Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles

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ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.04.077
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2018
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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
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Publisher Copyright: © 2018 The Authors. Published by Elsevier B.V.
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Artificial neural network , Battery pack , Electric vehicles , State-of-charge , Artificial neural network , Battery pack , Electric vehicles , State-of-charge , General Computer Science , SDG 11 - Sustainable Cities and Communities , SDG 7 - Affordable and Clean Energy , Funding Info , This work has been partially financed by the Spanish Ministry of Economy and Competitiveness within the framework of the project DEMS: “Sistema distribuido de gestión de energía en redes eléctricas inteligentes (TEC2015-66126-R)". , This work has been partially financed by the Spanish Ministry of Economy and Competitiveness within the framework of the project DEMS: “Sistema distribuido de gestión de energía en redes eléctricas inteligentes (TEC2015-66126-R)".
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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