A new method to perform Lithium-ion battery pack fault diagnostics – Part 1: Algorithm development and its performance analysis

Abstract

The successful integration of Li-ion batteries in the automotive industry paved the way for their integration into a new generation of aircraft with zero emission. However, potential hazards associated with Li-on batteries are a major concern for the safety-critical aerospace sector. Removing or reducing the hazards from Li-ion batteries while preferable, requires fundamental changes to the battery chemistry. Another way to address this is the implementation of fault diagnostics and prognostics for forecasting or detecting the presence of faults before an aircraft is airborne. This article, part of a three-part series, presents a novel algorithm exploiting the data collected while charging a battery pack for diagnosing faults. The algorithm works off-board, thus not adding any computational burden or weight to the aircraft while minimising aircraft turn-around time, which is crucial for commercial flight operation. This paper presents the underlying method of the algorithm involving the computation of resistance, charge energy and incremental capacity curve using the charging data followed by a combined horizontal and vertical comparative scheme for diagnosis of faults. The performance of the proposed algorithm was validated via module-level experiments considering three cell inactivation faults. Results indicated that the algorithm successfully segregated faulty and unfaulty cells in multiple test scenarios with different number of faults in a laboratory-assembled module. Upon testing the algorithm in experiments conducted using a real-life aerospace battery module, there was a slight reduction in the performance, attributed to greater thermal variation. Nevertheless, the results indicated promising prospects for practical implementation in eVTOL batteries.