Lithium ion battery health state estimation method based on codec model

A lithium-ion battery, codec technology, applied in the field of lithium battery SOH estimation based on the attention mechanism codec model, can solve problems such as limitation

Inactive Publication Date: 2020-10-27
TIANJIN UNIV
View PDF8 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although traditional data-driven algorithms can effectively use prior information, they are also limited by the selected features, while deep learning algorithms have more powerful data processing capabilities and nonlinear mapping capabilities. Some SOH and R

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Lithium ion battery health state estimation method based on codec model
  • Lithium ion battery health state estimation method based on codec model
  • Lithium ion battery health state estimation method based on codec model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0038] This embodiment provides a lithium battery SOH estimation method based on an attention mechanism codec model, which includes an encoder and a decoder. The encoder encodes the voltage and current time series of a single charge and discharge cycle according to certain rules, and outputs a coded sequence containing relevant intrinsic features, and then the decoder combines attention weights to extract specific information from it to achieve the final SOH estimation.

[0039] The specific structure of the codec model built in this paper is as follows: figure 1 . The encoder is mainly composed of convolutional neural network (CNN), gated recurrent unit (GRU) and other models, and ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a lithium ion battery health state estimation method based on a codec model, which comprises the following steps of: (1) acquiring battery charging and discharging period dataincluding terminal voltage and current data and the maximum discharging capacity in each charging and discharging period; and (2) constructing a codec model of an attention mechanism according to thecharacteristics of the acquired data, wherein the codec model comprises an encoder and a decoder, voltage and current values are used as encoder input, an SOH estimated value of the battery is obtained and used as decoder output, and the number of nodes of each layer is determined; (3) preprocessing and normalizing the data acquired in the step (1), inputting the data into the codec model with randomly initialized weight, and minimizing the output error of the codec model through Adam algorithm training; and (4) inputting a new test sample into the codec model trained in the step (3), and calculating a prediction error so as to evaluate the accuracy of model prediction.

Description

technical field [0001] The invention belongs to the field of lithium battery PHM, and relates to a lithium battery SOH estimation method based on an attention mechanism codec model. Background technique [0002] Lithium-ion batteries have broader application prospects due to their high energy density, long cycle life, and low self-discharge rate, including consumer electronics, power storage, and transportation. However, the performance of lithium batteries is affected by factors such as charge and discharge cycles, temperature, and aging. Among them, the state of health (SOH) and remaining service life (RUL) are important factors to evaluate whether the battery can operate. Therefore, real-time and accurate SOH and RUL estimation methods have become an urgent requirement to ensure the safe and stable operation of lithium batteries. The existing SOH estimation methods are mainly divided into the following three categories: direct measurement method, modeling method and data...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/084G06N3/048G06N3/045
Inventor 王萍刘昊天
Owner TIANJIN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap