Benchmarking the Transferability of Real‐Time State of Charge Algorithms to Sodium‐Ion Cells Using an Open‐Source Diagnostics Framework

Despite the abundance of battery state estimation algorithms in the BMS literature, their applicability to emerging cell chemistries remains uncertain, as evaluating their performance across diverse use cases is complex, resource‐intensive, and time‐consuming. In this work, we introduce an evaluation framework designed to assess the performance of diagnostic algorithms across various applications and external conditions. Our framework relies on simulations of different operational scenarios grouped into categories and evaluates algorithm performance using statistical metrics that represent accuracy, bias, and precision. While our framework can be used for various state estimators and applications, we demonstrate its functionality by benchmarking three common and real‐time capable State of Charge (SOC) algorithms across three cell types, including a sodium‐ion battery. Within our case study, we show that the tested model‐based algorithms offer excellent transferability for the specific sodium‐ion battery considering different operation conditions. Additionally, we demonstrate that the characteristic OCV(SOC) relationship of the sodium‐ion cell allows for the use of lower‐quality and more affordable sensors, as the cell is less sensitive to measurement inaccuracies. Overall, our open‐source framework supports the systematic assessment of diagnostic algorithm transferability and provides a foundation for informed decision‐making when selecting algorithms for a specific application and cell type.