Automatic magnetic impurity sample separation

Abstract

Apparatus and related methods relate to separating ferromagnetic impurities from bulk battery materials. In an illustrative example, a ferromagnetic impurity separation system (FISS) may receive a sample container that includes a closed sample of bulk battery material. For example, the FISS may include a variable magnetic flux generator (e.g., an electromagnet) disposed at a location separate from the sample container. The FISS may also include an agitation unit having a translating motor and a rotating motor configured to rotate the sample container about the central axis and translate a position of the central axis of the sample container. For example, in an operating mode, the variable magnetic flux generator and agitation unit may apply a predetermined magnetic flux, a predetermined angular velocity, and a predetermined velocity to the sample container. Various embodiments can advantageously simply separate ferromagnetic impurities from a closed sample without contaminating the sample.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 520,097, titled "High - Precision Magnetic Particle Collector", filed on August 17, 2023, by Jongwook Mah.

[0003] This application also claims the benefit of U.S. Provisional Application No. 63 / 583,138, titled "Automatic Magnetic Impurity Sample Isolation", filed on September 15, 2023, by Jongwook Mah et al.

[0004] This application hereby incorporates by reference in its entirety the foregoing applications. Technical Field

[0005] Various embodiments generally relate to battery manufacturing.

[0006] Background

[0007] Energy storage devices can include, for example, batteries. Batteries can be made of various chemistries. For example, lithium - ion batteries, sometimes referred to as Li - ion batteries, can be used in portable electronic devices and electric vehicles due to their high energy density, rechargeability, and lightweight characteristics. For example, batteries can power smartphones, laptop computers, electric vehicles, and / or a wide range of other applications.

[0008] For example, a battery can include three components: a cathode, an anode, and an electrolyte. As an illustrative example, in a Li - ion battery, for instance, the cathode can include a lithium - based compound, the anode can include graphite, and the electrolyte can include a lithium salt dissolved in a solvent. During charging, for example, lithium ions can move from the cathode to the anode through the electrolyte. For example, during discharging, the lithium ion flow back to the cathode, releasing electrical energy to power various devices. A variety of cathode materials can be used in lithium batteries, including lithium cobalt oxide (LiCoO2), lithium iron phosphate (LiFePO4), lithium manganese oxide (LiMn2O4), and / or lithium nickel cobalt manganese oxide (Li(NiCoMn)O2).

[0009] Magnetic impurities (e.g., iron, cobalt, nickel, manganese, copper, chromium, zinc, lead) can be present in Li - ion battery cathode materials. Manufacturers may desire to reduce magnetic impurities from battery materials (e.g., Li - ion cathode materials).

[0010] Overview

[0011] Devices and related methods relate to separating ferromagnetic impurities from bulk battery materials. In an illustrative example, a ferromagnetic impurity separation system (FISS) can receive a sample container enclosing a sample that includes bulk battery materials. For example, the FISS can include a variable magnetic flux generator (e.g., an electromagnet) disposed at a location separated from the sample container. The FISS can also include an agitation unit having a translation motor and a rotation motor configured to rotate the sample container about a central axis and translate the position of the central axis of the sample container along one or more axes. For example, in an operating mode, the variable magnetic flux generator and the agitation unit can apply a predetermined magnetic flux, a predetermined angular velocity, and a predetermined velocity to the sample container. Various embodiments can advantageously and concisely separate ferromagnetic impurities from a closed sample without contaminating the sample.

[0012] Various embodiments can achieve one or more advantages. For example, some embodiments can apply a gradually decreasing magnetic flux and kinetic energy to the sample container to advantageously remove paramagnetic impurities. Some embodiments can apply an acid, for example, to flush ferromagnetic impurities from the sample container to advantageously prepare for inductively coupled plasma (ICP) analysis. Some embodiments can apply ultrapure water, for example, to flush ferromagnetic impurities from the sample container to advantageously prepare for optical analysis. For example, some embodiments can advantageously provide on-line impurity monitoring. Some embodiments can advantageously apply a magnetic flux of up to 15,000 gauss to the sample container, for example.

[0013] Details of various embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims. Brief Description of the Drawings

[0015] Figure 1 An exemplary battery production system (BPS) employed in an illustrative use case scenario is depicted.

[0016] Figure 2 is a block diagram depicting an exemplary ferromagnetic impurity separation system (FISS).

[0017] Figure 3A 、 Figure 3B 、 Figure 3C 、 Figure 3D 、 Figure 3E and Figure 3F depict exemplary ferromagnetic impurity separator systems.

[0018] Figure 4A 、 Figure 4B 、 Figure 4C 、 Figure 4D and Figure 4EDepicts a first embodiment of an exemplary In - line Battery Material Collector (IBMC).

[0019] Figure 5A and Figure 5B Depicts a second embodiment of an exemplary IBMC.

[0020] Figure 6 Depicts a third embodiment of an exemplary IBMC.

[0021] Figure 7A Is a block diagram depicting an exemplary Voltammetric Analysis Module (VAM).

[0022] Figure 7B Shows an exemplary response plot of a potential scan and the response from an exemplary sample solution including iron, chromium, zinc, and nickel.

[0023] Figure 8 Is a flowchart illustrating an exemplary in - line ferromagnetic impurity monitoring method.

[0024] Figure 9 Is a flowchart illustrating an exemplary ferromagnetic impurity separation method.

[0025] Figure 10 Is a flowchart illustrating an exemplary in - line voltammetric analysis method.

[0026] Figure 11 Is a flowchart illustrating an exemplary voltammetric analysis calibration method.

[0027] Like reference symbols in the various figures indicate like elements.

[0028] Detailed description of illustrative embodiments

[0029] For the sake of helping understanding, this document is organized as follows. First, to help introduce the discussion of various embodiments, reference Figures 1 - 2 is made to introduce the Ferromagnetic Impurity Separation System (FISS). Second, this introduction leads to reference Figures 3A - 3F for the description of some exemplary embodiments of the FISS. Third, reference Figures 4A - 6 is made to describe the application of an in - line sampling device in an exemplary in - line magnetic impurity analysis system. Fourth, reference Figures 7A - 8 is made, and the discussion turns to exemplary embodiments that illustrate systems and methods for using voltammetric analysis to determine the magnetic impurity metric of a batch of bulk battery materials. Fifth, reference Figures 9 - 11 is made, and this document describes exemplary devices and methods that can be used to separate and measure ferromagnetic impurities in a battery production system. Finally, this document discusses additional embodiments, exemplary applications, and aspects related to ferromagnetic impurity monitoring and measurement systems for battery production.

[0030] Figure 1 Illustrates an exemplary battery production system (BPS) employed in an illustrative use case scenario. In this example, BPS 100 includes a battery production line 105. For example, the battery production line 105 can be a production line for lithium-ion batteries. For example, the battery production line 105 can be a production line for nickel-cadmium batteries. For example, the battery production line 105 can be a production line for solid-state batteries. For example, the battery production line 105 can be a production line for lead-acid batteries. For example, the battery production line 105 can be a production line for other solid-state batteries. For example, the battery production line 105 can include facilities for manufacturing the electrodes of the battery.

[0031] In this example, the battery production line 105 receives battery cathode material (BCM 110). For example, the battery production line 105 can use BCM 110 to produce electrodes for the battery. In some embodiments, BCM 110 can be in powder form (e.g., bulk in crates of cathode powder). For example, BCM 110 can include lithium nickel manganese cobalt oxide (NMC) cathode powder. In other examples, BCM 110 can include lithium powder (e.g., lithium nickel cobalt manganese oxide (NCM), lithium nickel cobalt aluminum oxide (NCA), lithium cobalt oxide (LiCoO2), lithium manganese oxide (LiMn2O4), lithium iron phosphate (LiFePO4)). In some embodiments, BCM 110 can include precursor cathode material (e.g., Ni powder, LiOH, iron oxide).

[0032] In some examples, depending on the type of cathode material used in the battery production line 105, BCM 110 can include magnetic impurities (e.g., zinc, chromium, iron, nickel). In some examples, the battery production line 105 can be contaminated with magnetic impurities. For example, magnetic impurities can adversely affect the quality of the batteries produced by the battery production line 105 (e.g., safety, lifespan, performance). Magnetic impurities can, for example, trap lithium ions, making them unavailable for the electrochemical reaction. For example, magnetic impurities can increase the internal resistance of the battery, thereby reducing the amount of electricity that can be delivered. For example, magnetic impurities can accelerate the degradation of the cathode material. For example, magnetic impurities can increase the risk of thermal runaway that causes the battery to overheat. For example, when the concentration of magnetic impurities is higher than an acceptable threshold, the affected batteries may have a higher risk of overheating. For example, the affected batteries may have a higher risk of not performing according to specifications (e.g., lower power output, longer charging time, lower storage capacity). In various examples, during production, if it is found that BCM 110 includes magnetic impurities above the acceptable threshold, the battery production line 105 may have to be stopped urgently so that the defective cathode material can be removed before production resumes. In such cases, for example, the operators of the battery production line 105 may incur high costs and significant production delays.

