CN107064816A - It is a kind of to strengthen the method that battery status estimates robustness - Google Patents
It is a kind of to strengthen the method that battery status estimates robustness Download PDFInfo
- Publication number
- CN107064816A CN107064816A CN201710239112.6A CN201710239112A CN107064816A CN 107064816 A CN107064816 A CN 107064816A CN 201710239112 A CN201710239112 A CN 201710239112A CN 107064816 A CN107064816 A CN 107064816A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- mtd
- msup
- mfrac
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
Strengthen the method that battery status estimates robustness the invention discloses a kind of, comprise the following steps：Step 1: setting up battery mathematical modeling according to battery behavior；Step 2: being adjusted by increasing redundant state variable to battery mathematical modeling；Step 3: carrying out On-line Estimation to all state variables in the battery mathematical modeling after adjustment using system state estimation method.Compared with prior art, the positive effect of the present invention is：The present invention by battery model is increased it is one or several, have physical significance or system state variables without physical significance, and accordingly change battery mathematics model equation, it can effectively improve battery status estimated accuracy in the case where there is model bias and system noise, accelerate the rate of convergence of estimated bias and strengthen the parameter adaptation of algorithm for estimating.The validity of the inventive method is demonstrated present invention obtains good estimation effect, and by test.
Description
Technical field
The invention belongs to cell art, and in particular to a kind of method that enhancing battery status estimates robustness.
Background technology
Battery management system is gathered in real time to parameters such as the temperature of battery, voltage, electric currents, and is calculated based on default
Method, it is to avoid the overcharging of battery, cross and put.The state estimation of battery is the core content of battery management system.One side of its accurate estimation
Face improves the ease for use of system in user level, and driver can drive in rational time range, maintain car body；
On the other hand in inside battery aspect, it can avoid the possible possible permanent damage of battery, extend making for lithium-ion-power cell
Use the life-span.
Battery status estimation initially be desirable to carry out by measuring the voltage of battery because the state-of-charge of battery and its
Open-circuit voltage has the corresponding relation of monotonicity, and the presence yet with complex characteristics such as battery polarization dynamics to estimate with voltage
The precision of battery status is very low, it is difficult to meet practicality demand.Therefore, the voltage by the way that battery is modeled and combined to battery
It is the current main method for improving BMS estimated accuracies to carry out estimated battery state with current signal.
The method of battery status estimation can divide based on model and the side for being not based on model from whether the angle based on model is said
Method.The typical method for being not based on model is current integration method, and the type of model includes electrochemistry mould in the method based on model
Type, neural network model, empirical equation model and circuit model.Wherein circuit model is because that can embody battery to a certain extent
The concern that is subject to of polarization dynamic characteristic it is more, relative battery status method of estimation, which is substantially all, concentrates on expansion karr
On graceful filtering method and its deformation method.
In view of the high complexity of battery behavior, any battery model is all difficult to the characteristic of accurate description battery, because base
In the battery status method of estimation of model, there can be estimated bias because of model bias.Influenceed to lead when by temperature, depth of discharge
When causing model bias larger, the precision of battery status can be reduced substantially.Furthermore the noise of actual condition is also difficult to Accurate Model, right
Battery status estimation also brings along obvious adverse effect.In consideration of it, needing to find a kind of battery status estimation of strong robustness
Method so that even if in the case where there is model bias and noise, it may have higher estimated accuracy.
The content of the invention
In order to overcome the disadvantages mentioned above of prior art, the present invention proposes the side that a kind of enhancing battery status estimates robustness
Method, it is intended to existing method is overcome to model bias and the sensitive shortcoming of system noise, even if there is model bias and system noise
In the case of sound, can also high-precision estimation be carried out to battery status, improve the robustness of estimation.
The technical solution adopted for the present invention to solve the technical problems is：It is a kind of to strengthen the side that battery status estimates robustness
Method, comprises the following steps：
Step 1: setting up battery mathematical modeling according to battery behavior；
Step 2: being adjusted by increasing redundant state variable to battery mathematical modeling；
Step 3: being carried out using system state estimation method to all state variables in the battery mathematical modeling after adjustment
On-line Estimation.
