Automated Battery Sorting System

Abstract

The present disclosure relates to a system, non-transitory computer-readable medium, and method for analyzing and sorting batteries according to a battery classification. In particular, in one or more embodiments, the disclosed system scans a target battery with multiple sensors and utilizes a classifier model to analyze the signals of the multiple sensors to determine a battery classification of the target battery. Also, in some embodiments, the disclosed system indicates the predicted battery classification to a battery sorting mechanism for sorting the target battery. Additional mechanisms and related methods for automated battery classification and sorting are disclosed.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 373,380, filed August 24, 2022, U.S. Patent Application No. 18 / 452,961, filed August 21, 2023, and U.S. Patent Application No. 18 / 452,974, filed August 21, 2023. Each of the foregoing applications is incorporated herein by reference in its entirety. [Background technology]

[0002] In recent years, the adoption of various types of batteries as an alternative to fossil fuels and other energy types has increased significantly. Additionally, the recent rise in popularity of electric vehicles and other electronic devices has resulted in a significant increase in the demand for battery production, as well as an increase in the demand for the safe and efficient recycling or disposal of batteries and battery materials.

[0003] Despite advances in battery production and implementation in various fields of use, existing methods for the mass recycling and / or disposal of batteries and battery materials face several drawbacks. For example, conventional systems can safely recycle or dispose of batteries of certain chemical compositions, such as lithium-ion batteries. However, certain battery compositions are difficult or impossible to recycle in a safe and efficient manner. Therefore, various classes of batteries must be sorted with a high level of certainty before recycling or disposal. Unfortunately, conventional methods of sorting and separating batteries typically require human attention and scrutiny of each battery, resulting in inefficiencies and often inaccuracies when processing large numbers of batteries for recycling and / or disposal.

[0004] These present additional challenges and problems with conventional battery recycling systems. Summary of the Invention

[0005] Embodiments of the present disclosure provide benefits and / or solve one or more of the aforementioned or other problems in the art with systems, apparatus, non-transitory computer-readable media, and methods for automatically classifying and sorting various classes of batteries. For example, the disclosed systems intelligently classify and sort batteries according to chemical composition, form factor, and / or other classifications utilizing various sensors and sorting mechanisms.

[0006] In some embodiments, for example, an apparatus for sorting batteries of various configurations includes a hopper mechanism configured to discharge the batteries individually onto a conveyor and a scanner mechanism with a plurality of sensors configured to determine an expected battery configuration for each battery. The apparatus also includes an array of sorting mechanisms configured to transfer the batteries into a plurality of bins according to the expected battery configuration. In one or more embodiments, additional sensors, such as infrared (IR) cameras, are implemented to detect abnormalities in discharged batteries, such as high temperatures.

[0007] Further, in one or more embodiments, the battery classification system receives a plurality of signals corresponding to the target battery from a plurality of sensors. For example, the plurality of sensors may comprise two or more of an X-ray scanning array, a three-dimensional (3D) scanner, and an RGB camera or an infrared camera. In response, utilizing a classifier model, the system determines a predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals. In one or more embodiments, the system indicates the predicted battery classification to a battery sorting mechanism.

[0008] Thus, the disclosed embodiments offer significant advantages over existing solutions, such as the increased efficiency enabled by automated battery sorting and separation. Additionally, the disclosed embodiments exhibit increased safety by eliminating the need for human interaction with potentially volatile or unstable batteries, by accurately predicting battery classification to avoid failures in subsequent processes, and by providing enhanced measures for detecting battery anomalies throughout the sorting and separation process.

[0009] Additional features and advantages of one or more embodiments of the present disclosure will be set forth in the description that follows, and in part will be obvious from the description, or may be learned by practice of such exemplary embodiments. [Brief explanation of the drawings]

[0010] The detailed description provides one or more embodiments with additional specificity and detail through the use of the accompanying drawings, as briefly described below. [Figure 1] FIG. 1 illustrates a perspective view of an automated battery sorting system according to one or more embodiments. [Figure 2] FIG. 2 illustrates an additional perspective view of an automated battery sorting system according to one or more embodiments. [Figure 3] FIG. 3 illustrates a top view of an automated battery sorting system according to one or more embodiments. [Figure 4] FIG. 4 illustrates a perspective view of a scanning bay of an automated battery sorting system according to one or more embodiments. [Figure 5A] FIG. 5A illustrates an additional perspective view of a scanning bay of an automated battery sorting system according to one or more embodiments. [Figure 5B] FIG. 5B illustrates an additional perspective view of a scanning bay of an automated battery sorting system according to one or more embodiments. [Figure 6]FIG. 6 illustrates a perspective view of a battery tipper and hopper assembly of an automated battery sorting system according to one or more embodiments. [Figure 7] FIG. 7 illustrates a diagram of an environment in which a battery classification system can operate, according to one or more embodiments. [Figure 8] FIG. 8 illustrates a schematic diagram of a battery classification system according to one or more embodiments. [Figure 9] FIG. 9 illustrates a battery classification system utilizing an RGB camera in accordance with one or more embodiments. [Figure 10] FIG. 10 illustrates a battery classification system utilizing a three-dimensional (3D) scanner and an X-ray scanning array, according to one or more embodiments. [Figure 11] FIG. 11 illustrates a flowchart of a series of operations for sorting and classifying batteries according to one or more embodiments. [Figure 12] FIG. 12 illustrates a block diagram of an exemplary computing device for implementing one or more embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] This disclosure describes one or more embodiments of a battery classification system that intelligently classifies batteries of various configurations based on inputs from various sensors. For example, in some implementations, the battery classification system utilizes a battery classifier machine learning model based on the various sensor signals to predict a battery classification for a target battery. Furthermore, some embodiments include a battery sorting mechanism configured to sort the battery according to the battery classification predicted by the battery classification system embodiment.

[0012] In some embodiments, for example, a battery classification system receives a plurality of signals corresponding to a target battery from a plurality of sensors. In response, the battery classification system utilizes a classifier machine learning model to determine a predicted battery classification of the battery from the plurality of battery classifications based on the plurality of signals. Also, in some embodiments, the battery classification system indicates the predicted battery classification to a battery sorting mechanism. Further, in some embodiments, the plurality of sensors comprises two or more of an X-ray scanning array, a three-dimensional (3D) scanner, an RGB camera, or an infrared camera.

[0013] Additionally, one or more embodiments of an apparatus for sorting batteries of various configurations include an arrangement of a hopper mechanism, a scanner mechanism, and a sorting mechanism. In some embodiments, the hopper mechanism is configured to individually discharge the plurality of batteries onto a conveyor. Further, the scanner mechanism, in some embodiments, comprises multiple types of sensors disposed about the conveyor and configured to determine a predicted battery configuration for each battery from the plurality of battery configurations. Also, in some embodiments, an arrangement of sorting mechanisms is disposed about the conveyor and configured to transfer the plurality of batteries from the conveyor to multiple bins based on the predicted battery configuration for each battery of the plurality of batteries.

[0014] Accordingly, the disclosed embodiments provide automated classification and sorting of batteries, including various classifications such as, but not limited to, chemical composition, form factor, and other configurations. Furthermore, utilizing various sensors to scan and analyze individual batteries, such as three-dimensional (3D) scanners, X-ray scanning arrays, RGB cameras, and infrared (IR) cameras, the disclosed system accurately and efficiently classifies and sorts batteries for further processing. Indeed, the disclosed system can identify / classify various classifications of batteries and sort large quantities of batteries accordingly, while improving safety and efficiency. Additionally, one or more disclosed embodiments utilize a classifier machine learning model to intelligently and efficiently predict battery classifications according to data provided by a database of battery classifications.

[0015] Thus, the disclosed battery classification and sorting system provides many advantages and benefits over conventional systems and methods. For example, by utilizing multiple sensor types and trained classifier models, the battery classification system improves accuracy compared to conventional systems. Specifically, the disclosed embodiments utilize a classifier machine learning model to compare and analyze various attributes provided by the sensors against a data library of battery classifications to intelligently identify battery classifications in an automated process.

