Power battery SOC (state of charge) estimation method based on expansion Kalman particle filter algorithm

A particle filter algorithm, extended Kalman technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as a large number of training data, particle degradation, algorithm jitter, etc., to achieve the effect of high estimation accuracy

Active Publication Date: 2013-12-25
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

The ampere-hour measurement method is simple and easy to implement, but the cumulative error is large, and the accuracy of the measuring equipment is high; the open circuit voltage method is only suitable for estimation after the battery has been left for a long enough time, and cannot be estimated in real time; the neural network can be estimated

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  • Power battery SOC (state of charge) estimation method based on expansion Kalman particle filter algorithm
  • Power battery SOC (state of charge) estimation method based on expansion Kalman particle filter algorithm
  • Power battery SOC (state of charge) estimation method based on expansion Kalman particle filter algorithm

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[0029] Below in conjunction with accompanying drawing, the technical scheme of invention is described in detail:

[0030] The present invention is applicable to the SOC estimation of each power battery. For different power battery models, after determining the discrete state space model of the extended Kalman particle filter, the SOC is estimated by using the extended Kalman particle filter method, wherein, in the extended Kalman particle filter After sampling, importance sampling is performed on the samples collected at each sampling time to obtain the particle set at each sampling time, and the extended Kalman particle filter is used to train the extended Kalman particle filter to estimate the SOC.

[0031] The technical scheme of the present invention is described below by taking the electrochemical composite battery model as an example, and the SOC estimation method of the power battery based on the extended Kalman particle filter algorithm described in the present inventio...

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Abstract

The invention discloses a power battery SOC (state of charge) estimation method based on an expansion Kalman particle filter algorithm. A conventional method has the problems of unsatisfied on-line estimation needs, large accumulation errors, diverging filtering and the like. An expansion Kalman filtering method and a particle filter method are combined and important density functions are generated through expansion Kalman filtering, so that a particle degeneration phenomenon is avoided and the estimation precision is enhanced. By using the provided method, the SOC of a battery can be effectively estimated and the precision is quite high, thereby being suitable for the SOC estimation of various batteries.

Description

technical field [0001] The invention discloses a power battery SOC estimation method based on an extended Kalman particle filter algorithm, and belongs to the technical field of lithium batteries. Background technique [0002] As a key technical component of an electric vehicle, the performance of the power battery directly affects the performance of the vehicle. The battery state of charge (State of Charge, SOC) is used to describe the amount of remaining battery power, which in turn reflects the driving range of electric vehicles. Power battery state of charge estimation is an important function of the battery management system, and it is also a technical difficulty that needs to be solved urgently in its development. Real-time and accurate SOC estimation is of great significance to battery performance, service life and the development of electric vehicles. The high nonlinearity of the power battery makes it difficult for many filtering methods to obtain accurate estimati...

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Application Information

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IPC IPC(8): G01R31/36
Inventor 周晓凤赵又群臧利国
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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