Smart systems and methods for identification and processing of spent lithium cells

A method, comprising:

• irradiating, via an X-ray radiation, a first plurality of energy storage devices, the X-ray radiation using an input radiation characterized by a first electromagnetic spectrum;
• detecting, via a chemical sensing device, an output radiation of the first plurality of energy storage devices, the output radiation generated via X-ray fluorescence (XRF);
• and
• dividing, based on the output radiation property via a first sorting instruction generated by a machine learning classification model, the first plurality of energy storage devices into a second plurality of energy storage devices and a third plurality of energy storage devices, the second plurality of energy storage devices including lithium-ion batteries (LIBs) and the third plurality of energy storage devices including batteries other than the LIBs.

A system, comprising:

• an X-ray radiation source configured to irradiate an energy storage device using an input radiation characterized by a first electromagnetic spectrum;
• a detector configured to detect an output radiation reflected or backscattered by the energy storage device, the detector forming at least a portion of an X-ray fluorescence (XRF) spectrometer;
• a compute device configured to generate an instruction, based on a machine learning classification model, to sort the energy storage devices based on the output radiation;
• and
• a sorting device configured to receive the instruction and sort the energy storage device based on the instruction.

A method, comprising:

• detecting, via a physical sensing device, at least one physical attribute of each of a plurality of energy storage devices;
• determining, via an element sensing unit, an elemental composition of each of the plurality of energy storage devices;
• providing, for each of the plurality of energy storage devices, at least one datum from at least one sensing unit to a controller configured to execute a machine learning classification model, the at least one datum including the at least one physical attribute and the elemental composition;
• and
• generating, via the machine learning classification model based on the at least one datum, a first instruction to sort the plurality of energy storage devices into lithium-ion batteries and other batteries and a second instruction to sort the lithium-ion batteries based on their material chemistries.

A system, comprising:

• an inlet configured to receive a plurality of energy storage devices;
• a physical sensing device configured to detect at least one physical attribute of each of the received plurality of energy storage devices;
• an element sensing unit configured to determine an elemental composition of each energy storage device;
• a guiding switch configured to route each energy storage device to one of a plurality of holding units;
• a packaging unit disposed prior to at least one of the plurality of holding units;
• and
• a controller configured to:
• receive, for each energy storage device, at least one datum from the element sensing unit,
• execute a machine learning classification model configured to generate a first instruction to sort the plurality of energy storage devices into lithium-ion batteries (LIBs) and other batteries, and a second instruction to sort the LIBs based on their material chemistries, and
• generate a signal configured to control the guiding switch and the packaging unit based on the first instruction and the second instruction.