Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information. (15th May 2020)

• Record Type: Journal Article
• Authors: Wang, Yue; Zeng, Xiaohua; Song, Dafeng
• Is Part Of: Energy. Volume 199(2020)
• Publication Date: 2020-05-15
• Subjects: Power-split hybrid electric bus -- Driving information data mining -- Hierarchical optimization -- Intelligent energy management strategy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117499
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Although hybrid electric buses (HEB) has fixed route condition, there are differences upon daily driving conditions affected by traffic, weather and so on. Thus, the rule-based strategy is difficult to get the best energy-saving result under this circumstance. To improve strategy adaptability and optimality, the paper presents a hierarchical optimization control strategy based on driving information. Driving information is first deeply explored and utilized from the history and future two dimensions, including typical cycle construction and future driving prediction. The upper control strategy adopts global optimization to plan SOC trajectory by using typical cycle construction from the overall perspective, determining the proportions of electric and hybrid modes, and realizing the rational use of electric energy. Low-level control realizes real-time optimal torque distribution based on the prediction of driving condition, which adapts to different driving conditions from the local real-time perspective. Finally, simulation and hardware-in-loop tests are performed under an actual bus route. In contrast to rule-based strategy, the hierarchical optimal intelligent strategy nearly achieves the global optimization results with 9.02% fuel efficiency. Therefore, the proposed optimization strategy improves the driving condition adaptability and fuel economy of fixed-route HEBs from global and local dimensions. Highlights: Hierarchical optimal intelligent energy management strategy based on driving information is proposed. Driving information data mining is developed from history and future two dimensions. An innovative intelligent algorithm is established for the driving condition prediction model. Optimal soc trajectory based on global optimization control strategy is designed.