Kalman filtering and data driving fusion battery SOC estimation method

A Kalman filter, data-driven technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of no specific standard for parameter setting and poor model adaptability.

Inactive Publication Date: 2016-11-09
HARBIN INST OF TECH
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Problems solved by technology

[0006] The purpose of the present invention is in order to solve the problem that above-mentioned prior art exists, namely for the parameter setting that widely used Kalman filter (Kalman Filter, KF) and its derivation method e

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  • Kalman filtering and data driving fusion battery SOC estimation method
  • Kalman filtering and data driving fusion battery SOC estimation method
  • Kalman filtering and data driving fusion battery SOC estimation method

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Embodiment Construction

[0113] The present invention will be described in further detail below in conjunction with the accompanying drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation is provided, but the protection scope of the present invention is not limited to the following embodiments.

[0114] Such as Figure 1 to Figure 8 as shown,

[0115] Kalman filter: Kalman filter is a statistical filtering method proposed in 1960, which repeatedly recursively iterates through the estimated value at the previous moment and the measured value at the current moment, and finally estimates from two values ​​​​with large errors a relatively accurate value. Kalman filtering uses state equations and measurement equations to describe the input-output relationship of the dynamic system. In the estimation process, the system state equations, measurement equations and statistical characteristics of white noise are used to for...

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Abstract

The invention discloses a Kalman filtering and data driving fusion battery SOC estimation method belonging to the battery SOC estimation method technology field. The invention provides a varied variance Kalman filter least square support vector machine (VVKF LSSVM) fusion method. Based on two equations of a KF, a noise variance, which is adapted to a current system state to the greatest extent, is set during every iteration, and a problem of declined precision caused by Kalman filtering noise variance initial value relied on artificial experience setting is solved. A least square support vector machine (LS SVM) is selected as the measurement equation of the KF, and by starting from a data angle, the SOC estimation method suitable for various batteries is completed by establishing a simple sample library, and the estimation precision is improved by dynamic modeling. A part of data in an NASA lithium battery data set and a CACLE lithium battery data set is used for experimenting to prove the superiority of the VVKF by comparing with the KF, and the validity of the whole method on the lithium battery SOC estimation.

Description

technical field [0001] The invention relates to a method for estimating battery SOC combined with Kalman filtering and data driving, and belongs to the technical field of battery SOC estimating methods. Background technique [0002] People usually only observe the terminal voltage to avoid overcharging and overdischarging of the battery. As people's requirements for battery usage increase, the state of charge (State of Charge, SOC) is the main parameter of battery management just like the oil gauge of a car. . A battery is a device that converts chemical energy into electrical energy, so its state of charge is unmeasurable. Usually, parameters such as terminal voltage, operating current, and internal resistance correspond to SOC, so that SOC can be estimated through these physical quantities. At present, there are many battery SOC estimation methods, which can be divided into open circuit voltage method, ampere-hour method, internal resistance method, model-based method, da...

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Application Information

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IPC IPC(8): G01R31/36
Inventor 彭喜元赵天意彭宇刘大同
Owner HARBIN INST OF TECH
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