Automated battery sorting

Abstract

A system and method for processing mixed batteries to separate lead batteries, lithium batteries, and non-conforming batteries for different battery recycling processes. Methods automate mixed battery sortation processes for economically scalable battery recycling. Methods automatically divert lead batteries and lithium batteries to be recycled in different processes as well as reduce volume of unknown batteries to be manually inspected.

Description

AUTOMATED BATTERY SORTINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 598,403, filed November 13, 2024, entitled “Automated Battery Sorting,” which is incorporated by reference herein in its entirety for all purposes.FIELD

[0002] The disclosed process, methods, and systems are directed to battery sortation, and more specifically to sortation of mixed batteries into lead batteries, lithium batteries, and other non-conforming batteries specific to a secondary lead battery recycling facility. In one example, the disclosed sortation process can be automated or partially automated.BACKGROUND

[0003] Lead battery recycling facilities often receive mixed battery scrap, including batteries containing materials not intended to be processed at a particular battery recycling facility. For example, risks may be associated with inadvertently processing non-lead batteries during a recycling process designed for lead batteries at a lead battery recycling facility. This is because lead batteries and non-lead batteries have different chemistries, which require different recycling processes. Thus, processing non-lead batteries using a recycling process designed for lead batteries may lead to reduced equipment life within the battery recycling facility, unanticipated accumulation of hazardous metals within the process flow stream, and significant impact to environmental protection processes. These effects may, in turn, lead to extended plant downtime for mitigation efforts.

[0004] Typical high-accuracy battery sortation processes are not at a scale adequate for lead battery recycling facilities. For example, current means of sorting lead batteries and non-lead batteries often results in either: false negatives allowing non-lead batteries into the recycling system, or false positives with inadvertent rejection of lead batteries, which must be manually identified and returned to the lead battery recycling stream. Manual removal and identification is labor intensive, which limits the overall volume capacity of a recycling facility and greatlyincreases cost. Further, current battery sortation is focused on small form batteries or individual cells, such as batteries with a mass of less than 1 kilogram, or operate at a slow processing rate, which limits the overall volume capacity of a recycling facility and increases costs. Thus, what is needed is a battery sortation process that is scalable to efficiently and economically sort battery scrap at high throughput, such as around 1,000 tons per day.SUMMARY

[0005] Disclosed herein are processes and systems useful for soiling batteries and battery scrap. An embodiment of the present disclosure can include a process for sorting mixed streams of batteries. The process can include receiving a stream of mixed batteries and initially processing the stream of mixed batteries to separate batteries containing ferrous metal into a stream of mixed batteries with steel from batteries not including steel into a first stream of lead batteries. The process can include a secondary ferrous detection step to return batteries containing only a small amount of steel, such as a nut or bolt that would be a part of the cable mounting terminal, back into the lead battery stream. The process can include a separation method to measure the discrete density of each battery of the stream of mixed batteries with steel to separate batteries including lead into a second stream of lead batteries from non-lead batteries into a stream of mixed non-lead batteries. The process can include various scanning methods. The process can include scanning the stream of mixed non-lead batteries to separate into three streams: batteries including lithium into a first stream of lithium batteries, batteries not containing lead nor lithium into a first stream of non-conforming batteries, and batteries of unknown conformation into a stream of unknown batteries. The process can include visual inspection. The process can include visually inspecting the stream of unknown batteries to separate batteries including lithium into a second stream of lithium batteries and batteries not conforming into a second stream of non-conforming batteries.

[0006] Embodiments of the present disclosure can also include an automated battery sortation system. The automated battery sortation system can include a first sensor that can process a stream of mixed batteries and, from the stream of mixed batteries, detect batteries including a ferrous metal. The automated battery sortation system can include a first diverter that can separate the batteries including the ferrous metal into a stream of mixed batteries with steel. The automated battery sortation system can include a second sensor that can measure a discretedensity of each battery of the stream of mixed batteries with steel and, based on the discrete densities, detect non-lead batteries. The automated battery sortation system can include a second diverter that can separate the non-lead batteries not including lead into a stream of mixed nonlead batteries. The automated battery sortation system can include a scanner that can scan the stream of mixed non-lead batteries to detect batteries including lithium, batteries not conforming to lead or lithium, and batteries unknown. The automated battery sortation system can include a third diverter that can separate the batteries including lithium into a stream of lithium batteries, the batteries not conforming into a stream of non-conforming batteries, and the batteries unknown into a stream of unknown batteries.

[0007] This Summary is provided to introduce a selection of concepts in a simplified form. It is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. A more extensive presentation of features, details, utilities, and advantages of the present invention as defined in the claims is provided in the following written description of various embodiments of the invention and illustrated in the accompanying drawings.

[0008] In one embodiment, a battery sorting system includes: a first stream of mixed batteries; a first device configured to detect ferrous material; a first diverter configured to separate the first stream of mixed batteries into: a first stream of lead batteries including at least one battery not including ferrous material; and a second stream of mixed batteries including at least one battery including ferrous material; a second device configured to detect lead batteries including ferrous material; a second diverter configured to separate the second stream of mixed batteries into: a second stream of lead batteries including at least one lead battery including ferrous material; and a third stream of mixed batteries including at least one non-lead battery; a third device configured to detect lithium batteries; a third diverter configured to separate the third stream of mixed batteries into: a first stream of lithium batteries including at least one lithium battery; a first stream of non-conforming batteries including at least one non-lithium battery; and a first stream of unknown batteries.

[0009] Optionally, in some embodiments, the system is at least partially automated.

[0010] Optionally, in some embodiments, the first device includes a ferrous metal detector.

[0011] Optionally, in some embodiments, a sensitivity range of the ferrous metal detector is adjusted based on a feedback datum from a downstream battery recycling process.

[0012] Optionally, in some embodiments, the first device includes two or more ferrous metal detectors in series or parallel.

[0013] Optionally, in some embodiments, the second device includes at least one or more of a density sensor, an optical sensor, a geometry sensor, or a mass detection sensor.

[0014] Optionally, in some embodiments, the third device includes an X-ray tomography scanner.

[0015] Optionally, in some embodiments, the system further includes a plurality of conveyor belts to continuously move the first stream of mixed batteries, the first stream of lead batteries, the second stream of mixed batteries, the second stream of lead batteries, the third stream of mixed batteries, the first stream of lithium batteries, the first stream of non-conforming batteries, and the stream of unknown batteries.

[0016] Optionally, in some embodiments, the third device is configured to receive a battery of up to, and including, 250 kilograms in weight.

[0017] Optionally, in some embodiments, the first stream of mixed batteries includes one or more lead batteries.

[0018] Optionally, in some embodiments, the first stream of mixed batteries includes 1,500 tons of batteries or less per day.

[0019] Optionally, in some embodiments, the system further includes: a fourth device configured to visually inspect the first stream of unknown batteries; and a fourth diverter configured to separate the stream of unknown batteries into: a second stream of lithium batteries including at least one of a visually confirmed lithium battery; and a second stream of nonconforming batteries including at least one of a visually confirmed non-conforming battery.

[0020] In some embodiments, a method for sorting batteries, includes: receiving a first stream of mixed batteries; a first processing step to separate the first stream of mixed batteries into: a first stream of lead batteries including at least one battery not including a ferrous material; and a second stream of mixed batteries including at least one battery including the ferrous material; a second processing step to separate the second stream of mixed batteries into: a second stream of lead batteries including at least a lead battery including the ferrous material; and a third stream of mixed batteries including at least one non-lead battery including the ferrous material; a third processing step to separate the third stream of mixed batteries into: a first stream of lithium batteries including at least one battery including lithium; a first stream of non-conformingbatteries including at least one battery not including lithium; and a stream of unknown batteries; and a fourth processing step to separate the stream of unknown batteries into: a second stream of lithium batteries including at least one battery including lithium; and a second stream of nonconforming batteries including at least one battery not including lithium.

[0021] Optionally, in some embodiments, the method is at least partially automated.

[0022] Optionally, in some embodiments, the method sorts 90% or greater of the first mixed battery stream automatically prior to a manual inspection and sortation.

[0023] Optionally, in some embodiments, the second processing step separates the second stream of mixed batteries based on a density measurement.

[0024] Optionally, in some embodiments, the second stream of lead batteries includes at least one battery with a density greater than or about 90 pounds per cubic foot and the third stream of mixed batteries includes at least one battery with a density less than 90 pounds per cubic foot.

[0025] Optionally, in some embodiments, the third processing step separates the third stream of mixed batteries based on a three-dimensional structure measurement without modifying or destroying batteries or battery cases.

[0026] Optionally, in some embodiments, the third processing step separates the third stream of mixed batteries based on one or more of a transmission rate or internal architecture differences via a detector and a machine learning program.

[0027] Optionally, in some embodiments, the fourth processing step separates the stream of unknown batteries using visual inspection.

[0028] Optionally, in some embodiments, the method is at least partially continuous, with respect to one or more of the first stream of mixed batteries, the first stream of lead batteries, the second stream of mixed batteries, the second stream of lead batteries, the third stream of mixed batteries, the first stream of lithium batteries, the first stream of non-conforming batteries, the stream of unknown batteries, the second stream of lithium batteries, and the second stream of non-conforming batteries.

[0029] In one embodiment, a method for sorting mixed streams of batteries, includes: receiving a stream of mixed batteries; processing the stream of mixed batteries to separate: batteries including ferrous metal into a stream of mixed batteries with steel; and batteries not including a ferrous metal into a first stream of lead batteries; measuring a discrete density of each battery of the stream of mixed batteries with steel to separate: batteries including lead into asecond stream of lead batteries; and non-lead batteries into a stream of mixed non-lead batteries; scanning the stream of mixed non-lead batteries to separate: batteries including lithium into a first stream of lithium batteries; batteries not conforming into a first stream of non-conforming batteries; and batteries unknown into a stream of unknown batteries; visually inspecting the stream of unknown batteries to separate: batteries including lithium into a second stream of lithium batteries; and batteries not conforming into a second stream of non-conforming batteries.

