A Practical Design for a Battery Diagnostic and Prognostic System Tool Suite

The development of battery diagnostic and prognostic solutions is essential to ensuring the reliability, safety, and longevity of energy storage systems across industries such as electric vehicles, grid energy storage, and aerospace. This paper presents a structured design approach for Ridgetop Group’s Battery Diagnostic and Prognostic System (BDPS) Tool Suite, which integrates modular technology elements into a unified platform that implements all five functional blocks of the IEEE 1856-2017 PHM Standard. Developed under a DOE-funded SBIR program for grid energy storage applications, the BDPS extends Ridgetop’s PHM methodologies and battery modeling expertise into new domains that demand near real-time state-of-health assessment, remaining useful life estimation, and failure mode prediction. The BDPS Tool Suite is a hardware-in-the-loop battery testing platform, combining electrochemical modeling, empirical cycle life testing, and advanced diagnostics. It utilizes commercial-off-the-shelf cycle life test systems with high-fidelity simulation tools, including the Advanced Electrolyte Model and the patented CellSage family of physics-based battery modeling tools. Additionally, the BDPS employs automated prognostic estimates using the Adaptive Remaining Useful Life Estimator to enhance anomaly detection and enable data-driven decision-making. The BDPS architecture comprises a robust data acquisition framework, advanced feature data extraction pipelines, multi-physics degradation modeling, and an automated advisory system, delivering end-to-end battery health management during cycle life testing. This paper details the industry need, key design considerations, the technical approach, and the system-level design of the BDPS based on a proven PHM methodology. By adhering to IEEE 1856-2017, the BDPS establishes a high-fidelity and scalable solution for mission-critical battery applications.