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On-line estimation method for state of charge of lithium ion battery based on extended single particle model

A single particle model, lithium-ion battery technology, applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., can solve the problem of battery SOC estimation error, inability to accurately describe battery characteristics, etc.

Inactive Publication Date: 2016-08-24
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, these models do not mechanistically analyze the root cause of the highly nonlinear external characteristics of the battery, so they cannot accurately describe the battery characteristics, resulting in certain errors in the estimation of battery SOC.

Method used

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  • On-line estimation method for state of charge of lithium ion battery based on extended single particle model
  • On-line estimation method for state of charge of lithium ion battery based on extended single particle model
  • On-line estimation method for state of charge of lithium ion battery based on extended single particle model

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specific Embodiment approach 1

[0046] Specific Embodiment 1: This embodiment is a detailed description of the method for establishing an extended single-particle model of a lithium-ion battery.

[0047] An extended single-event modeling approach for lithium-ion batteries includes the following steps:

[0048] Step 1: Solve the average lithium ion concentration in the positive and negative active particles and the lithium ion concentration on the particle surface;

[0049] Assuming that the active particles in the positive and negative electrodes are spherical particles with equal radii, the reactive ion current densities everywhere in the electrodes are also equal. Then the reactive ion current density at the boundary of the positive and negative current collectors is:

[0050] j n = IR n 3 A F ( 1 ...

specific Embodiment approach 2

[0107] Specific implementation mode two: the unscented Kalman filter is a nonlinear Gaussian state estimator based on the minimum variance estimation criterion, which uses the nonlinear optimal Gaussian filter as the basic theoretical framework, and uses unscented Posterior mean and posterior covariance after propagation of the linear system.

[0108] This embodiment is to illustrate the online estimation of the state of charge of a lithium-ion battery by using an unscented Kalman filter based on the extended single-event model described in the present invention.

[0109] Given the n-dimensional discrete-time nonlinear system, combined with the state-space equation, it can be known that n=1:

[0110] X k + 1 = f ...

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Abstract

The invention discloses an on-line estimation method for the state of charge (SOC) of a lithium ion battery based on an extended single particle model. The method comprises the following steps: 1) establishing a lithium ion battery single particle model; 2) solving the concentration distribution problem of liquid-phase lithium ions based on a BP (Back Propagation) neural network; 3) solving liquid-phase lithium ion concentration distribution of each area in the single particle model using the trained BP neural network to optimize the single particle model; and 4) implementing on-line estimation of the SOC of the lithium ion battery by adopting unscented Kalman filter based on the extended single particle model. The method considering the liquid-phase lithium ion concentration distribution of each area in the single particle model improves the simulation precision of the single particle model, and overcomes the defect of low precision of the single particle model under medium and high multiplying power conditions. The extended single particle model can better describe the nonlinear characteristic of the battery, and the SOC precision estimated by adopting unscented Kalman filter based on the extended single particle model is higher.

Description

technical field [0001] The invention belongs to the technical field of battery state-of-charge estimation, and relates to a method for establishing an extended single-event model of a lithium-ion battery and an online estimation method for a state-of-charge of a lithium-ion battery based on an unscented Kalman filter. Background technique [0002] A lithium-ion battery is an energy storage device that converts chemical energy into electrical energy. Lithium-ion batteries are widely used due to their high energy density, long cycle life, low self-discharge rate, and non-pollution. [0003] Accurate battery modeling for Li-ion batteries is an important prerequisite for realizing Li-ion battery state estimation. Based on the electrochemical model of the internal reaction of the lithium-ion battery, it can accurately reflect the changes of the internal microscopic quantities of the battery and the changes of the external characteristics of the battery. Compared with the empiri...

Claims

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/387
Inventor 陈则王崔鹰飞王友仁
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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