Jiménez Bermejo, DavidFraile Ardanuy, JesúsCastaño Solís, SandraMerino, JuliaÁlvaro Hermana, Roberto2026-05-062026-05-062018Jiménez-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.2018.04.07710.1016/j.procs.2018.04.077https://hdl.handle.net/20.500.14454/5872Ponencia 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 2018Due 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.eng© 2018 The AuthorsArtificial neural networkBattery packElectric vehiclesState-of-chargeconference paper2026-05-061877-0509