Lithium ion power battery state-of-health estimation method based on machine learning

A power battery and machine learning technology, applied in the direction of secondary battery repair/maintenance, instrumentation, calculation, etc., can solve the problem of no decay physical model, and achieve the effect of reducing the amount of calculation, high estimation accuracy, and improving estimation accuracy

Active Publication Date: 2019-10-18
JIANGSU UNIV
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Problems solved by technology

There is currently no accurate physical model of recession

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  • Lithium ion power battery state-of-health estimation method based on machine learning
  • Lithium ion power battery state-of-health estimation method based on machine learning
  • Lithium ion power battery state-of-health estimation method based on machine learning

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Embodiment Construction

[0021] The specific technical solutions of the present invention will be further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited thereto.

[0022] A method for estimating the state of health of a lithium-ion power battery based on machine learning, comprising the following steps:

[0023] Step (1), set up the equivalent circuit model of lithium-ion power battery, can select Thevenin equivalent circuit model for use, or second-order RC equivalent circuit model, the present embodiment takes Thevenin equivalent circuit as example (such as figure 1 ), among them, U OC Indicates the open circuit voltage of the battery, U t Indicates the terminal voltage of the battery, R 0 is the ohmic internal resistance of the battery, U p , R p 、C p Indicates battery polarization voltage, resistance, capacitance; I L Charge and discharge current for the battery. according to figure 1 The schematic diagram of ...

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Abstract

The invention discloses a lithium ion power battery state-of-health estimation method based on machine learning. The method is used for estimating the state-of-charge (SOC) and the state-of-health ofa power battery in real time. Parameter identification is performed on an equivalent circuit model by establishing the equivalent circuit model of the lithium ion battery, and then a Uoc-SOC model isestablished, and the SOC is estimated. Training is carried out by using a large amount of offline data to obtain a neural network model which takes Uoc-SOC model parameters as input and takes the maximum available capacity as output. The method further has the following steps: performing curve fitting on Uoc and the SOC at the same moment to obtain to-be-identified parameters in the model, inputting the to-be-identified parameters into the neural network model obtained by training to obtain the maximum available capacity, returning the obtained Uoc-SOC model parameters and the maximum available capacity to the SOC estimation step, and updating the parameters of a state equation and an observation equation. According to the lithium ion battery state-of-health estimation method provided by the invention, online estimation is carried out for the battery health state, parameter updating is carried out on SOC estimation, and the estimation precision is improved.

Description

Technical field [0001] The invention relates to the technical field of state estimation of electric vehicle power battery management systems, and specifically to the joint estimation of the state of charge and health state of the power battery. Background technique [0002] With the depletion of global oil resources and the increasingly serious environmental pollution, electric vehicles have attracted people's attention as an energy-saving, environmentally friendly and sustainable means of transportation. As the power source of electric vehicles, the performance of power battery packs has always been the focus of research. When the battery of an electric vehicle is used, it needs to work within a reasonable range of voltage, current, and temperature. Therefore, the use of electric batteries in electric vehicles needs to be effectively managed. The specific equipment for battery management in electric vehicles is the Battery Management System (BMS). It not only ensures the...

Claims

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Application Information

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IPC IPC(8): G01R31/388G01R31/367G01R31/392G06F17/50H01M10/42
CPCG01R31/388G01R31/367G01R31/392H01M10/42G06F30/20Y02E60/10
Inventor 何志刚李尧太盘朝奉周洪剑魏涛
Owner JIANGSU UNIV
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