[0016] Furthermore, by utilizing an automated process for determining battery classification with a trained classifier model, the disclosed system improves efficiency compared to conventional systems. Specifically, the disclosed embodiments intelligently analyze and determine classifications for large numbers of batteries, but in most implementations do so automatically and without requiring human consideration for each battery. Thus, the disclosed system can efficiently sort batteries into groups according to their predicted classifications.

[0017] Additionally, the disclosed system improves the safety of processes for identifying and sorting batteries for recycling and / or disposal. Specifically, the disclosed system reliably predicts battery classification to prevent inadvertent disposal of batteries through incorrect procedures. For example, batteries with certain chemical compositions may be impossible or uneconomical to process through typical processes, while other types of batteries are easily recycled through known procedures. Furthermore, by utilizing sensors such as infrared (IR) cameras to scan batteries for anomalies (e.g., high temperatures), the disclosed system can prevent accidents involving volatile materials typical of many batteries.

[0018] As explained by the preceding discussion, the present disclosure utilizes various terms to describe the features and advantages of the disclosed system. Additional details regarding the meaning of such terms are now provided. For example, as used herein, the term "machine learning model" refers to a model that can be trained and / or tuned based on inputs to approximate an unknown function. For example, the term "machine learning model" can include, but is not limited to, a random forest model, a decision tree (e.g., a series of gradient-boosted decision trees (e.g., the XGBoost algorithm)), a multilayer perceptron, linear regression, a support vector machine, a deep learning architecture, a deep learning transformer (e.g., a self-attention transformer), or a logistic regression. In other embodiments, the machine learning model includes a neural network such as a convolutional neural network, a recurrent neural network (e.g., an LSTM), a graph neural network, a self-attention transformer neural network, a diffusion neural network, or a generative adversarial neural network.

[0019] As used herein, the term "X-ray attenuation" refers to the decrease in intensity of X-rays as they traverse a substance, which may be caused by the absorption or deflection of photons from the X-ray beam. X-ray attenuation can be affected by different factors, such as the beam energy and the atomic energy of the material absorbing the X-rays. In particular, the disclosed system takes the detected X-ray attenuation into account when determining the chemical and / or material composition of a target battery, as discussed in more detail below.

[0020] As used herein, the term "object recognition" refers to computer techniques for locating and identifying objects in images. For example, the term "object recognition" can include, but is not limited to, appearance-based methods, edge matching, segmentation methods, grayscale matching, gradient matching, object detection neural networks, decision trees, or any of a variety of models trained to recognize objects in images.

[0021] Additional details will now be provided in connection with exemplary figures depicting exemplary embodiments and implementations of the disclosed methods, apparatus, and systems. For example, FIGS. 1-6 illustrate various views of an automated battery sorting system 100 according to one or more embodiments. Specifically, FIG. 1 illustrates a perspective view of the automated battery sorting system 100, FIG. 2 shows an additional perspective view of the automated battery sorting system 100, and FIG. 3 illustrates a top view of the automated battery sorting system 100. Additionally, FIG. 4 illustrates a perspective view of the scanning bays 102 and 104 of the automated battery sorting system 100, and FIGS. 5A-5B illustrate additional perspective views of the scanning bays 102 and 104. Additionally, FIG. 6 illustrates a perspective view of the tipper mechanism 110 and hopper 112 of the automated battery sorting system 100.

[0022] As illustrated in FIGS. 1-3 , the automated battery sorting system 100 includes a tipper mechanism 110 configured to receive multiple batches of batteries for sorting. In some embodiments, the tipper mechanism 110 includes a large chamber or bin 126 into which unsorted batteries can be placed to begin the sorting process. In some embodiments, for example, a load of batteries can be transferred directly into the bin 126 from a battery collection container, barrel, truck bed, pallet, or the like. Additionally, the tipper mechanism 110 can include devices for moving batteries from the bin 126 into the hopper 112, such as, but not limited to, a movable platform, slides, pneumatic lifters and / or tippers, or a conveyor belt. Thus, batteries placed in the bin 126 pass from the bin 126, through the tipper mechanism 110, and into the hopper 112, where they are forced to pass individually for subsequent scanning. In some embodiments, for example, the hopper includes a vibrating table configured to separate batteries within the batches by shape factor and / or weight. For example, in some implementations, the hopper 112 removes smaller batteries from the group of articles so that the smaller batteries are separated from the relatively larger batteries. Additional details and examples regarding the tipper mechanism 110 and the hopper 112 are described below in connection with FIG.

[0023] As further illustrated in FIGS. 1-3 , in some embodiments, individual batteries pass from hopper 112 onto inclined conveyor 114 and drop into feeder 116 for individual scanning, sorting, and separation. Alternatively, in one or more embodiments, batteries are directed directly from hopper 112 into the scanning area of ​​automated battery sorting system 100. For example, the exit of hopper 112 can be located directly above or otherwise adjacent to the scanning area or horizontal conveyor of automated battery sorting system 100. Furthermore, in some embodiments, automated battery sorting system 100 includes one or more actuators and / or structures configured to arrange arriving batteries before entering a subsequent scanning area. For example, automated battery sorting system 100 can arrange individual batteries into associated storage bins for a single measurement and a single discharge / shunt. Additionally, in some embodiments, one or more arrangement mechanisms (e.g., actuators, structures, etc.) can position arriving batteries at a specific angle relative to x-ray and other scanning sources to enhance measurement accuracy. The alignment mechanism may include, but is not limited to, guide rails, pneumatic arms, vibrating tables, expansion / non-contraction brackets, gates, and the like.

[0024] In the illustrated embodiment, each battery slides or otherwise passes through a feeder 116 individually toward the first scanning bay 102 and the second scanning bay 104. In particular, the first scanning bay 102 includes one or more sensors, such as, but not limited to, one or more RGB cameras, infrared (IR) cameras, or 3D scanners. Additionally, the 3D scanners may include, but are not limited to, one or more of an optical camera, a 3D laser scanner, a computed tomography scanner, a structured light 3D scanner, a LiDAR, or a time-of-flight laser scanner. Accordingly, in one or more embodiments, the automated battery sorting system 100 utilizes various visual sensors to scan each battery within the first scanning bay 102. As described further below (e.g., in connection with FIGS. 7-10 ), the automated battery sorting system 100 utilizes the various sensors to determine attributes of each battery based on the sensor signals and to predict a battery classification or category for each battery based on the determined attributes.

[0025] Additionally, the sensor(s) in the first scanning bay 102, and optionally the second scanning bay 104, of the automated battery sorting system 100, comprise an X-ray scanning array configured to emit and detect an X-ray beam as it passes over each battery for further analysis (i.e., providing additional input to the battery classifier model). In some embodiments, either or both of the first scanning bay 102 and the second scanning bay 104 further include an alignment mechanism configured to position each battery in an ideal position and / or orientation for the scanning procedure. Such an alignment mechanism may include, for example, a series of bumpers / rails, movable actuators, tumblers, or other devices operable to reposition smaller batteries among larger batteries. Also, as mentioned, in response to scanning each battery in the first and second scanning bays 102 and 104 to determine various attributes of each battery, the automated battery sorting system 100 utilizes a classifier model to determine a predicted battery classification for each battery.

[0026] In any event, automated battery sorting system 100 includes at least one scanning bay and multiple sensors. As shown in FIGS. 1-3 , in at least one specific example, automated battery sorting system 100 includes two scanning bays 102 and 104, each housing a different type of scanner. In one or more implementations, first scanning bay 102 includes one or more sensors that detect / measure / acquire a first type of attribute of the batteries. Similarly, second scanning bay 104 includes one or more additional sensors that detect / measure / acquire a second type of attribute of the batteries that is different from the first type of attribute.

[0027] For example, in one or more implementations, the first scanning bay 102 includes one or more sensors that detect / measure / acquire one or more visible attributes of the battery (e.g., a first type of attribute). In particular, the one or more sensors of the first scanning bay 102 detect / measure / acquire one or more of the following: battery size (e.g., volume, height, width), battery color, battery shape, text on the battery, etc. Similarly, in one or more implementations, the second scanning bay 104 includes one or more sensors that detect / measure / acquire one or more invisible attributes of the battery (e.g., a second type of attribute). In particular, the one or more sensors of the second scanning bay 104 detect / measure / acquire one or more of the following: battery temperature, battery X-ray attenuation, one or more data of the battery atomic number, battery composition, battery weight, etc.

[0028] 1-3, the automated battery sorting system 100 includes one or more pedestals 124 to provide screener or operator access to the scanning bays 102 and 104 and other areas of the system. Additionally, the illustrated screener pedestal 124 provides access to support cabinets 122 that house various components of the scanning system for bays 102 and 104, such as generators, IO feeds, analog controls, and / or digital operator consoles.

[0029] Additionally, some embodiments of the automated battery sorting system 100 include one or more infrared cameras positioned at one or more locations within the system to monitor for hot batteries and, in some cases, include a safety mechanism. For example, the safety mechanism can include one or more of an alarm, a fire extinguisher, a battery dunk tank, or an emergency power-off (EPO). The safety mechanism is configured to activate in response to a signal from at least one of the multiple types of sensors. For example, the automated battery sorting system 100 includes a safety receptacle (e.g., a dunk tank) for disposing of batteries with elevated temperatures detected by the one or more infrared cameras. The safety receptacle includes a flame-retardant material. Alternatively, the automated battery sorting system 100 is configured to add a fire-preventing material to the safety receptacle when the battery is added to the safety receptacle. As used herein, the term "safety receptacle" refers to a receptacle for storing batteries and other potentially hazardous materials. In one or more embodiments, the safety receptacle may be made of a durable, fire-resistant material, such as metal. Additionally, the terms "fire retardant material" or "fire retardant" refer to materials utilized for the prevention of fires associated with metals, flammable liquids, or volatile materials such as lithium-ion batteries. For example, in some implementations, the fire retardant may include mineral-based fire extinguishing agents such as vermiculite, perlite, expanded clay, expanded polystyrene (EPS), CellBlockEX, and other fire, heat, and / or smoke suppressant compounds.

[0030] Also, in one or more embodiments, various components of automated battery sorting system 100 are covered with shock-absorbing material (e.g., rubber) to cushion the impact of batteries as they pass through the system. Additionally, in some embodiments, automated battery sorting system 100 includes an anti-static coating on its metal surfaces to reduce the risk of electrical shorting of the batteries.

[0031] As illustrated, after passing through scanning bays 102 and 104, each battery continues along conveyor 106 toward a plurality of sorting bins 108. Automated battery sorting system 100 transfers each target battery to one of sorting bins 108 according to the expected classification for each battery. In particular, sorting bins 108 include a bin for each battery configuration of a plurality of battery configurations. Additionally, in one or more implementations, one or more of the sorting bins 108 comprise a safety bin (e.g.,

[0044] ). In some embodiments, for example, the automated battery sorting system 100 utilizes a plurality of actuators or gates (i.e., diverters) to move target batteries into associated ones of the sorting bins 108. For example, as shown in FIG. 2, some embodiments include a movable actuator 202 selectively positioned to push, pull, or guide arriving batteries into the sorting bins 108 according to their expected classification. Alternatively, or in addition, embodiments of the automated battery sorting system 100 may include a sorting mechanism comprising pneumatic actuators, guides, hoists, cranes, platforms, or alternative means for diverting batteries into respective ones of the sorting bins 108. Furthermore, in some embodiments, the automated battery sorting system 100 includes one or more sensors configured to verify that the batteries have been received by the correct sorting bin 108. For example, the verification sensor may include, but is not limited to, a camera or a pressure sensor integrated with the sorting bin 108.

[0032] 1-3 illustrate an automated battery sorting system 100 having two rows of sorting bins 108. Specifically, FIGS. 1-3 illustrate a row of sorting bins on each side of a conveyor 106. In alternative implementations, the automated battery sorting system 100 includes more or fewer than two rows of sorting bins. For example, in one or more implementations, the automated battery sorting system 100 includes one row of sorting bins 108 on one side of the conveyor 106. Alternatively, the automated battery sorting system 100 includes three or four rows of sorting bins 108. For example, the automated battery sorting system 100 includes multiple rows of sorting bins 108 at different vertical heights on both sides of the conveyor 106.

[0033] As also shown, the automated battery sorting system 100 includes return conveyors 118, 120, and 128 for returning batteries to the hopper 112 (or, alternatively, the receiving bin 126 of the tipper mechanism 110) if additional scanning and / or analysis is required. For example, in some cases, a battery may be insufficiently classified if, for example, the battery was not properly scanned for some reason or if a previously unseen battery configuration is encountered. In at least such cases, the battery is returned to the front of the automated battery sorting system 100, and the scanning and sorting process is repeated. In some embodiments, batteries with indeterminate or otherwise insufficient battery classification are sorted into designated sorting bins 108 for alternative handling. In some implementations, unidentified batteries can pass the end of the conveyor 106 to drop into one or more bins 130 located at the end of the conveyor 106.

[0034] Furthermore, in some embodiments, instead of (or in addition to) sorting bins 108 located proximate to conveyor 106, automated battery sorting system 100 may include additional conveyors proximate main conveyor 106 and configured to deliver batteries to individual bins or to another area for sorting and / or further processing. Thus, in some embodiments, the aforementioned actuators may divert batteries onto the additional conveyors according to the predicted classification for each battery.

[0035] 4 and 5A-5B, scanning bays 102 and 104 include one or more sensors for scanning batteries as they pass from feeder 116 and across conveyor 106. For example, scanning bay 102 includes a scanning bar 402 that includes one or more of a three-dimensional (3D) scanner, an RGB camera, or an infrared (IR) camera. In additional embodiments, the scanning bay 102 includes additional sensors in additional locations to provide for scanning the batteries at various angles and / or distances for each battery as it passes through the scanning bay 102. For example, in some embodiments, multiple cameras are provided at various locations to capture images of each side of each battery. For example, in addition to or as an alternative to being located within the scanning bar 402, an RGB camera is positioned on the side of the conveyor to capture side views of the batteries as they pass through the scanning bay 102. Furthermore, in some embodiments, an additional sensor is provided to measure the weight of each battery for additional input into predicting the corresponding battery configuration.

[0036] 4 , the scan bay 104 includes an X-ray scanning array comprised of an X-ray emitter 404 and an X-ray detector 406. In one or more embodiments, for example, the X-ray emitter is mounted to a housing of the scan bay 104 and oriented to emit an X-ray beam toward the conveyor 106. Correspondingly, in one or more embodiments, the X-ray detector 406 is mounted below the conveyor 106, and the X-ray detector 406 detects and measures the X-ray beam emitted from the X-ray emitter 404 through each battery as it passes through the scan bay 104. Furthermore, in one or more embodiments, the X-ray detector 406 is housed within a retractable drawer 412, thus facilitating adjustment and maintenance of the X-ray detector 406. In additional implementations, the scanning bay 104 includes multiple X-ray scanning arrays. For example, the scanning bay 104 includes two, three, or more X-ray detector arrays, each arranged at a different angle relative to the batteries disposed on the conveyor 106. In particular, in addition to measuring X-ray attenuation passing vertically through the batteries as shown in FIG. 4, the scanning bay 104 includes an X-ray scanning array that measures X-ray attenuation passing horizontally through the batteries. In such implementations, the automated battery sorting system 100 includes an X-ray emitter on one side of the conveyor 106 and an X-ray detector on the other side of the conveyor 106. In any event, in light of the disclosure herein, it will be understood that the scanning bays 102, 104 may include sensors on one or more sides of the conveyor 106, vertically above and / or below the conveyor 106, and at one or more acute angles (e.g., 30 degrees, 45 degrees, 60 degrees, 120 degrees, 135 degrees, 150 degrees) relative to the conveyor 106.

[0037] 4 and 5A-5B, the support cabinet 122 includes a generator 408 for powering the X-ray scanning array and / or other components of the automated battery sorting system 100. The example support cabinet further includes a power box 410 (i.e., an input / output module) and a control assembly 502 (see FIG. 5A). While the control assembly 502 shown is an analog controller associated with the X-ray scanning array, embodiments of the automated battery sorting system 100 may also include an operations console that can control sensors in both the scanning bays 102 and 104, as well as other aspects of the battery sorting and sorting system of the automated battery sorting system 100.

[0038] 6 , the automated battery sorting system 100 also includes a tipper mechanism 110 with a receiving bin 126 for receiving battery articles for sorting and separation. With the battery articles loaded into the receiving bin 126 of the tipper mechanism 110, an actuator 606 of the tipper mechanism 110 raises the battery articles into the hopper 112 for dispersion toward subsequent sections of the automated battery sorting system 100. In some embodiments, for example, the actuator 606 rotates a chamber (i.e., tip) of the tipper mechanism 110 upward until the batteries are dispersed from an opening 608 adjacent the hopper 112. Additional examples of actuators for moving the battery articles from the receiving bin 126 to the hopper 112 include, for example, a conveyor belt, a movable platform, a drop chute (e.g., with the receiving bin 126 positioned above the hopper 112), etc. Alternatively, the batteries may be loaded directly into the hopper 112 by hand, conveyor, forklift, or other loading mechanism.

[0039] As previously described, hopper 112 vibrates and separates the batteries and distributes them onto inclined conveyor 114, directly to feed chute 116, or otherwise toward a scan area of ​​automated battery sorting system 100. For example, in some embodiments, hopper 112 is configured to individually distribute the batteries onto inclined conveyor 114 (see FIGS. 1-3) and then through tapered exit 604. As illustrated, hopper 112 includes a vibration table 602 configured to separate and guide the batteries toward tapered exit 604. In some embodiments, hopper 112 also includes narrowing rails or sidewalls that further guide the batteries toward tapered exit 604 as they are vibrated away from each other by vibration table 602. In some embodiments, the batteries are guided through the tapered chamber with or without the use of a vibration table.

[0040] 6 , hopper 112 may further include a weigh gate 610. In one or more embodiments, for example, weigh gate 610 may include one or more sensors that detect individual batteries as they pass through weigh gate 610 toward tapered exit 604. In some embodiments, weigh gates, such as weigh gate 610, are positioned at one or more selected locations in automated battery sorting system 100 to keep track of the amount of batteries passing through various sections of automated battery sorting system 100.

[0041] As mentioned above, the disclosed embodiments include a battery classification system configured to intelligently classify batteries of various types or configurations (i.e., various classifications) based on multiple sensor signals, such as those described above in connection with the automated battery sorting system 100 of Figures 1-6. For example, Figure 7 illustrates a schematic diagram of a battery classification system 704 operating in accordance with one or more embodiments.

[0042] 7, in some embodiments, a computing device 702 includes a battery classification system 704 in communication with a plurality of sensors 710 and a battery sorting mechanism 720. In alternative embodiments, the battery classification system 704 is included on one or more server devices that communicate with the computing device 702, the plurality of sensors 710, and / or the battery sorting mechanism 720 over a network. Indeed, a variety of configurations of computing devices, server devices, storage devices, sensors, and other components are contemplated by the present disclosure.

[0043] 7, the battery classification system 704 includes a classifier model 706 and a database 708 that includes multiple battery classifications and their associated attributes (i.e., the chemistry, form factors, and other attributes of the batteries within each of the multiple battery classifications). Thus, the battery classification system 704 is trained by comparing and / or using the battery classification data included in the database 708 to make battery classification predictions based on signals from multiple sensors 710. As mentioned above, in one or more embodiments, the classifier model 706 includes a machine learning model trained to classify batteries based on multiple signals from the multiple sensors 710.

[0044] 7, in some embodiments, the plurality of sensors 710 includes an X-ray scanning array 712 (e.g., as described above in connection with the second scanning bay 104, the X-ray emitter 404, and the X-ray detector 406), a three-dimensional (3D) scanner 714 (e.g., as described above in connection with the first scanning bay 102 and the scanning bar 402), one or more RGB cameras 716 (e.g., as described above in connection with the first scanning bay 102 and the scanning bar 402), and / or an infrared camera 718 (e.g., as described above in connection with the first scanning bay 102 and the scanning bar 402). Indeed, embodiments of the battery classification system 704 may include any or all of the sensors of the plurality of sensors 710 shown, as well as additional sensors for the batteries to be scanned / imaged. Furthermore, while FIGS. 1-6 illustrate example types, configurations, and arrangements of sensors for scanning batteries, alternative embodiments include different types, configurations, and arrangements of the plurality of sensors 710. Additionally, in some embodiments, sensor data is received indirectly from multiple sensors 710, such as from an alternate source or in a data package received with each target battery.

[0045] 7, in one or more embodiments, the battery sorting system 704 communicates with a battery sorting mechanism 720 that includes a plurality of actuators 722 for sorting batteries into a plurality of sorting bins 724 (e.g., as described above in connection with FIGS. 1-6). For example, in response to predicting a battery classification for a target battery, the battery sorting system 704 indicates the predicted classification to the battery sorting mechanism 720, and in some implementations, the location of the target battery on the conveyor 106. In response, the battery sorting mechanism 720 utilizes one or more of the plurality of actuators 722 (e.g., actuator 202) to move the target battery into the sorting bin 724 that corresponds to the predicted battery classification.

[0046] Additionally, in some embodiments, the battery classification system 704 is configured to detect anomalies in the batteries and respond accordingly. For example, as shown in FIG. 7, the plurality of sensors 710 includes an infrared camera 718, which is configured to monitor batteries for high temperatures or other thermal anomalies. Upon detecting a high temperature (or other undesirable thermal condition) above a certain threshold, the battery classification system 704 indicates the anomaly to the battery sorting mechanism 720, which then activates one or more alarms 726, stops the sorting process, and / or ejects the target battery (e.g., moves the target battery into a safety receptacle (e.g., a dunk tank or other enclosure)).

[0047] As described above, the battery classification system 704 can predict a battery classification based on signals from multiple sensors utilizing a classifier model. For example, FIG. 8 illustrates a battery classification system 704 utilizing a classifier machine learning model 802 to determine a battery classification 826, according to one or more embodiments. Specifically, FIG. 8 shows the battery classification system 704 receiving multiple signals from sensors 816 and utilizing a classifier machine learning model 802 to determine the battery classification 826 from multiple battery classifications stored in a database 814. The battery classification can include various categories of batteries, such as, but not limited to, chemical composition, size, intended use, commercial brand, model type / number, etc. Battery chemical compositions can include, for example, lithium, lithium-ion, aluminum-ion, magnesium-ion, sodium-ion, potassium-ion, alkaline, nickel, carbon-zinc, silver oxide, aluminum-air, zinc-air, zinc-carbon, zinc chloride, zinc-ion, lead-acid, or any of a variety of battery compositions received for classification by the battery classification system 704.

[0048] 8, the plurality of sensors 816 includes a three-dimensional (3D) scanner, an X-ray scanning array 820, one or more RGB cameras 822, and an infrared (IR) camera 824. The sensors 816 acquire / measure signals that are passed to the battery classification system 704. The battery classification system 704 determines one or more attributes of the battery from the signals. The classifier machine learning model 802 determines the battery classification from the one or more attributes.

[0049] In the illustrated embodiment, for example, the battery classification system 704 receives an image from the 3D scanner 818. From the image, the battery classification system 704 determines one or more physical attributes of the battery in the image. For example, the battery classification system 704 determines one or more dimensions 804 of the target battery from the signal (e.g., data) received from the 3D scanner 818. Specifically, the battery classification system 704 determines one or more of the height, width, length, or volume of the target battery. The classifier machine learning model 802 generates a battery classification 826 based at least in part on the dimensions 804.

[0050] Alternatively, in one or more implementations, for example, the battery classification system 704 operates on signals received from the 3D scanner 818 to determine or generate attributes of the target battery. For example, the battery classification system 704 generates a three-dimensional profile of the target battery. Specifically, the battery classification system 704 uses one or more images or 3D scans to reconstruct the three-dimensional shape or profile of the target battery. In other words, the battery classification system 704 determines the shape of the target battery from signals from the sensor 816. The classifier machine learning model 802 generates the battery classification 826 based at least in part on the shape or profile of the target battery.

[0051] The battery classification system 704 also determines one or more indicators of X-ray attenuation 806 from signals received from the X-ray scanning array 820 of the target battery. In one or more embodiments, for example, the X-ray scanning array 820 writes X-ray data associated with the target battery to a cache memory in one or more data subsets (i.e., chunks). Thus, the battery classification system 704 can access one or more data subsets for the target battery and, if multiple subsets correspond to a single battery, can concatenate the multiple subsets for subsequent processing. In some embodiments, the battery classification system 704 receives signals from the X-ray scanning array 820, monitors the temperature of one or more scintillators of the X-ray scanning array 820, and, if necessary, recalibrates the X-ray gain lookup table according to the static offset and gain rate per pixel as each changes in real time (e.g., with temperature). In one or more embodiments, the battery classification system 704 utilizes an X-ray gain lookup table to determine the X-ray attenuation 806 and normalizes the signals from the detectors of the X-ray scanning array 820 to obtain attenuation measurements adjusted according to the real-time updates described above. Additionally, in embodiments, the battery classification system 704 utilizes a time decay model to reduce the effects of afterglow from the scintillator exhibited in any given signal from the X-ray scanning array 820.

[0052] Additionally, in some embodiments, the battery classification system 704 aligns high and low energy pixels from the signal of the X-ray scanning array 820 to generate high / low energy pairs (e.g., as indicated in the database 814) to determine which portions of the received pixels fall into defined regions associated with a predetermined chemical composition of the battery. Also, in one or more embodiments, the battery classification system 704 aligns the segmented 3D scans for the target battery (e.g., received from the 3D scanner 818) with corresponding low-energy and high-energy scans received from the X-ray scanning array 820 to utilize the distribution and ratio of battery height to magnitude of X-ray attenuation in determining the chemical composition or category of the target battery. Accordingly, the battery classification system 704 described herein can store the inferred prediction and / or confidence level in memory or transmit such results directly to the classifier machine learning model 802 for further analysis or to the battery sorting mechanism 828 to sort the target battery according to the inferred prediction and / or confidence level.

[0053] In one or more implementations, the battery classification system 704 receives the X-ray attenuation values ​​806 from the X-ray scanning array 820, and using these signals, the battery classification system 704 determines attributes of the target battery. For example, the battery classification system 704 determines the battery chemistry or material composition of the battery from the attenuation values ​​806. The classifier machine learning model 802 generates a battery classification 826 based at least in part on the battery chemistry or material composition of the target battery.

[0054] More specifically, the battery classification system 704 receives X-ray attenuation values ​​806 in the form of high-energy and low-energy decay values ​​for the target battery. In one or more implementations, the battery classification system 704 generates a decay energy curve by plotting the high-energy decay values ​​versus the low-energy decay values. The battery classification system 704 determines the battery chemistry from the decay energy curve. For example, the battery classification system 704 stores known decay energy curves for different battery chemistries in a database 814. The battery classification system 704 maps the generated decay energy curve to the known decay energy curve to determine the battery chemistry of the target battery.

[0055] Additionally, the battery classification system 704 receives one or more images (e.g., signals) of the target battery from the RGB camera 822. The battery classification system 704 generates one or more attributes from the one or more images. For example, the battery classification system 704 utilizes character recognition 808 and object recognition 810 to determine additional attributes of the target battery. In one or more embodiments, for example, the RGB camera 822 includes one or more overhead cameras mounted / positioned to view each target battery from each side (e.g., top, front, rear, side) of the target battery. The battery classification system 704 can utilize a camera driver to segment the batteries from the RGB images using background subtraction techniques, store the segmented images in a cache memory (e.g., a Redis cache comprising an in-memory key-value store for image and data storage and retrieval), and associate the placement and / or location of each battery with the stored image. Thus, the battery classification system 704 can access the segmented images from memory and perform object and / or character recognition on the associated battery. For example, the battery classification system 704 determines printed characters or codes on a label of the target battery, and the classifier machine learning model 802 generates a battery classification 826 based at least in part on the printed characters or codes of the target battery.

[0056] In one or more implementations, the battery classification system 704 utilizes a classifier machine learning model 802 to compare words, characters, and other recognized data to different battery types in a database 814. In some embodiments, the classifier machine learning model 802 includes one or more deep learning models configured to perform object recognition to identify the specific make and / or model of each target battery.

[0057] Additionally, in some embodiments, the battery classification system 704 receives one or more image frames from the RGB camera 822, segments each image frame to locate one or more individual batteries within the frame, links image frames containing the same battery (e.g., using the battery's shape or outline), performs character recognition 808 and / or object recognition 810, and associates the resulting object / character output with the battery ID and / or physical location of the battery (e.g., x-y position on the battery sorter's conveyor belt).

[0058] Additionally, the classifier machine learning model 802 receives signals corresponding to the temperature 812 and / or thermal profile of the target battery from the infrared camera 824. As mentioned above, in some embodiments, the battery classification system 704 can activate one or more safety measures in response to detecting via signals from the infrared camera 824 that the battery exhibits a high temperature (e.g., upon determining that the temperature of the target battery reaches a threshold temperature) or other thermal anomaly. For example, in response to detecting a thermal anomaly in the target battery, the battery classification system 704 can activate an alarm and / or a fire extinguisher (or flame retardant), evacuate the target battery to a dunk tank or other disposal location, shut down the battery sorting equipment, and / or notify / warn a user of the anomaly.

[0059] 8 , the classifier machine learning model 802 determines a battery classification 826 of the target battery based on one or more of dimensions 804, X-ray attenuation 806, character recognition 808, object recognition 810, or temperature 812 indicated by signals received from the sensors 816. In some embodiments, for example, the classifier machine learning model 802 aggregates inferences generated from signals of the various sensors 816 (as described above) to generate a final battery classification. For example, in one or more implementations, the classifier machine learning model 802 utilizes a decision tree to generate the battery classification 826 based on attributes of the target battery determined from signals from the sensors 816. Alternatively, the classifier machine learning model 802 includes a classification neural network. In such implementations, the classification neural network concatenates values ​​of the attributes of the target battery. The classification neural network utilizes a first set of neural network layers (e.g., an encoder) to generate a feature vector from the concatenated attribute values. The classification neural network utilizes a second set of neural network layers (e.g., a decoder) to generate the battery classification 826, which forms the feature vector.

[0060] Additionally, in one or more embodiments, if the classifier machine learning model 802 is unable to determine the battery classification within a threshold confidence level, the battery classification system 704 issues an error signal to the sorting mechanism 828. In such a case, for example, the sorting mechanism 828 can reroute the subject battery for a repeated classification attempt or replace the battery in a bin designated for batteries with an uncertain classification.

[0061] Further, in some embodiments, the battery classification system 704 indicates the battery classification 826 to a sorting mechanism 828 for sorting of the target battery. In one or more implementations, for example, the battery classification system 704 provides the location of the classified battery, such as coordinates relative to a conveyor belt, and the sorting mechanism 828 utilizes the transmitted location to inform a sorting actuator when moving the battery into a corresponding sorting bin or onto a corresponding additional conveyor, as described above. In some embodiments, the classifier machine learning model 802 includes a machine learning model trained to predict battery classifications as described herein. Alternatively, some embodiments include a combination of a classical (e.g., mathematical) model and one or more machine learning models.

[0062] As described above, in one or more embodiments, the battery classification system 704 utilizes images from one or more RGB cameras as input to determine the battery classification of the target battery. For example, FIG. 9 illustrates a battery classification system 704 utilizing images from one or more RGB cameras 904 to determine a battery type inference 920 for a target battery 902.

[0063] For example, one or more RGB cameras 904 provide one or more images 908 of a label included on the target battery 902, from which a classifier model 912 performs character recognition 914 (e.g., OCR) and compares the recognized characters to a database 918. For example, characters recognized by character recognition 914 can be compared to characters in database 918 or used to retrieve model numbers, serial numbers, and other relevant information that may be useful in determining a battery type inference 920 for the target battery 902.

[0064] Additionally or alternatively, the one or more RGB cameras 904 provide one or more profile images 906 of the target battery 902, the one or more profile images 906 showing or comprising a form factor 910 of the target battery 902. In response, the classifier model 912 utilizes the object detection machine learning model 916 to compare the visual attributes of the target battery 902 with data stored in a database 918 and determine a battery type inference 920 for the target battery 902. Further, the classifier model 912 can be trained to recognize known form factors or other battery attributes based on the form factors and attributes of battery configurations found in the database 918.

[0065] For example, in one or more embodiments, the classifier model 912 comprises an object detection machine learning model 916 for classifying batteries in a digital image. In one or more embodiments, the object detection machine learning model 916 comprises a deep learning convolutional neural network (CNN). For example, in some embodiments, the object detection machine learning model 916 comprises a region-based (R-CNN). Specifically, the object detection machine learning model 916 includes lower neural network layers and upper neural network layers. Generally, the lower neural network layers collectively form an encoder, and the upper neural network layers collectively form a decoder. In one or more embodiments, the encoder includes a convolutional layer that encodes the digital image into a feature vector, which is output from the encoder and provided as input to the decoder. In various implementations, the decoder includes a fully connected layer that analyzes the feature vector and outputs a battery classification. In one or more implementations, the object detection machine learning model 916 provides a prediction that a battery in an image is a respective battery classification of a plurality of battery classifications. For example, the object detection machine learning model 916 generates a classification vector with predictions (e.g., numbers between 0 and 1) that indicate the predicted percentage (e.g., between 1 and 100%) that the battery in the image is each of a plurality of battery classifications. Thus, if there are 100 different battery classifications, the object detection machine learning model 916 may generate a classification vector with 100 entries. The classifier model 912 selects the battery type inference 920 as the battery classification with the highest predicted percentage.

[0066] As described above, some embodiments of the battery classification system 704 utilize an X-ray scanning array and / or a 3D scanner to determine one or more attributes of a target battery, such as battery chemistry, to predict the battery classification. For example, FIG. 10 illustrates a battery classification system 704 utilizing input from a 3D scanner and an X-ray scanning array 1006 to determine a battery chemistry inference 1018 for a target battery 1002.

[0067] For example, in an exemplary embodiment, the battery classification system 704 receives or infers dimensions 1008 from the 3D scanner 1004 and high-energy decay 1010 and low-energy decay 1012 measurements from the X-ray scanning array 1006. In response, the battery classification system 704 utilizes a decay model 1016 of the classifier model 1014 to determine a battery chemistry inference 1018. In one or more embodiments, the X-ray scanning array 1006 includes a differential X-ray scanning array made up of multiple X-ray scanning arrays positioned at different angles or positions relative to the target battery 1002.

[0068] In one or more embodiments, the battery classification system 704 jointly processes signals from the X-ray scanning array 1006 and the 3D scanner 1004 for the target battery 1002 when determining the battery chemistry inference 1018. Upon segmenting the target battery 1002, for example, the battery classification system 704 can utilize images provided by the 3D scanner 1004 to determine dimensions 1008, including the area, center of gravity, and / or one or more rectangular crops of the target battery 1002. In one or more embodiments, the battery classification system 704 utilizes the dimensions 1008 and the signals from the X-ray scanning array 1006 to generate an array (e.g., a hash map) of the high energy decay 1010, the low energy decay 1012, and the dimensions 1008 of the target battery 1002. In such an embodiment, the battery classification system 704 can utilize the decay model 1016 of the classifier model 1014 to generate a battery chemistry inference 1018 based on the sequence of dimensions 1008, high energy decay 1010, and low energy decay 1012 for the target battery 1002.

[0069] Additionally, as described above, embodiments of the battery classification system 704 utilize various signals from various sensors to determine the battery classification of the target battery. For example, in some embodiments, the battery classification system 704 utilizes both the battery type inference 920 of FIG. 9 and the battery chemistry inference 1018 of FIG. 10 to determine the battery classification of the target battery. Indeed, embodiments of the battery classification system 704 may utilize any combination of data from the various sensors in conjunction with a classifier model to predict the battery classification of the target battery.

[0070] 1-10 and their corresponding descriptions and embodiments provide several different methods, systems, devices, and non-transitory computer-readable media for a battery classification system 704. In addition to the foregoing, one or more embodiments may also be described in terms of a flowchart including operations for achieving a particular result, as shown in FIG. 11. FIG. 11 may be performed with more or fewer operations. Furthermore, operations may be performed in a different order. Additionally, operations described herein may be repeated or performed in parallel with each other or with different instances of the same or similar operations.

[0071] As mentioned above, FIG. 11 illustrates a flowchart of a series of operations 1100 for classifying and sorting batteries according to one or more embodiments. While FIG. 11 illustrates operations according to one embodiment, alternative embodiments may omit, add, rearrange, and / or modify any of the operations shown in FIG. 11. The operations of FIG. 11 may be performed as part of a method. Alternatively, a non-transitory computer-readable medium may comprise instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of FIG. 11. In some embodiments, a system may perform the operations of FIG. 11.

[0072] As shown, FIG. 11 illustrates an example sequence of operations 1100 for classifying and sorting batteries according to multiple battery classifications. The sequence of operations 1100 may include an operation 1102 of scanning a target battery using multiple sensors. For example, in some embodiments, operation 1102 includes receiving multiple signals corresponding to the target battery from the multiple sensors. In one or more embodiments, operation 1102 may include receiving multiple signals from the multiple sensors corresponding to one or more detected attributes of the target battery, where the one or more detected attributes include one or more of the target battery's dimensions, battery chemistry, printed text, or form factor. Further, in some embodiments, the multiple sensors comprise two or more of an X-ray scanning array, a three-dimensional (3D) scanner, an RGB camera, or an infrared camera.

[0073] 11 , the series of operations 1100 may include an operation 1104 for utilizing a classifier machine learning model to determine a battery classification. For example, in some embodiments, operation 1104 includes utilizing a classifier machine learning model to determine a predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals. In one or more implementations, operation 1104 may include utilizing a classifier machine learning model to determine a predicted battery classification of the target battery from the plurality of battery classifications based on one or more detected attributes of the target battery.

[0074] Further, in some embodiments, operation 1104 may include determining material attributes (e.g., battery chemistry) of the target battery based on one or more X-ray attenuation measurements received from an X-ray scanning array for the target battery, and determining a predicted battery classification for the target battery based on the material attributes. For example, in some embodiments, operation 1104 may include receiving X-ray attenuation data for the target battery from an X-ray scanning array of sensors, and determining a predicted battery classification for the target battery based on the X-ray attenuation data.

[0075] In one or more embodiments, operation 1104 may include determining dimensions of the target battery based on scan data received from a three-dimensional (3D) scanner for the target battery, and determining a predicted battery classification for the target battery based on the dimensions. Relatedly, in one or more embodiments, operation 1104 may include receiving scan data for the target battery from a multiple-sensor three-dimensional (3D) scanner, determining multiple dimensions of the target battery based on the scan data, and determining a predicted battery classification for the target battery based on the multiple dimensions.

[0076] In some embodiments, operation 1104 may include determining a plurality of printed characters or codes disposed on the target battery based on image data received from the RGB camera for the target battery, and determining a predicted battery classification for the target battery based on the plurality of printed characters or codes. Relatedly, in some embodiments, operation 1104 may include receiving one or more label images from the RGB camera of the multiple sensors, utilizing object character recognition (OCR) to identify the plurality of printed characters or codes from the one or more label images, and determining a predicted battery classification for the target battery based on the plurality of printed characters or codes.

[0077] Further, in one or more embodiments, operation 1104 includes receiving one or more label images and one or more profile images of the target battery from the RGB cameras of the multiple sensors, determining printed characters of the target battery from the one or more label images using optical character recognition, determining a form factor of the target battery from the one or more profile images, and utilizing a classifier machine learning model to determine a predicted battery classification of the target battery based on the printed characters and the form factor. Also, in some embodiments, operation 1104 may include receiving one or more profile images of the target battery from the RGB cameras of the multiple sensors, determining a form factor of the target battery based on the one or more profile images of the target battery, and determining a predicted battery classification of the target battery based on the form factor.

[0078] Further, in some embodiments, operation 1104 may include determining dimensions of the target battery based on scanning data from a multi-sensor 3D scanner, determining a battery chemistry of the target battery based on X-ray attenuation data from a multi-sensor X-ray scanning array, and utilizing a classifier machine learning model to determine a predicted battery classification of the target battery based on the dimensions and battery chemistry.

[0079] 11 , the series of operations 1100 can include an operation 1106 for indicating a battery classification to a battery sorting mechanism. For example, in some embodiments, operation 1106 includes indicating the expected battery classification to a battery sorting mechanism. Also, in some embodiments, operation 1106 includes utilizing the battery sorting mechanism to transfer the target battery to a bin that corresponds to the target battery's expected battery classification.

[0080] Also, in some embodiments, operation 1106 may include indicating to a battery sorting mechanism a predicted battery classification of the target battery and additional predicted battery classifications of the additional battery, and utilizing the battery sorting mechanism to transport each battery of the target battery and the additional battery individually to multiple bins respectively associated with multiple battery classifications.

[0081] Further, in one or more embodiments, the series of operations 1100 can include receiving an additional plurality of signals from the multiple sensors corresponding to the additional battery, and utilizing a classifier machine learning model to determine an additional predicted battery classification of the additional battery from the multiple battery classifications based on the additional plurality of signals. Also, in some embodiments, the series of operations 1100 can include utilizing a battery sorting mechanism to transfer the target battery to a first bin corresponding to the predicted battery classification of the target battery, determining for the additional target battery a predicted battery classification of the additional target battery based on the additional plurality of signals from the multiple sensors, and utilizing the battery sorting mechanism to transfer the additional target battery to a second bin corresponding to the predicted battery classification of the additional target battery.

[0082] Further, in some embodiments, the series of operations 1100 may include determining a temperature or a temperature gradient of the target battery from a signal received from the infrared camera for the target battery, and activating an alarm in response to determining that the temperature or temperature gradient exceeds a threshold. Further, in one or more embodiments, the series of operations 1100 may include receiving an indication from the infrared camera of the plurality of sensors of a dangerous anomaly detected in at least one battery of the additional batteries, and in response, transferring the at least one battery to a dunk tank containing a flame retardant.

[0083] Embodiments of the present disclosure may comprise or utilize special purpose or general purpose computers including computer hardware such as, for example, one or more processors and system memory, as described in more detail below. Embodiments within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and / or data structures. In particular, one or more of the processes described herein may be embodied, at least in part, in a non-transitory computer-readable medium and implemented as instructions executable by one or more computing devices (e.g., any of the media content access devices described herein). Generally, a processor (e.g., a microprocessor) receives instructions from a non-transitory computer-readable medium (e.g., memory) and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein.

[0084] Computer-readable media may be any available media that can be accessed by a general-purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media. Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, the disclosed embodiments may comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media.

[0085] Non-transitory computer-readable storage media (devices) include RAM, ROM, EEPROM, CD-ROM, solid-state drives (“SSDs”) (e.g., RAM-based), flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage, or other medium that can be used to store desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer.

[0086] A "network" is defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules and / or other electronic devices. When information is transferred or provided to a computer over a network or another communications connection (wired, wireless, or a combination of wired or wireless), the computer properly views the connection as a transmission medium. Transmission media can include networks and / or data links that can be used to transport desired program code means in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer. Combinations of the above should also be included within the scope of computer-readable media.

[0087] Furthermore, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures may be automatically transferred from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link may be buffered in RAM within a network interface module (e.g., a "network interface card") and eventually transferred to computer system RAM and / or less volatile computer storage media (devices) within the computer system. Thus, it should be understood that non-transitory computer-readable storage media (devices) may be included in computer system components that also (or primarily) utilize transmission media.

[0088] Computer-executable instructions include, for example, instructions and data that, when executed by a processor, cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a certain function or group of functions. In some embodiments, computer-executable instructions are executed by a general-purpose computer to transform the general-purpose computer into a special-purpose computer that implements elements of the disclosure. Computer-executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. While the subject matter has been described in language specific to structural features and / or methodological acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to such features or acts. Rather, the described features and acts are disclosed as exemplary forms of implementing the claims.

[0089] Those skilled in the art will appreciate that the present invention can be practiced in networked computing environments having many types of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, cell phones, PDAs, tablets, pagers, routers, switches, etc. The present disclosure can also be practiced in distributed system environments where tasks are performed by local and remote computer systems that are linked through a network (either by hardwired data links, wireless data links, or a combination of hardwired and wireless data links). In a distributed system environment, program modules may be located in both local and remote memory storage devices.

[0090] Embodiments of the present disclosure may also be implemented in a cloud computing environment. As used herein, the term "cloud computing" refers to a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing may be used in markets to provide ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources that can be rapidly provisioned through virtualization, released with low management effort or through interaction with a service provider, and scaled accordingly.

[0091] The cloud computing model may comprise various characteristics, such as on-demand self-service, broad network access, resource pooling, rapid scalability, and scalable services. The cloud computing model may also expose various service models, such as Software as a Service ("SaaS"), Platform as a Service ("PaaS"), and Infrastructure as a Service ("laaS"). The cloud computing model may also be deployed using different deployment models, such as private cloud, community cloud, public cloud, and hybrid cloud. Additionally, as used herein, the term "cloud computing environment" refers to an environment in which cloud computing is utilized.

[0092] 12 illustrates a block diagram of an exemplary computing device 1200 that may be configured to perform one or more of the processes described above. It should be understood that one or more computing devices, such as computing device 1200, may represent the computing devices described above (e.g., computing device 702). In one or more embodiments, computing device 1200 may be a mobile device (e.g., a mobile phone, smartphone, PDA, tablet, laptop, camera, tracker, watch, wearable device, etc.). In an embodiment, computing device 1200 may be a non-mobile device (e.g., a desktop computer or another type of client device). Additionally, computing device 1200 may be a server device that includes cloud-based processing and storage capabilities (e.g., a local server that processes custom TCP or HTTP messages).

[0093] As shown in FIG. 12 , computing device 1200 may include one or more processors 1202, memory 1204, storage device 1206, input / output interface 1208 (or “VO interface 1208”), and a communication interface 1210 that may be communicatively coupled via a communication infrastructure (e.g., bus 1212). Although computing device 1200 is illustrated in FIG. 12 , the components illustrated in FIG. 12 are not intended to be limiting. In other embodiments, additional or alternative components may be used. Moreover, in some embodiments, computing device 1200 includes fewer components than those illustrated in FIG. 12 . The components of computing device 1200 illustrated in FIG. 12 are now described in further detail.

[0094] In particular embodiments, processor 1202 includes hardware for executing instructions, such as those making up a computer program. By way of example, and not by way of limitation, to execute instructions, processor 1202 may retrieve (or fetch) instructions from an internal register, an internal cache, memory 1204, or storage device 1206, decode them, and execute them.

[0095] The computing device 1200 includes a memory 1204 coupled to a processor 1202. The memory 1204 may be used to store data, metadata, and programs for execution by the processor. The memory 1204 may include one or more of volatile and non-volatile memory, such as random access memory, read-only memory, solid-state hard disk, flash, phase-change memory, or other types of data storage. The memory 1204 may be internal or distributed memory.

