@inbook{ac488462a0194da2abcb45774a7bd0f3,
title = "Towards an optimal deep neural network for SOC estimation of Electric-Vehicle Lithium-ion battery cells",
abstract = "This paper has identified a minimal configuration of a DNN architecture and hyperparameter settings to effectively estimate SOC of EV battery cells. The results from the experimental work has shown that a minimal configuration of hidden layers and neurons can reduce the computational cost and resources required without compromising the performance. This is further supported by the number of epochs taken to train the best DNN SOC estimations model. Hence, demonstrating that the risk of overfitting estimation models to training datasets can also be reduced. This is further supported by the generalisation capability of the best model demonstrated through the decrease in error metrics values from test phase to those in validation phase.",
author = "Muhammad Anjum and Moizzah Asif and Jonathan Williams",
year = "2020",
month = nov,
day = "4",
doi = "10.1007/978-3-030-63916-7",
language = "English",
isbn = "978-3-030-63915-0",
series = "Springer Proceedings in Energy",
publisher = "Springer",
pages = "11--18",
editor = "Iosif Mporas and Kourtessis, {Pandelis } and Amin Al-Habaibeh and Abhishek Asthana and Vladimir Vukovic and John Senior",
booktitle = "Energy and Sustainable Futures",
address = "Germany",
edition = "1",
note = "The International Conference on Energy and Sustainable Futures (ICESF), ICESF ; Conference date: 10-09-2020 Through 11-09-2020",
}