The present disclosure relates to a battery test system for a vehicle that includes a camera configured to capture an image of a vehicle identification number located on the vehicle, the camera being coupled to a processor which determines characters of the vehicle identification number from the image of the camera and correlates the characters of the vehicle identification number to a vehicle identification number database to receive battery parameters for the vehicle, a battery tester that is removably connected to terminals of a battery of the vehicle and configured to receive battery test results, and a display which conveys information relating to the battery parameters and the battery test results.

A secondary battery control device, on a Q-dQ/dV curve, when a capacity between two characteristic points or two points mathematically equivalent thereto is represented by α, a dQ/dV value at any extreme point of a plurality of extreme points plotted on the Q-dQ/dV curve or a point mathematically equivalent thereto is represented by β, a product of the α and the β is represented by X, and constants obtained in advance from a relationship between the X in a correction sample and a deterioration degree of the correction sample are represented by A and B, a deterioration degree SOH of the secondary battery is corrected to SOH=AX+B . . . (1). A battery pack including this secondary battery control device is highly safe, contributes to the stable supply of energy and contributes to sustainable development goals.

A secondary battery control device, on a Q-dQ/dV curve, when a capacity between two characteristic points or two points mathematically equivalent thereto is represented by α, a dQ/dV value at any extreme point of a plurality of extreme points plotted on the Q-dQ/dV curve or a point mathematically equivalent thereto is represented by β, a product of the α and the β is represented by X, and constants obtained in advance from a relationship between the X in a correction sample and a deterioration degree of the correction sample are represented by A and B, a deterioration degree SOH of the secondary battery is corrected to SOH=AX+B . . . (1). A battery pack including this secondary battery control device is highly safe, contributes to the stable supply of energy and contributes to sustainable development goals.

Provided is a method for determining a micro short circuit of a lithium ion secondary battery which is capable of determining the presence or absence of a micro short circuit of the lithium ion secondary battery in a short time. A relaxation process after the interruption of a discharging current or a charging current is analyzed and a voltage fluctuation component due to micro short circuit is separated and used in the determination. Specifically, a method for determining a micro short circuit that determines presence or absence of the micro short circuit in a lithium ion secondary battery is provided which includes: a relaxation decomposition step of decomposing a change of a cell voltage in a relaxation process after interrupting charging current during charging or discharging current during discharging into a plurality of decomposition relaxation components; and a micro short circuit determination step of determining presence or absence of micro short circuit by determining presence or absence of a component in which a voltage drop has occurred due to the micro short circuit, among the plurality of decomposition relaxation components.

Provided is a method for determining a micro short circuit of a lithium ion secondary battery which is capable of determining the presence or absence of a micro short circuit of the lithium ion secondary battery in a short time. A relaxation process after the interruption of a discharging current or a charging current is analyzed and a voltage fluctuation component due to micro short circuit is separated and used in the determination. Specifically, a method for determining a micro short circuit that determines presence or absence of the micro short circuit in a lithium ion secondary battery is provided which includes: a relaxation decomposition step of decomposing a change of a cell voltage in a relaxation process after interrupting charging current during charging or discharging current during discharging into a plurality of decomposition relaxation components; and a micro short circuit determination step of determining presence or absence of micro short circuit by determining presence or absence of a component in which a voltage drop has occurred due to the micro short circuit, among the plurality of decomposition relaxation components.

A state of charge of a battery is estimated by a battery model specific to the battery. The battery model provides a section-wise defined correlation of terminal voltage values depending on state of charge values. Each of sections of the battery model delimits a monotonic dependence of the correlation from others of the sections. By segmenting the correlation of terminal voltage values depending on a state of charge value into sections, each segment within such the section-wise defined correlation of terminal voltage values depending on state of charge values is mathematically spoken a bi-unique function suitable for transformation into an inverse function defined within the section. The estimated state of charge may be continuously refined by an iterative feedback loop including coulomb counting for estimating a battery charge value, where refined estimated battery charge values state of charge values at a previous cycle are projected forward to the current cycle.

This application relates to a battery test system and a battery test method. The battery test system according to an embodiment comprises: an extrusion apparatus configured to be disposed on a first surface of a battery; and a pressure apparatus, disposed above the extrusion apparatus, where the pressure apparatus is configured to apply a predetermined force to the battery in predetermined duration through the extrusion apparatus. The battery test system and the battery test method provided in this application are able to more reasonably evaluate the safety of the soft package battery and identify the risk caused by the defect of the soft package battery.

This application relates to a battery test system and a battery test method. The battery test system according to an embodiment comprises: an extrusion apparatus configured to be disposed on a first surface of a battery; and a pressure apparatus, disposed above the extrusion apparatus, where the pressure apparatus is configured to apply a predetermined force to the battery in predetermined duration through the extrusion apparatus. The battery test system and the battery test method provided in this application are able to more reasonably evaluate the safety of the soft package battery and identify the risk caused by the defect of the soft package battery.

An ECU is configured to execute SOC estimation control for estimating an SOC of a battery. The ECU obtains “first voltage” indicating an OCV of the battery in the SOC estimation control. The ECU controls an engine and a PCU such that the battery is charged with an amount of electric power equal to or larger than a prescribed amount, when the first voltage is within a voltage range where hysteresis occurs. The ECU obtains “second voltage” indicating an OCV of the charged battery, and estimates the SOC of the battery from the second voltage.

The present disclosure discloses a method, device and computer readable storage medium for estimating SOC of a lithium battery. State data and corresponding SOC values of lithium batteries under different working conditions are collected to establish a sample set, and clustering analysis is performed on the sample set to obtain a plurality of sample subsets; obtain sub-model functions of the plurality of sample subsets; the state data of a sample to be tested is respectively added into the state data of each of the sample subsets to calculate a change value of the state data of each of the sample subsets before and after the adding operation, and at least one sub-model close to the sample to be tested is selected as the selected sub-model according to the change value; a weight is assigned to the selected sub-model to calculate the SOC value of the sample to be tested.