[0033] BPS100 includes a ferromagnetic impurity separation system (FISS115). In some embodiments, FISS115 can separate magnetic (ferromagnetic) impurities from the BCM 110. For example, FISS115 can precisely separate ferromagnetic impurities from the BCM 110 by separating ferromagnetic impurities from the paramagnetic impurities in the BCM 110. The separated magnetic impurities can be analyzed, for example, in the sample analysis system 125. In some instances, the separated impurities can be transferred to another magnetic impurity analysis facility. For example, thus, before the BCM 110 is used in the battery production line 105, the BCM 110 can be advantageously verified as acceptable for production.

[0034] In some instances, by way of example and not limitation, FISS115 can operate in a laboratory environment. For example, a bulk cathode material powder can be sampled (e.g., by obtaining a predetermined sample amount), and the sample can be brought to FISS115. FISS115 can separate the target magnetic impurities (e.g., ferromagnetic impurities). For example, FISS115 can be used to determine the quality (e.g., purity level) of the bulk material before allowing the bulk material to enter the production line. For example, if the bulk material exceeds the maximum magnetic impurity threshold, the bulk material can be rejected or cleaned before entering the facility.

[0035] In this instance, the battery production line 105 can include an in-line battery material collector (IBMC 120). For example, the IBMC 120 can sample the BCM 110 used in the battery production line 105 from time to time (e.g., periodically, continuously) to verify the quality of the BCM 110. In this instance, the IBMC 120 can transfer the sampled BCM 110a to the sample analysis system 125. For example, the sample analysis system 125 can determine the concentration of the target (iron) magnetic impurities in the BCM 110.

[0036] Optionally, as Figure 1 shown, the IBMC 120 can transfer the sampled BCM 110a to FISS115. For example, before analyzing the ferromagnetic impurities in the sample analysis system 125, the BPS100 can use FISS115 to advantageously separate the ferromagnetic impurities from the sampled BCM 110a. For example, the analysis results using the ferromagnetic impurities from FISS115 can be more accurate. Refer to Figures 4A - 6 for a more detailed description of various embodiments of the IBMC 120.

[0037] FISS115 includes a sample container 130, a ferromagnetic impurity separation controller (FISC 135), and a sample collection module 140. In some embodiments, the sample container 130 may include a cavity configured to receive (e.g., manually load, automatically receive from the IBMC 120) a BCM sample 110b (e.g., a sampled BCM 110a that is directly sampled from the BCM 110 prior to production).

[0038] In this example, the sample container 130 is connected to the FISC 135. The FISC 135 includes a variable magnetic flux module 145, a agitation module 150, and a flushing module 155. For example, the variable magnetic flux module 145 may generate a varying magnetic flux (e.g., 1,200 gauss - 20,000 gauss) in the sample container 130. In some embodiments, the variable magnetic flux module 145 may be positioned outside the sample container 130 such that the BCM sample 110b can be physically separated from the variable magnetic flux module 145. In some examples, the separation between the variable magnetic flux module 145 and the BCM sample 110b may advantageously prevent the BCM sample 110b from being contaminated by the variable magnetic flux generated by the variable magnetic flux module 145.

[0039] As shown, the variable magnetic flux module 145 includes an electromagnet 160. For example, the FISC 135 may use a control signal to control the electromagnet 160 to generate a selected magnetic flux. For example, the impurity separation process may include a multi - cycle (2 cycles, 3 cycles, 6 cycles, …, N cycles) separation process. For example, the variable magnetic flux module 145 may apply magnetic fluxes of different intensities to the sample container 130 in each separation cycle. For example, by applying magnetic fluxes of multiple intensities to the sample container 130, the FISS115 can separate paramagnetic particles and ferromagnetic particles from the BCM sample 110b.

[0040] In some embodiments, the variable magnetic flux module 145 may further include a permanent magnet 165. For example, the variable magnetic flux module 145 can be controlled to generate a selected magnetic flux in the sample container 130 using both the electromagnet 160 and the permanent magnet 165. In some embodiments, the FISC 135 can be configured to generate a magnetic flux of less than 10,000 gauss using only the permanent magnet 165. For example, for magnetic flux ranges above 10,000 gauss, the FISC 135 can use both the electromagnet 160 and the permanent magnet 165. In some embodiments, the FISC 135 can be configured to generate a high magnetic flux (e.g., above 6000 gauss). In some embodiments, the variable magnetic flux module 145 can vary the magnetic strength of the permanent magnet 165. For example, the variable magnetic flux module 145 can control the distance between the permanent magnet 165 and the sample container 130 to advantageously vary the magnetic flux within the sample container 130 during different cycles of the impurity separation process.

[0041] For example, the agitation module 150 can agitate the BCM sample 110b during the impurity separation process. For example, the FISC 135 can control the agitation module 150 to agitate the sample container 130 (e.g., rotate, shake left and right, shake up and down) during the application of the magnetic flux by the variable magnetic flux module 145. As shown, the agitation module 150 includes a rotary motor 170 and a translation motor 175. For example, the rotary motor 170 and the translation motor 175 can include an AC motor. For example, the rotary motor 170 and the translation motor 175 can include a DC motor. For example, the rotary motor 170 and the translation motor 175 can include a stepper motor. For example, the rotary motor 170 and the translation motor 175 can include a servo motor. For example, the rotary motor 170 and the translation motor 175 can include a pneumatic motor. For example, the rotary motor 170 and the translation motor 175 can include a hydraulic motor.

[0042] As shown in this example, the rotary motor 170 can rotate the sample container 130 along the rotation axis R. For example, the translation motor 175 can translate the sample container 130 in Euclidean space along one or more of the x, y, and z (3) axes. In some instances, the agitation module 150 can, for example, advantageously break up aggregations of magnetic impurities and allow for more accurate separation and detection of magnetic impurities in the BCM sample 110b.

[0043] The rinsing module 155 includes an ultrapure water source 180 and an acid solution source 185. In some embodiments, the FISC 135 can rinse the sample container 130 between each cycle of the impurity separation process to discharge unwanted particles (e.g., paramagnetic impurities) from the sample container 130. As a non-limiting illustrative example, at the end of each cycle, magnetic impurities can be separated from the BCM sample 110b at the wall of the sample container 130. For example, the FISC 135 can then apply a lower magnetic flux to the sample container 130 to reduce the attachment strength between the paramagnetic impurities and the wall. Rinsing the wall with ultrapure water at this time can, for example, rinse the paramagnetic impurities from the sample container 130. In some embodiments, the remaining material can then experience a gradually decreasing magnetic flux in the next cycle, while the paramagnetic impurities are gradually washed away (e.g., using ultrapure water) in each subsequent cycle.

[0044] Once the minimum magnetic flux is reached, in some embodiments, the variable magnetic source can be operated to turn off such that there is substantially no magnetic flux. For example, the sample collection module 140 can collect the remaining particles from the sample container 130 (e.g., using ultrapure water, using an acid rinse). For example, the sample collection module 140 can operate with the acid solution source 185 to collect the remaining particles using an acid solution. For example, collecting the remaining particles with an acidic solution can advantageously facilitate the analysis of the remaining particles using inductively coupled plasma (ICP) analysis. In some embodiments, the final solution can include various types of impurities when collected with an acidic solution. In another example, the sample container can be washed with ultrapure water to produce a partial sample (e.g., without an acidic solution) for optical or spectroscopic methods (SEM, XRF) to physically observe the sample and its shape and composition.

[0045] For example, the collected particles can mainly include ferromagnetic magnetic impurities. As shown, the collected particles can be transferred to the sample analysis system 125 for analysis. For example, the sample analysis system 125 can determine the presence and / or concentration of magnetic impurities in the BCM 110. In some embodiments, the sample analysis system 125 can be configured to identify and detect target magnetic impurities.

[0046] In this example, the sample analysis system 125 includes a voltammetric analysis module 190 and an ICP analysis module 195. For example, the voltammetric analysis module 190 can apply anodic stripping voltammetry to determine the presence of target magnetic impurities in the purified sample. Refer to Figures 7A - 7B and Figure 10 Further describe various embodiments of using the voltammetric analysis module 190 to analyze samples of the BCM 110. In various embodiments, the sample analysis system 125 can use optical (e.g., scanning electron microscopy) analysis, spectroscopic (e.g., X-ray fluorescence, X-ray diffraction) analysis, and / or elemental (e.g., ICP) analysis to determine the collected particles.

[0047] In some embodiments, FISS115 can advantageously allow for the detection of magnetic impurities in BCM 110. For example, some embodiments can advantageously prevent the manufacturing line from being contaminated with impure battery materials. Some embodiments can advantageously reduce the incidence of battery fires and / or battery failures due to the presence of magnetic impurities.

[0048] In various embodiments, a method for separating ferromagnetic impurities from bulk battery material (e.g., BCM 110) can include agitating a sample of bulk battery material (e.g., BCM sample 110b) in a closed container (e.g., sample container 130) by inducing kinetic energy on a rotational axis (e.g., using a rotational motor 170) and a translational axis (e.g., using a translational motor 175), and (e.g., simultaneously) applying a time-varying magnetic flux to the sample (e.g., using a variable magnetic flux module 145).

