Lithium ion battery thermal process space-time modeling method based on dual-scale manifold learning

A lithium-ion battery and manifold learning technology, which is applied in the field of lithium-ion battery thermal process research, can solve problems such as the imperfect structure of the manifold structure graph and the large amount of calculation

Active Publication Date: 2019-11-08
GUANGDONG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] The present invention provides a lithium-ion battery thermal process based on dual-scale manifold learning to overcome the technical defects of large amount of calculation an

Method used

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  • Lithium ion battery thermal process space-time modeling method based on dual-scale manifold learning
  • Lithium ion battery thermal process space-time modeling method based on dual-scale manifold learning
  • Lithium ion battery thermal process space-time modeling method based on dual-scale manifold learning

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Experimental program
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Embodiment 1

[0089] Such as figure 1 As shown, the spatio-temporal modeling method of lithium-ion battery thermal process based on dual-scale manifold learning includes the following steps:

[0090] S1: According to the manifold learning method, construct a set of nonlinear spatial basis functions for time / space separation;

[0091] S2: Use the Galerkin method to truncate the nonlinear space basis function to obtain a physics-based time model;

[0092] S3: Use the extreme learning machine to evaluate and learn the unknown model structure and parameters existing in the time model;

[0093] S4: Based on the nonlinear spatial basis function and temporal model, the spatial-temporal model of LIBs is reconstructed using the spatio-temporal synthesis method.

[0094] Wherein, the concrete process of described step S1 comprises:

[0095] S11: Construct neighbor graphs and supplementary graphs to represent the manifold structure in the original space;

[0096] S12: Calculate the local linear we...

Embodiment 2

[0151] More specifically, the implementation of the scheme is carried out by taking 60Ah LiFePO4 / graphite rechargeable lithium-ion batteries (LIBs) as an example.

[0152] In the specific implementation process, the battery is regarded as a two-dimensional distribution heat process, and the temperature difference along the thickness direction is not considered. 20 thermocouple sensors are located on the surface of the battery for temperature data acquisition. Such as figure 2 As shown, the sensors marked with "cross" are used for model estimation, while the sensors marked with "circle" are used for model validation. In this experiment, the battery is subjected to cyclic charge and discharge experiments by the battery thermal system (BTS) integrated battery tester, incubator, battery management system (BMS) and host computer, such as image 3 shown. The input current and corresponding measured voltage can be measured with an integrated battery meter.

[0153] In the specif...

Embodiment 3

[0156] More specifically, in order to verify the performance of the model, the present invention uses three commonly used spatial BFs optimal learning methods for comparison: local manifold learning (LLE), global manifold learning (ISOMAP) and Karhunen–Loève (KL) method . The first two methods only consider a single nonlinear spatial information in the process of model dimensionality reduction, and the third method is a linear model dimensionality reduction technique. In order to facilitate comparison with other methods, the present invention introduces five error indicators. The role of these indicators can be categorized as follows:

[0157] (1) Spatio-temporal prediction error (SPE): evaluates the deviation between the model output value and the measured output value.

[0158]

[0159] (2) Mean Square Error (RMES): Similar to SPE, this indicator is also used to evaluate prediction bias. However, SPE is a vector or matrix related to the sample dimension, while RMSE can...

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Abstract

The invention provides a lithium ion battery thermal process space-time modeling method based on dual-scale manifold learning, which comprises the following steps: constructing a group of nonlinear space basis functions for time/space separation according to a manifold learning method; truncating the nonlinear space basis function by adopting a Galerkin method to obtain a time model based on physics; carrying out evaluation learning on unknown model structures and parameters existing in the time model by utilizing an extreme learning machine; and reconstructing an LIBs space-time model by using a space-time synthesis method based on the nonlinear space basis function and the time model. According to the lithium ion battery thermal process space-time modeling method based on dual-scale manifold learning provided by the invention, local and global nonlinear manifold structure information is considered at the same time through a BFs learning method, so that the method is superior to a modeling method based on local linear embedding (LLE) and isometric mapping (ISOMAP); and the method is suitable for space-time dynamic modeling of a distributed parameter system DPS.

Description

technical field [0001] The invention relates to the technical field of lithium-ion battery thermal process research, and more specifically, to a space-time modeling method for lithium-ion battery thermal process based on dual-scale manifold learning. Background technique [0002] Rechargeable lithium-ion batteries (LIBs) have the advantages of high specific energy, high energy density and low environmental pollution, and have gradually become the power source of electric vehicles (EVs) and hybrid electric vehicles (HEVs) in recent years [1][2]. However, they have not been widely used in the automotive industry because temperature effects would limit the performance of the battery [3]–[5]. When batteries are charged or discharged, they generate heat through electrochemical reactions and ohmic heating. Conversely, the heat generated can affect the battery's safety, lifespan, and performance. Therefore, the battery should work within the proper operating temperature range. U...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06F17/10G06N3/04G06N3/08
CPCG06F17/10G06N3/08G06N3/045Y02E60/10
Inventor 徐康康杨海东印四华朱成就
Owner GUANGDONG UNIV OF TECH
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