Development of an optimal energy management strategy for a Jetta MK5 hybrid vehicle

Abstract

This thesis employs MATLAB/Simulink/Simscape for a simulation analysis, aiming to determine the optimal fuel efficiency for parallel hybrid electric vehicles. The simulation model integrates theoretical frameworks and actual data to precisely assess hybrid powertrain performance across diverse driving conditions. Model accuracy is verified by comparing simulation results with experimental data. The thesis shows the impact of key parameters such as battery capacity, electric motor power, engine specifications, vehicle body, and driving cycles on hybrid electric vehicle fuel economy. The findings suggest that parallel hybrid electric vehicles can achieve notably high fuel efficiency. The thesis proposes a dynamic method for developing the powertrain of parallel-hybrid cars, incorporating real-world measurements and a genetic algorithm to optimize Proportional Integral Derivative parameters for synchronous motor regulation.