[0049] For example, the method can further include applying a phase sequence with a reduced magnetic flux and a correspondingly reduced rotational speed (e.g., N cycles of an impurity separation process) such that paramagnetic particles are separated from the ferromagnetic impurities. For example, the method can further include collecting the ferromagnetic impurities separated from the sample by rinsing the closed container with ultrapure water (e.g., using an ultrapure water source 180) and / or with an acid (e.g., using an acid solution source 185). In some instances, the method can include using ICP mass spectrometry to analyze the ferromagnetic impurities.

[0050] Accordingly, various embodiments can advantageously generate a measure (e.g., concentration, level, presence) of ferromagnetic impurities in battery materials to be used in battery production line 105. Using IBMC 120, some embodiments can provide near real-time on-line detection of magnetic impurities in BCM110. For example, battery production line 105 can include an on-line detector by combining IBMC 120, FISS115, and / or sample analysis system 125. The on-line detector can automatically generate a measure representing the quality of BCM 110 without contaminating BCM 110 due to direct contact with liquids and / or permanent magnets.

[0051] Figure 2 is a block diagram depicting an exemplary ferromagnetic impurity separation system (FISS). In this example, FISS200 includes FISC 135( Figure 1)。The FISC 135 includes a processor 205. The processor 205 can include, for example, one or more processing units. The processor 205 is operatively coupled to a communication module 210. The communication module 210 can include, for example, wired communication. The communication module 210 can include, for example, wireless communication. In the depicted example, the communication module 210 is operatively coupled to a variable magnetic flux module 145, a agitation module 150, a rinsing module 155, and a user interface 215. For example, the user interface 215 can receive control signals from a user. For example, the control signals can include a signal to start an impurity separation process. For example, the control signals can include a determination of a selected list of target magnetic impurities. For example, the control signals can include an input of the amount (e.g., weight, volume) of the BCM sample 110b in the sample container 130. For example, the control signals can include the type of BCM 110.

[0052] The user interface 215 can also include a display for the user. For example, the user interface 215 can display analysis results (e.g., metrics generated by the sample analysis system 125, the presence of each of the target impurities). For example, the user interface 215 can display warnings and / or other system messages to the user. For example, the user interface 215 can display the processing status of the FISS 115 to the user.

[0053] The processor 205 is operatively coupled to a memory module 220. The memory module 220 can include, for example, one or more memory modules (e.g., random access memory (RAM)). The processor 205 includes a storage module 225. The storage module 225 can include, for example, one or more storage modules (e.g., non-volatile memory). In the depicted example, the storage module 225 includes a process control engine 230, a magnetic flux control engine 235, an agitation application engine 240, a rinsing control engine 245, and a sample collection engine 250.

[0054] For example, the process control engine 230 can determine the separation process to be performed in the FISC 135. For example, the process control engine 230 can determine the number of cycles to be performed in the impurity separation process. For example, the process control engine 230 can determine the magnetic flux and kinetic energy to be applied to the sample container 130 in each cycle based on the type of BCM 110 and / or the target impurities to be analyzed in the BCM 110.

[0055] As shown, the processor 205 is also coupled to a data repository 255. The data repository 255 includes a separation process profile 260. For example, the process control engine 230 can retrieve the separation process profile 260 to determine the parameters of the impurity separation process when receiving a signal to start the impurity separation process in the sample container 130. For example, the separation process profile 260 can include multiple cycles in the impurity separation process. For example, the separation process profile 260 can include the duration of each step in each cycle of the impurity separation process. For example, the separation process profile 260 can include a time-varying profile of the magnetic flux and kinetic energy to be applied to the sample container 130 in each cycle of the impurity separation process.

[0056] For example, the magnetic flux control engine 235 can control the variable magnetic flux module 145. For example, the magnetic flux control engine 235 can transmit control signals to the electromagnet 160 and the permanent magnet 165 to adjust the magnetic flux within the sample container 130. For example, the magnetic flux control engine 235 can generate control signals based on the magnetic flux application profile 265 in the data repository 255. In some embodiments, according to the required magnetic intensity, the magnetic flux application profile 265 can include a combination of control signals for the electromagnet 160 and the permanent magnet 165 to generate the required magnetic intensity.

[0057] The agitation application engine 240 can, for example, control the agitation module 150. For example, the agitation application engine 240 can transmit control signals to the rotary motor 170 and the translation motor 175 to adjust the kinetic energy to be applied to the sample container 130. For example, the agitation application engine 240 can generate control signals based on the agitation application profile 270 in the data repository 255. In some embodiments, according to the required kinetic energy (e.g., specified in the separation process profile 260), the agitation application profile 270 can include a combination of control signals for the rotary motor 170 (e.g., to generate the angular velocity (ω) of the sample container 130) and the translation motor 175 (e.g., to generate the velocity (v) of the sample container 130) to generate the required kinetic energy.

[0058] As a non-limiting illustrative example, the process control engine 230 can determine that the impurity separation process includes N cycles according to the separation process profile 260. For example, the process control engine 230 can select the separation process profile 260 based on the type of target impurity and the BCM 110. For each of the N cycles, the separation process profile 260 can specify the magnetic flux and kinetic energy to be applied to the sample container 130 at different times during the cycle.

[0059] Based on the separation process profile 260, the process control engine 230 can use the magnetic flux control engine 235 and the agitation application engine 240 to generate a specified magnetic flux and kinetic energy to the sample container 130 at a specified time. For example, the separation process profile 260 can specify that in the i-th cycle, the variable magnetic flux module 145 and the agitation module 150 apply the i-th predetermined magnetic flux (Φ_i), the i-th predetermined angular velocity (ω_i), and the i-th predetermined velocity (v_i) to the sample container 130 for a predetermined separation time (e.g., 2 seconds, 10 seconds, 30 seconds, 60 seconds).

[0060] After the predetermined separation time, in each cycle, for example, the separation process profile 260 can include a rinse cycle. For example, the process control engine 230 can activate the rinse control engine 245 in the rinse cycle to rinse out the paramagnetic impurities using ultrapure water.

[0061] In some embodiments, the separation process profile 260 can also include a rinse magnetic flux to be applied to the sample container 130 in the rinse cycle. For example, in the i-th cycle, the separation process profile 260 can include applying the i-th rinse magnetic flux (Φ_ri) to the sample container 130. In some embodiments, Φ_ri < Φ_i to advantageously separate the paramagnetic impurities from the ferromagnetic impurities.

[0062] In various embodiments, the magnetic fluxes (Φ_1, Φ_2,..., Φ_N) applied in subsequent cycles i = 1, 2,..., N of the separation process profile 260 can be monotonically decreasing, such that Φ_1 > Φ_2 >... > Φ_N. For example, accordingly, the paramagnetic impurities are gradually removed from each subsequent separation cycle. In some embodiments, the separation process profile 260 can also include kinetic energies (KE_1, KE_2,..., KE_N) corresponding to the predetermined angular velocities (ω_i) and predetermined velocities (v_i) that are monotonically decreasing for each separation cycle i = 1, 2,..., N.

[0063] For example, after N separation cycles, the separation process profile 260 can include a sample collection cycle. For example, the sample collection engine 250 can control the variable magnetic flux module 145, the agitation module 150, the rinse module 155, and the sample collection module 140 to collect the remaining particles from the sample container 130.

[0064] In various embodiments, in the sample collection cycle, the rotation motor 170 can be controlled to rotate the sample container 130 at a low speed. For example, after the rotation of the sample container 130 is completed, the translation motor 175 can be controlled to be in the collection position. Refer to Figure 3F describes some exemplary sample collection processes.

[0065] Figure 3A 、 Figure 3B 、Figure 3C , Figure 3D , Figure 3E and Figure 3F depict exemplary ferromagnetic impurity separator systems. As Figure 3A shown, the FISS 300 can include a hole 305. For example, a sample container 130 enclosing a BCM sample 110b (e.g., in powder form) can be inserted into the FISS 300 through the hole 305 for the impurity separation process. In this instance, a user can use the user interface 215 to control the FISS 300. For example, the user can input the weight of the BCM sample 110b in the sample container 130. For example, the user can input the target impurities to be separated from the BCM sample 110b in the sample container 130.

[0066] Figure 3B shows a transparent view of the FISS 300. As shown, the sample container 130 is (removably) coupled to the agitation module 150 and the electromagnet 160. As shown, the electromagnet 160 is disposed outside the sample container 130 such that the BCM sample 110b can be physically separated from the electromagnet 160.

[0067] The agitation module 150 can agitate the sample container 130. For example, during the application of a current magnetic flux by a variable magnetic source, the container can be agitated (e.g., rotated about a central axis R, shaken left and right, shaken up and down along a translation axis 310). Agitation can, for example, advantageously break up aggregations of magnetic impurities and allow for a more accurate separation and detection of magnetic impurities in the sample container 130.

[0068] In one embodiment, the electromagnet 160 can be controlled to provide a first magnetic flux to separate paramagnetic particles and ferromagnetic particles (e.g., from the BCM sample 110b). For example, the first magnetic flux can include a high magnetic intensity. At the same time, for example, the agitation module 150 can apply a high rate or rotation to the sample container 130. For example, all magnetic impurities (e.g., paramagnetic particles, ferromagnetic particles) can be separated from the BCM sample 110b. In some embodiments, the sample container can undergo multiple separation cycles. At the completion of each separation cycle, the sample container 130 can then gradually undergo a lower magnetic flux and / or a lower rotational speed. For example, the FISS 300 can induce kinetic energy on two axes (e.g., a rotational axis and a translation axis) to advantageously maximize the extraction potential of the sample.

