GA AND WOA-BASED OPTIMIZATION FOR ELECTRIC POWERTRAIN EFFICIENCY

This study presents an optimum vehicle architecture along with a design methodology that optimizes the motor power, battery capacity, and propulsion ratio for two different driving profiles using Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA). A virtual electric vehicle model was created in MATLAB/Simulink and validated with real-world driving. The vehicle architecture was optimized with GA and WOA based on the cost, range, gradability, and maximum speed outputs obtained from the "hybrid driving" and "urban driving" behaviours. According to the results obtained in the study, it was found that GA optimization can create a vehicle architecture suitable for long-distance and high-performance driving and can provide shorter optimization times. On the other hand, it was seen that WOA optimization can create vehicle architectures with lower costs, higher maximum speeds, and improved gradability in urban driving. It was found that e-motor 4 and battery 2 can provide the most optimum vehicle architecture solution on a component basis.