[0030] In one embodiment, an automated battery sortation system includes: a first sensor configured to process a stream of mixed batteries and, from the stream of mixed batteries, detect batteries including ferrous metal; a first diverter configured to separate the batteries including ferrous metal into a stream of mixed batteries with steel; a second sensor configured to measure a discrete density of each battery of the stream of mixed batteries with steel and, based on the discrete densities, detect non-lead batteries; a second diverter configured to separate the non-lead batteries into a stream of mixed non-lead batteries; a scanner configured to scan the stream of mixed non-lead batteries to detect batteries including lithium, batteries not conforming, and batteries unknown; and; a third diverter configured to separate the batteries including lithium into a stream of lithium batteries, the batteries not conforming into a stream of non-conforming batteries, and the batteries unknown into a stream of unknown batteries.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG. 1 is an annotated flow chart of one embodiment of the disclosed processes and systems, which can be referred to as Automated Battery Sortation (Process Overview).

[0032] FIG. 2 is a flow diagram of mixed battery sortation.

[0033] FIG. 3 is a flow chart of an automated method of sorting batteries according to the system of FIG. 1.

[0034] FIG. 4 is a perspective view of an embodiment of the system of FIG. 1.

[0035] FIG. 5 A illustrates an embodiment ID#, Pbl; Chemistry, Flooded Lead Acid; CellShape, Plate; Brand & Model, NAPA Legend 7575; Capacity, 12v, 800CCA; Dimensions (in), 9.5 x 7.2 x 6.8; Weight (kg), 15.7 of a battery suitable for processing with the systems and methods disclosed herein.

[0036] FIG. 5B is an example of an X-ray image of the battery of FIG. 5A taken at a first imaging energy.

[0037] FIG. 5C is an example of an X-ray image of the battery of FIG. 5A taken at a second imaging energy.

[0038] FIG. 6A illustrates an embodiment ID#, Pb2; Chemistry, Flooded Lead Acid; Cell Shape, Plate; Brand & Model, BikeMaster Performance 6N6-3B-1; Capacity, 6v, 6Ah;Dimensions (in), 4.3 x 3.8 x 2.2; Weight (kg), 1.1.

[0039] FIG. 6B is an example of an X-ray image of the battery of FIG. 6A taken at a first imaging energy.

[0040] FIG. 6C is an example of an X-ray image of the battery of FIG. 6A taken at a second imaging energy.

[0041] FIG. 7A illustrates an embodiment ID#, PbAl; Chemistry, Lead Absorbent Glass Mat (AGM); Cell Shape, Plate; Brand & Model, NAPA Legend Premium AGM 9865; Capacity, 12v, 75Ah; Dimensions (in), 11.9 x 7.4 x 6.7; Weight (kg), 21.7.

[0042] FIG. 7B is an example of an X-ray image of the battery of FIG. 7A taken at a first imaging energy.

[0043] FIG. 7C is an example of an X-ray image of the battery of FIG. 7A taken at a second imaging energy.

[0044] FIG. 8A illustrates an embodiment ID#, PbA2; Chemistry, Lead AGM; Cell Shape, Plate; Brand & Model, Super Start Powersport AGM ETX9; Capacity, 12v, 120A; Dimensions (in), 5.8 x 4.1 x 3.4; Weight (kg), 3.3.

[0045] FIG. 8B is an example of an X-ray image of the battery of FIG. 8A taken at a first imaging energy.

[0046] FIG. 8C is an example of an X-ray image of the battery of FIG. 8A taken at a second imaging energy.

[0047] FIG. 9A illustrates an embodiment ID#, PbA3; Chemistry, Lead AGM; Cell Shape, Plate; Brand & Model, Duracell Ultra AGM DURA12-8F; Capacity, 12v, 8Ah; Dimensions (in), 5.9 x 3.7 x 2.6; Weight (kg), 2.3.

[0048] FIG. 9B is an example of an X-ray image of the battery of FIG. 9A taken at a first imaging energy.

[0049] FIG. 9C is an example of an X-ray image of the battery of FIG. 9A taken at a second imaging energy.

[0050] FIG. 10A illustrates an embodiment ID#, PbNCl; Chemistry, Lead AGM; Cell Shape, Plate; Brand & Model, Odyssey Extreme Series AGM PC545; Capacity, 12v, 150A; Dimensions (in), 7.0 x 5.1 x 3.3; Weight (kg), 5.6.

[0051] FIG. 10B is an example of an X-ray image of the battery of FIG. 10A taken at a first imaging energy.

[0052] FIG. 10C is an example of an X-ray image of the battery of FIG. 10A taken at a second imaging energy.

[0053] FIG. 11 A illustrates an embodiment ID#, PbNC2; Chemistry, Lead AGM; CellShape, Plate; Brand & Model, Tripplite UPS SMART1500LCDT; Capacity, 120v, 1500VA, 900W; Dimensions (in), 15.2 x 8.0 x 5.7; Weight (kg), 11.8.

[0054] FIG. 1 IB is an example of an X-ray image of the battery of FIG. 11 A taken at a first imaging energy.

[0055] FIG. 11C is an example of an X-ray image of the battery of FIG. 11 A taken at a second imaging energy.

[0056] FIG. 12A illustrates an embodiment ID#, PbNC3; Chemistry, Lead AGM; Cell Shape, Plate; Brand & Model, SuperPack 1200 Jump Pack 51200; Capacity, 12v, 1200A; Dimensions (in), 10.5 x 9.5 x 6.6; Weight (kg), 5.8.

[0057] FIG. 12B is an example of an X-ray image of the battery of FIG. 12A taken at a first imaging energy.

[0058] FIG. 12C is an example of an X-ray image of the battery of FIG. 12A taken at a second imaging energy.

[0059] FIG. 13 A illustrates an embodiment ID#, PbNC4; Chemistry, Lead AGM; CellShape, Plate; Brand & Model, SuperPack 750 Jump Pack 55750; Capacity, 12v, 750A; Dimensions (in), 10.8 x 9.2 x 4.6; Weight (kg), 3.8

[0060] FIG. 13B is an example of an X-ray image of the battery of FIG. 13 A taken at a first imaging energy.

[0061] FIG. 13C is an example of an X-ray image of the battery of FIG. 13 A taken at a second imaging energy.

[0062] FIG. 14A illustrates an embodiment ID#, Li2; Chemistry, Lithium Iron Phosphate; Cell Shape, Unknown; Brand & Model, Tracker Lithium TLi60; Capacity, 12.8v, 60Ah, 768Wh; Dimensions (in), 10.2 x 8.2 x 6.6; Weight (kg), 7.3.

[0063] FIG. 14B is an example of an X-ray image of the battery of FIG. 14A taken at a first imaging energy.

[0064] FIG. 14C is an example of an X-ray image of the battery of FIG. 14A taken at a second imaging energy.

[0065] FIG. 15A illustrates an embodiment ID#, Lil; Chemistry, Lithium Iron Phosphate;Cell Shape, Unknown; Brand & Model, Harley Davidson 66000174; Capacity, 12v, 6Ah, 72Wh; Dimensions (in), 5.9 x 5.7 x 3.5; Weight (kg), 1.32.

[0066] FIG. 15B is an example of an X-ray image of the battery of FIG. 15A taken at a first imaging energy.

[0067] FIG. 15C is an example of an X-ray image of the battery of FIG. 15 A taken at a second imaging energy.

[0068] FIG. 16A illustrates an embodiment ID#, Li3; Chemistry, Unknown; Cell Shape, Cylindrical 18650; Brand & Model, Kobalt KB 540-06; Capacity, 40v, 5Ah, 172Wh;Dimensions (in), 6.7 x 4.5 x 2.2; Weight (kg), 1.42.

[0069] FIG. 16B is an example of an X-ray image of the battery of FIG. 16A taken at a first imaging energy.

[0070] FIG. 16C is an example of an X-ray image of the battery of FIG. 16A taken at a second imaging energy.

[0071] FIG. 17A illustrates an embodiment ID#, Li4; Chemistry, Unknown; Cell Shape, Cylindrical 18650; Brand & Model, Black & Decker LBXR36; Capacity, 40v, 54Wh;Dimensions (in), 5.0 x 3.3 x 2.7; Weight (kg), 0.71.

[0072] FIG. 17B is an example of an X-ray image of the battery of FIG. 17A taken at a first imaging energy.

[0073] FIG. 17C is an example of an X-ray image of the battery of FIG. 17A taken at a second imaging energy.

[0074] FIG. 18A illustrates an embodiment ID#, Li5; Chemistry, Unknown; Cell Shape, Cylindrical 18650; Brand & Model, Eclipse Oxygen Systems Pawer Cartridge 2400 6107 RevU;Capacity, 14.4v, 2x 95Wh Modules; Dimensions (in), 7.5 x 5.1 x 2.3; Weight (kg), 1.5.

[0075] FIG. 18B is an example of an X-ray image of the battery of FIG. 18A taken at a first imaging energy.

[0076] FIG. 18C is an example of an X-ray image of the battery of FIG. 18A taken at a second imaging energy.

[0077] FIG. 19A illustrates an embodiment ID#, Li6; Chemistry, Lithium Mangenese Oxide; Cell Shape, Unknown; Brand & Model, Philips M3863A 453564142082 RevII; Capacity, 12v, 4.2Ah; Dimensions (in), 6.8 x 2.6 x 1.4; Weight (kg), 0.36.

[0078] FIG. 19B is an example of an X-ray image of the battery of FIG. 19A taken at a first imaging energy.

[0079] FIG. 19C is an example of an X-ray image of the battery of FIG. 19A taken at a second imaging energy.

[0080] FIG. 20A illustrates an embodiment ID#, Li7; Chemistry, Unknown; Cell Shape, Unknown; Brand & Model, Dell 6MT4T; Capacity, 7.6v, 62Wh; Dimensions (in), 9.2 x 3.7 x 0.3; Weight (kg), 0.30.

[0081] FIG. 20B is an example of an X-ray image of the battery of FIG. 20A taken at a first imaging energy.

[0082] FIG. 20C is an example of an X-ray image of the battery of FIG. 20A taken at a second imaging energy.

[0083] FIG. 21A illustrates an embodiment ID#, Li8; Chemistry, Unknown; Cell Shape, Unknown; Brand & Model, Honeywell CK65-BTSC; Capacity, 3.6v, 7Ah, 25.2Wh; Dimensions (in), 3.5 x 1.6 x 1.0; Weight (kg), 0.13.

[0084] FIG. 21B is an example of an X-ray image of the battery of FIG. 21A taken at a first imaging energy.

[0085] FIG. 21C is an example of an X-ray image of the battery of FIG. 21 A taken at a second imaging energy.

[0086] FIG. 22A illustrates an embodiment ID#, Li9; Chemistry, Unknown; Cell Shape, Cylindrical 18650; Brand & Model, Unknown - Blue Wrapped Small Cells; Capacity, Each Cell - 3.6v / 4.2v, 2.5Ah; Dimensions (in), 9.0 x 6.9 x 2.7; Weight (kg), 4.4.

[0087] FIG. 22B is an example of an X-ray image of the battery of FIG. 22A taken at a first imaging energy.

[0088] FIG. 22C is an example of an X-ray image of the battery of FIG. 22A taken at a second imaging energy.

[0089] FIG. 23A illustrates an embodiment ID#, LilO; Chemistry, Unknown; Cell Shape, Cylindrical 32700; Brand & Model, Unknown - Blue Wrapped Large Cells; Capacity, Unknown; Dimensions (in), 10.3 x 6.4 x 5.7; Weight (kg), 11.6.

[0090] FIG. 23B is an example of an X-ray image of the battery of FIG. 23A taken at a first imaging energy.

[0091] FIG. 23C is an example of an X-ray image of the battery of FIG. 23A taken at a second imaging energy.

[0092] FIG. 24A illustrates an embodiment ID#, Lil i; Chemistry, Unknown; Cell Shape, Cylindrical 18650; Brand & Model, Ryobi OP40401; Capacity, 40v, 4.0Ah, 144Wh; Dimensions (in), 6.5 x 4.0 x 3.1; Weight (kg), 0.35.

[0093] FIG. 24B is an example of an X-ray image of the battery of FIG. 24A taken at a first imaging energy.

[0094] FIG. 24C is an example of an X-ray image of the battery of FIG. 24A taken at a second imaging energy.

[0095] FIG. 25A illustrates an embodiment ID#, Lil2; Chemistry, Unknown; Cell Shape, Unknown; Brand & Model, Shida 230407 756580; Capacity, 14.8v, 3.0Ah, 44.4Wh; Dimensions (in), 3.6 x 2.6 x 1.2; Weight (kg), 1.3.

[0096] FIG. 25B is an example of an X-ray image of the battery of FIG. 25A taken at a first imaging energy.

[0097] FIG. 25C is an example of an X-ray image of the battery of FIG. 25A taken at a second imaging energy.

[0098] FIG. 26 A illustrates an embodiment ID#, Lil3; Chemistry, Unknown; Cell Shape, Unknown; Brand & Model, TFDC P12-270-FP 210983; Capacity, 12.8v, 3.0Ah, 38.4Wh; Dimensions (in), 6.0 x 1.9 x 1.3; Weight (kg), 0.41.

[0099] FIG. 26B is an example of an X-ray image of the battery of FIG. 26A taken at a first imaging energy.

[0100] FIG. 26C is an example of an X-ray image of the battery of FIG. 26A taken at a second imaging energy.

[0101] FIG. 27A illustrates an embodiment ID#, Lil4; Chemistry, Unknown; Cell Shape, Cylindrical 18650; Brand & Model, Dewait DCB204; Capacity, 20v, 4Ah, 80Wh; Dimensions (in), 4.6 x 2.9 x 2.4; Weight (kg), 0.64.

[0102] FIG. 27B is an example of an X-ray image of the battery of FIG. 27A taken at a first imaging energy.

[0103] FIG. 27C is an example of an X-ray image of the battery of FIG. 27A taken at a second imaging energy.

[0104] FIG. 28 A illustrates an embodiment ID#, Li 15; Chemistry, Unknown; Cell Shape, Unknown; Brand & Model, NIU Energy 171-ISR18 / 659-26; Capacity, 60v, 26Ah, 1560Wh; Dimensions (in), 12.1 x 7.3 x 5.8; Weight (kg), 10.7.

[0105] FIG. 28B is an example of an X-ray image of the battery of FIG. 28A taken at a first imaging energy.

[0106] FIG. 28C is an example of an X-ray image of the battery of FIG. 28A taken at a second imaging energy.

[0107] FIG. 29A illustrates an embodiment ID#, Lil6; Chemistry, Unknown; Cell Shape, Cylindrical 32700; Brand & Model, Unknown - Unwrapped Grey Large Cell Module; Capacity, Each Cell - 3.2v; Dimensions (in), 10.4 x 6.4 x 5.4; Weight (kg), 11.6.

[0108] FIG. 29B is an example of an X-ray image of the battery of FIG. 29A taken at a first imaging energy.

[0109] FIG. 29C is an example of an X-ray image of the battery of FIG. 29A taken at a second imaging energy.

[0110] FIG. 30A illustrates an embodiment ID#, LP2; Chemistry, Lithium Polymer; Cell Shape, Unknown; Brand & Model, Schumacher SL1396; Capacity, 37Wh; Dimensions (in), 6.9 x 3.3 x 1.5; Weight (kg), 0.46.

[0111] FIG. 30B is an example of an X-ray image of the battery of FIG. 30A taken at a first imaging energy.

[0112] FIG. 30C is an example of an X-ray image of the battery of FIG. 30A taken at a second imaging energy.

[0113] FIG. 31A illustrates an embodiment ID#, LP1; Chemistry, Lithium Polymer; Cell Shape, Unknown; Brand & Model, E-Flight High-Power Series EFLB21003S; Capacity, l l.lv, 2.1 Ah, 23.5Wh; Dimensions (in), 4.0 x 1.3 x 1.2; Weight (kg), 0.19.

[0114] FIG. 3 IB is an example of an X-ray image of the battery of FIG. 31 A taken at a first imaging energy.

[0115] FIG. 31C is an example of an X-ray image of the battery of FIG. 31A taken at a second imaging energy.

[0116] FIG. 32A illustrates an embodiment ID#, LP3; Chemistry, Lithium Polymer; Cell Shape, Unknown; Brand & Model, Schumacher SL1612; Capacity, 12v, 44.4Wh; Dimensions (in), 9.0 x 4.0 x 2.4; Weight (kg), 1.2.

[0117] FIG. 32B is an example of an X-ray image of the battery of FIG. 32A taken at a first imaging energy.

[0118] FIG. 32C is an example of an X-ray image of the battery of FIG. 32A taken at a second imaging energy.

[0119] FIG. 33A illustrates an embodiment ID#, NC4; Chemistry, nickel cadmium (NiCd);Cell Form, Unknown; Brand & Model, Dewait DC9096; Capacity, 18v; Dimensions (in), 5.5 x 4.5 x 3.4; Weight (kg), 0.97.

[0120] FIG. 33B is an example of an X-ray image of the battery of FIG. 33A taken at a first imaging energy.

[0121] FIG. 33C is an example of an X-ray image of the battery of FIG. 33A taken at a second imaging energy.

[0122] FIG. 34A illustrates an embodiment ID#, NCI; Chemistry, NiCd; Cell Form, Flooded; Brand & Model, Americad EDE-15; Capacity, Unknown; Dimensions (in), 10.4 x 7.6 x 4.5; Weight (kg), 6.7.

[0123] FIG. 34B is an example of an X-ray image of the battery of FIG. 34A taken at a first imaging energy.

[0124] FIG. 34C is an example of an X-ray image of the battery of FIG. 34A taken at a second imaging energy.

[0125] FIG. 35A illustrates an embodiment ID#, NC2; Chemistry, NiCd; Cell Form, Unknown; Brand & Model, PowerLuber 1401; Capacity, 14.4v; Dimensions (in), 4.6 x 3.9 x 3.5; Weight (kg), 0.75.

[0126] FIG. 35B is an example of an X-ray image of the battery of FIG. 35A taken at a first imaging energy.

[0127] FIG. 35C is an example of an X-ray image of the battery of FIG. 35A taken at a second imaging energy.

[0128] FIG. 36A illustrates an embodiment ID#, NC3; Chemistry, NiCd; Cell Form, Unknown; Brand & Model, Dewait DW9072; Capacity, 12v; Dimensions (in), 4.1 x 3.7 x 3.2; Weight (kg), 0.55.

[0129] FIG. 36B is an example of an X-ray image of the battery of FIG. 36A taken at a first imaging energy.

[0130] FIG. 36C is an example of an X-ray image of the battery of FIG. 36A taken at a second imaging energy.

[0131] FIG. 37A illustrates an embodiment ID#, NC5; Chemistry, NiCd; Cell Form, Flooded; Brand & Model, TLX+180, T13FFCF, MFR09052, KM180P; Capacity, Unknown; Dimensions (in), 9.8 x 4.2 x 3.2; Weight (kg), 3.9.

[0132] FIG. 37B is an example of an X-ray image of the battery of FIG. 37A taken at a first imaging energy.

[0133] FIG. 37C is an example of an X-ray image of the battery of FIG. 37A taken at a second imaging energy.

[0134] FIG. 38A illustrates an embodiment ID#, NC6; Chemistry, NiCd; Cell Form, Flooded; Brand & Model, Salt VHP KH-3 MFR09052; Capacity, Unknown; Dimensions (in), 9.3 x 3.1 x 1.4; Weight (kg), 1.5.

[0135] FIG. 38B is an example of an X-ray image of the battery of FIG. 38A taken at a first imaging energy.

[0136] FIG. 38C is an example of an X-ray image of the battery of FIG. 38A taken at a second imaging energy.

[0137] FIG. 39A illustrates an embodiment ID#, NC7; Chemistry, NiCd; Cell Form, Flooded; Brand & Model, Saft VP 170 KH MFR09052; Capacity, Unknown; Dimensions (in), 6.7 x 2.3 x 1.3; Weight (kg), 0.66.

[0138] FIG. 39B is an example of an X-ray image of the battery of FIG. 39A taken at a first imaging energy.

[0139] FIG. 39C is an example of an X-ray image of the battery of FIG. 39A taken at a second imaging energy.

[0140] FIG. 40A illustrates an embodiment ID#, NC8; Chemistry, NiCd; Cell Form, Unknown; Brand & Model, Motorola SNN4305B TFDGE12.EEF; Capacity, 6v; Dimensions (in), 4.7 x 2.3 x 0.5; Weight (kg), 0.14.

[0141] FIG. 40B is an example of an X-ray image of the battery of FIG. 40A taken at a first imaging energy.

[0142] FIG. 40C is an example of an X-ray image of the battery of FIG. 40A taken at a second imaging energy.

[0143] FIG. 41 A illustrates an embodiment ID#, NC9; Chemistry, NiCd; Cell Form, Cylindircal; Brand & Model, P / N 100003A064, P / N 1000030118; Capacity, Unknown; Dimensions (in), 6.4 x 2.6 x 2.4; Weight (kg), 1.32.

[0144] FIG. 41B is an example of an X-ray image of the battery of FIG. 41 A taken at a first imaging energy.

[0145] FIG. 41C is an example of an X-ray image of the battery of FIG. 41 A taken at a second imaging energy.

[0146] FIG. 42A illustrates an embodiment ID#, NM1; Chemistry, nickel metal hydride (NiMH); Cell Form, Prismatic; Brand & Model, Ford Motorcraft BXE-203; Capacity, 12v; Dimensions (in), 15.3 x 7.0 x 4.6; Weight (kg), 18.9.

[0147] FIG. 42B is an example of an X-ray image of the battery of FIG. 42A taken at a first imaging energy.

[0148] FIG. 42C is an example of an X-ray image of the battery of FIG. 42A taken at a second imaging energy.

[0149] FIG. 43 A illustrates an embodiment ID#, NM2; Chemistry, NiMH; Cell Form,Unknown; Brand & Model, Schwing 98384265; Capacity, 7.2v, 3.3Ah; Dimensions (in), 5.9 x 2.0 x 1.1; Weight (kg), 0.42.

[0150] FIG. 43B is an example of an X-ray image of the battery of FIG. 43A taken at a first imaging energy.

[0151] FIG. 43C is an example of an X-ray image of the battery of FIG. 43A taken at a second imaging energy.

[0152] FIG. 44A illustrates an embodiment ID#, NM3; Chemistry, NiMH; Cell Form, Unknown; Brand & Model, MAX USA IP509H; Capacity, 9.6v, 3.3Ah; Dimensions (in), 4.2 x 3.5 x 2.9; Weight (kg), 0.65.

[0153] FIG. 44B is an example of an X-ray image of the battery of FIG. 44A taken at a first imaging energy.

[0154] FIG. 44C is an example of an X-ray image of the battery of FIG. 44A taken at a second imaging energy.

[0155] FIG. 45 A illustrates an embodiment ID#, NM4; Chemistry, NiMH; Cell Form, Unknown; Brand & Model, Extended SuperCell 115R0000395157; Capacity, Unknown; Dimensions (in), 4.5 x 3.7 x 1.7; Weight (kg), 0.48.

[0156] FIG. 45B is an example of an X-ray image of the battery of FIG. 45A taken at a first imaging energy.

[0157] FIG. 45C is an example of an X-ray image of the battery of FIG. 45A taken at a second imaging energy.

[0158] FIG. 46A illustrates an embodiment ID#, NM5; Chemistry, NiMH; Cell Form, Cylindrical; Brand & Model, Unknown; Capacity, 560Wh; Dimensions (in), 8.5 x 6.1 x 5.0; Weight (kg), 9.1.

[0159] FIG. 46B is an example of an X-ray image of the battery of FIG. 46A taken at a first imaging energy.

[0160] FIG. 46C is an example of an X-ray image of the battery of FIG. 46A taken at a second imaging energy.

[0161] FIG. 47 is a flow chart of an embodiment of a method of training an artificial intelligence or machine learning algorithm to sort batteries according to the systems and methods disclosed herein.

[0162] FIG. 48 is a flow chart of an embodiment of a method of classifying and / or sorting a battery with a trained artificial intelligence or machine learning algorithm according to the systems and methods disclosed herein.

[0163] FIG. 49 is a simplified block diagram of components of a computing system of the systems (or components thereof) disclosed herein.DETAILED DESCRIPTION

[0164] The disclosed compositions, devices, methods, processes, and systems are directed to sorting streams of mixed batteries and mixed battery scrap, and separating into streams of lead batteries, lithium batteries, and non-conforming batteries. In some embodiments, the disclosed streams may include batteries of unknown or non-conforming type. The disclosed methods, processes and systems are useful in preventing non-lead batteries, such as lithium batteries andnon-conforming batteries, such as NiMH and NiCd, from entering a recycling process designed for recycling lead batteries. The disclosed methods, processes and systems can be automated or partially automated. For example, automated systems described herein can sort a majority of mixed batteries and battery scrap, such as 90% or greater, for example 95% or greater, prior to manual inspection and sortation. In this way, the disclosed methods, processes and systems can be scalable to increase the overall volume capacity of a recycling facility.

[0165] In at least one example, the automated battery sortation system can include a first sensor that can process a plurality of mixed batteries and, from the plurality of mixed batteries, detect batteries including a ferrous metal. The automated battery sortation system can include a first diverter that can automatically divert or otherwise separate the batteries including a ferrous metal from batteries not including a ferrous metal, e.g., separate the batteries including a ferrous metal into a stream of mixed batteries with steel. In various embodiments, non-ferrous containing batteries may contain an amount of ferrous material below a threshold amount. The automated battery sortation system can include a second sensor that can measure a discrete density of each battery of the stream of mixed batteries with steel and, based on the discrete densities, distinguish between lead and non-lead batteries. The automated battery sortation system can include a second diverter that can automatically divert or otherwise separate the nonlead batteries from batteries including lead, e.g., separate the non-lead batteries into a stream of mixed non-lead batteries. The automated battery sortation system can include a scanner that can scan the stream of mixed non-lead batteries to detect batteries including lithium, batteries not conforming, and, if present, batteries unknown. The automated battery sortation system can include a third diverter that can automatically divert or otherwise separate the batteries including lithium into a stream of lithium batteries, the batteries not conforming into a stream of nonconforming batteries, and the batteries unknown into a stream of unknown batteries.

[0166] Turning to the figures, illustrative embodiments of the present disclosure will now be discussed in more detail. FIG. 1 is an annotated flow chart of a process 100 for automated battery sortation. For example, the process 100 can sort a high throughput of mixed batteries and separate the mixed batteries into lead batteries, lithium batteries, and non-confirming batteries. In this example, a high throughput can be about on the order of 1,000 tons, e.g., the process 100 can sort about 1,000 tons of mixed batteries per day. In other embodiments, the disclosed process may throughput in excess of about 1,000 tons per day.

[0167] The process 100 can include receiving or obtaining a stream of mixed batteries (Block 105). For example, a battery recycling facility, equipment at a battery recycling facility such as a conveyor belt, or the like can receive the stream of mixed batteries. The stream of mixed batteries can include end-of-life batteries and battery scrap, e.g., batteries and battery scrap discarded, recycled, or otherwise not in use. The stream of mixed batteries can include different types of batteries, e.g., with different chemical structures or chemistries. For example, the stream of mixed batteries can include at least pail of at least one lead battery, lithium battery, or non-confirming battery, e.g., a non-lead, non-lithium battery. For example, the stream of mixed batteries can include battery scrap of a lead battery, lithium battery, or non-confirming battery. The process 100 can include receiving or obtaining less than or equal to about 1,500 tons of mixed batteries, e.g., per day. For example, FIG. 2 depicts 1,000 tons of mixed batteries received in one day, this is for illustration only and is not intended to be limiting, as other embodiments may receive greater than or less than about 1,000 tons per day, and some embodiments may receive much greater 1,000 tons per day.

[0168] Lead or lead-acid batteries can include a non-ferrous metal and can have a density that is generally greater than 90 pounds per cubic foot and less than 180 pounds per cubic foot, e.g., about 135 pounds per cubic foot. Lithium batteries can include a ferrous metal, such as steel, and can have a density generally less than lead batteries. For example, lithium batteries can have a density that is generally greater than 54 pounds per cubic foot and less than 90 pounds per cubic foot, e.g., about 72 pounds per cubic foot. Non-conforming batteries can include a ferrous metal or a non-ferrous metal and can have a density generally less than lead batteries, e.g., less than 135 pounds per cubic foot. Additionally, lead batteries, lithium batteries, and nonconforming batteries can have different internal geometries and / or cell configurations, e.g., lithium batteries often contain a circuit board for multi-cell voltage and temperature management, NiMH batteries contain a perforated steel cathode collector, or NiCd batteries can contain free liquid electrolyte. Battery specific geometries or internal component layout and construction combined with the discrete densities continue to evolve and expand.Ferrous Metal Detector

[0169] The process 100 can include passing the stream of mixed batteries through at least one ferrous metal detector to determine whether a ferrous metal is present in the stream of mixedbatteries, e.g., to determine whether a ferrous metal is detected in a single battery of the stream of mixed batteries (Block 110). For example, each battery of the stream of mixed batteries can be processed via at least one metal detector capable of detecting ferrous metal. Ferrous metals can include steel or iron-enriched materials.

[0170] The sensitivity range of the ferrous metal detector can be adjusted. For example, the sensitivity range of the ferrous metal detector can be set to a sensitivity that may identify a battery as being non-ferrous even if the battery contains some level of ferrous material in the battery. In other examples, the sensitivity range of the ferrous metal detector can be varied from low-sensitivity to high- sensitivity. In these examples, with the sensitivity range of the ferrous metal detector set to low-sensitivity, false negative identification can be high. The sensitivity range of the ferrous metal detector can be adjusted based on feedback from the downstream battery recycling process, e.g., based on a tolerance of a lead battery recycling process for processing non-conforming batteries or lithium batteries. In other examples, the sensitivity range of the ferrous metal detector can be adjusted based on other steps of the process 100 described above and below e.g., the time to complete lines 140A, B, described in more detail below.

[0171] The process 100 can include diverting a battery of the stream of mixed batteries to another step of the process 100, e.g., based on the result of the ferrous metal detector. For example, a conveyor belt, or similar means, can deliver or otherwise move a battery to the ferrous metal detector and based on whether the detector detects a level of a ferrous metal, deliver or otherwise move the battery to another step. In some examples, two or more ferrous metal detectors can be arranged in series. In these examples, the process 100 can include moving the batteries to the next step after being processed by each ferrous metal detector, e.g., after being processed by the last ferrous metal detector in the series. In other examples, the process 100 can include multiple ferrous metal detectors in parallel and may include moving the batteries to the next step after being processed by any one of the ferrous metal detectors, e.g., prior to being processed by one of the ferrous metal detectors. For example, the process 100 can include a secondary ferrous detection step to return batteries containing only a small amount of steel, such as a nut or bolt that would be a part of the cable mounting terminal, back into the lead battery stream. The process 100 can include delivering or otherwise moving each battery of the stream of mixed batteries to the next step continuously, e.g., without slowing or without stopping the stream of batteries being processed such as at Block 110. For example, a conveyor diverter,or similar means, can automatically divert batteries with a ferrous metal detected to a new stream, or alternatively divert batteries without a ferrous metal detected to a new stream. In some embodiments, the ferrous metal containing batteries and the non-ferrous metal containing batteries may each be diverted to new streams. In this way, the throughput of the process 100 can be maintained, minimally reduced, or not reduced.

[0172] In cases where a ferrous metal is not detected in a battery of the stream of mixed batteries, the process 100 can proceed to Block 115 (Line 110A). For example, not detecting a ferrous metal in a battery of the stream of mixed batteries can indicate the battery is a lead battery. In these examples, batteries of the stream of mixed batteries where a ferrous metal is not detected by the ferrous metal detector, e.g., lead batteries, can be diverted to a new stream, such as a first stream of lead batteries, and compiled (Block 115). For example, FIG. 2 depicts 700 tons of the 1,000 tons of mixed batteries received compiled as lead batteries. In other words, about 70% of the mixed batteries received can be compiled as lead batteries after the stream of mixed batteries are passed through the ferrous metal detector.

[0173] In cases where a ferrous metal is detected in a battery of the stream of mixed batteries, the process 100 can proceed to Block 120 (Line 110B). For example, detecting a ferrous metal such as steel in a battery of the stream of mixed batteries can indicate the battery is not a lead battery. In these examples, batteries of the stream of mixed batteries where a ferrous metal is detected by the ferrous metal detector can be compiled or otherwise diverted to further processing means, e.g., diverted by a first diverter to a new stream, such as a stream of mixed batteries with steel. For example, FIG. 2 depicts 300 tons of the 1,000 tons of mixed batteries received separated for further processing. In other words, about 30% of the mixed batteries received can be separated or otherwise diverted for further processing after the stream of mixed batteries is passed through the ferrous metal detector.Geometry and Mass Detection or Density Sensors

[0174] The process 100 can include passing the batteries with a ferrous metal detected through additional sensors to further detect lead batteries (Block 120). For example, the process 100 can include directing the batteries with a ferrous metal detected, e.g., the stream of mixed batteries with steel, through at least one density sensor, at least one optical or geometry sensor, and / or through at least one scale or mass detection sensor. The process 100 can includecalculating a discrete density measurement, e.g., for each battery via the optical and / or scale sensors. Non-lead batteries can have a lower density than lead batteries. For example, a battery with a low discrete density can indicate the battery does not include lead, e.g., is a lithium battery or non-conforming battery, and a battery with a higher discrete density can indicate the battery does include lead, e.g., is a lead battery. For example, a lead battery can have a density of about 135 pounds per cubic foot, whereas a lithium battery can have a density of about 72 pounds per cubic foot.

[0175] The process 100 can include diverting each battery passed through the optical and / or scale sensors to another step of the process 100, e.g., based on the discrete density measurement of the battery. For example, a conveyor belt, or similar means, can deliver or otherwise move each battery to the optical and / or scale sensors from the ferrous metal detector and, based on the discrete density measurement of each battery, deliver or otherwise move each battery to another step. The process 100 can include delivering or otherwise moving each battery of the stream of mixed batteries with steel passed through the optical and / or scale sensors to the next step continuously, e.g., without slowing or without stopping the stream of batteries being processed such as at Block 120. For example, a conveyor diverter, or similar means, can automatically divert batteries with a low discrete density measured to a new stream, or alternatively divert batteries with a higher discrete density measured. In some embodiments, the low discrete density batteries and the higher discrete density batteries may each be diverted to new streams. In this way, the throughput of the process 100 can be maintained, minimally reduced, or not reduced.

[0176] In cases where the discrete density measurement indicates a battery includes lead, the process 100 can proceed to Block 115 (Line 120A). For example, a higher discrete density measurement can indicate a battery includes lead. In this example, a higher discrete density measurement can be greater than about 90 pounds per cubic foot. In these examples, batteries with a higher discrete density measurement, e.g., lead batteries, can be diverted to a new stream, such as a second stream of lead batteries, and compiled (Block 115). For example, FIG. 2 depicts an additional 280 tons of the 1,000 tons of mixed batteries received compiled as lead batteries. In other words, about 28% of the mixed batteries received can be compiled as lead batteries after the stream of mixed batteries with steel are passed through the optical and / or scale sensors. For example, between Blocks 110 and 120 of FIG. 1, about 980 tons out of 1,000 tons of mixed batteries received can be automatically sorted as lead batteries, as depicted in FIG. 2, which canthen proceed to a lead battery recycling process. In other words, about 98% of the mixed batteries received can be compiled as lead batteries after the stream of mixed batteries are passed through the ferrous metal detector and the stream of mixed batteries with steel are passed through the optical and / or scale sensors. In some embodiments, a stream of batteries may be lesser or greater amounts of lead batteries, e.g., 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or even 100% lead-containing batteries. In this way, the overall efficiency of a lead battery recycling process can be improved, e.g., compared to only manual sortation.Additionally, the overall risks associated with recycling non-lead batteries via a lead battery recycling process can be reduced.

[0177] In cases where the discrete density measurement indicates a battery does not include lead, e.g., is a non-lead battery, the process 100 can proceed to Block 125 (Line 120B). For example, a low discrete density measurement can indicate that a battery does not include lead. In this example, a low discrete density measurement can be less than or equal to about 90 pounds per cubic foot. In these examples, batteries of the stream of mixed batteries with steel with a low discrete density measurement can be compiled or otherwise diverted to further processing means, e.g., diverted by a second diverter to a new stream, such as a stream of mixed non-lead batteries. For example, FIG. 2 depicts 20 tons of the 1,000 tons of mixed batteries received separated for further processing. In other words, about 2% of the mixed batteries received can be separated or otherwise diverted for further processing after the stream of mixed batteries with steel is passed through the optical and / or scale sensors.X-Ray Tomography (XRT) Scanner

[0178] The process 100 can include passing the batteries with discrete density measurements indicating that the batteries do not include lead through a scanner to detect lithium (Block 125). For example, the process 100 can include directing each battery of the batteries with low discrete density measurements, e.g., the stream of mixed non-lead batteries, through at least one XRT scanner. The XRT scanner can receive large batteries, e.g., batteries up to about 250 kilograms. For example, the XRT scanner can receive and scan a battery of about 150 kilograms. The XRT scanner can measure the three-dimensional structure of each battery without modifying or destroying the battery or battery case.

