A Mobility Performance Assessment on Plug-in EV Battery

Seyed Mohammad Rezvanizaniani, Yixiang Huang, Chuan Jiang, and Jay Lee
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Technical Brief
AttachmentSizeTimestamp
ijphm_12_011.pdf561.74 KBFebruary 28, 2013 - 4:33pm

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.

Publication Year: 
2012
Publication Volume: 
3
Publication Control Number: 
011
Page Count: 
8
Submission Keywords: 
Battery SoC
Mobility
Road condition
driving behavior
recurrent neural networks
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
  
 
 
 

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