Description:
Background
Current battery management systems, such as those in electric vehicles, grid storage and consumer electronics, can only estimate bulk metrics like capacity and resistance, which miss key degradation mechanisms. While electrochemical impedance spectroscopy (EIS) provides detailed insight into battery health, it requires specialized hardware and long measurement times, making it impractical for real-world deployment.
Technology Description
USC Researchers have developed a novel software-based diagnostic method that extracts laboratory-grade battery health information from ~60 seconds of voltage relaxation data using existing battery management system (BMS) signals. The technology uses a structure-preserving modeling approach to identify electrochemical time constants that describe internal battery processes such as charge transfer, interphase transport, and diffusion. The output provides the same diagnostic and prognostic insights as electrochemical impedance spectroscopy (EIS), a gold-standard laboratory technique, without requiring specialized hardware, long measurement times, or chemistry-specific calibration.
Benefits
- ~60 seconds vs. 10–60 minutes for diagnostic insight
- No additional hardware (uses existing BMS sensors)
- Deployable as firmware/software update
- Chemistry-agnostic across multiple battery types
- Early-life prognosis before capacity fade is visible
- Detects manufacturing defects missed by conventional metrics
Stage of Development
- Validated across 850+ cells from five independent datasets
- Algorithm ready for embedded deployment
Applications
- Electric vehicles: real-time on-board health monitoring
- Battery manufacturing: single-pulse defect screening
- Second-life batteries: rapid valuation of retired cells
- Grid-scale storage: fleet level diagnostics
Keywords
Battery diagnostics, battery management system, state of health, electric vehicles