A Multi-Objective Power Component Optimal Sizing Model for Battery Electric Vehicles 2021-01-0724

With recent advances in electric vehicles, there is a plethora of powertrain topologies and components available in the market. Thus, the performance of electric vehicles is highly sensitive to the choice of various powertrain components. This paper presents a multi-objective optimization model that can optimally select component sizes for batteries, supercapacitors, and motors in regular passenger battery-electric vehicles (BEVs). The BEV topology presented here is a hybrid BEV which consists of both a battery pack and a supercapacitor bank. Focus is placed on optimal selection of the battery pack, motor, and supercapacitor combination, from a set of commercially available options, that minimizes the capital cost of the selected power components, the fuel cost over the vehicle lifespan, and the 0-60 mph acceleration time. Available batteries, supercapacitors, and motors are from a market survey. The considered lifespan is taken as 10 years, and the traveling distance is estimated at 50.9 miles per day using a combination of standard driving cycles. The resulting optimization problem is solved with the help of a quasi-static powertrain model which is developed using MATLAB/Simulink. A Genetic Algorithm is used to find the optimal solution in the case study. Normalized weighting factors are given to help users meeting their preferred performance during the power component design. Battery packs in the case study are chosen from LiFePO4 18650 cells with total capacity up to 100 kWh. Seven available types of supercapacitors along with 6 popular motors are also included in the design options. Two samples of the design results are compared to analyze the relevant tradeoff between performance indicators and cost.