Computationally Efficient Li-Ion Battery Aging Model for Hybrid Electric Vehicle Supervisory Control Optimization Clemson-ICAR

Format:

Conference/Event

Author/Creator:

Zhang, Zhang, author.

Contributor:

Filipi, Zoran

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

This paper presents the development of an electrochemical aging model of LiFePO4-Graphite battery based on single particle (SP) model. Solid electrolyte interphase (SEI) growth is considered as the aging mechanism. It is intended to provide both sufficient fidelity and computational efficiency required for integration within the HEV power management optimization framework. The model enables assessment of the battery aging rate by considering instantaneous lithium ion surface concentration rather than average concentration, thus enhancing the fidelity of predictions. In addition, an approximate analytical method is applied to speed up the calculation while preserving required accuracy. Next, this aging model are illustrated two applications. First is hybrid electric powertrain system model integration and simulation. The SP model with analytical solutions is compared with two book-end models, namely the SP model with finite difference method (FDM) on the high-fidelity side, and the average solution on the low computational power side. The result shows that SP model with approximate analytical solutions matches well FDM's prediction, and only has a slight penalty on the computation load comparing with the average solution. Second, the SP model with approximate analytical solution is applied to supervisory control optimization, and the tradeoff between minimizing fuel consumption and active lithium ion loss is studied under different battery temperature

Notes:

Vendor supplied data

Publisher Number:

2017-01-0274

Access Restriction:

Restricted for use by site license