Lithium ion battery SOC prediction method based on recurrent neural network

A cyclic neural network and lithium-ion battery technology, which is applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of predicting SOC values ​​and the disappearance of neural network gradients, avoiding accurate determination, improving prediction accuracy, and widening SOC. The effect of value prediction

Inactive Publication Date: 2018-09-11
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] In view of this, the object of the present invention is to provide a lithium-ion battery SOC prediction method based on cyclic neural network, by improving the neural network gradient disappearance problem, provide a more convenient,

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  • Lithium ion battery SOC prediction method based on recurrent neural network
  • Lithium ion battery SOC prediction method based on recurrent neural network
  • Lithium ion battery SOC prediction method based on recurrent neural network

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[0089] figure 1 To collect test data for a complete charge-discharge test, set different specific conditions for the test lithium-ion battery, such as constant current charging, constant current discharge, and constant temperature resistance measurement, to design a test plan for the compound pulse power test (HPPCTest). Taking into account the different battery parameters corresponding to different charging and discharging directions, the original good HPPC cycle test has been improved. Discharge the fully charged battery at a constant current for about 50 minutes, then let the battery stand for 15 minutes and wait for its parameters to stabilize, then charge it with a constant current for about 50 minutes, and then let the battery stand for 15 minutes. A complete charge and discharge test.

[0090] figure 2 A kind of lithium-ion battery SOC prediction discharge flow chart based on the long-short-term memory model provided by the present invention, combined with this figur...

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Abstract

The present invention relates to a lithium ion battery SOC (State Of Charge) prediction method based on a recurrent neural network, belonging to the field of electric vehicle battery management systems. The method comprises the steps of: employing a sliding window algorithm to improve battery external parameter data obtained through several experiments and a data set commonly formed by corresponding SOC values at present, employing the improved recurrent neural network, namely a long short-term memory network method to establish a power battery SOC estimation model, performing repeated verification through experiments to obtain a network layer function and a gradient regulation method, and finally, setting different learning rates to verify a prediction result of the model. The lithium ionbattery SOC (State Of Charge) prediction method can accurately predicate an SOC value at the next moment, is high in prediction precision, short in training duration and low in cost, and can be widely applied to a battery management system on the electric vehicle power battery.

Description

technical field [0001] The invention belongs to the field of battery management systems for electric vehicles, and relates to a lithium-ion battery SOC prediction method based on a cyclic neural network. Background technique [0002] The energy problem in today's society is becoming more and more serious, and electric vehicles have gradually become the mainstream of the industry due to their advantages such as energy saving and cleanliness, and one of the important parts is the battery management system (BMS). A complete battery pack on an electric vehicle contains a large number of single cells, each of which affects the system characteristics of the battery pack. How to accurately understand the current state of the battery, grasp the application characteristics of the system, and apply it to the operation and maintenance, management control, planning and design of the system has become an important research content of the system. The current remaining capacity of the bat...

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Application Information

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IPC IPC(8): G01R31/36
Inventor 王瑶程安宇程自立黄天祥余奇卓宏刚
Owner CHONGQING UNIV OF POSTS & TELECOMM
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