Abstract: Disclosed are a Mamba model-based lithium battery lifetime prediction method and system in the technical field of energy storage in renewable energy power systems, the method including: acquiring aging feature matrices of a plurality of lithium batteries and inputting the same into a Mamba model for training, extracting a hidden state matrix sequence of each of the lithium batteries based on the trained Mamba model, and obtaining a cycle count sequence corresponding to a target cycle life prediction point of each of the lithium batteries through norm calculation and variational mode decomposition; constructing a linear relationship model for lithium battery lifetime prediction based on cycle count sequences corresponding to target states of health of all of the lithium batteries and cycle count sequences corresponding to target cycle life prediction points of all of the lithium batteries.

Abstract: Disclosed are a method, device, medium, and product for extracting feature maps from operational data of lithium batteries. The method includes: acquiring operational data of a lithium battery; selecting target operational data from the operational data to obtain a plurality of target data groups; sorting a plurality of operational data in each of the target data groups to obtain an initial matrix; performing mean-variance normalization processing on each column of the initial matrix to obtain a normalized matrix; calculating a covariance matrix of the normalized matrix; performing singular value decomposition on the covariance matrix to obtain an eigenvector matrix; calculating a dimensionality reduction matrix corresponding to each of the target data groups according to the eigenvector matrix; scaling the dimensionality reduction matrix to construct a three-dimensional feature map matrix; and converting the three-dimensional feature map matrix into an image to obtain a feature image of the lithium battery.

Abstract: The present disclosure discloses a parameter extraction method for an energy storage management system based on natural language interaction, and belongs to the technical field of energy storage for new energy power systems, comprising: dividing parameter types, collecting natural language interaction corpora, labeling parameters, constructing a word segmentation set, coding natural language interaction corpora, calculating prior probabilities and conditional probabilities, persisting the prior probabilities and the conditional probabilities, inputting new corpora, loading the prior probabilities and the conditional probabilities, calculating posterior probabilities according to a naive Bayes formula, and extracting parameters corresponding to a maximum value of the posterior probabilities. According to the present disclosure, parameters are extracted using a natural language interaction manner, which reduces a threshold of use of conventional energy storage management systems.

Abstract: Disclosed are an internal resistance consistency evaluation method and system for a dynamically reconfigurable battery module in the technical field of energy storage in power systems, where the method includes: calculating a current change rate by introducing a nominal capacity of battery cells; selecting a previous sampling time point of a sampling time point at which the current change rate is greater than or equal to a target current change rate as a target sampling time point; when the number of the target sampling time points is greater than a preset value, calculating an internal resistance of each of the battery cells at the target sampling time points according to the target sampling time points, and constructing an internal resistance matrix; utilizing the internal resistance matrix and applying an improved internal resistance consistency evaluation algorithm to calculate and obtain an internal resistance consistency result of the dynamically reconfigurable battery module.

Abstract: The present disclosure discloses a bus voltage modulation method for a series dynamic reconfiguration battery system, comprising: collecting a voltage value of each battery module, and arranging in a descending order according to magnitudes of the voltage values; selecting a battery module corresponding to the highest voltage value; determining whether the serial number i of the selected battery module is less than a total number N of series battery modules, if a determination result is YES, accumulating voltage values corresponding to the selected battery modules, and determining whether a voltage accumulation value of the battery modules is within a preset range; and repeating the above content until an output bus voltage meets requirements for the preset range. A dynamic reconfiguration battery system based on a software-defined technology of the present disclosure can reconstruct and output a desired bus voltage according to voltages of respective battery modules in real time.

Abstract: Disclosed is a battery aging assessment method based on multi-source and multi-scale high-dimensional state space modeling in the field of energy storage in renewable power systems. The method includes: acquiring a time series of each discharge process within a preset number of discharge cycles of a sample battery; determining a first state transition path and a second state transition path based on discharge parameters corresponding to the time series; establishing a benchmark working-state transition path; calculating multiple sample distances between the second state transition path and the benchmark working-state transition path; training a battery aging assessment model using the sample distances as input and corresponding target state-of-health values as output; calculating a target distance between a state transition path of a to-be-predicted target battery and the benchmark working-state transition path.