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Battery SOH prediction method based on unsupervised transfer learning

A technology of transfer learning and prediction method, which is applied in the field of battery SOH prediction based on unsupervised transfer learning, which can solve problems such as difficulty in guaranteeing the reliability of data-driven models, prediction of new battery performance, and difficulty in ensuring the same data distribution of training data.

Active Publication Date: 2021-02-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Application Information

AI Technical Summary

Problems solved by technology

Researchers have proposed a variety of data-driven SOH estimation methods. However, current research mainly focuses on the modeling process of specific power battery health states under experimental conditions. How to predict the performance of new batteries without historical SOH data is still a problem. unresolved issues
Due to different battery types and usage environments, it is difficult to ensure that the training data and the predicted object have the same data distribution. When the training data and the actual predicted battery data distribution are different, the reliability of the general data-driven model will be difficult to guarantee.

Method used

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  • Battery SOH prediction method based on unsupervised transfer learning
  • Battery SOH prediction method based on unsupervised transfer learning
  • Battery SOH prediction method based on unsupervised transfer learning

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Embodiment

[0056] For the convenience of description, the relevant technical terms appearing in the specific implementation are explained first:

[0057] ANN (Artificial Neural Network): artificial neural network;

[0058] newff: the function of training feedforward and backpropagation network in matlab

[0059] GPR (Gaussian Process Regression) Gaussian process regression;

[0060] fitgpr: the function of training the GPR model in the GPML toolbox of matlab

[0061] KNN (K-Nearest Neighbor) K nearest neighbor node algorithm;

[0062] fitcknn: the function of training KNN in matlab

[0063] TCA (Transfer Component Analysis) migration component analysis;

[0064] JDA (Joint Domain Adaptation) joint domain matching;

[0065] DDA (Dual Domain Adaption) double domain matching, our new domain matching algorithm;

[0066] figure 1 It is a flow chart of the battery SOH prediction method based on unsupervised transfer learning in the present invention.

[0067] In this example, if figur...

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Abstract

The invention discloses a battery SOH prediction method based on unsupervised transfer learning. The method comprises the steps: extracting the features of two batteries, carrying out the adjustment of two feature spaces through a domain matching algorithm, enabling the conditional distribution and edge distribution of the two feature spaces to be matched, and enabling the weights of samples related to the two spaces to be increased and the weight of the irrelevant samples to be reduced through the adjustment of the weights of the samples; in this way, data of one battery can be used for training in a domain matching mode; the SOH of the other battery is predicted through a common machine learning algorithm, and therefore the method has the advantages of being high in applicability, high in prediction precision, easy to implement and the like.

Description

technical field [0001] The invention belongs to the technical field of battery state of health assessment, and more specifically, relates to a battery SOH prediction method based on unsupervised transfer learning. Background technique [0002] The state of health of batteries is closely related to the range, safety and reliability of electric vehicles. Due to the complex mechanism of battery degradation and numerous influencing factors, accurate and reliable estimation of the battery's state of health (SOH) is a difficult issue in battery management technology. [0003] The data-driven method represented by machine learning is flexible, does not require modeling, and has good nonlinear mapping capabilities. It is currently a research hotspot in this field. Researchers have proposed a variety of data-driven SOH estimation methods. However, current research mainly focuses on the modeling process of specific power battery health states under experimental conditions. How to pre...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/392
CPCG01R31/367G01R31/392
Inventor 盛瀚民刘鑫邵晋梁陈凯周圆
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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