[0069] As shown, the FISS 300 includes more than one electromagnet 160 to form an electromagnetic sleeve 315. In some instances, the electromagnetic sleeve 315 can pulsate with different harmonization relative to the sample container 130 to fully extract ferromagnetic impurities from other particles.

[0070] Figure 3C illustrates an exemplary separation cycle in the impurity separation process in FISS 300 (e.g., the i-th cycle among the N cycles as described in Figure 2 . As shown by the arrows, the sample container 130 is filled with fluid 320 (e.g., ultrapure water) during the separation cycle. In this example, FISS 300 can apply a magnetic flux Φ, a translational velocity v, and an angular velocity ω to the sample container 130 (e.g., simultaneously). For example, FISS 300 can apply the magnetic flux Φ, the translational velocity v, and the angular velocity ω based on the separation process profile 260. For example, the magnetic impurities in the BCM sample 110b dissolved and / or mixed in the fluid 320 can be attracted towards the outer wall of the sample container 130.

[0071] For example, after each separation cycle, FISS 300 can perform a rinse cycle to remove unwanted particles from the sample container 130. As Figure 3D shown, during the rinse cycle, the rinse control engine 245 can use the rotary motor 170 and the translational motor 175 to control the agitation module 150 to place the sample container 130 at a pre-determined displacement d_0 and a pre-determined angle θ_0. For example, at d_0 and θ_0, the output port of the sample container 130 can be aligned with the waste collection container 325 of the sample collection module 140.

[0072] In some embodiments, the rinse control engine 245 can control the variable magnetic flux module 145 to apply a medium magnetic intensity to the sample container and control the agitation module 150 to apply a low rotation rate to the sample container during the rinse cycle before the sample container 130 reaches the position (d_0, θ_0). In various embodiments, the rinse cycle for each subsequent separation cycle can include different combinations of magnetic intensity and rotation speed to advantageously and effectively extract unwanted impurities. As Figure 3E shown, when the sample container 130 reaches the predetermined position (d_0, θ_0), FISS 300 can rinse the sample container 130 with the rinse fluid 330 (e.g., ultrapure water) to facilitate the collection of unwanted particles in the waste collection container 325.

[0073] After completing all (e.g., N) separation cycles, separated magnetic impurities (e.g., ferromagnetic impurities) can also be collected, as Figure 3F shown. In this example, the sample collection module 140 can include a sample collection container 335. For example, the sample collection container 335 can be a 50 ml sterile tube. For example, after collecting the sample in the sample collection container 335, the user can use the side door 340 (as Figure 3Acollect the sample collection container 335 (e.g., for further analysis of impurities) as shown in []. For example, a user can transport the magnetic impurities collected in the sample collection container 335 for analysis (e.g., in the sample analysis system 125). For example, the magnetic impurities can be analyzed to determine the concentration of magnetic impurities in the BCM 110. Some embodiments can advantageously prevent contamination of the manufacturing line. Some embodiments can advantageously reduce the incidence of battery fires and / or battery failures due to the presence of magnetic impurities.

[0074] In some embodiments, the FISS 300 can include an automatic sampling module. For example, the FISS 300 can include a dispenser unit for temporarily storing the pre-prepared sample collection container 335 including ferromagnetic impurities. For example, the dispenser unit can be configured in a format similar to a vending machine for a user to immediately grab the pre-prepared sample container for immediate analysis.

[0075] Figure 4A , Figure 4B , Figure 4C , Figure 4D and Figure 4E depicts a first embodiment of an exemplary in-line battery material collector (IBMC). In this example, the IBMC 400 can be disposed next to the battery powder transport chute (CPTC 405). The CPTC 405 can include a continuous flow of battery material 415 (e.g., BCM 110, cathode powder) in the battery production line 105.

[0076] The IBMC 400 includes an impurity collector 410. For example, the impurity collector 410 can collect magnetic impurities using a magnetic setting. As shown in [], when the battery material 415 flows through the CPTC 405, the impurity collector 410 can magnetically attract the magnetic impurities to be collected in the impurity collector 410. In some embodiments, the impurity collector 410 can include a vibrating panel with an electromagnet. In some embodiments, the impurity collector 410 can include a mechanism for separating the battery material 415 from the magnetic impurities. Figure 4E [], when the battery material 415 flows through the CPTC 405, the impurity collector 410 can magnetically attract the magnetic impurities to be collected in the impurity collector 410. In some embodiments, the impurity collector 410 can include a vibrating panel with an electromagnet. In some embodiments, the impurity collector 410 can include a mechanism for separating the battery material 415 from the magnetic impurities.

[0077] In some embodiments, the impurity collector 410 can include an in-line impurity separator (IIS). For example, the IIS can include a predetermined number (1, 2, 3, 5) of electromagnets with a fixed magnetic intensity to generate a varying magnetic flux to separate magnetic impurities from the BCM. For example, after collecting the magnetic impurities through the IIS, the impurity collector 410 can transfer the collected magnetic impurities (e.g., transfer to an aqueous solution) for voltammetric analysis testing (e.g., as described in reference []. Figures 7A - 7B []).

[0078] In this example, sampling particles from the battery material 415 can be collected in the impurity collector 410 based on an activation signal of magnetism (e.g., via a magnet located around and / or in the impurity collector 410). For example, the activation signal can be transmitted at a sampling time (e.g., periodically, manually). In some examples, after receiving a deactivation signal, the impurity collector 410 can turn off the magnetism to release the sampled particles back into the CPTC 405 (as shown as the returned particles 420). For example, sampled particles can be advantageously collected without interrupting the flow of the CPTC 405. In some embodiments, the impurity collector 410 can include a sample analysis module. For example, the sample analysis module can include anodic stripping voltammetry. In some embodiments, the impurity collector 410 can include a voltammetric analysis module 190 to perform voltammetric analysis.

[0079] Figure 5A and Figure 5B A second embodiment of an exemplary IBMC is depicted. In this example, the IBMC 500 includes an intermittent sampling device 505. As Figure 5A shown, the intermittent sampling device 505 can collect the battery material 415 from a continuous flow of battery material through the through-hole 510.

[0080] The controller 515 is operatively coupled to the intermittent sampling device 505. For example, the controller 515 can be an automated system. For example, the controller 515 can be an operator who manually controls the intermittent sampling device 505. In some embodiments, the controller 515 can control the intermittent sampling device 505 to collect a sample 520 of the battery material 415 by moving the intermittent sampling device 505 ( Figure 5B ). As Figure 5B shown, after collecting the sample 520, the intermittent sampling device 505 can release the sample into the impurity collector 410 for analysis. For example, the sample 520 can be removed from the battery material 415 without being released back into the CPTC 405, to advantageously prevent contamination of the battery material 415.

[0081] Figure 6 A third embodiment of an exemplary IBMC is depicted. For example, the IBMC 600 can directly extract magnetic impurities from the CPTC 405. As shown, the IBMC 600 includes a magnet 605 and a magnet sleeve 610. In operation, when the battery material 415 is flowing, the operator can insert the IBMC 600 into the CPTC 405. For example, the magnetic impurities can be collected on the magnet sleeve 610 for analysis in, for example, the sample analysis system 125.

[0082] Figure 7Ais a block diagram depicting an exemplary Voltammetric Analysis Module (VAM). In this example, VAM 700 is analyzing sample mixture 705. For example, sample mixture 705 can be an aqueous suspension solution. Sample mixture 705 includes material sample 710 and metal indicator 715. For example, the metal indicator can include a metal chelating agent. For example, metal indicator 715 can be added to material sample 710 after sampling material sample 710 from IBMC 120. For example, metal indicator 715 can include Calcon (Color Index (C.I.) 15705) metal indicator.

[0083] VAM 700 also includes a potential application device 720. For example, potential application device 720 can apply a range of potentials in the sample mixture 705 during voltammetric analysis. In this example, potential application device 720 is coupled to a Target Impurity Identification Module (TIIM 725). TIIM 725 can control the potential applied to sample mixture 705. Additionally, TIIM 725 can receive a response (e.g., a current response) from sample mixture 705 at each of the applied potentials.

[0084] TIIM 725 includes a processor 730 coupled to a VA engine 735. For example, VA engine 735 can be stored in a storage module containing programmable instructions for performing voltammetric analysis. For example, VA engine 735 can determine the range of potentials to be applied to sample mixture 705 based on the type of material sample 710 and metal indicator 715. For example, VA engine 735 can generate an impurity metric representing the impurity level in material sample 710.

[0085] As shown, VA engine 735 retrieves a Ferromagnetic Impurity (FMI)-Potential Correlation Profile (FPCP 740) and a normalization correlation profile 745. For example, VA engine 735 can generate an estimated impurity ratio of the bulk cell material (e.g., BCM 110) based on the impurity metric, FPCP 740, and normalization correlation profile 745.

