A battery diagnosis apparatus and a battery diagnosis method for accurately diagnosing a secondary battery are proposed. A pulse current generator, a voltage measuring instrument that measures a voltage response to application of a current pulse, a first data processing device that obtains a chronopotentiogram (CP) indicating a change in the voltage response over time and normalizes the CP, a database that saves normalized data, and a second data processing device that uses a correlation between the saved data and a battery state expressing factor prepared in advance to make a battery diagnosis are used. Desirably, the current pulse is a current in the same direction at the time of data obtainment and at the time of a diagnosis. Further, a noise filter for an input signal of the CP and resampling means for reducing the number of pieces of data input to the first data processing device are provided.

A method and system for monitoring a non-rechargeable battery of an IoT device are described. Voltage values of the non-rechargeable battery when a load is applied to the non-rechargeable battery are determined over a first interval of time. Based at least in part on these voltage values a determination of whether a voltage of the non-rechargeable battery has decreased for more than a predetermined threshold is performed. In response to determining based at least in part on the voltage values that the voltage of the non-rechargeable battery has decreased for more than the predetermined threshold, a prediction of an end of life parameter value for the non-rechargeable battery is performed, and the end of life parameter value is transmitted to an electronic device of an administrator of the IoT device.

A method and system for monitoring a non-rechargeable battery of an IoT device are described. Voltage values of the non-rechargeable battery when a load is applied to the non-rechargeable battery are determined over a first interval of time. Based at least in part on these voltage values a determination of whether a voltage of the non-rechargeable battery has decreased for more than a predetermined threshold is performed. In response to determining based at least in part on the voltage values that the voltage of the non-rechargeable battery has decreased for more than the predetermined threshold, a prediction of an end of life parameter value for the non-rechargeable battery is performed, and the end of life parameter value is transmitted to an electronic device of an administrator of the IoT device.

A sorting mechanism includes a flap that selects a first path connecting from a battery outlet of a charging section to a battery inlet of a first compartment section as a path to introduce a first battery to the first compartment section in a case of housing the first battery in the first compartment section, and that selects a second path connecting from the battery outlet of the charging section to a battery inlet of a second compartment section as a path to introduce a second battery to the second compartment section in a case of housing the second battery in the second compartment section.

The invention provides a battery pack, a method and a vehicle. The battery pack battery has a cell group. The cell group comprises a lithium iron phosphate cell and a ternary lithium-ion cell, wherein the lithium iron phosphate cell and the ternary lithium-ion cell are arranged in series with each another.

In one aspect the invention provides an assessment apparatus which includes two terminal connectors configured to electrically connect the assessment apparatus to the positive and negative terminals of a battery being assessed. The apparatus also includes a response measurement system configured to measure the terminal voltage and current of the battery when supplied with at least one alternating test current having a frequency less than 1 Hz and/or less than an impedance transition frequency associated with the battery being assessed. Also provided is a processor in communication with the response measurement system and being configured to output a performance assessment indicator for the battery being assessed by calculating at least one impedance for the battery using terminal voltage and current measurements communicated by the response measurement system.

A method and electrode assembly for evaluating performance of an electrode uses a negative electrode on which a negative electrode active material is applied to both surfaces of a negative electrode collector; a positive electrode on which a positive electrode active material is applied to both surfaces of a positive electrode collector and which is stacked with the negative electrode; two separators stacked between the negative electrode and the positive electrode; a reference electrode stacked between the two separators; and a second negative electrode stacked between the separator, which is relatively close to the negative electrode, of the two separators and the negative electrode.

Provided are a battery charging method and device, and a storage medium that are applicable to a lithium-ion battery with an N/P range of 0.5 to 1.1. The method includes: obtaining, by a power management system, a state of health (SOH) loss of the lithium-ion battery; and determining a charge cut-off voltage of a next charge process based on the SOH loss, an initial charge cut-off voltage of the lithium-ion battery, and a voltage correction factor, where the initial charge cut-off voltage is determined based on the N/P, and the charge cut-off voltage increases with increase of a count of charging. The N/P range is 0.5 to 1.1, thereby reducing the dosage of the negative electrode and reducing cost. In addition, the charge cut-off voltage of the next charge process is increased based on the SOH loss, thereby ensuring that the lithium-ion battery is free from lithium plating.

Provided are a battery charging method and device, and a storage medium that are applicable to a lithium-ion battery with an N/P range of 0.5 to 1.1. The method includes: obtaining, by a power management system, a state of health (SOH) loss of the lithium-ion battery; and determining a charge cut-off voltage of a next charge process based on the SOH loss, an initial charge cut-off voltage of the lithium-ion battery, and a voltage correction factor, where the initial charge cut-off voltage is determined based on the N/P, and the charge cut-off voltage increases with increase of a count of charging. The N/P range is 0.5 to 1.1, thereby reducing the dosage of the negative electrode and reducing cost. In addition, the charge cut-off voltage of the next charge process is increased based on the SOH loss, thereby ensuring that the lithium-ion battery is free from lithium plating.

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.