Energy storage battery capacity prediction method, server and computer readable storage medium

A prediction method and energy storage battery technology, applied in the field of battery energy storage, can solve the problems of slow prediction speed, difficult to determine the initial parameters of the data-driven method, and inability to achieve accurate modeling, and achieve the effect of improving prediction accuracy.

Active Publication Date: 2021-08-10
HEBEI UNIV OF TECH +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the prediction methods for the remaining capacity of the battery are mainly the model method and the data-driven method. Because the model method cannot simultaneously consider the influence of the environment and

Method used

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  • Energy storage battery capacity prediction method, server and computer readable storage medium
  • Energy storage battery capacity prediction method, server and computer readable storage medium
  • Energy storage battery capacity prediction method, server and computer readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] Please refer to Figure 1-Figure 4 A block diagram of a method for predicting the capacity of an energy storage battery provided in this application, including the following steps:

[0055] S100: Preprocessing:

[0056] Acquiring battery data, and preprocessing the battery data;

[0057] Select battery capacity degeneration feature quantity in described battery data, as the input of Elman neural network, select battery capacity as the output of Elman neural network; Described battery data is divided into training set and test set;

[0058] Specifically, the battery is an energy storage battery; the battery data is acquired by using battery charging and discharging equipment to collect data from the battery.

[0059] Specifically, performing preprocessing on the battery data includes: performing normalization and noise reduction processing on the battery data.

[0060] S200: Initialization: Initialize the cuckoo search algorithm parameters and generate N initial solutio...

Embodiment 2

[0116] Please refer to Figure 8 The functional block diagram of the server or server computer system 600 provided in this application, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor executes the computer program The program is to realize the steps of the energy storage battery capacity prediction method described in any one of the above.

[0117] like Figure 8 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can execute according to a program stored in a read only memory (ROM) 602 or a program loaded from a storage portion into a random access memory (RAM) 603 Various appropriate actions and treatments. Various programs and data necessary for system operation are also stored in RAM 603 . The CPU 601 , ROM 602 , and RAM 603 are connected to each other through a bus 604 . An input / output (I / O) interface 605 is also connected to the bus 604 .

[0118] The fol...

Embodiment 3

[0121] The present application also provides a computer-readable storage medium, the computer-readable storage medium has a computer program, and when the computer program is executed by a processor, the steps of the energy storage battery capacity prediction method described in any one of the above are implemented.

[0122] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programm...

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PUM

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Abstract

The invention provides an energy storage battery capacity prediction method, a server side and a computer readable storage medium, and the method comprises the following steps: obtaining battery data, and carrying out the preprocessing of the battery data; selecting a battery capacity degradation characteristic quantity and a battery capacity from the battery data; initializing cuckoo search algorithm parameters and generating an initial solution, and calculating an adaptive value of the initial solution; generating a new solution according to a step length formula and a nest position formula, comparing adaptive values of the initial solution and the new solution, and iterating to obtain an optimal solution; endowing the weight and threshold information of the optimal solution to a parameter space of the Elman neural network, and performing parameter fine tuning training; and inputting the test set into the trained Elman neural network, and outputting a prediction result of the battery capacity. Through the method, the prediction speed and the prediction precision of the residual capacity of the battery can be improved.

Description

technical field [0001] The present disclosure generally relates to the technical field of battery energy storage, and specifically relates to a method for predicting the capacity of an energy storage battery, a server, and a computer-readable storage medium. Background technique [0002] Energy storage batteries are widely used in new energy vehicles, power station energy storage and other fields, and their reliability and service life are the focus of attention. Accurately predicting the remaining battery capacity of energy storage batteries is of great significance and value to ensure the healthy use of batteries and prolong the service life of batteries. The research on methods for predicting the remaining battery capacity of energy storage batteries has received extensive attention and attention. [0003] At present, the prediction methods for the remaining capacity of the battery are mainly the model method and the data-driven method. Because the model method cannot sim...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00G06N3/04G06N3/08H02J7/00
CPCG06Q10/04G06N3/08G06N3/006H02J7/0048G06N3/044G06N3/045Y02E60/10
Inventor 李练兵李思佳李佳祺李脉李东颖张佳伟刘汉民王阳赵建华任杰
Owner HEBEI UNIV OF TECH
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