[0086] For example, FPCP 740 can include a standard calibration curve between various response values from potential application device 720 and known concentrations of known impurities (e.g., calibration bulk materials). For example, FPCP 740 can be generated by testing various known amounts of standard materials (e.g., as material sample 710) with VAM 700. For example, FPCP 740 can represent the relationship between the signal magnitude received from potential application device 720 and the actual amount of impurities in material sample 710.

[0087] For example, the normalization-related profile 745 may include the relationship between the impurities collected in the material sample 710 (e.g., via IBMC 120, IBMC 400, IBMC 500, and / or IBMC 600) and the actual amount of impurities in the battery material (e.g., battery material 415). For example, IBMC 120 may collect 3%-10% of the actual magnetic impurities in the stream of BCM 110. For example, when the actual reading of the magnetic impurities in BCM 110 is 100 ppb (parts per billion), IBMC 120 may detect 5 ppb from the material sample 710 collected online from BCM 110. For example, as an illustrative example, the normalization-related profile 745 may include a multiplication factor in the ratio of up to 5 ppb to 100 ppb to generate an actual reading representing a metric corresponding to the bulk material of the material sample 710.

[0088] Figure 7B An exemplary response plot showing a potential scan and the response from an exemplary sample solution including iron, chromium, zinc, and nickel is shown. In this example, the response plot 770 includes the range of the potential (V) applied to the material sample 710 and the corresponding current response (A). In this example, the response plot 770 includes peaks representing the separation potentials of the various elements in the VAM 700. For example, iron (Fe) may include a separation potential at V1. For example, chromium (Cr) may include a separation potential at V2. For example, zinc (Zn) may include a separation potential at V3. For example, nickel (Ni) may include a separation potential at V4.

[0089] In some embodiments, based on the FPCP 740, the VA engine 735 may identify the peaks (V1-4) corresponding to each of the target impurities (Fe, Cr, Zn, Ni). For example, the VA engine 735 may integrate the areas respectively indicated by the areas 775a-775d below each peak (V1-4) to generate a response value. In some embodiments, the VA engine 735 may generate a sample metric representing the concentration of the impurities in the material sample 710 based on the response value. For example, the VA engine 735 may also generate the actual concentration of the bulk material of the material sample 710 corresponding to the response plot 770 according to the sample metric and the normalization-related profile 745.

[0090] Figure 8FIG. is a flowchart showing an exemplary online ferromagnetic impurity monitoring method 800. For example, the exemplary online ferromagnetic impurity monitoring method 800 may be performed by the BPS 100 using the IBMC 120, the sample analysis system 125, and / or the FISS 115. In this example, when a signal to start monitoring ferromagnetic impurities (FMI) of the bulk material is received in step 805, the exemplary online ferromagnetic impurity monitoring method 800 begins. For example, the controller 515 may receive a signal to collect a sample from the battery material 415 to monitor ferromagnetic impurities.

[0091] Next, at decision point 810, it is determined whether the VAM is calibrated with actual impurity analysis. For example, the VA engine 735 may check whether the FPCP 740 and the normalization-related profile 745 are valid. If the VAM is not calibrated, then in step 815, a calibration method for the VAM is performed (e.g., as described in Figure 11 ). After step 815 or if the VAM is calibrated, then in step 820, a sample of the bulk material is collected. For example, the BCM sample 110b may be collected by the IBMC 120. For example, the BPS 100 may use the IBMC 400, the IBMC 500, and / or the VAM 700 to collect a sample of the battery material 415.

[0092] After collecting the sample, in step 825, an impurity value is generated using a voltammetry analyzer. For example, the VAM 700 may be used to generate a sample metric representing the concentration of one or more target impurities using the FPCP 740. In step 830, the calibration profile and the actual concentration value of the VAM are retrieved. For example, the normalization-related profile 745 is retrieved by the VA engine 735. In step 835, an actual impurity metric for the bulk material is generated based on the calibration curve, and the exemplary online ferromagnetic impurity monitoring method 800 ends. For example, the VA engine 735 may generate the actual proportion of ferromagnetic impurities of the BCM 110 based on the normalization-related profile 745 and the generated sample metric.

[0093] Figure 9 FIG. is a flowchart showing an exemplary ferromagnetic impurity separation method 900. For example, the FISC 135 may perform the exemplary ferromagnetic impurity separation method 900 to separate ferromagnetic impurities from other particles in the BCM 110.

[0094] In this example, when a sample is received in a sample container, the exemplary ferromagnetic impurity separation method 900 begins at step 905. For example, when the sample container 130 is inserted into the FISS 115, the FISC 135 may receive a signal. Next, at step 910, the signal is received to initiate magnetic separation of one or more target ferromagnetic impurities (TFIs). For example, the signal may be received at the user interface 215. At step 915, a separation process profile is retrieved based on the TFIs. For example, the process control engine 230 may retrieve the separation process profile 260.

[0095] At step 920, N is determined, where N is the number of separation cycles to be applied to the sample, and i is set to 0. For example, the process control engine 230 may determine N based on the separation process profile 260 and the TFIs. Next, at step 925, i is set to i + 1. At step 930, the i-th cycle (Φ_i, ω_i, v_i) of kinetic energy and magnetic flux is applied to the sample. For example, the i-th cycle (Φ_i, ω_i, v_i) of kinetic energy and magnetic flux may also be time-varying within the i-th cycle. For example, the i-th cycle (Φ_i, ω_i, v_i) of kinetic energy and magnetic flux may be specified in the magnetic flux application profile 265 and the agitation application profile 270.

[0096] After applying the i-th cycle (Φ_i, ω_i, v_i) of kinetic energy and magnetic flux, at step 935, the magnetic flux and / or kinetic energy is reduced. Next, at step 940, unwanted particles are rinsed from the sample container using ultrapure water. For example, the rinse module 155 may rinse the paramagnetic impurities in the sample container 130 in each rinse cycle.

[0097] At step 945, it is determined whether i is equal to N. For example, if N cycles have not been reached, then i is not equal to N, and step 925 is repeated. If i = N, then at step 950, the sample particles are collected by rinsing the sample container with a collection fluid. For example, the sample collection module 140 may use an acid to collect the ferromagnetic impurities separated at the sample container 130.

[0098] At decision point 955, it is determined whether more tests are to be performed. For example, the BPS 100 may include a criterion for testing a predetermined amount of sample per ton. For example, the BPS 100 may require testing 300 g of sample 20 times per metric ton in order to advantageously generate a deterministic result with a predetermined confidence level. If more tests are required, then step 905 is repeated. If no more tests are required, then the exemplary ferromagnetic impurity separation method 900 ends.

[0099] Figure 10is a flowchart illustrating an exemplary online voltammetry analysis method 1000. For example, the VA engine 735 may perform the exemplary online voltammetry analysis method 1000 to generate a metric representative of ferromagnetic impurities in a bulk material (e.g., BCM 110). In this example, at step 1005, the exemplary online voltammetry analysis method 1000 begins when an input signal identifying the TFI is received. Next, at step 1010, a water-suspended sample (WSS) containing the TFI is received. For example, the sample mixture 705 is received.

[0100] At decision point 1015, it is determined whether the WSS is clean. For example, the VAM 700 may check whether the sample mixture 705 contains no more than 2 ppm of total dissolved solids. If it is determined that the WSS is clean, then at step 1020, a potential scan range to be applied to the WSS is determined based on the TFI. For example, the VA engine 735 may determine the potential scan range based on the user-specified TFI (e.g., in response to V in FIG. 770). If the WSS is determined to be not clean, then an error signal is generated at step 1025, and the exemplary online voltammetry analysis method 1000 ends.

[0101] At step 1030, after determining the potential scan range, the potential scan range is applied to the WSS. For example, the VA engine 735 may control the potential application device 720 to apply the determined potential to the sample mixture 705. Next, at step 1035, a response value is determined based on the integration of the peaks corresponding to each TFI. For example, the integration values 775a - 775d are generated.

[0102] At step 1040, a sample impurity value is generated based on the response value and the FMI-potential correlation profile. For example, the VA engine 735 may generate a sample metric based on the integration values 775a - 775d and the FPCP 740. At decision point 1045, it is determined whether any detected FMI concentration is greater than a predetermined detection threshold. For example, if there are any magnetic impurities, the VAM 700 may be configured to detect impurity concentrations as low as one part per trillion (ppt) level. For example, the predetermined detection threshold may be 200 ppb. If the detected FMI concentration is higher than the predetermined detection threshold, step 1025 is repeated. If the detected FMI concentration is not higher than the predetermined detection threshold, then an actual impurity value is generated at step 1050 based on the sample impurity value and the standardized correlation profile, and the exemplary online voltammetry analysis method 1000 ends. For example, the VA engine 735 may generate an actual metric based on the sample metric and the standardized correlation profile 745.

[0103] In some embodiments, the VAM 700 can be configured such that the total value of magnetic impurities can be less than 50 ppb. For example, when the sample mixture 705 includes impurities exceeding 50 ppb, the bulk material of the sample mixture 705 may be contaminated. For example, the VA engine 735 can immediately notify the user.

