Development of energy management system based on a rule-based power distribution strategy for hybrid power sources. (15th May 2019)

• Record Type: Journal Article
• Authors: Wang, Yujie; Sun, Zhendong; Chen, Zonghai
• Is Part Of: Energy. Volume 175(2019)
• Page Start: 1055
• Page End: 1066
• Publication Date: 2019-05-15
• Subjects: Hybrid power source system -- Energy management system -- Power distribution strategy -- Power capability prediction; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.03.155
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The proton exchange membrane fuel cell has become a good candidate for the power source of future green transportations due to its excellent performance of high efficiency and cleanness. To overcome the slow dynamic characteristic, the fuel cells are always grouped with other energy storage devices such as lithium-ion batteries and supercapacitors. The development of energy management system and design of the power distribution strategy are critical issues for the hybrid power source system. In previous work, few studies have comprehensively considered the criteria of power capabilities of the batteries and supercapacitors which are intimately correlated with the power requirements of accelerating, gradient climbing and regenerative braking. The development of power distribution strategy considering the constraints of the power capability is urgent and critical to the safety and longevity of the hybrid energy storage system. In this paper, a distributed energy management system is developed for the hybrid power source system based on a rule-based power distribution strategy. The presented power distribution strategy has comprehensively considered the criterias of the demand power, the remaining capacity and power capability of the hybrid power source system. Moreover, the Bayes Monte Carlo approach is employed for co-estimation of the remaining capacity and power capability of the batteries and supercapacitors. Compared with the existing rule-based strategy without considering the restrictions of the power capability, the presented strategy has better rationality in terms of fuel economy and dynamic property. The presented energy management strategy can extend the lifespan and improve the economy of the hybrid energy storage system by employing the charge and discharge limits of power capability and residual capacity. Highlights: The model framework is developed for a multi-sources hybrid system. A rule-based power distribution strategy with power prediction is presented. The Bayes approach is used for remaining capacity and power capability estimation. The fuel economy and dynamic property are analyzed with different driving cycles.