Fast screening of lithium-ion batteries for second use with pack-level testing and machine learning

Fast and accurate screening of retired lithium-ion batteries is critical to an efficient and reliable second use with improved performance consistency, contributing to the sustainability of renewable energy sources. However, time-consuming testing, representative criteria extraction, and large module-to-module inconsistencies at the end of first life all pose great challenges for fast screening. This paper proposes a fast screening approach with pack-level testing and machine learning to evaluate and classify module-level aging, where disassembly of the battery pack and individual testing of modules are not required. Dynamic characteristic-based criteria are designed to extract the comprehensive performance of the retired modules, making the approach applicable for battery packs with module state-of-charge inconsistencies up to 30%. Adaptive affinity propagation clustering is utilized to classify the modules and further accelerate the screening progress. The proposed approach is implemented and validated by conducting pack-level and module-level experiments with a retired battery pack consisting of 95 modules connected in series. The screening time is reduced by at least 50% compared with approaches that require module-level testing. Reasonable static performance consistency and better dynamic performance consistency, as well as higher screening stability, are achieved, with average overall performance improvements of 18.94%, 4.83% and 34.41% compared with the three benchmarks, respectively. Its adaptability to a larger current rate shows promise for large-scale applications in second-use screening.