[0104] Figure 11 FIG. is a flowchart illustrating an exemplary voltammetric analysis calibration method 1100. For example, the VA engine 735 can perform the exemplary voltammetric analysis calibration method 1100 to generate a normalized correlation profile 745. In this example, when a signal to start calibrating the VAM is received in step 1105, the exemplary voltammetric analysis calibration method 1100 begins. Next, in step 1110, the actual impurity value of the battery material is determined. For example, the VA engine 735 can determine the actual impurity value based on the standardized material used in the calibration process.

[0105] In step 1115, the VAM is used to generate a measured impurity value. For example, the VA engine 735 can operate the potential application device 720 to obtain the measured impurity value. For example, the measured impurity value can be generated according to the FPCP 740. In step 1120, the calibration curve is updated based on the actual impurity value and the measured impurity value. For example, the VA engine 735 can use the ratio between the actual impurity value and the measured impurity value to update the normalized correlation profile 745.

[0106] At decision point 1125, it is determined whether the calibration curve is stable. For example, the VA engine 735 can perform a test to determine whether the normalized correlation profile 745 is stable based on the error between the impurity value generated using the normalized correlation profile 745 and the actual impurity value. If the calibration curve is not stable, step 1115 is repeated. If the calibration curve is stable, then in step 1130, the calibration curve is saved in the data repository, and the exemplary voltammetric analysis calibration method 1100 ends.

[0107] Although various embodiments have been described with reference to the accompanying drawings, other embodiments are also possible. In some embodiments, the flushing module 155 can include a mechanical brushing unit (MBU) to mechanically remove unwanted particles from the sample container 130. For example, the MBU can include a boom to brush off unwanted particles from the walls of the sample container 130 during a flushing cycle. For example, the MBU can include a blower to remove unwanted particles from the sample container 130 using air.

[0108] Although reference has been made to Figures 1 - 7B the described exemplary system, other embodiments can be deployed in other industrial, scientific, medical, commercial, and / or residential applications.

[0109] In an illustrative example, a method for separating magnetic impurities from a sample can include weighing the sample and placing the sample into a container. In a second step, the container can be sealed with a lid and inserted into a magnetic impurity separator. In a third step, the lid of the container can be attached to the machine. In a fourth step, the sample is rotated at a high speed with a high magnetic intensity. This can separate all magnetic impurities from the bulk sample, for example. For example, the machine can frequently change from high speed to low speed while in a rotation cycle. The electromagnetic sleeve can pulsate, for example, with different degrees of coordination to fully extract the particles. The device can release kinetic energy, for example, on two axes to maximize the extraction potential of the sample. In a fifth step, the machine performs a rinse cycle. The rinse cycle can occur over a long period of time. The rinse cycle can rotate, for example, at a low speed with a medium magnetic field intensity. This can separate all unwanted impurities, for example. This step can remove unwanted impurities that may be present from the bulk powder sample, for example. This step can be repeated multiple times, for example, to ensure the quality of the rinse. For example, the electromagnet can be set to change a proprietary level for efficient rinsing.

[0110] In a sixth step, the rinse cycle can occur, for example, over a short period of time. The rinse cycle can occur, for example, at a low magnetic intensity. The rinse cycle can occur, for example, at a medium speed. This step can remove all unwanted impurities, for example. This step can remove unwanted impurities that may be present in the sample container, for example. This step can be repeated multiple times, for example, to ensure the quality of the rinse. For example, the electromagnet can be set to change a proprietary level for efficient rinsing.

[0111] Between step 4, step 5, and step 6, a seventh step, called a discharge step, can be performed by the machine, for example, to release waste or the sample from the container according to the steps. This can allow the material to be obtained with minimal contamination, for example.

[0112] In an eighth step, the user turns the settings of the machine to have no magnetic intensity. Then the user collects all the clean magnetic impurities. The solution can be conditioned, for example, with optical (SEM) analysis, spectroscopic (XRF, XRD) analysis, or elemental (ICP) analysis. The sample will be collected by the user into a 50 mL sterile test tube.

[0113] In various embodiments, some bypass circuit implementations can be controlled in response to signals from analog or digital components, which can be discrete, integrated, or a combination of each. Some embodiments can include programmed devices, programmable devices, or some combination thereof (e.g., PLA, PLD, ASIC, microcontroller, microprocessor), and can include one or more data repositories (e.g., cells, registers, blocks, pages), which provide single-level or multi-level digital data storage capabilities and can be volatile, non-volatile, or some combination thereof. Some control functions can be implemented in hardware, software, firmware, or any combination thereof.

[0114] A computer program product can contain a set of instructions that, when executed by a processor device, cause the processor to perform specified functions. These functions can be performed in conjunction with a controlled device operably communicable with the processor. A computer program product that can include software can be stored in a data repository tangibly embedded on a storage medium, such as an electronic storage device, a magnetic storage device, or a rotating storage device, and can be fixed or removable (e.g., hard disk, floppy disk, thumb drive, CD, DVD).

[0115] Although examples of systems that can be portable have been described with reference to the above figures, other embodiments can be deployed in other processing applications, such as desktop and networked environments.

[0116] For example, a temporary auxiliary energy input can be received from a rechargeable or disposable battery, which can enable use in portable or remote applications. For example, some embodiments can operate using other DC voltage sources, such as a 9V (nominal) battery. An alternating current (AC) input, which can be provided, for example, from a 50 / 60Hz power port or from a portable generator, can be received via a rectifier and appropriate scaling. The provision of an AC (e.g., sine wave, square wave, triangular wave) input can include a line frequency converter to provide voltage boost, voltage buck, and / or isolation.

[0117] Although specific features of the architecture have been described, other features can be incorporated to improve performance. For example, cache (e.g., L1, L2, ...) technology can be used. Random access memory can be included, for example, to provide scratch pad memory and / or to load stored executable code or parameter information for use during runtime operations. Other hardware and software can be provided to perform operations, such as a network using one or more protocols or other communication, wireless (e.g., infrared) communication, stored operating energy and power (e.g., battery), switching and / or linear power circuits, software maintenance (e.g., self-test, upgrade), etc. One or more communication interfaces can be provided to support data storage and related operations.

[0118] Some systems can be implemented as computer systems that can be used with various embodiments. For example, various embodiments can include digital circuits, analog circuits, computer hardware, firmware, software, or combinations thereof. The apparatus can be implemented in a computer program product tangibly embodied in an information carrier, such as in a machine-readable storage device, for execution by a programmable processor; and the method can be performed by a programmable processor executing an instruction program to perform the functions of various embodiments by operating on input data and generating output. Various embodiments can advantageously be implemented in one or more computer programs executable in a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and / or at least one output device. A computer program is a set of instructions that can be used directly or indirectly in a computer to perform a particular activity or produce a particular result. The computer program can be written in any form of programming language, including a compiled or interpreted language, and the computer program can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0119] As an example, suitable processors for executing instruction programs include both general-purpose microprocessors and special-purpose microprocessors, which may include one of the single processors or multiple processors of any type of computer. Generally, the processor will receive instructions and data from read-only memory or random access memory or both. The key elements of a computer are the processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include one or more mass storage devices for storing data files, or be operatively coupled to communicate with the one or more mass storage devices; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, by way of example, including semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, an ASIC (application specific integrated circuit).

[0120] In some embodiments, each system may be programmed with the same or similar information and / or initialized with substantially the same information stored in volatile memory and / or non-volatile memory. For example, a data interface may be configured to perform auto-configuration, auto-download, and / or auto-update functions when coupled to a suitable host device such as a desktop computer or a server.

[0121] In some embodiments, one or more user interface features may be custom-configured to perform specific functions. A variety of embodiments may be implemented in a computer system including a graphical user interface and / or an Internet browser. To provide interaction with the user, some embodiments may be implemented on a computer having a display device. The display device may include, for example, an LED (light-emitting diode) display. In some embodiments, the display device may include, for example, a CRT (cathode ray tube). In some embodiments, the display device may include, for example, an LCD (liquid crystal display). The display device (e.g., a monitor) may be used, for example, to display information to the user. Some embodiments may include, for example, a keyboard and / or a pointing device (e.g., a mouse, a touchpad, a trackball, a joystick), such that the user may provide input to the computer through the keyboard and / or the pointing device.

[0122] In various embodiments, the system can communicate using suitable communication methods, devices, and technologies. For example, the system can communicate with compatible devices (e.g., devices capable of transferring data to and / or from the system) using point-to-point communication, in which messages are transmitted directly from a source to a receiver over a dedicated physical link (e.g., an optical fiber link, point-to-point wiring, daisy chain). Components of the system can exchange information via analog or digital data communication in any form or medium, including packet-based messages over a communication network. Examples of communication networks include, for example, LAN (Local Area Network), WAN (Wide Area Network), MAN (Metropolitan Area Network), wireless networks, and / or optical networks, computers and networks forming the Internet, or some combination thereof. Other embodiments can transmit messages by broadcasting to all or substantially all devices connected together by a communication network, such as by using an omnidirectional radio frequency (RF) signal. Still other embodiments can transmit messages characterized by high directivity, such as RF signals transmitted using a directional (i.e., narrow beam) antenna or infrared signals that can optionally be used in conjunction with focusing optics. Other embodiments are possible using appropriate interfaces and protocols, such as, by way of example and not limitation, USB 2.0, Firewire, ATA / IDE, RS-232, RS-422, RS-485, 802.11a / b / g, Wi-Fi, Ethernet, IrDA, FDDI (Fiber Distributed Data Interface), token ring, multiplexing techniques based on frequency division, time division, or code division, or some combination thereof. Some embodiments can optionally incorporate features such as error detection and correction (ECC) for data integrity, or security measures such as encryption (e.g., WEP) and password protection.

