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Adaptive UKF (Unscented Kalman Filtering)-based lithium battery SOC (State Of Charge) estimation method

A lithium battery, self-adaptive technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of battery model filtering accuracy reduction, filtering divergence, etc.

Inactive Publication Date: 2017-09-12
FUJIAN UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is to provide a lithium battery SOC estimation method based on self-adaptive UKF, which overcomes the problem that the uncertainty of the battery model and the unknown statistical characteristics of the noise in the UKF algorithm may cause a decrease in filtering accuracy or even filter divergence.

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  • Adaptive UKF (Unscented Kalman Filtering)-based lithium battery SOC (State Of Charge) estimation method
  • Adaptive UKF (Unscented Kalman Filtering)-based lithium battery SOC (State Of Charge) estimation method
  • Adaptive UKF (Unscented Kalman Filtering)-based lithium battery SOC (State Of Charge) estimation method

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Embodiment Construction

[0051] Such as figure 1 As shown, a lithium battery SOC estimation method based on adaptive UKF includes the following steps:

[0052] Step 1: Perform a quick calibration experiment on the lithium battery to obtain the relationship curve between SOC and open circuit voltage OCV;

[0053] Step 2: Establish a model of the lithium battery to be tested, and identify the parameters of the battery model through battery charge and discharge experiments. The present invention uses the following mathematical model to describe the battery terminal voltage characteristics:

[0054] Z k = K 0 -Ri k -K 1 / x k -K 2 x k +K 3 In(x k )+K 4 In(1-x k )

[0055] Among them, Z k refers to the battery terminal voltage, i refers to the battery current, R refers to the internal resistance of the battery, the value is different during charging and discharging, set it as R+ during charging, and R- during discharging, K 0 ~K 4 is a constant;

[0056] Step 3: Establish a discrete state-s...

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Abstract

The invention discloses an adaptive UKF (Unscented Kalman Filtering)-based lithium battery SOC (State Of Charge) estimation method which comprises the following steps: performing a quick calibration experiment on a lithium battery, thus obtaining an SOC and OCV (Open Circuit Voltage) relation curve; building a model for the lithium battery to be detected, identifying parameters of a battery model through battery charging and discharging experiments, and building a discrete state space model of a battery system; estimating a battery SOC through an improved UKF algorithm. The method disclosed by the invention solves the problems of reduction of filtering precision and even generation of filtering divergence possibly due to the uncertainty and an unknown noise statistical property of the battery model in the UKF algorithm.

Description

【Technical field】 [0001] The invention belongs to the technical field of lithium batteries, in particular to a lithium battery SOC estimation method based on self-adaptive UKF. 【Background technique】 [0002] Batteries are the main energy carrier and power source of electric vehicles, and are also the main components of the electric vehicle body. Accurate estimation of battery SOC can not only improve the capacity utilization efficiency of batteries, but also prolong the service life of batteries. Due to the complex electrochemical properties and physical reactions inside the battery, SOC cannot be measured directly, and is generally estimated by external parameters of the battery, such as voltage and operating current. For the state estimation problem of the battery dynamic nonlinear hybrid system, a single nonlinear filtering algorithm is currently used for estimation. As one of the earliest nonlinear filtering methods, the EKF method has been widely used in engineering. ...

Claims

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367G01R31/387
Inventor 刘成武邓青杨志
Owner FUJIAN UNIV OF TECH
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