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Construction method of battery soh prediction model based on deep learning

A predictive model and deep learning technology, applied in the field of artificial intelligence, can solve problems such as SOH battery state differences, achieve the effect of reducing data volume, maintaining data characteristics, and improving training efficiency

Active Publication Date: 2022-07-12
中汽信息科技(天津)有限公司 +1
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  • Abstract
  • Description
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Problems solved by technology

[0003] The current battery SOH evaluation technology is mostly based on laboratory data, and there is a big difference between the obtained SOH and the battery state in real use

Method used

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  • Construction method of battery soh prediction model based on deep learning
  • Construction method of battery soh prediction model based on deep learning
  • Construction method of battery soh prediction model based on deep learning

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Embodiment Construction

[0026] In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0027] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicate...

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Abstract

The embodiment of the present invention discloses a method for constructing a battery SOH prediction model based on deep learning, which includes: acquiring characteristic data of the battery during multiple charging processes and the SOH after charging; The feature data of a continuous time segment; the feature data of each continuous time segment is used as a sample to train the deep learning network, so that the output of the deep learning network is close to the SOH after each charge; the trained deep learning network is composed of A battery SOH prediction model; wherein the deep learning network includes: a restricted Boltzmann machine model and a fully connected layer; the restricted Boltzmann machine model is used to reduce the feature data of each continuous time segment The fully connected layer is used to perform fully connected calculation on the feature data after dimensionality reduction to obtain the output of the deep learning network. In this embodiment, the prediction result of the model is more in line with the actual use state of the battery.

Description

technical field [0001] Embodiments of the present invention relate to the field of artificial intelligence, and in particular, to a method for constructing a battery SOH prediction model based on deep learning. Background technique [0002] With the application of power batteries in new energy vehicles, the energy density and output efficiency of batteries are continuously increasing. Reasonable evaluation of the SOH (State Of Healthy) of batteries is conducive to the cascade utilization of power batteries and the recycling of scrap materials. [0003] The current battery SOH evaluation technology is mostly based on laboratory data, and the obtained SOH is quite different from the actual battery state in use. SUMMARY OF THE INVENTION [0004] The embodiment of the present invention provides a method for constructing a battery SOH prediction model based on deep learning, so that the prediction result of the model is more in line with the actual use state of the battery. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367G01R31/392G06N3/04G06N3/08
CPCG01R31/367G01R31/392G06N3/084G06N3/045Y02W30/84
Inventor 杨亮张衡王文斌王铁王军雷王华珺
Owner 中汽信息科技(天津)有限公司
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