Simulation Framework for the Optimization of HEV Design Parameters: Incorporating Battery Degradation in a Lifecycle Economic Analysis. Issue 15 (2015)

• Record Type: Journal Article
• Authors: Vora, Ashish P.; Jin, Xing; Hoshing, Vaidehi; Guo, Xiaofan; Shaver, Gregory; Tyner, Wallace; Holloway, Eric; Varigonda, Subbarao; Kupe, Joachim
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 15(2015)
• Page Start: 195
• Page End: 202
• Publication Date: 2015
• Subjects: Hybrid electric vehicles -- powertrain simulation -- battery life -- design optimization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.10.028
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The optimal design of hybrid electric vehicle powertrains from a systems perspective is critical to realize the maximum benefits of hybridization for a given application, especially in the heavy-duty vehicle space due to the large number of unique applications. This paper proposes a novel framework that enables parametric design optimization of hybrid electric vehicles while accounting for the degradation of the electric battery and its impact over the lifecycle of the vehicle. This framework captures the impact of battery degradation on fuel consumption and battery replacement over the vehicle life by integrating a battery model capable of predicting degradation, and degraded performance, into the drivecycle simulation. These results are incorporated into a lifecycle economic analysis that enables the use of specific economic metrics (including net present value, payback period, and internal rate of return) as optimization objectives. To demonstrate the framework, the electric motor and battery sizes are optimized for a North American transit bus application. The results show that the optimal component sizes depend on the metric of interest, i.e. different optimum parameter sets are obtained when the objective is different. Further, these optimum parameter sets are different if the objective is simply the "day 1" fuel consumption. For example, while optimizing for fuel consumption leads to selection of the largest available battery pack and electric motor, optimizing for payback period leads to the selection of a smaller battery back. Lastly it was also observed that the fuel consumption increases by up to 10% from "day 1 " to End-of-Life of the battery. These results highlight the utility of the proposed framework in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades.