CN107290685A - A kind of application of Unscented kalman filtering method based on internal resistance model in battery state of charge estimation - Google Patents
A kind of application of Unscented kalman filtering method based on internal resistance model in battery state of charge estimation Download PDFInfo
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- CN107290685A CN107290685A CN201710615126.3A CN201710615126A CN107290685A CN 107290685 A CN107290685 A CN 107290685A CN 201710615126 A CN201710615126 A CN 201710615126A CN 107290685 A CN107290685 A CN 107290685A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
Abstract
The invention discloses a kind of application of Unscented kalman filtering method based on internal resistance model in battery state of charge estimation.The application uses internal resistance of cell equivalent model, and by the internal resistance of cell and battery state of charge（SOC）As state-space model, based on this model use Unscented kalman filtering device（UFK）To the state of charge of battery（SOC）Estimated.Internal resistance of cell equivalent model clearly expresses the relation between the internal state variable of battery and external electrical characteristic.The built-in variable of battery can be calculated according to the variable quantity of external electrical characteristic, in Practical Project utilization, amount of calculation is simplified, and be easy to parameter identification.Unscented kalman filtering device（UFK）The internal resistance of the Space admittance can be constantly adjusted, with compensation model deviation so that the estimation to battery state of charge is more accurate.This method not only avoid the calculating of complexity, and precision is accurate, is easily realized in engineering application.
Description
Technical field
The present invention relates to new energy battery management system technical field, it is specially a kind of based on internal resistance model without mark karr
Application of the graceful filtering method in battery state of charge estimation.
Background technology
Battery management system is one of indispensable core component of electric automobile, and the stability and accuracy of its system are determined
The efficient usability and service life of battery are determined.Estimation wherein to battery state of charge SOC directly affects battery management system
The performance of system.The state of charge of battery has directly reacted the state of battery, is an important parameter of battery remaining power.And it is electric
The size of pond residual capacity directly affects the mileage that electric automobile can continue to traveling, therefore battery remaining power is carried out accurately
Estimation it is particularly significant.
The state of charge of battery not only reflects the course continuation mileage of electric automobile, and can show different battery capacities
Size.Therefore the state of charge of battery is the harmonious offer condition of power battery pack, and then ensures the one of power battery pack
Cause property, gives full play to the overall performance of power battery pack, extends the service life of electric automobile power battery group, it is to avoid power electric
The infringement of pond group.
Accurate estimation in real time is carried out in terms of electric automobile energy utilization rate is improved to the state of charge of power battery pack
Have great importance.But in the actual operating state of electric automobile power battery, by external environment condition and inside battery
The influence of the factors such as electrical characteristic, it is a nonlinear quantity of state that the state of charge of battery, which becomes, have impact on the accurate fixed of estimation.
It is particularly important how nonlinear state amount is handled.
The content of the invention
It is an object of the invention to provide a kind of Unscented kalman filtering method based on internal resistance model in battery charge shape
Application in state estimation, to solve the problems mentioned in the above background technology.
To achieve the above object, the present invention provides following technical scheme：A kind of Unscented kalman filter based on internal resistance model
Application of the wave method in battery state of charge estimation, is comprised the following steps that：
Step one：State equation is set up according to internal resistance of cell model
V=Voc-I×Re+C
Step 2：Quantity of state and margin of error initialization
Step 3：Calculate Sigma points
(3)
Step 4：Time propagation equation
Step 5：Measurement updaue equation
Step 6：Unscented kalman filtering
v0(k)=E-I (k) × R (k)+C+ ε
Wherein, QmaxIt is battery rated capacity, ω1、ω2It is process noise, ε is measurement noise, and Δ T is discrete time.
It is preferred that, need not to nonlinear system when being estimated using Unscented kalman filtering method battery charge levels
Processing, is brought directly to the nonlinear equation of battery model, the non-linear form of changeable internal damp bvattery equivalent-circuit model is as follows：
Y (k)=vocv(x1,k)-x2(k)×u(k)-0.0028+ε
It is preferred that, converted using UT, approximation linear function is converted into approximation probability density function.
It is preferred that, set up Space admittance.
Compared with prior art, the beneficial effects of the invention are as follows：Design and establish a kind of battery state of charge estimation and calculate
Method, i.e. the Unscented kalman filtering method based on internal resistance model, this method establishes the internal resistance of cell and battery state of charge
(SOC) state-space model, and internal resistance compensation can be carried out to model according to actual state, it is ensured that the reliability of estimation result,
Accuracy and accuracy.Simplify simultaneously and calculate, this method is used in engineering practice.
