Abstract: Method for analyzing and predicting state of health of a lithium battery includes: collecting battery data within a predetermined time interval, where the battery data comprises at least battery operating time, current, and voltage; determining a target area based on the battery data; establishing a relationship between a cumulative capacity of the target area and the state of health of the battery; correlating the number of cycles in the current state of the battery with the state of health of the battery using the cumulative capacity of the target area and determining a relationship between the number of cycles and the state of health of the battery; and predicting the state of health of the battery in the future according to the relationship between the number of cycles and the state of health of the battery. The method is simple and easy to operate, and practical as well.

Abstract: A heterogeneous computing terminal for task scheduling is provided, including a processing unit and multiple computing units. The heterogeneous computing terminal generates a task scheduling strategy. The target computing unit acquires a target upgrade program, is upgrade based on the target upgrade program, processes a target task and sends a processing result to the processing unit. The processing unit combines processing results sent by multiple target computing units to obtain a processing result of an initial task set. The heterogeneous computing terminal can upgrade the computing unit online, that is, dynamically update the computing unit, thereby greatly expanding the versatility of the computing unit of hardware. Scheduling strategy adapted to the task can be generated, and the computing unit can dynamically provide better task processing capabilities. The computing efficiency of the heterogeneous computing terminal is improved.

Abstract: A method for calculating a state of charge of a battery is provided. The method includes: estimating a first state of charge of the battery by using a first state of charge estimation algorithm; estimating a second state of charge of the battery by using a second state of charge estimation algorithm; determining a first weight corresponding to the first state of charge and a second weight corresponding to the second state of charge based on state data of the battery; and calculating the state of charge of the battery based on the first state of charge and the first weight, and the second state of charge and the second weight. With method for calculating a state of charge of a battery in the present disclosure, the accuracy of calculating the state of charge of the battery can be improve d with a lower computation cost.

Abstract: A method for monitoring battery cell data comprises: collecting the first set of the battery cell data in real time; retrieving estimation parameter data corresponding to the first set of the battery cell data from a battery cell parameter database in real time; based on the estimation parameter data of the first set of the battery cell data, processing the first set of the battery cell data through a battery model to acquire the second set of the battery cell data in real time; acquiring an error value between the first set and the second set of the battery cell data in real time; acquiring a monitoring result of the first set of the battery cell data in real time based on the error value and an error threshold, the monitoring result is either the error value being greater than the error threshold, or less than the error threshold.

Abstract: A method and a device for forecasting an electric load, an electronic device, and a computer-readable storage medium are provided. The method includes: acquiring historical load data prior to a forecast date; generating a load sequence based on the historical load data; performing variational mode decomposition on the load sequence, to obtain multiple intrinsic mode components and a residual that are corresponding to the load sequence; and inputting the multiple intrinsic mode components and the residual into respective forecasting models, and determining a load value on the forecast date based on forecasting results of all the forecasting models. With the method and the device, the electronic device, and the computer-readable storage medium, the load sequence is decomposed based on the variational mode decomposition, so that more dimensional feature information in the load sequence can be mined. Moreover, the residual is also used as valid data.

Abstract: A thermal-runaway warning method, system, and terminal for a power station are provided. The method comprises: obtaining collected data of a battery module comprised in the power station, wherein the collected data comprises collected temperature values of the battery module collected by temperature measuring devices; normalizing the collected data to obtain a standard dataset; training a CNNLSTM-based temperature predicting model based on the standard dataset to obtain a trained CNNLSTM-based temperature predicting model; and obtaining a predicted temperature value of the battery module within an output time window based on the trained CNNLSTM-based temperature predicting model, and determining whether there is a risk of thermal runaway in the power station based on the predicted temperature value.

Abstract: A battery overheat warning method, a battery overheat warning system, a storage medium, and an electronic device are provided. The battery overheat warning method comprises: obtaining working condition parameters and geometric parameters of a battery; obtaining a theoretical temperature of a target position in the battery based on the working condition parameters and the geometric parameters of the battery; obtaining an actual temperature of the target position in the battery by using a temperature transducer; and sending an overheat warning signal based on the theoretical temperature and the actual temperature of the target position in the battery. The battery overheat warning method has relatively high timeliness and accuracy.

Abstract: A hot-plugging edge computing system includes at least an elastic database, a data processing module, and hot-plugging computing modules. The elastic database is configured to store data through an SSD array with a configurable number of SSDs. The data processing module is connected to the elastic database and each of the hot-plugging computing modules, and controls data storage in the elastic database, monitors and assigns a task to each of the hot-plugging computing modules. Each of the hot-plugging computing modules performs data computing based on the corresponding task assigned by the data processing module, and returns a computing result to the corresponding data processing module.

Abstract: A method for estimating an internal resistance of a lithium battery, a storage medium, and an electronic device are provided. The method includes: sampling a current and a voltage of the lithium battery at a preset sampling interval; inputting the current and the voltage of the lithium battery to an internal resistance estimation equivalent circuit model, determining to-be-estimated parameters of the model, and determining expressions of an open circuit voltage and an internal resistance of the battery; obtaining a range of each of the to-be-estimated parameters of the model based on a memory factor of the model; and inputting the range of each of the to-be-estimated parameters to an internal resistance distribution model to obtain a total internal resistance of the battery, and determining a distribution of the total internal resistance by repeatedly running the internal resistance distribution model, to obtain different estimated values of the total internal resistance.

Abstract: A method for analyzing battery life degradation, a storage medium, and an electronic device are provided. The method includes: obtaining battery data of a device, where the battery data includes a voltage and a current of a battery of the device; estimating an open circuit voltage of the battery based on the battery data; establishing a function curve between a state of charge and the open circuit voltage of the battery; extracting a life degradation curve based on the function curve; and performing life degradation analysis on the battery based on the life degradation curve. In the present disclosure, a dQ/dV curve of the battery can be accurately extracted under complex working conditions, thereby ensuring accuracy of the battery life degradation analysis, and achieving desirable practical applicability under complex working conditions.