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Cell SOH online estimation method based on fractional order neural network and double-volume Kalman filtering

A neural network and neural network model technology, applied in the field of battery health management, can solve problems such as inability to converge, slow training speed, etc., and achieve the effects of effective function approximation ability, fast convergence speed, and high prediction accuracy

Active Publication Date: 2017-12-05
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, the neural network model also has certain problems. Its training speed is slow, and there will be situations where it cannot converge. However, the neural network with fractional order improvement can not only improve its convergence speed, but also improve the prediction accuracy of the model.

Method used

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  • Cell SOH online estimation method based on fractional order neural network and double-volume Kalman filtering
  • Cell SOH online estimation method based on fractional order neural network and double-volume Kalman filtering
  • Cell SOH online estimation method based on fractional order neural network and double-volume Kalman filtering

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Embodiment Construction

[0050] The invention will be described in further detail below in conjunction with the accompanying drawings.

[0051] The schematic diagram of online estimation of battery dynamic system is as follows: figure 1 Shown, concrete operation of the present invention comprises the following steps:

[0052] In step 1, the visible state of the dynamic system is measured by sensors.

[0053] In step 2, the historical data of the same type of battery is used as the initial training data set, and the neural network algorithm improved by the fractional order theory is used to train the model, and an equivalent model representing the nonlinear relationship of the battery system is obtained.

[0054] The flowchart of the fractional order neural network algorithm in step 2 is as follows figure 2 As shown, the specific process is as follows:

[0055] Step 2.1, the topology of the fractional order neural network is a three-layer network structure (1 input layer, 1 hidden layer and 1 outpu...

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Abstract

The invention discloses a cell state-of-health (SOH) online estimation method based on the fractional order neural network and double-volume Kalman filtering and belongs to the cell health management field. The method comprises steps that (1), a visible state quantity of a cell is acquired through a sensor; (2), a fractional order neural network model is trained offline; (3), real-time data acquired in the step (1) is added to an initial training data set, and cell characteristics can be more accurately described through the model; (4), a discrete state space model is established to present a mapping function between a hidden cell state and a visible state; and (5), a double-volume Kalman filtering algorithm is utilized to carry out online update of the fractional order neural network model, and online estimation of the hidden cell state is further carried out. The method is advantaged in that the storage cell model can be updated online, the model is made to continuously change to adapt to the dynamically-changed environment, and efficiency and accuracy of cell health management are improved.

Description

technical field [0001] The invention discloses an online battery SOH (state of health) estimation method based on a fractional order neural network and a double-volume Kalman, belonging to the field of battery health management. Background technique [0002] With the continuous development of the national economy, the modernization requirements in the fields of energy, electric power, transportation, communication, and environmental protection are constantly increasing. The battery system as a backup energy source is being widely used. For all power supply systems that are not allowed to cut off, the battery pack is an indispensable backup power system. Moreover, the battery system is more and more widely used in various industries. . Whether the battery is running normally or not directly affects the normal, reliable and safe operation of various equipment in the application field. Therefore, accurately estimating the state of health of the battery is of great significanc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36G06N3/04G06N3/08
CPCG06N3/08G01R31/367G01R31/392G06N3/048
Inventor 陈则王林娅朱晓栋崔江王友仁
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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