[0179] XRT displays color according to transmission which is a function of the atomic weight of the constituent elements present. This provides a potential basis of comparison. Transmission properties of each battery, as measured or otherwise detected by the XRT scanner, can differentiate between battery chemistries based on transmission rates as well as known internal architecture differences, e.g., via a machine learning program. For example, the internal geometry of a battery and / or cell configuration of a battery can indicate that a battery includes the presence of a battery management system which would indicate that a battery includes lithium or is otherwise a lithium battery. For example, lithium batteries with more than one cell can require a battery management system, thus the presence of a battery management system can indicate the battery is a lithium battery. In yet another example, batteries containing specific geometries, such as the perforated steel cathode collector found in most NiMH batteries, would indicate that battery is not a lithium battery.

[0180] The process 100 can include diverting each battery passed through the XRT scanner to another step of the process 100, e.g., based on the XRT scan of the battery. For example, a conveyor belt, or similar means, can deliver or otherwise move each battery to the XRT scanner from the optical and / or scale sensors and, based on the XRT scan of each battery, deliver or otherwise move each battery of the stream of mixed non-lead batteries to another step. The process 100 can include delivering or otherwise moving each battery passed through the XRT scanner to the next step continuously, e.g., without slowing or without stopping the stream of batteries being processed such as at Block 125. For example, a conveyor diverter, or similar means, can automatically divert batteries with an XRT scan indicating a lithium battery to a new stream, or alternatively divert batteries with an XRT scan that does not indicate a lithium battery. In some embodiments, the low discrete density batteries and the higher discrete density batteries may each be diverted to new streams. In this way, the throughput of the process 100 can be maintained, minimally reduced, or not reduced.

[0181] In cases where the XRT scan indicates that a battery includes lithium, the process 100 can proceed to Block 130 (Line 125A). For example, batteries with an XRT scan that indicates the battery includes lithium can be diverted, e.g., by a third diverter, to a new stream, such as a first stream of lithium batteries, and compiled (Block 130). For example, FIG. 2 depicts 18 tons of the 1,000 tons of mixed batteries received compiled as lithium batteries. In other words, about 1.8% of the mixed batteries received can be compiled as lithium batteries after the stream ofmixed non-lead batteries are passed through the XRT scanner. In this example, about 18 tons out of 1,000 tons of mixed batteries received can be automatically sorted as lithium batteries, as depicted in FIG. 2, which can then proceed to lithium battery recycling. In this way, the overall efficiency of lithium battery recycling can be improved, e.g., compared to manual sortation.

[0182] In cases where the XRT scan indicates that a battery does not include lithium, the process 100 can proceed to Block 135 (Line 125B). For example, batteries with an XRT scan that indicates the battery does not include lithium can be diverted, e.g., by the third diverter, to a new stream, such as a first stream of non-conforming batteries, and compiled (Block 135). For example, FIG. 2 depicts less than 2 tons of the 1,000 tons of mixed batteries received compiled as non-conforming batteries, e.g., neither lithium nor lead batteries. In other words, less than or equal to about 0.2% of the mixed batteries received can be compiled as non-conforming batteries after the stream of mixed non-lead batteries are passed through the XRT scanner. In this example, about 2 tons out of 1,000 tons of mixed batteries received can be automatically sorted as non-conforming batteries, as depicted in FIG. 2. In this way, the overall risks from nonconforming batteries can be reduced.

[0183] In cases where the XRT scan indicates that it is unknown whether a battery includes lithium or not, e.g., the machine learning program associated with the XRT scanner does not make a determination based on the XRT scan, the process 100 can proceed to Block 140 (Line 125C). For example, an XRT scan of a battery may not indicate whether the battery includes lithium or not. In these examples, batteries of the stream of mixed non-lead batteries with an inconclusive XRT scan can be compiled or otherwise diverted to further processing means, e.g., diverted by the third diverter to a new stream, such as a stream of unknown batteries. For example, FIG. 2 depicts less than 1 ton of the 1,000 tons of mixed batteries received separated for further processing. In other words, less than or equal to about 0.1% of the mixed batteries received can be separated or otherwise diverted for further processing after the stream of mixed non-lead batteries are passed through the XRT scanner.

[0184] In some embodiments, X-ray images may be taken at two or more imaging energies. For example, a first image may be taken at a first imaging energy and a second image may be taken at a second imaging energy different than the first imaging energy. In many examples, an image taken at a first imaging energy (e.g., FIGS. 5B, 6B, etc.) is at a lower imaging energy than an image taken at a second imaging energy (e.g., FIGS. 5C, 6C, etc.)Visual Identification

[0185] The process 100 can include additionally processing the batteries with an XRT scan that does not indicate whether a battery includes lithium or not, to identify batteries including lithium (Block 140). For example, the process 100 can include directing each battery of the batteries with an XRT scan indicating that it is unknown whether a battery includes lithium or not, e.g., the stream of unknown batteries, to at least one manual identifier. The manual identifier can inspect, e.g., visually, each battery and manually sort and separate batteries including lithium from non-conforming batteries. The manual identifier can additionally update the machine learning program associated with the XRT scanner at Block 125 with the data used to determine whether the unknown battery includes lithium or not.

[0186] The process 100 can include diverting each battery inspected by the manual identifier to another step of the process 100, e.g., based on the results of the inspection. For example, a conveyor belt, or similar means, can deliver or otherwise move each battery to the manual identifier from the XRT scanner and based on the determination of the manual identifier, deliver or otherwise move each battery of the stream of unknown batteries to another step. For example, a conveyor diverter, or similar means, can automatically divert batteries that are non-conforming, as determined by the manual identifier, to a new stream, or alternatively divert batteries that include lithium, as determined by the manual identifier. In some embodiments, the lithium batteries and the non-conforming batteries may each be diverted to new streams. In this way, the throughput of the process 100 can be maintained, minimally reduced, or not reduced.

[0187] In cases where the manual identifier determines that a battery does not include lithium, e.g., is a non-confirming battery, the process 100 can proceed to Block 135 (Line 140A). For example, batteries with a determination that the battery does not include lithium can be diverted to a new stream, such as a second stream of non-conforming batteries, and compiled (Block 135). For example, FIG. 2 depicts less than 2 tons total of the 1,000 tons of mixed batteries received, compiled as non-conforming batteries, e.g., neither lithium batteries nor lead batteries. In other words, between Blocks 125 and 140 of FIG. 1, about 2 tons out of 1,000 tons of mixed batteries received can be sorted as non-conforming batteries, as depicted in FIG. 2, after the stream of mixed non-lead batteries are passed through the XRT scanner and the stream of unknown batteries are passed through the visual inspection. In other words, less than or equalto about 0.2% of the mixed batteries received can be compiled as non-conforming batteries. In this way, the overall risks from non-conforming batteries can be reduced.

[0188] In cases where the manual identifier determines that a battery includes lithium, the process 100 can proceed to Block 130 (Line 140B). For example, batteries with a determination that the battery includes lithium can be diverted to a new stream, such as a second stream of lithium batteries, and compiled (Block 130). For example, FIG. 2 depicts 18 tons total of the 1,000 tons of mixed batteries received, compiled as lithium batteries. In other words, between Blocks 125 and 140 of FIG. 1, about 18 tons out of 1,000 tons of mixed batteries received can be sorted as lithium batteries, as depicted in FIG. 2, after the stream of mixed non-lead batteries are passed through the XRT scanner and the stream of unknown batteries are passed through the visual inspection, which can then proceed to a lithium battery recycling process. In other words, about 1.8% of the mixed batteries received can be compiled as lithium batteries. In this way, the overall efficiency of a lithium battery recycling process can be improved, e.g., compared to only manual sortation.

[0189] FIG. 3 illustrates an example method 300 for automatically sorting batteries.Although the example method 300 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the function of the method 300. In other examples, different components of an example device or system that implements the method 300 may perform functions at substantially the same time or in a specific sequence.

[0190] According to some examples, the method 300 includes a block 302 to divert a first stream of mixed batteries 304.

[0191] According to some examples, the method 300 includes a block 306 to separate the first stream of mixed batteries 304 into a first stream of lead batteries 308 and a second stream of mixed batteries 310.

[0192] According to some examples, the method 300 includes a block 312 to separate a second stream of mixed batteries 310 into a second stream of lead batteries 314 and a third stream of mixed batteries 316.

[0193] According to some examples, the method 300 includes a block 318 to separate the third stream of mixed batteries 316 into a first stream of lithium batteries 320, a first stream of non-conforming batteries 322, and a first stream of unknown batteries 324.

[0194] According to some examples, the method 300 includes a block 326 to separate the first stream of unknown batteries 324 into a second stream of lithium batteries 328 and a second stream of non-conforming batteries 330.

[0195] FIG. 4 depicts an embodiment of an automatic battery sorting system 200, which is an embodiment of the system 100. FIG. 4 includes at least one of a front-end battery pallet dumping units 402, a ferrous metal detector 404, a scale and camera 406, an XRT 408, robotic arms 410, and a manual identifier 412.

[0196] In this particular embodiment, batteries are received by the front-end battery pallet dumping units 402, which are then received as a first stream of mixed batteries 201. The first stream of mixed batteries 201 are passed through a first device 202, which in this particular embodiment comprises of the ferrous metal detector 404. The ferrous metal detector 404 and a first diverter 203, which in this embodiment is one of the robotic aims 410, separate the first stream of mixed batteries 201 into a first stream of lead batteries 204 and a second stream of mixed batteries 205.

[0197] Subsequently, a second device 206 receives the second stream of mixed batteries 205. In this particular embodiment, the second device 206 includes the scale and camera 406. The scale and camera 406 and the second diverter 207, which in this embodiment is another of the robotic arms 410, separate batteries based on discrete density measurements into the second stream of lead batteries 208 (not shown) and the third stream of mixed batteries 209. In this embodiment, the second stream of lead batteries 208 (not shown) is immediately combined with the first stream of lead batteries 204 into a first combined stream of lead batteries 219.