[0096] The computing device 1200 includes a storage device 1206 for storing data or instructions. By way of example and not limitation, the storage device 1206 may include the non-transitory storage media described above. The storage device 1206 may include a hard disk drive, a flash memory, a universal serial bus drive, or a combination of these or other storage devices.

[0097] In some embodiments, data storage in storage device 1206 may include a remote dictionary server (Redis) data structure with an in-memory key-value database for storing and indexing cached data throughout the battery classification and sorting process described herein. In an embodiment, for example, each sensor array of multiple sensor arrays (e.g., RGB cameras, 3D scanners, X-ray scanning arrays) stores images and / or related information with time-dependent key values ​​(e.g., “rgb:390:8910”) representing the x-y coordinates of the respective image. Thus, battery classification system 704 can associate information from each sensor array pipeline with the respective physical coordinates when aggregating sensor signals to determine the battery classification of the target battery. Alternatively, or additionally, battery classification system 704 can store a sorted list of coordinates for each image, such that all images within a given range of coordinates are easily accessible when filtering for signals corresponding to the target battery.

[0098] As shown, computing device 1200 includes one or more I / O interfaces 1208 that are provided to enable a user to provide input (such as user strokes), receive output from, and otherwise transfer data from computing device 1200. These I / O interfaces 1208 may include a mouse, a keypad or keyboard, a touchscreen, a camera, an optical scanner, a network interface, a modem, other known I / O devices, or a combination of such I / O interfaces 1208. A touchscreen may be actuated with a stylus or a finger.

[0099] The I / O interface 1208 may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In one embodiment, input / output interface 1208 is configured to provide graphical data to a display for presentation to a user. The graphical data may include one or more graphical user interfaces and / or any other graphical content that may serve a particular implementation.

[0100] The computing device 1200 may further include a communication interface 1210. The communication interface 1210 may include hardware, software, or both. The communication interface 1210 provides one or more interfaces for communication (e.g., packet-based communication) between the computing device and one or more other computing devices or one or more networks. By way of example, and not limitation, the communication interface 1210 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wired-based network, or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as Wi-Fi. The computing device 1200 may further include a bus 1212. The bus 1212 may include hardware, software, or both that couple the components of the computing device 1200 to one another.

[0101] In the foregoing specification, the invention has been described with reference to specific example embodiments. Various embodiments and aspects of the invention are described with reference to the details discussed herein, and the accompanying drawings illustrate various embodiments. The above description and drawings are illustrative of the invention and should not be construed as limiting the invention. Numerous specific details are set forth in order to provide a thorough understanding of various embodiments of the invention.

[0102] The present invention may be embodied in other specific forms without departing from the spirit and essential characteristics thereof. The described embodiments are to be considered in all respects as illustrative only and not restrictive. For example, methods described herein may be performed with fewer or more steps / actions, or the steps / actions may be performed in a different order. In addition, steps / actions described herein may be repeated or performed in parallel or concurrently with each other for different instances of the same or similar steps / actions. The scope of the present invention is therefore indicated by the appended claims, rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. 1. An apparatus for sorting batteries, comprising: a feeding mechanism including a hopper disposed above a conveyor and configured to individually discharge a plurality of batteries onto the conveyor; a scanner mechanism disposed about the conveyor, the scanner mechanism comprising a plurality of types of sensors configured to obtain attributes of each battery of the plurality of batteries, the plurality of types of sensors including a three-dimensional (3D) scanner configured to determine geometric dimensions of each battery of the plurality of batteries; an array of sorting mechanisms disposed about the conveyor and comprising at least one actuator configured to selectively transfer the plurality of batteries from the conveyor to a plurality of bins based on a predicted battery configuration of each battery of the plurality of batteries determined based on the acquired attributes of each battery.

2. 10. The apparatus of claim 1, wherein the feeding mechanism further comprises a vibrating table disposed above the conveyor and configured to separate and direct individual batteries of the plurality of batteries.

3. 10. The apparatus of claim 1, further comprising one or more scanning bays configured to receive, via the conveyor, individual batteries of the plurality of batteries to be scanned by the plurality of types of sensors.

4. The apparatus of claim 1 , wherein the multiple types of sensors further comprise one or more of an X-ray scanning array, an RGB camera, or an infrared camera.

5. 10. The apparatus of claim 1, further comprising one or more processors configured to determine the predicted battery configuration for each battery based on sensor data from one or more sensors of the plurality of types of sensors.

6. 10. The apparatus of claim 1, further comprising at least one return conveyor disposed at an end of the conveyor and configured to return unsorted batteries to the beginning of the conveyor.

7. 10. The apparatus of claim 1, further comprising a safety mechanism including one or more of an alarm, a fire extinguisher, a battery dunk tank, or an emergency power off (EPO), the safety mechanism configured to activate in response to a signal from at least one sensor of the plurality of types of sensors.

8. 1. An apparatus for classifying batteries, comprising: one or more scanning bays configured to receive batteries of a plurality of battery configurations; two or more scanner mechanisms associated with the one or more scanning bays, the two or more scanner mechanisms comprising a sensor configured to scan each battery as it passes through the one or more scanning bays, the two or more scanner mechanisms comprising an x-ray scanning array and a three-dimensional (3D) scanner; one or more processors configured to determine a predicted battery configuration for each battery based on sensor data from sensors of the two or more scanner mechanisms.

9. 10. The apparatus of claim 8, further comprising a hopper mechanism configured to individually discharge the batteries onto a conveyor.

10. 10. The apparatus of claim 9, wherein the one or more scanning bays are positioned around the conveyor to receive batteries individually discharged by the hopper mechanism.

11. 11. The apparatus of claim 10, further comprising an array of sorting mechanisms positioned around the conveyor after the one or more scanning bays, the array of sorting mechanisms comprising one or more actuators configured to transfer each battery from the conveyor to a respective bin of a plurality of bins based on the predicted battery configuration for each battery.

12. 10. The apparatus of claim 8, wherein the X-ray scanning array is configured to determine material attributes of each battery.

13. 10. The apparatus of claim 8, wherein the three-dimensional (3D) scanner is configured to determine geometric dimensions of each battery.

14. 10. The apparatus of claim 8, wherein the one or more processors are configured to determine the predicted battery configuration for each battery by utilizing a machine learning model that processes the sensor data from the sensors of the two or more scanner mechanisms.

15. 1. An apparatus for sorting batteries of various configurations, comprising: a feeding mechanism including a hopper disposed above a conveyor and configured to individually discharge a plurality of batteries onto the conveyor; one or more scanner mechanisms proximate to the conveyor, the one or more scanner mechanisms comprising multiple types of sensors configured to scan each battery as it passes on the conveyor; one or more sorting mechanisms disposed about the conveyor and comprising one or more actuators configured to transfer the plurality of batteries from the conveyor to a plurality of bins based on an expected battery configuration of each battery determined based on attributes of each battery obtained using the one or more scanner mechanisms; a safety mechanism including one or more of a fire extinguisher, a battery dunk tank, or an emergency power off (EPO), the safety mechanism being configured to activate in response to a signal from at least one sensor of the plurality of types of sensors.

16. 16. The apparatus of claim 15, wherein the one or more scanner mechanisms comprise an x-ray scanning array comprising an x-ray generator positioned above the conveyor and an x-ray detector positioned below the conveyor.

17. 16. The apparatus of claim 15, wherein the one or more scanner mechanisms comprise two or more of an X-ray scanning array, a three-dimensional (3D) scanner, an RGB camera, or an infrared camera.

18. 16. The apparatus of claim 15, further comprising a return conveyor after the one or more scanner mechanisms, the return conveyor configured to return batteries for which an accurate predicted battery configuration cannot be determined for the one or more scanner mechanisms.

19. 16. The apparatus of claim 15, wherein the one or more sorting mechanisms comprise one or more actuators configured to push, pull, or guide batteries into respective ones of the plurality of bins.

20. 16. The apparatus of claim 15, wherein the safety mechanism is configured to activate in response to detecting a dangerous anomaly in a battery of the plurality of batteries.

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