Multi-objective Optimal Sizing and Energy Management of Hybrid Energy Storage System for Electric Vehicles

Hybrid energy storage system (HESS) composed of lithium-ion battery and supercapacitors has been recognized as one of the most promising solutions to face against the high cost, low power density and short cycle life of the battery-only energy storage system, which is the major headache hindering the further penetration of electric vehicles. In this work, the HESS sizing and energy management problem of an electric race car is investigated as a case study to improve the driving mileage and battery cycle life performance. Compared with the existing research, the distinctive features of this work are: (1) A dynamic model and a degradation model of the battery are employed to describe the dynamic behavior and to predict the cycle life of the battery more precisely; (2) Considering the fact that the design and control problems are coupled in most cases, in order to achieve a global optimal design solution and an implementable real-time energy management system, a Bi-level multi-objective sizing and control framework based on non-dominated sorting genetic algorithm-II and fuzzy logic control (FLC) is proposed to size the HESS and to optimize the membership functions of a FLC based EMS at the same time; (3) In order to improve the optimization efficiency, a vectorized fuzzy inference system which allows large scale of fuzzy logic controllers operating in parallel is devised. At last, the Pareto optimal solutions of different HESSs are obtained and compared to show the achieved enhancements of the proposed Bi-level optimal sizing and energy management framework.