Back to results
US 12,521,766 B1Granted
Smart systems and methods for identification and processing of spent lithium cells
AssigneeLi Industries, Inc.
InventorsDavid Young, Xu Han, Zheng Li, Panni Zheng
Life of this patent
Estimating the term…
Abstract
A method includes using machine learning to classify and sort energy storage devices based on at least one of the detected chemical or physical properties. In some embodiments, the method can include irradiating an energy storage device with an input radiation and detecting the output radiation reflected or backscattered by the energy storage device. The method may further include detecting a physical property of the energy storage device.
Claims31
A method, comprising:
- detecting, via a physical sensing device, a physical property of a first plurality of energy storage devices;
- dividing, based on the physical 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 other batteries;
- detecting, via a chemical sensing device, an output radiation of the second plurality of energy storage devices including the lithium-ion batteries, the output radiation generated via an X-ray fluorescence (XRF) process;
- dividing, based on the output radiation indicative of their cathode material chemistries via a second sorting instruction generated by the machine learning classification model, the second plurality of energy storage devices including the lithium-ion batteries into a fourth plurality of energy storage devices and a fifth plurality of energy storage devices.
1 more element
A method, comprising:
- detecting, via a chemical sensing device, an output radiation of a first plurality of energy storage devices, the output radiation generated via an X-ray fluorescence (XRF) process;
- 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 other batteries;
- detecting, via a physical sensing device, a physical property of the second plurality of energy storage devices including the lithium-ion batteries, indicative of their cathode material chemistries;
- dividing, based on the physical property via a second sorting instruction generated by the machine learning classification model, the second plurality of energy storage devices including the lithium-ion batteries into a fourth plurality of energy storage devices and a fifth plurality of energy storage devices.
1 more element
A system, comprising:
- a 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;
- a physical sensing device configured to detect a physical property of an energy storage device;
- a compute device configured to generate an instruction based on a machine learning classification model, to sort the energy storage device based on the output radiation and the physical property,
- wherein the instruction to sort the energy storage device includes:
- a first instruction to sort a plurality of energy storage devices into lithium ion batteries (LIBs) and other batteries;
- and
- a second instruction to further sort the sorted LIBs based on their cathode material chemistries.
5 more elements
A method, comprising:
- detecting, via a physical sensing device, a physical property of a first plurality of energy storage devices;
- dividing, based on the physical 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 other batteries;
- detecting, via a chemical sensing device, an output radiation of the second plurality of energy storage devices including the lithium-ion batteries, the detecting the output radiation including:
- irradiating the second plurality of energy storage devices using an input radiation characterized by a first electromagnetic spectrum;
- sensing the output radiation reflected or backscattered by the second plurality of energy storage devices;
- determining a second electromagnetic spectrum of the output radiation that is characteristic of a chemical composition;
- and
- comparing the second electromagnetic spectrum characteristic of a chemical composition with a reference electromagnetic spectrum to identify the chemical composition of the energy storage device;
- and
- dividing, based on the output radiation indicative of their cathode material chemistries via a second sorting instruction generated by the machine learning classification model, the second plurality of energy storage devices including the lithium-ion batteries into a fourth plurality of energy storage devices and a fifth plurality of energy storage devices.
7 more elements
A method, comprising:
- detecting, via a chemical sensing device, an output radiation of a first plurality of energy storage devices, the detecting the output radiation including:
- irradiating the first plurality of energy storage devices using an input radiation characterized by a first electromagnetic spectrum;
- sensing the output radiation reflected or backscattered by the first plurality of energy storage devices;
- determining a second electromagnetic spectrum of the output radiation that is characteristic of a chemical composition;
- and
- comparing the second electromagnetic spectrum characteristic of a chemical composition with a reference electromagnetic spectrum to identify the chemical composition of the energy storage device;
- 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 other batteries;
- detecting, via a physical sensing device, a physical property of the second plurality of energy storage devices including the lithium-ion batteries, indicative of their cathode material chemistries;
- dividing, based on the physical property via a second sorting instruction generated by the machine learning classification model, the second plurality of energy storage devices including the lithium-ion batteries into a fourth plurality of energy storage devices and a fifth plurality of energy storage devices.
6 more elements
Dependencies are inferred from the claim text.