Big data generation platform for battery faults under real-world variances

There is an increasing demand for real-time data-driven fault diagnosis of lithium-ion batteries that can predict battery faults at an early stage to avoid safety issues and improve battery reliability. However, such prediction methods require large amounts of data, generally obtained through experiments or during the operation phase, resulting in substantial economic and time efforts. In this context, generating realistic battery pack data that covers all sensor values a battery management system receives, as well as including fault models, is of particular interest and can mitigate the need to perform extensive laboratory testing. This paper focuses on the systematic development of a data generation platform capable of simulating a large scale of battery packs with random battery faults and generating big data for the following battery fault diagnostics. Initially, the electrical, thermal, and aging modeling of a battery pack is performed. After this, four types of faults, namely hard short circuit, soft short circuit, abnormal internal resistance, and abnormal contact resistance, are modeled using equivalent circuit models. To generate realistic data, both cell-to-cell variations and pack-level variations are considered. Variations included are, for example, the manufacturing quality, temperatures, aging processes, road conditions, state of charge, and fault severity. By combining the battery pack models, fault models, and the different variations through Monte Carlo simulations, a large data set representing different packs with varying levels of inconsistencies is generated.