Real time energy management strategy for a fast charging electric urban bus powered by hybrid energy storage system. (1st October 2016)

• Record Type: Journal Article
• Authors: Yu, Huilong; Tarsitano, Davide; Hu, Xiaosong; Cheli, Federico
• Is Part Of: Energy. Volume 112(2016)
• Page Start: 322
• Page End: 331
• Publication Date: 2016-10-01
• Subjects: Energy management strategy -- Hybrid energy storage system -- Fuzzy logic controller -- Genetic algorithm -- Model in the loop; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.06.084
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this paper, an innovative real time energy management strategy design approach is proposed for a fast charging electric urban bus with hybrid energy storage system composed of conventional batteries and supercapacitors. After modeling, a multi-objective optimization problem taking into account cycle life of the battery, total energy consumption and specific requirement that minimizing the use of battery is formulated. A quantifiable evaluation model is firstly derived to evaluate different kinds of strategies. Then a conventional fuzzy logic control based energy management strategy with features of intelligence and adaptability is proposed, but the simulation result shows that even after long time tuning it can not achieve the desired result with the manual set membership functions. Thereafter, an optimal energy management strategy based on dynamic programming is developed as a benchmark to see the room for improvement. Finally, an innovative model in the loop optimization approach based on genetic algorithm is proposed to optimize the membership functions of the conventional fuzzy logic based energy management strategy. Simulation results demonstrate that the overall performance of optimized fuzzy logic based energy management strategy can be improved significantly and can even approach the optimal results of dynamic programming. Highlights: Evaluation model include battery cycle life, efficiency and specific requirement. Conventional fuzzy logic and dynamic programming based strategies. Model in the loop multi-objective optimization of the member functions. Genetic algorithm optimized fuzzy logic energy management strategy.