Data-driven snapshot methods leveraging data fusion to estimate state of health for maritime battery systems

Abstract

The number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of these ships, it is important to monitor the capacity that can be stored in the batteries, and classification societies typically require that this can be verified by independent tests—annual capacity tests. However, this paper discusses data-driven alternatives based on operational sensor data collected from the batteries. There are different strategies for such data-driven state of health (SOH) estimation. Some approaches require full operational history of the batteries in order to predict SOH, and this may be impractical due to several reasons. Thus, methods that are able to give reliable estimation of SOH based on only snapshots of the data streams are more attractive from a practical point of view. In this paper, data-driven snapshot methods are explored and applied to degradation data from battery cells cycled in different laboratory tests. Hence, data from different sources are fused together with the aim of achieving better predictions. The paper presents the battery data show how relevant features can be extracted from snapshots of the data and presents data-driven models for SOH estimation. It is discussed how such methods could be utilized in a data-driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for nearly 40% of the tested cells, although the fusion of data from different laboratory tests did not improve results significantly. Results are greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available.