Transformer-Based Time-Series Algorithms for Battery Health Analysis and Related Methods, Software, and Systems

Abstract

Methods of analyzing battery health using deep-learning models based on transformer architecture. The methods can be used to determine, for example, battery state of charge (SOC), state of health (SOH), or remaining useful life (RUL), or any combination thereof. In some embodiments, portions of multivariate battery data, such as capacity, energy, time, temperature, voltage, current, etc., input into a model of the present disclosure are randomly masked and subsequently reconstructed by the model to learn contextual information and multivariate interaction. In some embodiments, the model employs self-attention mechanisms to train without explicit labeled data and estimated SOC, SOH, and/or RUL. In some embodiments, the model is applied to various downstream tasks like anomaly detection, SOX estimation, and/or RUL prediction, among others, using a flexible adaptor that is independent of the pretrained model. Related software and systems are also disclosed.

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