[0123] In various embodiments, a computer system can include Internet of Things (IoT) devices. IoT devices can include objects embedded with electronic devices, software, sensors, actuators, and network connections that enable these objects to collect and exchange data. IoT devices can send data to another device via an interface and thus be used with wired or wireless devices. IoT devices can collect useful data and then autonomously transfer data between other devices.

[0124] Various instances of a module can be implemented using a circuit that includes various electronic hardware. By way of example and not limitation, the hardware can include transistors, resistors, capacitors, switches, integrated circuits, other modules, or some combination thereof. In various instances, the module can include analog logic, digital logic, discrete components, traces, and / or memory circuits fabricated on a silicon substrate that includes various integrated circuits (e.g., FPGA, ASIC), or some combination thereof. In some embodiments, the module can involve the execution of pre-programmed instructions, software executed by a processor, or some combination thereof. For example, many modules may involve both hardware and software.

[0125] In an illustrative aspect, a ferromagnetic impurity separation system can include a sample container configured to encapsulate a sample of bulk battery material. For example, the ferromagnetic impurity separation system can include a variable magnetic flux generator disposed at a location separated from the sample container. For example, the variable magnetic flux generator can be configured to generate a variable magnetic flux at the sample container.

[0126] For example, the ferromagnetic impurity separation system can include a agitation unit coupled to the sample container. For example, the agitation unit can include a rotary motor configured to rotate the sample container about a central axis. For example, the agitation unit can include a translation motor configured to translate the sample container. For example, the sample container can be displaced in Euclidean space.

[0127] For example, in an operating mode, the variable magnetic flux generator and the agitation unit can operate in a first separation cycle. For example, the variable magnetic flux generator and the agitation unit can apply a first predetermined magnetic flux (Φ_1), a first predetermined angular velocity (ω_1), and a first predetermined velocity (v_1) to the sample container. For example, ferromagnetic impurities can be separated from the sample.

[0128] For example, the first separation cycle can include applying a first rinse magnetic flux (Φ_r1) to the sample container. For example, Φ_r1 < Φ_1. For example, the first separation cycle can include rinsing the sample container with ultrapure water.

[0129] For example, the operating mode can include N separation cycles. For example, in each i-th cycle, where i can be an integer and 1 ≤ i ≤ N, the variable magnetic flux generator and the agitation unit can apply an i-th predetermined magnetic flux (Φ_i), an i-th predetermined angular velocity (ω_i), and an i-th predetermined velocity (v_i) to the sample container. For example, the variable magnetic flux generator can apply an i-th rinse magnetic flux (Φ_ri) to the sample container. For example, Φ_ri < Φ_i. For example, the operating mode can include rinsing the sample container with ultrapure water.

[0130] For example, the magnetic fluxes (Φ_1, Φ_2, … Φ_N) applied in subsequent cycles i = 1, 2, …, N can be configured to monotonically decrease. For example, Φ_1 > Φ_2 > … > Φ_N. For example, paramagnetic impurities and ferromagnetic impurities that may be present in the sample container can be separated in each subsequent cycle.

[0131] For example, the ferromagnetic impurity separation system can include a data repository that can include a magnetic flux application profile and an agitation application profile. For example, the ferromagnetic impurity separation system can include a controller operably coupled to the data repository and configured to control a variable magnetic flux generator and an agitation unit. For example, in each cycle i, the controller can be configured to adjust the i-th predetermined magnetic flux (Φ_i), the i-th predetermined angular velocity (ω_i), and the i-th predetermined velocity (v_i) based on the magnetic flux application profile and the agitation application profile retrieved from the data repository.

[0132] For example, the operating mode can also include collecting ferromagnetic impurities by flushing the sample container with an acid. For example, the variable magnetic flux generator can include an electromagnet. For example, the variable magnetic flux generator can also include a permanent magnet.

[0133] For example, the variable magnetic flux generator can be configured to selectively provide a magnetic flux in the range of 1000 gauss to 12000 gauss. For example, the variable magnetic flux generator and the agitation unit can simultaneously apply Φ_1, ω_1, and v_1 to the sample container.

[0134] In an illustrative aspect, an on-line ferromagnetic impurity analysis system can include a data repository that includes an instruction program. For example, the on-line ferromagnetic impurity analysis system can include a processor operably coupled to the data repository. For example, when the processor executes the instruction program, the processor can cause operations to be performed to automatically generate the ferromagnetic impurity concentration of the bulk battery material based on a sample of the bulk battery material collected within a battery production line.

[0135] For example, the operations can include receiving a signal identifying a target impurity element to be detected in the sample. For example, the sample can include a chelating agent and a solution of water-suspended bulk battery material sampled from the production line. For example, the operations can include determining a range of potentials to be applied to the sample based on the target impurity element. For example, the operations can include determining a response value corresponding to the target impurity element from the sample during the application of the potential range. For example, the response value can include an integral of the peak current response corresponding to the target impurity element.

[0136] For example, the operation may include generating a measured impurity concentration of each target impurity element based on a response value and a normal correlation profile. For example, the normal correlation profile may include a correlation between the response value and the concentration of the target impurity element in the sample. For example, the operation may include generating an actual concentration of impurities in the bulk battery material based on the measured impurity concentration and the standardized correlation profile. For example, the standardized correlation profile may include a correlation between the measured impurity concentration in the sample and the actual concentration in the bulk battery material. For example, the ferromagnetic impurity concentration of the bulk battery material may be monitored within the production line of the battery.

[0137] For example, the operation may further include determining the standard correlation profile by correlating a known concentration of a standardized target impurity sample with the response value corresponding to the standardized target impurity sample.

[0138] For example, the operation may further include determining the standardized correlation profile by using an operation to correlate a more accurate measurement result of the actual concentration of the target impurity element with the corresponding measured impurity concentration of the target impurity element.

[0139] For example, the operation may further include using a ferromagnetic impurity separation system to perform a separation operation to generate a more accurate measurement result of the actual concentration of the target impurity element. For example, the separation operation may include encapsulating a sample of the bulk battery material used for calibration in a sample container. For example, the separation operation may include applying N cycles of the separation process to the sample container, where N ≥ 1. For example, in each i-th cycle of the separation process, where 1 ≤ i ≤ N, the separation process may include determining a magnetic flux (Φ_i), a i-th predetermined angular velocity (ω_i), and a i-th predetermined velocity (v_i) to be applied to the sample container. For example, the separation process may include applying Φ_i, ω_i, and v_i to the sample container. For example, the magnetic fluxes (Φ_1, Φ_2, … Φ_N) applied in subsequent cycles i = 1, 2, …, N may be configured to monotonically decrease. For example, Φ_1 > Φ_2 > … > Φ_N. The separation process may include flushing the sample container to collect particles with a collection fluid. For example, the ferromagnetic impurities may be separated from the bulk battery material used for calibration without contamination. For example, the range of Φ_i may be between 1000 gauss and 12000 gauss.

[0140] In an illustrative aspect, a ferromagnetic impurity separation method may include receiving a sample of bulk battery material encapsulated in a sample container. For example, the ferromagnetic impurity separation method may include initiating a separation process that includes N separation cycles. For example, in each i-th cycle, where i may be an integer and 1 ≤ i ≤ N, the separation process may include applying a predetermined angular velocity (ω_i), a predetermined velocity (v_i), and a predetermined magnetic flux (Φ_i) to the sample container (930). For example, the predetermined magnetic fluxes (Φ_1, Φ_2, … Φ_N) applied in subsequent cycles i = 1, 2, …, N may be configured to monotonically decrease. For example, Φ_1 > Φ_2 > … > Φ_N. For example, the ferromagnetic impurity separation method may include flushing the sample container to collect particles with a collection fluid. For example, ferromagnetic impurities may be separated from a sample of bulk battery material that includes paramagnetic impurities.

[0141] For example, the predetermined angular velocity and the predetermined velocity of each separation cycle may be configured. For example, the kinetic energies (KE_1, KE_2, … KE_N) corresponding to the predetermined angular velocity and the predetermined velocity of each separation cycle i = 1, 2, …, N may be configured to monotonically decrease.

[0142] For example, for each of the i-th separation cycles, the method may further include applying a flushing magnetic flux (Φ_i1) to the sample container. For example, Φ_i1 < Φ_i. For example, for each of the i-th separation cycles, the method may further include flushing the sample container with ultrapure water. For example, paramagnetic impurities in the sample may be discharged from the sample container.

[0143] For example, the collection fluid may include an acid. For example, the method may further include using inductively coupled plasma (ICP) analysis to analyze the collected particles. For example, the range of Φ_i may be between 1000 gauss and 12000 gauss.

[0144] For example, a ferromagnetic impurity separation system according to any one of [0120 - 28] may be combined with an on-line ferromagnetic impurity analysis system according to any one of [0129 - 0134]. For example, a ferromagnetic impurity separation system according to any one of [0120 - 28] may be combined with a ferromagnetic impurity separation method according to any one of [0135 - 38].

