Dimensioning and Power Management of Hybrid Energy Storage Systems for Electric Vehicles with Multiple Optimization Criteria

TL;DR

This paper presents a bi-level multi-objective framework using genetic algorithms and fuzzy logic control to optimize the size and power management of hybrid energy storage systems in electric vehicles, enhancing efficiency and performance.

Contribution

It introduces a novel combined optimization approach with a vectorized fuzzy inference system for large-scale, real-time control of hybrid energy storage systems in EVs.

Findings

Pareto optimal solutions demonstrate improved system performance.

The vectorized fuzzy inference system increases optimization efficiency.

Enhanced hybrid energy storage system designs extend battery cycle life.

Abstract

Hybrid energy storage systems that combine lithium-ion batteries and supercapacitors are considered as an attractive solution to overcome the drawbacks of battery-only energy storage systems, such as high cost, low power density, and short cycle life, which hinder the popularity of electric vehicles. A properly sized hybrid energy storage system and an implementable real-time power management system are of great importance to achieve satisfactory driving mileage and battery cycle life. However, dimensioning and power management problems are quite complicated and challenging in practice. To address these challenges, this work proposes a Bi-level multi-objective design and control framework with the non-dominated sorting genetic algorithm-II and fuzzy logic control as key components, to obtain an optimal sized hybrid energy storage system and the corresponding optimal real-time power management system based on fuzzy logic control simultaneously. In particular, a vectorized fuzzy inference system is devised, which allows large-scale fuzzy logic controllers to run in parallel, thereby improving optimization efficiency. Pareto optimal solutions of different hybrid energy storage systems incorporating both optimal design and control parameters are obtained and compared to show the achieved enhancements of the proposed approach.