Generation of Synthetic Battery Data with Capacity Variation

Abstract

In this article an approach is presented based on the use of measured experimental data from conventional battery packs to generate {it synthetic} operational data for subsequent use in monitoring, predicting and controlling battery pack state of health. Generally speaking, experimentation-based synthetic data is effective in extensive simulation models possessing many varied operating conditions. The results presented in this article focus on proof of concept and are part of a comprehensive study into general capacity estimation and capacity fade prediction in battery packs. Experimental data is derived from scaled operational cycles with multiple charge and discharge pulses applied repetitively on a commercially available battery pack. The resulting synthetically generated data, using Markov chain approaches, has the flexibility of matching user-imposed conditions and can be of any length.Therefore, the focus in this article is the generation of sufficient training data for models built from machine learning techniques, utilizing only a relatively limited amount of actual data. In the context of the overall ongoing study, the behavior of the battery pack is characterized by features and a supervised learning approach is adopted in order to estimate capacity fade during real-time operation without the use of specific capacity tests.