Compared with prior art, the positive effect of the present invention is：The present invention by battery model increase it is one or several,
There are physical significance or the system state variables without physical significance, and accordingly change battery mathematics model equation, can exist
In the case of model bias and system noise, effectively improve battery status estimated accuracy, accelerate estimated bias rate of convergence and
Strengthen the parameter adaptation of algorithm for estimating.Present invention side is demonstrated present invention obtains good estimation effect, and by test
The validity of method.
Brief description of the drawings
Examples of the present invention will be described by way of reference to the accompanying drawings, wherein：
Fig. 1 is battery equivalent circuit model.
Fig. 2 is that robust strengthens the fault-tolerant ability to model bias.
Fig. 3 is that robust strengthens the fault-tolerant ability to estimated state deviation.
Fig. 4 is that robust strengthens the influence to method of estimation parameter adaptation.
Embodiment
It is a kind of to strengthen the method that battery status estimates robustness, comprise the following steps：
Step 1: setting up battery mathematical modeling：
According to battery behavior, suitable model state variable x, input variable u, process noise w are set up, noise and mould is measured
Shape parameter θ, and set up battery system equation：
Y=g (t, x, u, θ, v)
And and then it is converted into discrete form：
xk+1=f (xk,uk,θ)+wk
yk=g (xk,uk,θ)+vk
Step 2: increasing redundancy state variable to battery mathematical modeling, and correspondingly adjust battery mathematical modeling：
Augmentation is carried out to state variable x：
S in formula is the state variable of augmentation, and accordingly adjustment battery model is as follows：
Step 3: being carried out using system state estimation method to all state variables in the battery mathematical modeling after adjustment
On-line Estimation：
M in formulakThe measured value of battery output quantity during for sampling instant k, LkFor the feedback oscillator of method of estimation used.
It should be noted that：
The battery model of the present invention, is not limited to any form, can be battery equivalent circuit model, electrochemical model etc..
The present invention gives the increased redundant state variable of battery model, and the number of redundant state variable is not limited, can be 1
Or it is multiple, and the physical significance of redundant variables is not limited, can have the variable of specific physical significance or without specific thing
Manage the variable of meaning.
The system state estimation method that the present invention is used, any kind of method of estimation is not limited, can be that Kalman estimates
Meter, H∞Estimation, sliding formwork estimation etc..
The inventive method is also not limited to which type of battery.
In order to describe the technology contents of the present invention in detail, algorithm characteristic realizes purpose and effect, below in conjunction with specific reality
Mode is applied system operation flow is described in detail.
The method of robustness is estimated with reference to the enhancing battery status that Fig. 1 present invention is set up, is comprised the following steps：
Step 1, battery is modeled using equivalent-circuit model, and sets up battery mathematical modeling；
As shown in Figure 1 be typical battery equivalent circuit model, including by battery charge state (State of Charge,
SoC) battery open circuit voltage (Open Circuit Voltage, OCV) of control, i.e. OCV (SoC)；Battery equivalent internal resistance is described
R0, the Order RC model that description battery electrochemical polarizes with concentration polarization, i.e. R1C1And R2C2.Set up the circuit model corresponding
Battery discrete system mathematical modeling, its state is：
The state space equation and output equation of mathematical modeling be respectively：
xk+1=Axk+BIk+Fwk (2)
Wherein wkFor process noise (i.e. current measurement noise), vkFor voltage measurement noise, ykExported for system, be with being
The port voltage of circuit shown in the Fig. 1 for state computation of uniting.With reference to the circuit model in Fig. 1, SoC excursion is taken as 0~
Each matrix in 100 (being expressed as a percentage), formula (2) is as follows：
Wherein Cap is battery capacity, τ1=R1C1, τ2=R2C2, Δ t is system communication cycle.
