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- W4387677132 abstract "The era of battery powered automotive mobility is picking up pace. There have been several electrification initiatives undertaken by countries across the globe to surmount rising pollution levels. Lithium-ion Electric Vehicle battery packs continue to be a leading source of energy for most of the on-road Electric Vehicles. A key scientific goal among battery researchers is precise inference of State-of-Charge metrics of such storage elements. State-of-Charge helps estimate the range of a vehicle. High-quality battery parameter data is vital to strengthen the current momentum of transportation electrification. As competition among battery pack and Electric Vehicle manufacturers increases over time, confidentiality clauses have prevented access of high-quality battery measurement data to researchers. Lack of data has hindered improvements in developing meticulous State-of-Charge algorithms. In this paper we employ a sparse self-attention approach to produce synthetic battery parameter data. We release the code of our methodology in the results section of the paper to enable reproduction of our key findings." @default.
- W4387677132 created "2023-10-17" @default.
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- W4387677132 date "2023-09-21" @default.
- W4387677132 modified "2023-10-17" @default.
- W4387677132 title "Extended Battery Parameter Synthetic Data Generation Using Sparse Attention Mechanism" @default.
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- W4387677132 doi "https://doi.org/10.1109/wintechcon58518.2023.10276501" @default.
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