FORECASTING TIMESERIES ANALYSIS FOR ELECTRICAL VEHICLE BATTERYSTATE UTILIZING A HYBRID TRANSFORMER ARCHITECTURE

Abstract: technologies and techniques for controlling an electrical system via battery forecasting for an electrical system. time series data is transformed into a series of battery image data including a multi-channel image representing a plurality of battery characteristics and/or battery usage characteristics derived from the time series battery data. an image vector is generated for each respective battery image data of the series, and each image vector is transformed via a first portion of a transformer architecture for sequential data processing and positional encoding. contextual information is extracted from the transformed image vectors, where each image vector is transformed via a second portion of the transformer architecture using learned weights from the first portion to generate a forecast of future battery characteristics and/or battery usage characteristics. a control command is generated for task-specific processing to modify operation of the electrical system, based on the forecast.

Inventor(s): Saeth Wannasuphoprasit, Gianina Alina Negoita, William A. Paxton

CPC Classification: G06F1/3212 (Means for saving power)

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