To certain battery, the parameter and numerical value needed for the model are as shown in table 1：
The model capacitance-resistance parameter of table 1
Step 2, increase the redundant state variable of mathematical modeling, and correspondingly adjust battery mathematical modeling, it is specific as follows：
Battery SoC is electric current I integration, and the bias current I of SoC estimated accuracies can be influenceed by being included in I measured valueb,
State as system carries out augmentation to original state, and On-line Estimation is carried out to it, and the system mode of the circuit model becomes
Measure for：
WhereinWithRespectively correspond to the voltage on resistance-capacitance network electric capacity (or resistance).By I-IbIt is used as input
The actual current of battery, its numerical value represents charging to be positive, and the state space equation and output equation of discrete system can be write respectively
For：
Correspondingly adjust the A in battery model, formula (8) and formula (9)a, BaAnd FaRespectively：
Step 3, in each sampling instant, using R∞Observer is estimated battery status, specific as follows：
R is set first∞Observer parameter P0, Q, W and V, II are unit battle array, and combine the sampled value m of battery current voltagek,
It is calculated as below in each sampling instant k：
By carrying out test checking to this method, test result is as in Figure 2-4.Figure it is seen that obvious when existing
Model bias when, the fault-tolerant ability of model bias is substantially better than before enhancing after enhancing, illustrates the method for the enhancing robustness
There is very strong fault-tolerant ability to model bias.From figure 3, it can be seen that when there is initial battery status deviation, making to estimate after enhancing
Evaluation to the convergent speed of actual value faster.If the estimation effect of estimator is very sensitive to its parameter value, mean that this is estimated
Gauge easily occurs that debugging is difficult or job insecurity phenomenon in practical application, therefore the estimator that can be engineered its parameter can
It is more wide better with scope, here it is the problem of parameter adaptation.From fig. 4, it can be seen that when strengthening without robust, some parameter W
Value properly whether have a strong impact on the estimated accuracy of battery status；And after robust enhancing, in W very wide span,
The estimated accuracy of battery status is barely affected, and illustrates that the robust Enhancement Method can substantially increase estimator parameter value model
Enclose, and then be effectively increased the parameter adaptation of estimation.
Claims (7)
1. a kind of strengthen the method that battery status estimates robustness, it is characterised in that：Comprise the following steps：
Step 1: setting up battery mathematical modeling according to battery behavior；
Step 2: being adjusted by increasing redundant state variable to battery mathematical modeling；
Step 3: being carried out using system state estimation method to all state variables in the battery mathematical modeling after adjustment online
Estimation.
2. the method that a kind of enhancing battery status according to claim 1 estimates robustness, it is characterised in that：The battery
Model includes battery equivalent circuit model, electrochemical model.
3. the method that a kind of enhancing battery status according to claim 2 estimates robustness, it is characterised in that：Step one institute
Stating the method for setting up battery mathematical modeling according to battery behavior is：
(1) the corresponding battery discrete system mathematical modeling of equivalent-circuit model is set up, its state variable is：
<mrow>
<mi>x</mi>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>S</mi>
<mi>o</mi>
<mi>C</mi>
</mrow>
</mtd>
<mtd>
<msup>
<mi>U</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<msub>
<mi>C</mi>
<mn>1</mn>
</msub>
</mrow>
</msup>
</mtd>
<mtd>
<msup>
<mi>U</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<msub>
<mi>C</mi>
<mn>2</mn>
</msub>
</mrow>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
Wherein：SoC represents battery charge state, R1C1And R2C2Represent the Order RC mould of battery electrochemical polarization and concentration polarization
Type；
(2) state space equation and output equation of founding mathematical models：
xk+1=Axk+BIk+Fwk
<mrow>
<msub>
<mi>y</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<mi>O</mi>
<mi>C</mi>
<mi>V</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>SoC</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mi>U</mi>
<mi>k</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<msub>
<mi>C</mi>
<mn>1</mn>
</msub>
</mrow>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>U</mi>
<mi>k</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<msub>
<mi>C</mi>
<mn>2</mn>
</msub>
</mrow>
</msubsup>
<mo>+</mo>
<msub>
<mi>R</mi>
<mn>0</mn>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>I</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>v</mi>
<mi>k</mi>
</msub>
</mrow>
Wherein：wkFor process noise, vkFor voltage measurement noise, ykExported for system, R0Represent battery equivalent internal resistance, OCV (SoC)
The battery open circuit voltage OCV controlled by battery charge state SoC is represented, and：
<mrow>
<mi>A</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<mi>B</mi>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>a</mi>
<mi>p</mi>
</mrow>
</mfrac>
<mo>&times;</mo>
<mn>100</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
<mrow>
<mi>F</mi>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>a</mi>
<mi>p</mi>
</mrow>
</mfrac>
<mo>&times;</mo>
<mn>100</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
Wherein：Cap is battery capacity, τ1=R1C1, τ2=R2C2, Δ t is system communication cycle.
