Vehicle battery state of health estimation method based on data driving

A battery state of health, data-driven technology, applied in the measurement of electricity, measurement of electrical variables, instruments, etc., can solve the problem of extracting the optimal feature subset of the health factor, and achieve the effect of improving the estimation accuracy and reliability and reducing the amount of calculation.

Inactive Publication Date: 2020-07-10
CHONGQING UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0004] In view of the above problems, there is currently no scheme to extract health factors based on the charging process and use a more effective screening strategy to screen the optimal feature subset to estimate battery SOH more accurately and reliably.

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  • Vehicle battery state of health estimation method based on data driving
  • Vehicle battery state of health estimation method based on data driving
  • Vehicle battery state of health estimation method based on data driving

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Embodiment Construction

[0055] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0056] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a vehicle battery state of health (SOH) estimation method based on data driving, and belongs to the technical field of battery management. The method comprises the following steps: selecting a to-be-tested battery, and collecting and arranging technical parameters of the battery; carrying out a cyclic aging experiment of the battery according to the vehicle charging working condition and the constant current or dynamic discharging working condition, and establishing a battery aging database according to the voltage, current, temperature and other information of the battery; extracting health factors of other batteries according to the screened subsets, and estimating SOH estimation verification of the batteries under other different discharge working conditions through the trained regression model; and embedding the trained model into a vehicle battery management system, extracting health factors covered by the subset from the vehicle, and performing SOH estimation. According to the method, the optimal feature subset is selected for model training by using a fusion method, so that the calculation amount can be effectively reduced, the model precision is improved, and reference is provided for actual vehicles.

Description

technical field [0001] The invention belongs to the technical field of battery management and relates to a data-driven method for estimating the state of health of a vehicle battery. Background technique [0002] The successful development of electric vehicles depends largely on the cycle performance, cost and safety of batteries. Rechargeable lithium-ion batteries are currently the best choice for electric vehicles due to their reasonable energy density and cycle life. Lithium-ion batteries will lead to higher energy densities and more complex battery dynamics, and the efficiency and safety of such batteries will be the focus of attention. An advanced battery management system (BMS) that monitors and optimizes battery behavior and safety is therefore essential to the entire electrification system. Reliable predictions of the battery's state of health (SOH) by the BMS will allow the battery to reach its full potential and maximum life expectancy before replacement or dispo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/392G01R31/367G01R31/385
CPCG01R31/392G01R31/367G01R31/385
Inventor 胡晓松车云弘邓忠伟李佳承刘波
Owner CHONGQING UNIV
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