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Power battery health state estimation method

A power battery and health state technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problem of large error of the extended Kalman filter algorithm

Inactive Publication Date: 2020-04-24
CHINA AUTOMOTIVE TECH & RES CENT +1
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Problems solved by technology

The most widely used of such algorithms is the extended Kalman filter algorithm, but the capacitance is approximated as an integer order in its model, resulting in its estimation accuracy heavily dependent on the accuracy of the noise variance, which makes the error of the extended Kalman filter algorithm relatively large

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Embodiment Construction

[0081] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0082] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0083] The invention adopts the second-order Thevenin equivalent circuit model of the lithium ion battery, and uses an adaptive unscented Kalman filter (AUKF) algorithm to estimate the state of the battery in real time. The adaptive unscented Kalman filter algorithm combines the unscented Kalman filter algorithm and the extended Kalman algorithm to establish a loop iterative relationship. The battery state is estimated with known battery parameters, and then the battery state is used as a known quantity to estimate the model parameters, and so on. Recursive calculation, real-time estimation of battery SOC and ohmic internal resistance. Using the functional correspondence between ohmic i...

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Abstract

The invention provides a power battery health state estimation method. A lithium ion battery second-order Thevenin equivalent circuit model is adopted, and an adaptive unscented Kalman filter (AUKF) algorithm is applied to carry out real-time estimation on a state of a battery. The adaptive unscented Kalman filter algorithm is combined with an unscented Kalman filter algorithm and an extended Kalman filter algorithm, a loop iteration relation is established, the battery state is estimated according to known battery parameters, then the battery state serves as a known quantity to estimate modelparameters, recursive operation is performed in the same manner, and SOC and an ohmic internal resistance of the battery are estimated in real time. And battery SOH can be estimated in real time by using a function corresponding relationship between the ohmic internal resistance and the battery SOH.

Description

technical field [0001] The invention belongs to the technical field of new energy vehicle battery management, and in particular relates to a method for estimating the state of health of a power battery. Background technique [0002] As the world's energy consumption increases day by day and air pollution becomes more and more serious, the development of new energy vehicles has become an important task for the development of modern industry. Among them, electric vehicles have attracted much attention due to their high efficiency and low pollution. Lithium-ion power battery pack is the only energy storage link in electric vehicles. When the performance of electric vehicle power battery pack drops to 80% of its original performance, it will no longer be suitable for use in electric vehicles. Therefore, it is necessary to accurately estimate the health status of the power battery to evaluate the current capacity of the battery in real time, which can not only make timely prepar...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/392G01R31/367G01R31/389G01R31/36
CPCG01R31/3648G01R31/367G01R31/389G01R31/392
Inventor 邓浩然方锐张亚军邝男男裴志豪齐创
Owner CHINA AUTOMOTIVE TECH & RES CENT
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