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Lithium battery temperature estimation method and system based on Bayesian neural network

A neural network and neural network model technology, applied in the field of battery thermal management, can solve the problems of uncertainty measurement, misleading decision makers, limited interpretability of results, etc., and achieve the effect of accurate internal temperature estimation

Active Publication Date: 2021-09-28
SUN YAT SEN UNIV
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Problems solved by technology

Current temperature estimation methods do not provide a measure of uncertainty and have limited interpretability of results, resulting in estimates from these models that may mislead decision makers

Method used

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  • Lithium battery temperature estimation method and system based on Bayesian neural network
  • Lithium battery temperature estimation method and system based on Bayesian neural network
  • Lithium battery temperature estimation method and system based on Bayesian neural network

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Embodiment Construction

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, it is only set for the convenience of illustration and description, and the order between the steps is not limited in any way. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art sexual adjustment.

[0037] refer to figure 1 , the invention provides a kind of lithium battery temperature estimation method based on Bayesian neural network, and this method comprises the following steps:

[0038] S1. Offline collection of battery electrochemical impedance spectroscopy data and corresponding temperature labels;

[0039] S2. Process the electrochemical impedance spectrum data of the battery based on the ARD algorithm to obtain temperature-related features and temperature-related impedance frequency points;

[0040] S...

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Abstract

The invention discloses a lithium battery temperature estimation method and system based on a Bayesian neural network. The method comprises the following steps: collecting battery electrochemical impedance spectroscopy data and a temperature label; processing the electrochemical impedance spectroscopy data of the battery based on an ARD algorithm to obtain temperature-dependent characteristics and temperature-dependent impedance frequency points; training a Bayesian neural network model based on the temperature-related features and the temperature labels, and collecting impedance data under the temperature-related impedance frequency points; and inputting the impedance imaginary part data into the temperature estimation model to obtain the internal estimated temperature and confidence interval of the battery at the current moment. The system comprises an offline data acquisition module, a temperature related data determination module, a model training module, an online data acquisition module and a temperature estimation module. According to the invention, accurate internal temperature estimation of the whole life cycle of the power battery is realized. The lithium battery temperature estimation method and system based on the Bayesian neural network can be widely applied to the field of battery thermal management.

Description

technical field [0001] The invention relates to the field of battery thermal management, in particular to a method and system for estimating the temperature of a lithium battery based on a Bayesian neural network. Background technique [0002] Lithium-ion batteries are widely used as power batteries for electric vehicles due to their outstanding features such as high energy density, low material price, good performance, non-toxic and pollution-free, and safety. Estimating the temperature of a Li-ion battery is critical for safety and control purposes. For example, high temperature accelerates battery aging, thereby reducing its life and performance, and can even cause thermal runaway of the battery, which in turn may lead to fire or explosion. Typical methods for battery temperature monitoring include installing thermocouples on the surface of the battery. There is a problem with this method. The heating of the battery comes from internal physical and chemical reactions, an...

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Application Information

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IPC IPC(8): G01R31/374
CPCG01R31/374
Inventor 谭晓军欧阳孔雷范玉千彭卫文吕鹏翔
Owner SUN YAT SEN UNIV
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