RT Conference Proceedings
T1 Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles
A1 Jiménez-Bermejo, David
A1 Fraile-Ardanuy, Jesús
A1 Castaño-Solis, Sandra
A1 Merino, Julia
A1 Alvaro-Hermana, Roberto
AB Due to urban pollution, transport electrification is being currently promoted in different countries. Electric Vehicles (EVs) sales are growing all over the world, but there are still some challenges to be solved before a mass adoption of this type of vehicles occurs. One of the main drawbacks of EVs are their limited range, for that reason an accurate estimation of the state-of-charge (SOC) is required. The main contribution of this work is the design of a Nonlinear Autoregressive with External Input (NARX) artificial neural network to estimate the SOC of an EV using real data extracted from the car during its daily trips. The network is trained using voltage, current and four different battery pack temperatures as input and SOC as output. This network has been tested using 54 different real driving cycles, obtaining highly accurate results, with a mean squared error lower than 1e-6 in all situations
SN 1877-0509
YR 2018
FD 2018
LA eng
NO Jiménez-Bermejo , D , Fraile-Ardanuy , J , Castaño-Solis , S , Merino , J & Alvaro-Hermana , R 2018 , ' Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles ' , Procedia Computer Science , vol. 130 , pp. 533-540 . https://doi.org/10.1016/j.procs.2018.04.077
NO Publisher Copyright: © 2018 The Authors. Published by Elsevier B.V.
DS TECNALIA Publications
RD 1 jul 2024