Battery health state identification method

A technology of battery state of health and identification method, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as complex parameter configuration, and achieve the effect of ensuring recognition accuracy, reducing difficulty, and reducing evaluation difficulty

Inactive Publication Date: 2018-07-20
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Although the physical model method has high accuracy, it has high requirements for the

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Embodiment Construction

[0017] Next, the specific implementation method of the present invention will be further elaborated in conjunction with the accompanying drawings.

[0018] Such as figure 1 Shown, the specific implementation process and principle of the present invention are as follows:

[0019] A. Use the power battery system model to extract the terminal voltage, current temperature and SOC raw data of the power battery in each state, and preprocess the raw data;

[0020] B. Perform feature extraction on the preprocessed battery data and perform normalization processing;

[0021] C. Establish a mixed Gaussian distribution model and a single Gaussian distribution model, use the characteristic sequence to initialize and re-estimate the parameters of the hidden semi-Markov model, and determine the hidden semi-Markov model that conforms to each state of the battery;

[0022] D. Collect test data in each state, after feature extraction, input it into the hidden semi-Markov model of each state, ...

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Abstract

The invention relates to a battery health state identification method, namely a real-time evaluation method for the health state of a battery. The method includes a feature extraction part and a stateidentification part. The health state of the battery is classified according to relation between the internal resistance of a power battery and a battery life. The terminal voltage, current and SOC original data in different states of the battery are obtained. A wavelet packet energy method is used to extract energy value features from the original data, and a characteristic vector used for a hidden semi-Markov model is established. Characteristic vectors in different states are used to train the hidden semi-Markov model in the corresponding states. Test data is substituted into the trained hidden semi-Markov model of the different states to calculate a forward probability value, and the health state of the battery at present is obtained by comparison. According to the method of the invention, complex parameter configuration is not needed, and the health state of the battery at present can be identified accurately in real time.

Description

technical field [0001] This patent belongs to the field of power batteries, and in particular relates to a battery health state identification method. Background technique [0002] With the gradual depletion of fossil energy, the development of new energy is crucial. As transportation is the main aspect of energy consumption, it is an irresistible trend for vehicles to transform from traditional fuel vehicles to electric vehicles. Power battery is the main power source of electric vehicles, its safety, economy and power are the key factors to determine the future development of electric vehicles. Improving battery safety and prolonging battery life are also bottlenecks that restrict the development and popularization of electric vehicles. As an important indicator for evaluating battery safety performance, battery health status has received more and more attention. Replacing a battery with a poor health condition in time according to the health condition of the battery ca...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367G01R31/392
Inventor 禄盛金泽魁马艺玮谢颖朴昌浩
Owner CHONGQING UNIV OF POSTS & TELECOMM
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