Open AccessEvaluation of battery modules state for electric vehicle using artificial neural network and experimental validation
Author(s) -
Liang Xinyu,
Bao Nengsheng,
Zhang Jian,
Garg Akhil,
Wang Shuangxi
Publication year - 2018
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.214
Subject(s) - battery (electricity) , artificial neural network , state of charge , computer science , electric vehicle , state of health , voltage , state (computer science) , power (physics) , automotive engineering , artificial intelligence , algorithm , engineering , electrical engineering , physics , quantum mechanics
Abstract This work undertakes research problem on prediction of state of battery modules used in electric vehicle. Past studies focussed extensively on the estimation of state of charge and state of health of a single cell/battery. However, the focus on the estimation of state of entire battery module is hardly studied. During the actual operation of electric vehicle, the environmental conditions (road slope and climate) and factors such as abnormal voltage and temperature conditions causes the deviations among the battery modules from its equilibrium state. As a result, its power efficiency and the life‐cycle decreases. One of the reasons of deviation is the defects in manufacturing of a battery module. For this purpose, the evaluation and estimation of state of battery modules is a priority to have information on which module among the given ones should be discharged first. Therefore, this study proposes the methodology for the evaluation of state of battery modules based on its current and temperature. Firstly, the rules are defined based on experiments to determine the priority for the discharge of each of the six modules. Dataset of 6000 samples obtained comprises of the 12 inputs (temperature and current) and 6 outputs (on‐off state of switch). Each output represents the priority of module to be discharged. To analyze this multi‐input multi‐output dataset, the popular artificial intelligence algorithm namely, artificial neural network with three training algorithms (Levenberg, Scaled conjugate and Bayesian regularization), using a number of neurons from 2 to 20 in the hidden layer is used. It was found that the model obtained using Levenberg algorithm performs the best.