A Comparative Analysis of Route‐Based Energy Management Systems for Phevs. Issue 1 (29th July 2015)

• Record Type: Journal Article
• Authors: Taghavipour, Amir; Vajedi, Mahyar; Azad, Nasser L.; McPhee, John
• Is Part Of: Asian journal of control. Volume 18:Issue 1(2016)
• Page Start: 29
• Page End: 39
• Publication Date: 2015-07-29
• Subjects: Plug‐in hybrid electric vehicle, energy management system, model predictive control, adaptive equivalent consumption minimization strategy; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1191
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: Plug‐in hybrid electric vehicle (PHEV) development seems to be essential step on the path to widespread deployment of electric vehicles (EVs) as the zero‐emission solution for the future of transportation. Because of their larger battery pack in comparison to conventional hybrid electic vehicles (HEVs), they offer longer electric range which leads to a superior fuel economy performance. Advanced energy management systems (EMSs) use vehicle trip information to enhance a PHEV's performance. In this study, the performance of two optimal control approaches, model predictive control (MPC) and adaptive equivalent consumption minimization strategy (A‐ECMS), for designing an EMS for different levels of trip information are compared. The resulting EMSs are fine‐tuned for the Toyota Prius plug‐in hybrid powertrain and their performances are evaluated by using a high‐fidelity simulation model in the Autonomie software. The results of simulation show that both MPC and A‐ECMS can approximately improve fuel economy up to 10% compared to the baseline Autonomie controller for EPA urban and highway drive cycles. Although both EMSs can be implemented in real time, A‐ECMS is 15% faster than MPC. Moreover, it is shown that the engine operating points are more sensitive to the battery depletion pattern than to different driving schedules.