes per point, noting that the values used in the models were interpolated between the points for each time step. With the control signal consisting of 2 distinct signals viz. speed and power, as 50 element time variant signals, a 100-D problem was formulated for the optimizer. The developed algorithms were simulated on the REEV model on MATLAB/SIMULINK platform. Simulation results show that the fuel consumption reduction of 12% and NOx emission reduction of 35% was achieved individually by deploying the optimal PMS strategy when compared with the baseline results.