[0198] A third device 210 subsequently receives the third stream of mixed batteries 209. In this embodiment, the third device 210 comprises the XRT 408. The XRT 408 and the third diverter 211, which in this embodiment is one of the robotic arms 410, then separate the third stream of mixed batteries 209 into a first stream of lithium batteries 212, a first stream of nonconforming batteries 213, and a stream of unknown batteries 214.

[0199] In this particular embodiment, the fourth device or agent 215 is comprised of the manual identifier 412. In this example, the manual identifier 412 separates the stream ofunknown batteries 214 into a second stream of lithium batteries 217 (not shown) and a second stream of non-conforming batteries 218 (not shown). The second stream of lithium batteries 217 (not shown) is immediately combined with the first stream of lithium batteries 212 into a first combined stream of lithium batteries 220. Likewise, the second stream of non-conforming batteries 218 (not shown) and the first stream of non-conforming batteries 213 are immediately combined into a first combined stream of non-conforming batteries 221.

[0200] FIGS. 5A - 46B show examples of batteries suitable for processing with the systems and methods disclosed herein. In some examples, the X-ray images of the batteries are taken at two different energies. In some embodiments, the methods herein include comparing, with a processing element, the two images (e.g., FIG. 5B and FIG 5C) to automatically highlight and determine different material densities in the battery.

[0201] In some embodiments, any of the optical images (e.g., FIGS. 5A, 6A, ... 46A) of the batteries, or the X-ray images of the batteries (e.g., FIGS. 5B, 5C, ... 46B, 46C) may be used to train an artificial intelligence or machine learning (AI / ML) algorithm to automatically detect battery types based on the images. For example, if an AI / ML algorithm such as a classifier is trained with a data set including the optical images, the systems disclosed herein may receive an optical image of a battery and automatically classify or sort the battery based on the optical image. For example, a robot 410 may receive a command from a processor executing the AI / ML algorithm to sort a battery based on an optical image thereof. Similarly, the AI / ML algorithm may be trained on one or more sets of X-ray images (e.g., taken at one or more energies) and may then recognize batteries based on similar X-ray images. Again, the systems disclosed herein may sort the battery based on the AI / ML recognition of a battery based on the X-ray images.

[0202] FIG. 47 illustrates an example method 4700 for training an AI / ML algorithm to identify batteries as part of any system or method disclosed herein. Although the example method 4700 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect the function of the method 4700. In other examples, different components of an example device or system that implements the method 4700 may perform functions at substantially the same time or in a specific sequence.

[0203] In some embodiments, any of the optical images (e.g., FIGS. 5A, 6A, ... 46A) of the batteries, or the X-ray images of the batteries (e.g., FIGS. 5B, 5C, ... 46B, 46C) may be used to train an artificial intelligence or machine learning (AI / ML) algorithm to automatically detect battery types based on the images. In some embodiments, optical and / or X-ray images are individually or collectively training data 4702.

[0204] According to some examples, the method 4700 includes receiving optical images at operation 4704. For example, a processing element 4902 executing a training routine for an AI / ML algorithm may receive optical images of the outside case of a battery. The training routine may also receive data that correlates the optical image to a particular type of battery, general battery chemistry, or chemical content.

[0205] According to some examples, the method 4700 includes receiving x-ray images at a first energy at operation 4706. For example, a processing element 4902 executing a training routine for an AI / ML algorithm may receive X-ray images of a battery at a first energy (e.g., (e.g., FIGS. 5B, 5C, ... 46B, 46C). The training routine may also receive data that correlates the X-ray image to a particular- type of battery, general battery chemistry, or chemical content.

[0206] According to some examples, the method 4700 includes receiving x-ray images at a at operation 4708. For example, a processing element 4902 executing a training routine for an AI / ML algorithm may receive X-ray images of a battery at a second energy (e.g., (e.g., FIGS.5B, 5C, ... 46B, 46C). The training routine may also receive data that correlates the X-ray image to a particular type of battery, general battery chemistry, or chemical content. In some embodiments, the AI / ML may be trained on a differential or comparison of X-ray images taken at two or more different energies.

[0207] According to some examples, the method 4700 includes training AI / ML algorithm at operation 4710 based on the training data 4702. For example, an artificial neural network, deep learning network, transformer, or other type of AI / ML algorithm may have weights of its neurons set based on the training data 4702.

[0208] FIG. 48 illustrates an example method 4800 for identifying and / or sorting batteries with an AI / ML algorithm, e.g., such as one trained by the method 4700. Although the example method 4800 depicts a particular sequence of operations, the sequence may be altered without departing from the scope of the present disclosure. For example, some of the operations depicted may be performed in parallel or in a different sequence that does not materially affect thefunction of the method 4800. In other examples, different components of an example device or system that implements the method 4800 may perform functions at substantially the same time or in a specific sequence.

[0209] According to some examples, the method 4800 includes receiving optical images at operation 4804. In the method 4800, any of the optical images and / or X-ray images may be sorting data 4802. For example, as a battery is processed by the systems disclosed herein, an optical camera may capture one or more images of the battery. The optical image may be fed to the trained AI / ML algorithm that then generates a command to the system to sort the battery, e.g., the system may actuate a diverter or a robot disclosed herein to move the battery to a stream based on the AI / ML algorithm identification of the battery class, type, chemical content, etc. In some embodiments, the optical image may include images of two or more batteries and the AI / ML algorithm may classify (and the systems may sort) individual batteries from a group or pile.

[0210] According to some examples, the method 4800 includes receiving x-ray images at a first energy at operation 4806. According to some examples, the method 4800 includes receiving x-ray images at a second energy at operation 4808. Similarly, the systems disclosed may scan incoming batteries with an X-ray sensor at one or more energies. In some examples, the sorting data includes a differential or comparison of X-ray images taken at two or more different energies.

[0211] According to some examples, the method 4800 includes classifying the battery at operation 4810. For example, the trained AI / ML algorithm may determine, based on the sorting data 4802 that a battery belongs to a certain class, type, chemical content, etc.

[0212] According to some examples, the method 4800 includes sorting battery at operation 4812. For example, the systems and methods disclosed herein may activate a diverter to move the identified battery to a particular stream.

[0213] FIG. 49 is a simplified block diagram of components of a computing system 4900 of the system 100 or system 200, such as the first device 202, second device 206, first diverter 203, second diverter 207, a robotic arm 410, or a computing system 4900 executing or training an AI / ML algorithm, etc. For example, the processing element 4902 and the memory component 4908 may be located at one or in several computing systems 4900. This disclosure contemplates any suitable number of such computing systems 4900. For example, the computing system 4900may be a desktop computing system, a mainframe, a blade, a mesh of computing systems 4900, a laptop or notebook computing system 4900, a tablet computing system 4900, an embedded computing system 4900, a system-on-chip, a single-board computing system 4900, or a combination of two or more of these. Where appropriate, a computing system 4900 may include one or more computing systems 4900; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. A computing system 4900 may include one or more processing elements 4902, an input / output I / O interface 4904, one or more external devices 4912, one or more memory components 608, and a network interface 4910. Each of the various components may be in communication with one another through one or more buses or communication networks, such as wired or wireless networks, e.g., a network. The components in FIG. 49 are exemplary only. In various examples, the computing system 4900 may include additional components and / or functionality not shown in FIG. 49.

[0214] The processing element 4902 may be any type of electronic device capable of processing, receiving, and / or transmitting instructions. For example, the processing element 4902 may be a central processing unit, microprocessor, processor, or microcontroller. Additionally, it should be noted that some components of the computing system 4900 may be controlled by a first processing element 4902 and other components may be controlled by a second processing element 4902, where the first and second processing elements may or may not be in communication with each other.

[0215] The VO interface 4904 allows a user to enter data in to computing system 4900, as well as provides an input / output for the computing system 4900 to communicate with other devices or services. The VO interface 4904 can include one or more input buttons, touch pads, touch screens, and so on.

[0216] The external device 4912 are one or more devices that can be used to provide various inputs to the computing systems 600, e.g., mouse, microphone, keyboard, trackpad, sensing element (e.g., a thermistor, humidity sensor, light detector, etc. The external devices 4912 may be local or remote and may vary as desired. In some examples, the external devices 4912 may also include one or more additional sensors.

[0217] The memory components 4908 are used by the computing system 4900 to store instructions for the processing element 4902 such as training data, instructions for executing ortraining the AI / ML algorithm, the steps of the methods disclosed herein, user preferences, alerts, etc. The memory components 4908 may be, for example, magneto-optical storage, read-only memory, random access memory, erasable programmable memory, flash memory, or a combination of one or more types of memory components.

[0218] The network interface 4910 provides communication to and from the computing system 4900 to other devices. The network interface 4910 includes one or more communication protocols, such as, but not limited to Wi-Fi, Ethernet, Bluetooth, etc. The network interface 4910 may also include one or more hardwired components, such as a Universal Serial Bus (USB) cable, or the like. The configuration of the network interface 4910 depends on the types of communication desired and may be modified to communicate via Wi-Fi, Bluetooth, etc.

[0219] The display 4906 provides a visual output for the computing system 4900 and may be varied as needed based on the device. The display 4906 may be configured to provide visual feedback to user and may include a liquid crystal display screen, light emitting diode screen, plasma screen, or the like. In some examples, the display 4906 may be configured to act as an input element for the user through touch feedback or the like.

[0220] It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure can be made without departing from the spirit of the present disclosure as defined in the appended claims. In methodologies directly or indirectly set forth herein, operations can be performed in any order, unless explicitly claimed otherwise or a specific order is inherently necessitated by the claim language. Additionally, some of the operations described can be skipped or not included in the process 100.