[0145] For example, an on-line ferromagnetic impurity analysis system according to any one of [0129 - 0134] may be combined with a ferromagnetic impurity separation system according to any one of [0120 - 28]. For example, an on-line ferromagnetic impurity analysis system according to any one of [0129 - 0134] may be combined with a ferromagnetic impurity separation method according to any one of [0135 - 38].

[0146] For example, the ferromagnetic impurity separation method according to any one of [0135-38] can be combined with the ferromagnetic impurity separation system according to any one of [0120-28]. For example, the ferromagnetic impurity separation method according to any one of [0135-38] can be combined with the on-line ferromagnetic impurity analysis system according to any one of [0129-0134].

[0147] A variety of embodiments have been described. However, it should be understood that various modifications can be made. For example, if the steps of the disclosed technology are performed in a different order, or if the components of the disclosed system are combined in a different manner, or if these components are supplemented with other components, advantageous results can be achieved. Accordingly, other embodiments are contemplated within the scope of the appended claims.

Claims

1. A ferromagnetic impurity separation system, comprising: A sample container (130) configured to encapsulate a sample of bulk battery material; A variable magnetic flux generator (145) disposed at a position separated from the sample container, wherein the variable magnetic flux generator is configured to generate a variable magnetic flux at the sample container; A stirring unit (150) coupled to the sample container, wherein the stirring unit includes: - A rotary motor (170) configured to rotate the sample container about a central axis; and, - A translation motor (175) configured to translate the sample container such that the sample container is displaced in Euclidean space, wherein, In an operating mode, the variable magnetic flux generator and the stirring unit operate in a first separation cycle, wherein the variable magnetic flux generator and the stirring unit apply a first predetermined magnetic flux (Φ_1), a first predetermined angular velocity (ω_1), and a first predetermined velocity (v_1) to the sample container such that ferromagnetic impurities are separated from the sample.

2. The ferromagnetic impurity separation system according to claim 1, wherein the first separation cycle further includes: Applying a first flushing magnetic flux (Φ_r1) to the sample container, wherein Φ_r1 < Φ_1; And, Flushing the sample container with ultrapure water.

3. The ferromagnetic impurity separation system according to claim 2, wherein the operating mode includes N separation cycles, wherein in each i-th cycle, where i is an integer and 1 ≤ i ≤ N, The variable magnetic flux generator and the stirring unit apply an i-th predetermined magnetic flux (Φ_i), an i-th predetermined angular velocity (ω_i), and an i-th predetermined velocity (v_i) to the sample container; The variable magnetic flux generator applies an i-th flushing magnetic flux (Φ_ri) to the sample container, wherein Φ_ri < Φ_i; and, Flushing the sample container with ultrapure water.

4. The ferromagnetic impurity separation system according to claim 3, wherein the magnetic fluxes (Φ_1, Φ_2,... Φ_N) applied in subsequent cycles i = 1, 2,..., N are configured to decrease monotonically such that Φ_1 > Φ_2 >... > Φ_N, such that paramagnetic impurities present in the sample container are separated from the ferromagnetic impurities in each subsequent cycle.

5. The ferromagnetic impurity separation system according to claim 3, further comprising: A data repository including a magnetic flux application profile and a stirring application profile; And, A controller operably coupled to the data repository and configured to control the variable magnetic flux generator and the stirring unit, wherein, in each cycle i, the controller is configured to adjust the i-th predetermined magnetic flux (Φ_i), the i-th predetermined angular velocity (ω_i), and the i-th predetermined velocity (v_i) based on the magnetic flux application profile and the stirring application profile retrieved from the data repository.

6. The ferromagnetic impurity separation system according to claim 2, wherein the operating mode further includes collecting the ferromagnetic impurities by flushing the sample container with acid.

7. The ferromagnetic impurity separation system according to claim 1, wherein the variable magnetic flux generator comprises an electromagnet.

8. The ferromagnetic impurity separation system according to claim 7, wherein the variable magnetic flux generator further comprises a permanent magnet.

9. The ferromagnetic impurity separation system according to claim 1, wherein the variable magnetic flux generator is configured to selectively provide a magnetic flux in the range of 1000 gauss to 12000 gauss.

10. The ferromagnetic impurity separation system according to claim 1, wherein the variable magnetic flux generator and the agitation unit simultaneously apply the Φ_1, the ω_1, and the v_1 to the sample container.

11. An on-line ferromagnetic impurity analysis system, comprising: A data repository (735) that includes an instruction program; and, A processor (730) operatively coupled to the data repository such that when the processor executes the instruction program, the processor causes operations to be performed to automatically generate a ferromagnetic impurity concentration of the bulk cell material based on a sample of the bulk cell material collected within a production line of a battery, the operations including: Receiving a signal (1005) identifying a target impurity element to be detected in the sample, wherein the sample comprises a chelating agent and a solution of a water-suspended bulk cell material sampled from the production line; Determining a range of potentials (1020) to be applied to the sample based on the target impurity element; During application of the range of potentials, determining a response value (1035) corresponding to the target impurity element from the sample, wherein the response value comprises an integral of a peak current response corresponding to the target impurity element; Generating a measured impurity concentration of each of the target impurity elements (1040) based on the response value and a standard correlation profile, wherein the standard correlation profile comprises a correlation between the response value and the concentration of the target impurity element in the sample; and, Generating an actual concentration of impurities in the bulk cell material (1050) based on the measured impurity concentration and a standardized correlation profile, wherein the standardized correlation profile comprises a correlation between the measured impurity concentration in the sample and the actual concentration in the bulk cell material, such that the ferromagnetic impurity concentration of the bulk cell material is monitored within the production line of the battery.

12. The on-line ferromagnetic impurity analysis system according to claim 11, wherein the operations further include determining the standard correlation profile by correlating a known concentration of a standardized target impurity sample with a response value corresponding to the standardized target impurity sample.

13. The on-line ferromagnetic impurity analysis system according to claim 11, wherein the operations further include determining the standardized correlation profile by using the operations to correlate a more accurate measurement of an actual concentration of the target impurity element with a corresponding measured impurity concentration of the target impurity element.

14. The on-line ferromagnetic impurity analysis system according to claim 13, wherein the operation further comprises using a ferromagnetic impurity separation system to perform a separation operation to generate a more accurate measurement result of the actual concentration of the target impurity element, wherein the separation operation comprises: Encapsulating a sample of the bulk cell material used for calibration in a sample container; Applying N cycles of the separation process to the sample container, where N≥1, and wherein, in each i-th cycle of the separation process, where 1≤i≤N, the separation process comprises: - Determining the magnetic flux (Φ_i), the i-th predetermined angular velocity (ω_i) and the i-th predetermined velocity (v_i) to be applied to the sample container; and, - Applying the Φ_i, the ω_i and the v_i to the sample container, wherein the magnetic fluxes (Φ_1, Φ_2,... Φ_N) applied in subsequent cycles i = 1, 2,..., N are configured to be monotonically decreasing, such that Φ_1>Φ_2>...>Φ_N; and, Rinsing the sample container to collect particles with a collection fluid, such that the ferromagnetic impurities are separated from the bulk cell material used for calibration without contamination.

15. The on-line ferromagnetic impurity analysis system according to claim 14, wherein the range of the Φ_i is between 1000 gauss and 12000 gauss.

16. A ferromagnetic impurity separation method, comprising: Receiving a sample (905) of the bulk cell material encapsulated in a sample container; Initiating a separation process comprising N separation cycles, wherein, in each i-th cycle, where i is an integer and 1≤i≤N, the separation process comprises applying a predetermined angular velocity (ω_i), a predetermined velocity (v_i) and a predetermined magnetic flux (Φ_i) (930) to the sample container, wherein the predetermined magnetic fluxes (Φ_1, Φ_2,... Φ_N) applied in subsequent cycles i = 1, 2,..., N are configured to be monotonically decreasing, such that Φ_1>Φ_2>...>Φ_N; And, Rinsing the sample container to collect particles with a collection fluid, such that the ferromagnetic impurities are separated from the sample of the bulk cell material containing paramagnetic impurities (950).

17. The ferromagnetic impurity separation method according to claim 16, wherein the predetermined angular velocity and the predetermined velocity of each separation cycle are configured such that the kinetic energies (KE_1, KE_2,... KE_N) corresponding to the predetermined angular velocity and the predetermined velocity of each separation cycle i = 1, 2,..., N are configured to be monotonically decreasing.

18. The ferromagnetic impurity separation method according to claim 16, wherein for each of the i-th separation cycles, the method further comprises: Applying a rinsing magnetic flux (Φ_i1) to the sample container, where Φ_i1<Φ_i; And, Rinsing the sample container with ultrapure water, such that the paramagnetic impurities in the sample are discharged from the sample container.

19. The ferromagnetic impurity separation method according to claim 16, wherein the collecting fluid contains an acid, and the method further includes analyzing the collected particles using inductively coupled plasma (ICP) analysis.

20. The ferromagnetic impurity separation method according to claim 16, wherein the range of Φ_i is between 1000 gauss and 12000 gauss.

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