Brief description of the drawings
Fig. 1 is the structural representation of internal resistance model equivalent-circuit model figure in the present invention；
Fig. 2 is UT shift theory figures in Unscented kalman filtering method in the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
Fig. 1-2 is referred to, the present invention provides a kind of technical scheme：A kind of Unscented kalman filtering side based on internal resistance model
Application of the method in battery state of charge estimation, is comprised the following steps that：
Step one：State equation is set up according to internal resistance of cell model
V=Voc-I×Re+C
Internal resistance of cell model is as shown in figure 1, wherein VocElectrokinetic cell open-circuit voltage is represented, is existed necessarily with battery SOC
Functional relation, ReThe internal resistance of cell is represented, V represents the port voltage of electrokinetic cell, and constant C is used for correction model error.
Step 2：Quantity of state and margin of error initialization
Step 3：Calculate Sigma points
(3)
Step 4：Time propagation equation
Step 5：Measurement updaue equation
Step 6：Unscented kalman filtering
v0(k)=E-I (k) × R (k)+C+ ε
Wherein, QmaxIt is battery rated capacity, ω1、ω2It is process noise, ε is measurement noise, and Δ T is discrete time.
Specifically, when being estimated using Unscented kalman filtering method battery charge levels, to nonlinear system without
Processing is needed, the nonlinear equation of battery model is brought directly to, the non-linear form of changeable internal damp bvattery equivalent-circuit model is as follows：
Y (k)=vocv(x1,k)-x2(k)×u(k)-0.0028+ε
Specifically, converted using UT, approximation linear function be converted into approximation probability density function, converted using UT,
Filtration efficiency is high, and filter effect is good.
Specifically, Space admittance is set up, Space admittance is established, it is considered to the actual operating state of battery,
Complicated computing is simplified.
Operation principle：The application uses internal resistance of cell equivalent model, and the internal resistance of cell and battery state of charge (SOC) are made
For state-space model, the state of charge (SOC) of battery is estimated based on this model use Unscented kalman filtering device (UFK)
Calculate, internal resistance of cell equivalent model clearly expresses the relation between the internal state variable of battery and external electrical characteristic, can
The built-in variable of battery is calculated according to the variable quantity of external electrical characteristic, in Practical Project utilization, amount of calculation is simplified, and
And it is easy to parameter identification, Unscented kalman filtering device (UFK) can constantly adjust the internal resistance of the Space admittance, to compensate
Model bias.
Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of changes, modification can be carried out to these embodiments, replace without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (4)
1. a kind of application of Unscented kalman filtering method based on internal resistance model in battery state of charge estimation, its feature exists
In：Comprise the following steps that：
Step one：State equation is set up according to internal resistance of cell model
V=Voc-I×Re+C
Step 2：Quantity of state and margin of error initialization
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Step 4：Time propagation equation
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Step 6：Unscented kalman filtering
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v0(k)=E-I (k) × R (k)+C+ ε
Wherein, QmaxIt is battery rated capacity, ω1、ω2It is process noise, ε is measurement noise, and Δ T is discrete time.
2. a kind of Unscented kalman filtering method based on internal resistance model according to claim 1 is estimated in battery state of charge
Application in calculation, it is characterised in that：When being estimated using Unscented kalman filtering method battery charge levels, to nonlinear system
System is brought directly to the nonlinear equation of battery model, the non-linear form of changeable internal damp bvattery equivalent-circuit model is as follows without processing：
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<mtr>
<mtd>
<msub>
<mi>&omega;</mi>
<mn>1</mn>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&omega;</mi>
<mn>2</mn>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Y (k)=vocv(x1,k)-x2(k)×u(k)-0.0028+ε
3. a kind of Unscented kalman filtering method based on internal resistance model according to claim 1 is estimated in battery state of charge
Application in calculation, it is characterised in that：Converted using UT, approximation linear function is converted into approximation probability density function.
4. a kind of Unscented kalman filtering method based on internal resistance model according to claim 1 is estimated in battery state of charge
Application in calculation, it is characterised in that：Set up Space admittance.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108459278A (en) * | 2018-07-05 | 2018-08-28 | 宁波均胜科技有限公司 | A kind of lithium ion battery internal resistance evaluation method synchronous with state-of-charge |
| CN108459278B (en) * | 2018-07-05 | 2021-01-19 | 宁波均胜科技有限公司 | Lithium ion battery internal resistance and charge state synchronous estimation method |
| CN110412472A (en) * | 2019-09-02 | 2019-11-05 | 西北工业大学 | A kind of battery charge state estimation method based on the filtering of normal state gamma |
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