Academic Journals Database
Disseminating quality controlled scientific knowledge

A Mobility Performance Assessment on Plug-in EV Battery

ADD TO MY LIST
 
Author(s): Seyed Mohammad Rezvanizaniani | Yixiang Huang | Chuan Jiang | Jay Lee

Journal: International Journal of Prognostics and Health Management
ISSN 2153-2648

Volume: 3;
Issue: 2;
Start page: 39;
Date: 2012;
Original page

Keywords: Battery SoC Mobility Road condition driving behavior recurrent neural network

ABSTRACT
This paper deals with mobility prediction of LiFeMnPO_4 batteries for an emission-free Electric Vehicle. The data-driven model has been developed based on empirical data from two different road types –highway and local streets –and two different driving modes – aggressive and moderate. Battery State of Charge (SoC) can be predicted on any new roads based on the trained model by selecting the drving mode. In this paper, the performance of Adaptive Recurrent Neural Network (ARNN) and regression is evaluated using two benchmark data sets. The ARNN model at first estimates the speed profile of the new road based on slope and then both slope and speed is going to be used as the input to estimate battery current and SoC. Through comparison it is found that if ARNN system is appropriately trained, it performs with better accuracy than Regression in both two road types and driving modes. The results show that prediction SoC model follows the Columb-counting SoC according to the road slope.

Tango Jona
Tangokurs Rapperswil-Jona

     Affiliate Program