Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sharma, Shivi; S.S., Neetha; Arya, Pranav; Prakash, Chandra Title A name given to the resource Predicting electric vehicle performance metrics using a convolution neural network-gated recurrent unit-attention based deep learning architecture Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Next Energy;Volume;11;Issue;;Article No.;100514; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.nxener.2026.100514" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.nxener.2026.100514</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028945893?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028945893?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma S., Computer Science Engineering Department, Maharishi Markandeshwar University (MMU), Haryana, Mullana, India; S.S. N., Department of Computer Science, Christ University, Bangalore, India; Arya P., Department of Artificial Intelligence &amp; Machine Learning, School of Engineering &amp; Technology, IIMT University, Meerut, India; Prakash C., Amity School of Engineering and Technology, Amity University Punjab, Punjab, Mohali, India Description An account of the resource The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods. 2026 The Authors Subject The topic of the resource Charge demand; EV performance; RUL; SOC Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 2949821X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online