A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence (“AI”) model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.

Embodiments disclosed herein include a device including a lithium-ion battery and a battery-management system. The battery-management system may be configured to measure charge states of the lithium-ion battery over a number of charging cycles at specified conditions. The battery-management system may also be configured to obtain an expression for lithium-metal-deposition (LMD)-based capacity fade. The battery-management system may also be configured to determine an LMD state of the lithium-ion battery responsive to a comparison between the measured charge states and the expression. Related devices and systems are also disclosed herein. Additional embodiments are directed to methods, systems, and/or devices configured to generate an expression for LMD-based capacity fade.

Embodiments disclosed herein include a device including a lithium-ion battery and a battery-management system. The battery-management system may be configured to measure charge states of the lithium-ion battery over a number of charging cycles at specified conditions. The battery-management system may also be configured to obtain an expression for lithium-metal-deposition (LMD)-based capacity fade. The battery-management system may also be configured to determine an LMD state of the lithium-ion battery responsive to a comparison between the measured charge states and the expression. Related devices and systems are also disclosed herein. Additional embodiments are directed to methods, systems, and/or devices configured to generate an expression for LMD-based capacity fade.

This application provides a method and device for estimating a SOC of a battery pack, and a battery management system. The battery pack includes a first cell without a plateau and a second cell with a plateau. At least one first cell is serially connected to the second cell. The method includes: determining a capacity variation of the battery pack based on a SOC variation of the first cell in comparison with an initial SOC of the first cell, and based on a nominal capacity of the first cell; obtaining an equalization capacity of the first cell and an equalization capacity of the second cell; and estimating a SOC of the second cell, and determining the SOC of the battery pack based on the SOC of the second cell.

The purpose of the present invention is to shorten the manufacturing time in a method for manufacturing a non-aqueous-electrolyte secondary cell. In the method for manufacturing a non-aqueous-electrolyte secondary cell according to one embodiment of the present invention, during initial charging/discharging of a non-aqueous-electrolyte secondary cell comprising a negative electrode that includes a negative-electrode active material, a positive electrode that includes a Li—Ni composite oxide represented by the general formula Li.sub.aNi.sub.xM.sub.(1-x)O.sub.2 (where 0<a≤1.2, 0.6≤x<1, and M is at least one element selected from Co, Mn, and Al) as a positive-electrode active material, and a non-aqueous electrolyte, charging is performed so that the positive electrode potential based on lithium is 4.1-4.25 V in an open circuit state after charging.

The purpose of the present invention is to shorten the manufacturing time in a method for manufacturing a non-aqueous-electrolyte secondary cell. In the method for manufacturing a non-aqueous-electrolyte secondary cell according to one embodiment of the present invention, during initial charging/discharging of a non-aqueous-electrolyte secondary cell comprising a negative electrode that includes a negative-electrode active material, a positive electrode that includes a Li—Ni composite oxide represented by the general formula Li.sub.aNi.sub.xM.sub.(1-x)O.sub.2 (where 0<a≤1.2, 0.6≤x<1, and M is at least one element selected from Co, Mn, and Al) as a positive-electrode active material, and a non-aqueous electrolyte, charging is performed so that the positive electrode potential based on lithium is 4.1-4.25 V in an open circuit state after charging.

The present disclosure relates to a battery management apparatus and method, which sets a degraded SOC region based on a SOC-based voltage difference between a charging voltage and a discharging voltage according to a SOC of a battery and estimates a degree of degradation of the battery based on the voltage difference in the set degraded SOC region.

The present invention discloses a testing method and a testing system for a safe operating window of a lithium-ion battery in a squeezed state. The testing system includes a mechanical loading device, a heating device, a lithium-ion battery tester and a measuring device. By comparing the influence of a combined use of two or more of mechanical abuse with two different fixed variables, thermal abuse, and electrical abuse on critical conditions of thermal runaway of the lithium-ion battery, the influence of the different forms of abuse on the critical conditions of thermal runaway of the lithium-ion battery can be compared qualitatively and quantitatively, and these data can also be used to determine the safe operating windows of the lithium-ion battery under different abuse conditions.

The present invention discloses a testing method and a testing system for a safe operating window of a lithium-ion battery in a squeezed state. The testing system includes a mechanical loading device, a heating device, a lithium-ion battery tester and a measuring device. By comparing the influence of a combined use of two or more of mechanical abuse with two different fixed variables, thermal abuse, and electrical abuse on critical conditions of thermal runaway of the lithium-ion battery, the influence of the different forms of abuse on the critical conditions of thermal runaway of the lithium-ion battery can be compared qualitatively and quantitatively, and these data can also be used to determine the safe operating windows of the lithium-ion battery under different abuse conditions.

Described herein is a device for autonomously monitoring a battery is provided. The device is integrated with the battery (e.g., by being electrically coupled to the battery). The device obtains measurement data by injecting electrical signals into the battery and measuring an electrical response of the battery. The device participates in an authentication protocol with a computing device to verify a unique identity of the device to the computing device. After performing the authentication protocol verifying the unique identity of the device, the device transmits battery data to the computer. Further, techniques for verifying the identity of the battery using measurement data obtained by the device are described herein. The techniques generate a battery signature using the measurement data that is then used to verify the identity of the battery. For example, the battery signature may be used to determine whether the battery is counterfeit or defective.