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 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: The invention provides a method and system for identifying electrochemical model parameters based on capacity change rate. The method includes: acquiring operating data from an actual operation process of a lithium battery and cleaning an actual operating data set from the operating data; generating a simulated operating data set through simulation of a preset electrochemical model; performing parameter identification based on a preset first loss function, the actual operating data set, and the simulated operating data set; calculating capacity convergence coefficient; comparing capacity convergence coefficient with a preset convergence threshold value; when greater than the preset convergence threshold value, regenerating the simulated operating data set; and when not greater than the preset convergence threshold value, outputting an electrochemical model parameter set as a parameter identification result.

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 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: The invention discloses a method, a system, a device, and a medium for detecting a battery state of health. The method includes extracting a target curve segment from a first charging characteristic curve of a battery under test; and determining, according to a corresponding relation between characteristic parameters of the target curve segment and the battery state of health, the battery state of health matching the characteristic parameters of the target curve segment, and determining the battery state of health as the battery state of health of the battery under test. By utilizing said corresponding relation as the calculation basis, the invention improves the reliability and accuracy of the detection of battery state of health detection. The invention also tracks instances of abusive conditions that accelerate battery aging, assessing whether such conditions accelerate battery degradation and issuing warnings accordingly.

Abstract: Service models may be generated in response to services exposed by two or more IaaS service providers, and an interface may be provided for selection and specification of the service models.

Abstract: Service models may be generated in response to services exposed by two or more IaaS service providers, and an interface may be provided for selection and specification of the service models.

Abstract: Service models may be generated in response to services exposed by two or more IaaS service providers, and an interface may be provided for selection and specification of the service models.

Abstract: A method includes performing a query of a database. The query includes joining a first Domain-Model-sub-class table with a Domain-Model table based on identity attributes present in both the first Domain-Model-sub-class table and Domain-Model table to produce a first joined table. The query further includes joining the first joined table with an access control list (ACL) entry table based on an ACL object identity attribute present in both the first joined table and the ACL entry table to produce a second joined table. The query also includes joining the second joined table with a second Domain-Model-sub-class table based on an ACL security identity present in both the second joined table and the second Domain-Model-sub-class table to produce a third joined table.

Abstract: Service models may be generated in response to services exposed by two or more IaaS service providers, and an interface may be provided for selection and specification of the service models.