An apparatus and method for testing a battery pack are provided. Measurement components are configured to measure parameters of battery components (e.g., batteries and battery connection components) within the battery pack. Trained models are applied to signals measured by the measurement components and provide an output indicative of a condition of the battery components.

According to an embodiment, a storage battery management device includes: a memory configured to store therein storage battery characteristics of a storage battery unit as a storage battery characteristics table; and one or more processors coupled to the memory. The one or more processors are configured to: acquire the storage battery characteristics based on storage battery information output from the storage battery unit; update the storage battery characteristics table based on the acquired storage battery characteristics; and estimate SOC of the storage battery unit by referring to the updated storage battery characteristics table.

According to an embodiment, a storage battery management device includes: a memory configured to store therein storage battery characteristics of a storage battery unit as a storage battery characteristics table; and one or more processors coupled to the memory. The one or more processors are configured to: acquire the storage battery characteristics based on storage battery information output from the storage battery unit; update the storage battery characteristics table based on the acquired storage battery characteristics; and estimate SOC of the storage battery unit by referring to the updated storage battery characteristics table.

A capacity judgment module and a capacity calibration method thereof are disclosed. The capacity judgment module is used to judge a capacity of a battery installed in an electronic device. The capacity judgment module includes a database, a voltage detection module and a processing module. The database is used to store the voltage-capacity comparison curve. The voltage detection module is used to obtain a voltage value interval between a maximum use voltage value and a minimum use voltage value of the electronic device so as to divide the voltage value interval into a plurality of levels. The processing module is used to query the voltage-capacity comparison curve to actually modify the plurality of levels of the voltage value interval of the electronic device and the battery capacity ratio according to the voltage-capacity comparison curve, thereby creating a new voltage-capacity comparison table,

A battery management system (BMS) for a vehicle includes a module for estimating the state of a rechargeable battery, such as its state of charge, in real time. The module includes a learning model for predicting the state of a battery based on the vehicle's usage and related factors unique to the vehicle, in addition to a sensed voltage, current and temperature of a battery.

The present invention relates to a method for determining a state of charge of a battery, including: (a) acquiring a state of charge of the battery at a current sampling time point tn; (b) acquiring a voltage Vn, a temperature Tn, and a charging rate Cn of the battery at the current sampling time point tn, and a voltage Vi of the battery at a sampling time point ti, and calculating a voltage difference Vn−Vi between the voltage Vn and the voltage Vi; (c) when the voltage difference Vn−Vi is greater than or equal to a preset voltage threshold, calculating a voltage change rate; and (d) when the voltage change rate is greater than or equal to a preset voltage change rate threshold for the first time, acquiring a corrected state of charge of the battery as an actual state of charge of the battery.

The present invention relates to a method for determining a state of charge of a battery, including: (a) acquiring a state of charge of the battery at a current sampling time point tn; (b) acquiring a voltage Vn, a temperature Tn, and a charging rate Cn of the battery at the current sampling time point tn, and a voltage Vi of the battery at a sampling time point ti, and calculating a voltage difference Vn−Vi between the voltage Vn and the voltage Vi; (c) when the voltage difference Vn−Vi is greater than or equal to a preset voltage threshold, calculating a voltage change rate; and (d) when the voltage change rate is greater than or equal to a preset voltage change rate threshold for the first time, acquiring a corrected state of charge of the battery as an actual state of charge of the battery.

An apparatus for estimating a state of a battery includes a memory configured to store a program of a neural network including a plurality of pre-trained predictive models and an adaptive hidden layer; and includes at least one processor configured to execute the program. The program includes an instruction for receiving battery data of a target battery, inputting the battery data to the adaptive hidden layer, selecting one predictive model from the plurality of pre-trained predictive models through the adaptive hidden layer, inputting the battery data to the selected predictive model, and outputting prediction data for a remaining useful life of the target battery through the selected predictive model.

An apparatus for estimating a state of a battery includes a memory configured to store a program of a neural network including a plurality of pre-trained predictive models and an adaptive hidden layer; and includes at least one processor configured to execute the program. The program includes an instruction for receiving battery data of a target battery, inputting the battery data to the adaptive hidden layer, selecting one predictive model from the plurality of pre-trained predictive models through the adaptive hidden layer, inputting the battery data to the selected predictive model, and outputting prediction data for a remaining useful life of the target battery through the selected predictive model.

Method provides accurate state-of-health (SOH) diagnostics and prognostics during the whole-life-service of a lithium-ion battery by considering the effects of state- of-charge (SOC) and SOH on certain parameters (such as consideration of nonlinearity of the terminal voltage) during the process of SOC diagnostics and prognostics. The method integrates Lebesgue sampling and equivalent circuit model (ECM) analysis, which greatly decreases computation cost and uncertainty accumulation to provide efficient acquisition of open circuit voltage (OCV) determinations for the ECM process. The OCV curve of the battery was obtained during Hybrid Pulse Power Characterization testing by fitting a series of selected OCV points after enough rest of the subject battery. Identified parameters of ECM are updated according to terminal voltage measurement to enable accurate SOC estimation and prediction during the period from full charge to full discharge of the battery. Parameter identification is re-conducted and an initial condition for SOC estimation is updated according to SOH to enable accurate SOC estimation during the whole-life-service of battery.