A Study on Optimizing SHEV Components Specifications and Control Parameter Values for the Reduction of Fuel Consumption by Using a Genetic Algorithm

For a series hybrid electric vehicle (SHEV), the electric motor is responsible for driving the wheels, while the engine drives the only generator to provide electricity. SHEVs set a control strategy to make the engine run near the fixed operating point with high thermal efficiency, thereby effectively reducing fuel consumption. The powertrain system of HEV is more complex than that of a conventional drive system using only an internal combustion engine, and it is time-consuming to obtain the optimal components specification values and control parameters. Therefore, automatic optimization methods are required nowadays. We used Genetic Algorithm (GA) as the optimization method and optimize powertrain specifications and control parameter values to reduce fuel consumption. The results show that it is an effective optimization method. In this research, we use a SHEV model constructed in MATLAB/Simulink and optimize the motor maximum torque, the capacity of the battery, and the control parameter values for starting and stopping the engine. Then, the degree of influence of each optimized parameter on the fuel consumption is analyzed. The components which have high sensitivity on fuel consumption are the battery capacity, the motor maximum torque and thresholds value for engine stop. As a result of optimization by GA, fuel consumption was reduced by 1.1% compared to the baseline. We are also verifying whether manual calibration of parameters or GA is the more efficient method. Manual calibration of parameters means setting all possible combinations of parameters, calculating the fuel consumption value for each combination and finding the minimum value. As it shows in the result, optimization can be achieved in less time with GA.