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Lithium battery SOC estimation method based on BP neural network

A BP neural network and lithium battery technology, which is applied in the field of lithium battery SOC estimation based on BP neural network, can solve the problems of battery overcharge and overdischarge, complexity, and reduced battery efficiency, and achieve strong nonlinear fitting ability and estimation The effect of high precision and good applicability

Inactive Publication Date: 2017-02-22
陈逸涵
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AI Technical Summary

Problems solved by technology

[0004] Inaccurate estimation of SOC will lead to overcharge and overdischarge of the battery, reducing the service life of the battery; at the same time, it will affect the accurate calculation of the battery life and reduce the efficiency of the battery
As an inherent characteristic of lithium batteries, SOC is difficult to be directly measured by sensors. It is affected by factors such as temperature, discharge current, self-discharge, and battery life during operation, which presents complex nonlinearities. Therefore, an accurate SOC estimation method is sought It is an important research topic in the current power battery industry

Method used

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  • Lithium battery SOC estimation method based on BP neural network
  • Lithium battery SOC estimation method based on BP neural network
  • Lithium battery SOC estimation method based on BP neural network

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Embodiment

[0056] refer to Figure 5 As shown, a lithium battery SOC estimation method based on BP neural network includes the following steps:

[0057] (1) Establish neuron model: BP neural network is a multi-layer neural network composed of input layer, hidden layer and output layer. Each layer is composed of several neurons. Neuron is the most basic neural network. The constituent unit of X = (x1, x2, ...) is the input of the neuron, y is the output of the neuron, W = (w1, w2, ...) is the adjustable input weight, B = b is the neuron The threshold value, f(net) is the activation function of the neuron, the input signal enters the neuron through the input weight connection, and the output y is obtained through the activation function;

[0058] (2) Set up BP neural network model: three-layer BP neural network comprises input layer, hidden layer, output layer, setting X=(x1, x2, ... xn) is the input matrix of network, and xn is input feature vector, W =(w0,w1,...wn) is the connection we...

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Abstract

The present invention discloses a lithium battery SOC estimation method based on a BP neural network. The method comprises the steps of (1) establishing a neuron model; (2) establishing a BP neural network model; (3) constructing a BP neural network algorithm; (4) obtaining the network sample data; (5) carrying out the sample SOC calculation. The neural network algorithm has the stronger non-linear fitting capability, and the relation between input and output can be obtained by training a lot of input and output samples on the condition of not needing to consider the internal structure of a lithium battery and for the external excitation, thereby being able to fitting the dynamic characteristics of the lithium battery very well to estimate the SOC of the battery. The method is high in estimation precision and can obtain higher precision on the condition of enough battery data samples, and the neural network SOC estimation has the very good applicability and is suitable for various power batteries.

Description

technical field [0001] The invention relates to the technical field of BP neural network application, in particular to a lithium battery SOC estimation method based on BP neural network. Background technique [0002] With the development of science and technology and industrial technology, the problem of energy crisis and air pollution is becoming more and more serious. According to the survey data of the Beijing Municipal Environmental Protection Bureau in 2013, motor vehicle emissions accounted for 31.1% of the total PM2.5 emissions. Considering the economy, technology and environmental protection, the advantages of electric vehicles are zero emission, low noise, and high efficiency. The development of electric vehicles will become one of the important ways to control air pollution and solve the energy crisis. At present, electric vehicles have problems such as short battery life and insufficient power performance, and the key technical issue in the development of electri...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
CPCG01R31/367
Inventor 陈逸涵
Owner 陈逸涵
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