Battery Assessment Tools

Battery diagnostics and assessment tools are specialized systems used to evaluate the condition, safety, and remaining life of batteries to determine whether they meet quality and safety standards for use, reuse, or recycling. These tools are critical for both End-of-Line testing in manufacturing—where electrical integrity and performance consistency are verified before shipment—and for testing and remanufacturing during early recycling workflows, where the focus is on identifying viable modules and minimizing waste.

Modern diagnostic systems combine electrical measurement techniques such as Electrochemical Impedance Spectroscopy (EIS), Direct Current Internal Resistance (DCIR), Open-Circuit Voltage (OCV) analysis, and Hybrid Pulse Power Characterization (HPPC) with data-driven models and AI-based analytics. Together, these approaches enable non-destructive, rapid, and traceable assessment of cells, modules, and packs across manufacturing and circular economy workflows.

Key Industrial Workflows

• End-of-Line (EoL) testing in manufacturing: Verifies that new modules and packs meet performance and safety specifications before shipment. Detects faults, imbalances, or connection errors early to ensure process stability and quality assurance.
• Testing & remanufacturing in recycling: Evaluates returned or unknown modules to determine reuse potential, builds reference data for new chemistries or designs, and classifies units for second-life or material recovery.
• Field and in-vehicle diagnostics: Enables continuous condition monitoring and predictive maintenance to extend operating life and reduce downtime.

Methodologies

1. Measurement-based diagnostics
   Physical testing methods directly measure voltage, current, impedance, and temperature under controlled excitation. They provide fast, accurate SOH readings and real-time fault detection—ideal for production and laboratory environments.
2. Data-driven and historical-model methods
   These systems leverage reference databases or battery fingerprint libraries and use machine learning or regression algorithms to correlate new measurements with historical test data. They can predict capacity fade, identify chemistry, and assess unknown or mixed modules with high accuracy.
3. Hybrid approaches
   The most advanced systems integrate both methods—performing a short EIS/DCIR scan and referencing historical models for faster and more accurate SOH estimation. Each new test enriches the database, improving traceability, standardization, and predictive precision across manufacturing, remanufacturing, and recycling workflows.