Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge
Prediction
- URL: http://arxiv.org/abs/2206.02555v1
- Date: Wed, 1 Jun 2022 15:31:06 GMT
- Title: Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge
Prediction
- Authors: Luca Biggio, Tommaso Bendinelli, Chetan Kulkarni, Olga Fink
- Abstract summary: We introduce a novel Transformer-based deep learning architecture which is able to simultaneously infer the ageing state from a limited number of voltage/current samples.
Our experiments show that the trained model is effective for input current profiles of different complexities and is robust to a wide range of degradation levels.
- Score: 2.670887944566458
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Electrochemical batteries are ubiquitous devices in our society. When they
are employed in mission-critical applications, the ability to precisely predict
the end of discharge under highly variable environmental and operating
conditions is of paramount importance in order to support operational
decision-making. While there are accurate predictive models of the processes
underlying the charge and discharge phases of batteries, the modelling of
ageing and its effect on performance remains poorly understood. Such a lack of
understanding often leads to inaccurate models or the need for time-consuming
calibration procedures whenever the battery ages or its conditions change
significantly. This represents a major obstacle to the real-world deployment of
efficient and robust battery management systems. In this paper, we propose for
the first time an approach that can predict the voltage discharge curve for
batteries of any degradation level without the need for calibration. In
particular, we introduce Dynaformer, a novel Transformer-based deep learning
architecture which is able to simultaneously infer the ageing state from a
limited number of voltage/current samples and predict the full voltage
discharge curve for real batteries with high precision. Our experiments show
that the trained model is effective for input current profiles of different
complexities and is robust to a wide range of degradation levels. In addition
to evaluating the performance of the proposed framework on simulated data, we
demonstrate that a minimal amount of fine-tuning allows the model to bridge the
simulation-to-real gap between simulations and real data collected from a set
of batteries. The proposed methodology enables the utilization of
battery-powered systems until the end of discharge in a controlled and
predictable way, thereby significantly prolonging the operating cycles and
reducing costs.
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