Data-Driven Prediction of Li-Ion Battery Degradation Using Predicted Features

For their emergent application in electric vehicles, the development of fast and accurate algorithms to monitor the health status of batteries and aid decision-making in relation to maintenance and replacement is now of paramount importance. Data-driven approaches are preferred due to the difficulties associated with defining valid models for system and parameter identification. In recent years, the use of features to enhance data-driven methods has become commonplace. Unless the data sets are from multiple batteries, however, such approaches cannot be used to predict more than one cycle ahead because the features are unavailable for future cycles, in the absence of different embedding strategies. In this paper, we propose a novel approach in which features are predicted for future cycles, enabling predictions of the state of health for an arbitrary number of cycles ahead, and, therefore, predictions for the end-of-life. This is achieved by using a data-driven approach to predict voltage and temperature curves for future cycles, from which important signatures of degradation can be extracted and even used directly for degradation predictions. The use of features is shown to enhance the state-of-health predictions. The approach we develop is capable of accurate predictions using a data set specific to the battery under consideration. This avoids the need for large multi-battery data sets, which are hampered by natural variations in the performance and degradation of batteries even from the same batch, compromising the prediction accuracy of approaches based on such data.

Automated summary

The development of fast and accurate algorithms for monitoring the health status of batteries and aiding decision-making in maintenance and replacement is crucial for their application in electric vehicles. Data-driven approaches are preferred due to difficulties in defining valid models for system and parameter identification. This paper proposes a novel approach that predicts features for future cycles, enabling predictions of the state of health and end-of-life for an arbitrary number of cycles.