Using dynamic neural networks for battery state of charge estimation in electric vehicles
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Fecha
2018
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Elsevier B.V.
Resumen
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.
Palabras clave
Artificial neural network
Battery pack
Electric vehicles
State-of-charge
Battery pack
Electric vehicles
State-of-charge
Descripción
Ponencia presentada en la 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018) / 8th International Conference on Sustainable Energy Information Technology (SEIT-2018), celebradas conjuntamente en Oporto, Portugal, entre el 8 y el 11 de mayo de 2018
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Cita
Jiménez-Bermejo, D., Fraile-Ardanuy, J., Castaño-Solis, S., Merino, J., & Álvaro-Hermana, R. (2018). Using dynamic neural networks for battery state of charge estimation in electric vehicles. Procedia Computer Science, 130, 533-540. https://doi.org/10.1016/J.PROCS.2018.04.077