Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, incorporates novel concepts such as Vector concept Seeded concept, has been developed minimize fuel consumption NOx emissions for an extended-range vehicle (EREV). An optimization problem formulated battery state charge (SOC) trajectory over entire driving cycle, vector size 50, be optimized via control lever consisting 50 engine/generator speed points spread same 2 h cycle. Thus, particle consisted SOC trajectory, having elements, points, resulting in 100-D problem. To improve convergence PSO, seeding particles was introduced. Additionally, further improvements were accomplished by adapting Time-Varying Acceleration Coefficients (TVAC) PSO Frankenstein’s features particles. MATLAB/SIMULINK platform used validate commercial model against similar ADVISOR using standard rule-based PMS algorithm. validated then simulation developed, modified algorithms through multi-objective strategy weighted sum fitness function. Simulation results show reduction 12% emission 35% achieved individually deploying algorithms. When applied, simultaneous 9.4% 7.9% when compared baseline with