Title: Trip-based stochastic prediction of battery state-of-charge for electric vehicles

Authors: Nitin Ranjan; Yaoyu Li

Addresses: Elution, Inc., N14 W23833 Stone Ridge Dr., Suite #400, Waukesha, WI 53188, USA. ' Department of Mechanical Engineering, University of Texas at Dallas, 800 W. Campbell Rd., Richardson, TX 75080, USA

Abstract: For electric vehicle (EV) operation, a major concern is whether the available on-board battery charge could sustain a specific trip or not. It is of practical benefit for EV driver to predict the battery energy demand for a specific trip a prior. This paper presents a trip specific scheme for estimating the battery state-of-charge (SOC) change based on the trip modelling in a probabalistic fashion. Assuming an approximate constant-acceleration model for trip segments, there are cases of accelerating, constant speed and decelerating segments. The distribution density functions of the segmental acceleration and mean speed are estimated from test driving cycle data. The stochastic characteristics of the SOC change for a specific trip is then obtained via a Monte Carlo type method. For an example trip in the greater Milwaukee area, simulation results show that for the example trip, the SOC change is 33.4% ± 6.4% with 95% confidence.

Keywords: electric vehicles; EVs; battery state-of-charge SOC determination; stochastic modelling; trip modelling; Monte Carlo simulation; on-board batteries; battery charging.

DOI: 10.1504/IJEHV.2011.044371

International Journal of Electric and Hybrid Vehicles, 2011 Vol.3 No.4, pp.353 - 368

Received: 03 Sep 2011
Accepted: 07 Nov 2011
Published online: 22 Dec 2011 *

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