A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems

Abstract

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.