Physical and chemical features of the battery are detected using sensors that are reliable in a robust operational environment.
Our proprietary software utilizes machine learning to analyze and sort batteries with high accuracy and speed.
Sorted batteries are automatically aggregated for storage and transportation.
Data Collected
Shape, size, weight, and elemental composition.
Label, shape, size, and outlook
Label, shape, size, and weight
Sorting Output
6+ categories: (1) Non- LIBs; (2) LIBs (LCO); (3) LIBs (NCA) (4) LIBs (NCM); (5) LIBs (LFP) (6) LIBs (LMO)
7 categories: (1) Alkaline / Zinc carbon; (2) NiMH; (3) Ni-Cd; (4) LIBs; (5) Button Cells; (6) 6 V Batteries; (7) Lead Batteries
4 categories: (1) Alkaline, (2) NiMH; (3) NiCd; (4) Primary LIBs
Flexibility/Reliability
High
Medium
Low
Accuracy
>99%
~90%
~97%
Efficiency
~2,000/batteries per hour
~800 batteries/hour
~10,000 batteries/hour
Key Cost Factors
Sensors, mechanical parts ($10-$20k)
Well-trained workers ($150-$200k/yr)
Sensors, mechanical parts ($10k-$20k)
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