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On-line identification method for intrinsic parameters of battery

An internal parameter and battery technology, which is applied in the field of online identification of battery internal parameters, can solve the problems of repeated system adjustment, instability, and the adaptive PID controller cannot respond to the system, and achieves the effect of improving control accuracy and extending capacity.

Inactive Publication Date: 2017-05-10
SOUTH CHINA AGRI UNIV
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Problems solved by technology

At present, the model parameter identification of batteries mostly adopts least squares parameter identification and Kalman filter identification. These methods are mostly used for parameter identification of time-invariant systems. It will reduce the adaptability of the system parameters, causing the adaptive PID controller to fail to respond accurately to the system, so that the system will fall into a new round of repeated adjustments or directly collapse due to instability

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  • On-line identification method for intrinsic parameters of battery
  • On-line identification method for intrinsic parameters of battery

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Embodiment Construction

[0023] The present invention will be further described below in conjunction with specific embodiment:

[0024] See attached Figures 1 to 2 As shown, this embodiment is for online identification of the internal parameters of the power lithium-ion battery.

[0025] The first step is to establish a more accurate mathematical model of the battery, with figure 2 As shown, there is an energy storage element C in the figure p , the model order of the lithium-ion battery can be determined as a first-order RC model, which is composed of an ideal voltage source, a capacitor and two resistors, and the two resistors represent the ohmic internal resistance R of the battery respectively 0 and polarization internal resistance R p , the capacitance represents the polarization capacitance C of the battery p , where the polarizing capacitance C p and polarization resistance R p in parallel, then with ohmic resistor R 0 and an ideal voltage source to form a series circuit; the terminal ...

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Abstract

The invention relates to an on-line identification method for intrinsic parameters of a battery, which is characterized in that a linear neural network is constructed by using input and output data of the battery on the basis of a standard equivalent first-order RC model, current and voltage data in a composite pulse test is taken as a training set, and parameter identification is performed on an equivalent circuit model by using a neural network parameter identification method. The on-line identification method identifies each parameter of a power storage battery online conveniently and efficiently, provides model parameters for battery power estimation, and is conducive to improving control effects of a battery management system, giving full play to the performance of the storage battery and prolonging the cycle life of the storage battery.

Description

technical field [0001] The invention relates to the technical field of battery detection, in particular to a method for online identification of battery internal parameters. Background technique [0002] With the increasing maturity of electric vehicle technology, electric vehicles have gradually entered people's lives. However, the three major problems of motor, battery and electronic control restrict the development of electric vehicles, among which the battery is an important "bottleneck" for the development of electric vehicles. Electric vehicle power battery packs have the problem of power imbalance during charging and discharging. In order to estimate the remaining power of the battery in real time, it is necessary to realize the online identification of battery model parameters. At present, the model parameter identification of batteries mostly adopts least squares parameter identification and Kalman filter identification. These methods are mostly used for parameter ...

Claims

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367
Inventor 陆华忠张震邦王海林吕恩利肖博一曽细强李斌杨益彬
Owner SOUTH CHINA AGRI UNIV
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