Paper Info
Title
Torque-leveling Threshold-changing Rule-based Control for Parallel Hybrid Electric Vehicles
Abstract
A novel rule-based control strategy is proposed for the energy management of parallel hybrid electric vehicles (HEVs): the torque-leveling threshold-changing strategy (TTS). In contrast to the most commonly used heuristic electric assist control strategy (EACS) that is designed based on the load following approach, the TTS proposes and applies the new fundamental concept of torque leveling. This mechanism operates the engine with a constant torque when the engine is active, thus ensuring the engine works at an efficient operating point. The TTS additionally extends and uses a design concept that has previously been proposed in the context of series HEVs, the threshold-changing mechanism, to operate the HEV in a charge-sustaining manner. By exploiting this new set of design principles for parallel HEVs, the TTS realizes energy source control sharing behavior that is reminiscent to optimization-based methods. To show its effectiveness, the TTS is implemented to a through-the-road (TTR) HEV and benchmarked against two conventional control strategies: dynamic programming (DP) and the EACS. The results show that the TTS, despite its simplicity, is able to deliver comparable fuel economy as the global optimization approach DP and thus achieve significant improvement compared to the EACS. In addition, to facilitate real-time application, a simplified version of the TTS (STTS) is also developed, which is able to deliver similar performance as the TTS but is more simple to implement in practice.
Year
DOI
Venue
2019
10.1109/tvt.2019.2916720
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
Engines,Hybrid electric vehicles,Torque,Batteries,Ice,Fuels,Wheels
Energy management,Dynamic programming,Rule-based system,Heuristic,Torque,Global optimization,Computer science,Operating point,Electronic engineering,Control engineering,Energy source
Journal
Volume
Issue
ISSN
68
7
0018-9545
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
xuefang li1182.74
Simos A. Evangelou2710.22