[0221] The description of certain embodiments included herein is merely exemplary in nature and is in no way intended to limit the scope of the disclosure or its applications or uses. In the included detailed description of embodiments of the present systems and methods, reference is made to the accompanying drawings which form a part hereof, and which are shown by way of illustration specific to embodiments in which the described systems and methods may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice presently disclosed systems and methods, and it is to be understood that other embodiments may be utilized, and that structural and logical changes may be made without departing from the spirit and scope of the disclosure. Moreover, for the purpose of clarity,detailed descriptions of certain features will not be discussed when they would be apparent to those with skill in the art so as not to obscure the description of embodiments of the disclosure. The included detailed description is therefore not to be taken in a limiting sense, and the scope of the disclosure is defined only by the appended claims.

[0222] From the foregoing it will be appreciated that, although specific embodiments of the invention have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the invention.

[0223] The particulars shown herein are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present disclosure and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of various embodiments of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the invention, the description taken with the drawings and / or examples making apparent to those skilled in the art how the several forms of the invention may be embodied in practice.

[0224] As used herein and unless otherwise indicated, the terms “a” and “an” are taken to mean “one”, “at least one” or “one or more”. Unless otherwise required by context, singular terms used herein shall include pluralities and plural teims shall include the singular.

[0225] Unless the context clearly requires otherwise, throughout the description and the claims, the words ‘comprise’, ‘comprising’, and the like arc to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to”. Words using the singular or plural number also include the plural and singular number, respectively. Additionally, the words “herein,” “above,” and “below” and words of similar import, when used in this application, shall refer to this application as a whole and not to any particular portions of the application.

[0226] All relative, directional, and ordinal references (including top, bottom, side, front, rear, first, second, third, and so forth) are given by way of example to aid the reader’s understanding of the examples described herein. They should not be read to be requirements or limitations, particularly as to the position, orientation, or use unless specifically set forth in the claims. Connection references (e.g., attached, coupled, connected, joined, and the like) are to be construed broadly and may include intermediate members between a connection of elements andrelative movement between elements. As such, connection references do not necessarily infer that two elements are directly connected and in fixed relation to each other, unless specifically set forth in the claims.

[0227] Of course, it is to be appreciated that any one of the examples, embodiments or processes described herein may be combined with one or more other examples, embodiments and / or processes or be separated and / or performed amongst separate devices or device portions in accordance with the present systems, devices and methods.

[0228] Finally, the above discussion is intended to be merely illustrative of the present system and should not be construed as limiting the appended claims to any particular embodiment or group of embodiments. Thus, while the present system has been described in particular detail with reference to exemplary embodiments, it should also be appreciated that numerous modifications and alternative embodiments may be devised by those having ordinary skill in the art without departing from the broader and intended spirit and scope of the present system as set forth in the claims that follow. Accordingly, the specification and drawings are to be regarded in an illustrative manner and are not intended to limit the scope of the appended claims.

[0229] As used herein, the terms “about” and “substantially”, when referring to a value or to an amount of a composition, mass, weight, temperature, time, volume, concentration, percentage, etc., are meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods or employ the disclosed compositions.

[0230] Every range of values (of the form, "from about a to about b," or, equivalently, "from approximately a to b," or, equivalently, "from approximately a-b" or, equivalently, "greater than about a and less than about b", for example) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values.As used herein, the term “and / or” when used in the context of a listing of entities, refers to the entities being present singly or in combination. Thus, for example, the phrase “A, B, C, and / or D” includes A, B, C, and D individually, but also includes any and all combinations and subcombinations of A, B, C, and D.

Claims

CLAIMSWe claim:

1. A battery sorting system comprising: a first stream of mixed batteries; a first device configured to detect ferrous material; a first diverter configured to separate the first stream of mixed batteries into: a first stream of lead batteries comprising at least one battery not including ferrous material; and a second stream of mixed batteries comprising at least one battery including ferrous material; a second device configured to detect lead batteries including ferrous material; a second diverter configured to separate the second stream of mixed batteries into: a second stream of lead batteries comprising at least one lead battery including ferrous material; and a third stream of mixed batteries comprising at least one non-lead battery; a third device configured to detect lithium batteries; a third diverter configured to separate the third stream of mixed batteries into: a first stream of lithium batteries comprising at least one lithium battery; a first stream of non-conforming batteries comprising at least one non-lithium battery; and a first stream of unknown batteries.

2. The system of claim 1, wherein the system is at least partially automated.

3. The system of claim 1, wherein the first device comprises a ferrous metal detector.

4. The system of claim 3, wherein a sensitivity range of the ferrous metal detector is adjusted based on a feedback datum from a downstream battery recycling process.

5. The system of claim 1, wherein the first device comprises two or more ferrous metal detectors in series or parallel.

6. The system of claim 1, wherein the second device comprises at least one or more of a density sensor, an optical sensor, a geometry sensor, or a mass detection sensor.

7. The system of claim 1, wherein the third device comprises an X-ray tomography scanner.

8. The system of claim 1, further comprising a plurality of conveyor belts to continuously move the first stream of mixed batteries, the first stream of lead batteries, the second stream of mixed batteries, the second stream of lead batteries, the third stream of mixed batteries, the first stream of lithium batteries, the first stream of non-conforming batteries, and the stream of unknown batteries.

9. The system of claim 1, wherein the third device is configured to receive a battery of up to, and including, 250 kilograms in weight.

10. The system of claim 1, wherein the first stream of mixed batteries comprises one or more lead batteries.

11. The system of claim 1, wherein the first stream of mixed batteries comprises 1,500 tons of batteries or less per day.

12. The system of claim 1, further comprising: a fourth device configured to visually inspect the first stream of unknown batteries; and a fourth diverter configured to separate the stream of unknown batteries into: a second stream of lithium batteries comprising at least one of a visually confirmed lithium battery; and a second stream of non-conforming batteries comprising at least one of a visually confirmed non-conforming battery.

13. A method for sorting batteries, comprising: receiving a first stream of mixed batteries; a first processing step to separate the first stream of mixed batteries into: a first stream of lead batteries comprising at least one battery not including a ferrous material; anda second stream of mixed batteries comprising at least one battery including the ferrous material; a second processing step to separate the second stream of mixed batteries into: a second stream of lead batteries comprising at least a lead battery including the ferrous material; and a third stream of mixed batteries comprising at least one non-lead battery including the ferrous material; a third processing step to separate the third stream of mixed batteries into: a first stream of lithium batteries comprising at least one battery including lithium; a first stream of non-conforming batteries comprising at least one battery not including lithium; and a stream of unknown batteries; and a fourth processing step to separate the stream of unknown batteries into: a second stream of lithium batteries comprising at least one battery including lithium; and a second stream of non-conforming batteries comprising at least one battery not including lithium.

14. The method of claim 13, wherein the method is at least partially automated.

15. The method of claim 13, wherein the method sorts 90% or greater of the first mixed battery stream automatically prior to a manual inspection and sortation.

16. The method of claim 13, wherein the second processing step separates the second stream of mixed batteries based on a density measurement.

17. The method of claim 16, wherein the second stream of lead batteries comprises at least one battery with a density greater than or about 90 pounds per cubic foot and the third stream of mixed batteries comprises at least one battery with a density less than 90 pounds per cubic foot.

18. The method of claim 13, wherein the third processing step separates the third stream of mixed batteries based on a three-dimensional structure measurement without modifying or destroying batteries or battery cases.

19. The method of claim 13, wherein the third processing step separates the third stream of mixed batteries based on one or more of a transmission rate or internal architecture differences via a detector and a machine learning program.

20. The method of claim 13, wherein the fourth processing step separates the stream of unknown batteries using visual inspection.

21. The method of claim 13, wherein the method is at least partially continuous, with respect to one or more of the first stream of mixed batteries, the first stream of lead batteries, the second stream of mixed batteries, the second stream of lead batteries, the third stream of mixed batteries, the first stream of lithium batteries, the first stream of nonconforming batteries, the stream of unknown batteries, the second stream of lithium batteries, and the second stream of non-conforming batteries.

22. A method for sorting mixed streams of batteries, comprising: receiving a stream of mixed batteries; processing the stream of mixed batteries to separate: batteries including ferrous metal into a stream of mixed batteries with steel; and batteries not including a ferrous metal into a first stream of lead batteries; measuring a discrete density of each battery of the stream of mixed batteries with steel to separate: batteries including lead into a second stream of lead batteries; and non-lead batteries into a stream of mixed non-lead batteries; scanning the stream of mixed non-lead batteries to separate: batteries including lithium into a first stream of lithium batteries; batteries not conforming into a first stream of non-conforming batteries; and batteries unknown into a stream of unknown batteries; visually inspecting the stream of unknown batteries to separate: batteries including lithium into a second stream of lithium batteries; and batteries not conforming into a second stream of non-conforming batteries.

23. An automated battery sortation system comprising: a first sensor configured to process a stream of mixed batteries and, from the stream of mixed batteries, detect batteries including ferrous metal; a first diverter configured to separate the batteries including ferrous metal into a stream of mixed batteries with steel; a second sensor configured to measure a discrete density of each battery of the stream of mixed batteries with steel and, based on the discrete densities, detect non-lead batteries; a second diverter configured to separate the non-lead batteries into a stream of mixed non-lead batteries; a scanner configured to scan the stream of mixed non-lead batteries to detect batteries including lithium, batteries not conforming, and batteries unknown; and; a third diverter configured to separate the batteries including lithium into a stream of lithium batteries, the batteries not conforming into a stream of non-conforming batteries, and the batteries unknown into a stream of unknown batteries.

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