4. the method that a kind of enhancing battery status according to claim 3 estimates robustness, it is characterised in that：Step 2 institute
Stating the method being adjusted by increasing redundant state variable to battery mathematical modeling is：
The meeting included in electric current I measured value is influenceed to the bias current I of SoC estimated accuraciesbIt is as redundant state variable, then electric
The system state variables of road model is taken as：
<mrow>
<msup>
<mi>x</mi>
<mi>a</mi>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>S</mi>
<mi>o</mi>
<mi>C</mi>
</mrow>
</mtd>
<mtd>
<msup>
<mi>U</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<msub>
<mi>C</mi>
<mn>1</mn>
</msub>
</mrow>
</msup>
</mtd>
<mtd>
<msup>
<mi>U</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<msub>
<mi>C</mi>
<mn>2</mn>
</msub>
</mrow>
</msup>
</mtd>
<mtd>
<msup>
<mi>I</mi>
<mi>b</mi>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
Wherein：WithResistance-capacitance network electric capacity or ohmically voltage are respectively corresponded to, by I-IbIt is used as input battery
Actual current, its numerical value represents charging to be positive, and the state space equation and output equation of discrete system are respectively：
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>a</mi>
</msubsup>
<mo>=</mo>
<msup>
<mi>A</mi>
<mi>a</mi>
</msup>
<msubsup>
<mi>x</mi>
<mi>k</mi>
<mi>a</mi>
</msubsup>
<mo>+</mo>
<msup>
<mi>B</mi>
<mi>a</mi>
</msup>
<msub>
<mi>I</mi>
<mi>k</mi>
</msub>
<mo>+</mo>
<msup>
<mi>F</mi>
<mi>a</mi>
</msup>
<msub>
<mi>w</mi>
<mi>k</mi>
</msub>
</mrow>
1
<mrow>
<msub>
<mi>y</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<mi>O</mi>
<mi>C</mi>
<mi>V</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>SoC</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mi>U</mi>
<mi>k</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<msub>
<mi>C</mi>
<mn>1</mn>
</msub>
</mrow>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>U</mi>
<mi>k</mi>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<msub>
<mi>C</mi>
<mn>2</mn>
</msub>
</mrow>
</msubsup>
<mo>+</mo>
<msub>
<mi>R</mi>
<mn>0</mn>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>I</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>I</mi>
<mi>k</mi>
<mi>b</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>v</mi>
<mi>k</mi>
</msub>
</mrow>
Wherein, Aa, BaAnd FaRespectively：
<mrow>
<msup>
<mi>A</mi>
<mi>a</mi>
</msup>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>a</mi>
<mi>p</mi>
</mrow>
</mfrac>
<mo>&times;</mo>
<mn>100</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>1</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
<mrow>
<msup>
<mi>B</mi>
<mi>a</mi>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>a</mi>
<mi>p</mi>
</mrow>
</mfrac>
<mo>&times;</mo>
<mn>100</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
<mrow>
<msup>
<mi>F</mi>
<mi>a</mi>
</msup>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>a</mi>
<mi>p</mi>
</mrow>
</mfrac>
<mo>&times;</mo>
<mn>100</mn>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mi>&Delta;</mi>
<mi>t</mi>
</mrow>
<msub>
<mi>&tau;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</msup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>.</mo>
</mrow>
5. the method that a kind of enhancing battery status according to claim 4 estimates robustness, it is characterised in that：The system
Method for estimating state includes Kalman methods of estimation, H∞Method of estimation, sliding formwork method of estimation.
6. the method that a kind of enhancing battery status according to claim 5 estimates robustness, it is characterised in that：Step 3 institute
State the side that using system state estimation method all state variables in the battery mathematical modeling after adjustment are carried out with On-line Estimation
Method is：In each sampling instant, using H∞Observer is estimated battery status：
H is set first∞Observer parameter P0, Q, W and V, II be unit battle array, and combine battery current voltage sampled value mk, every
Individual sampling instant k is calculated as below：
<mrow>
<msub>
<mi>K</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<msup>
<mi>A</mi>
<mi>a</mi>
</msup>
<msub>
<mi>P</mi>
<mrow>
<mi>K</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>T</mi>
<mi>k</mi>
</msub>
<msubsup>
<mi>C</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<msup>
<mi>V</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>;</mo>
</mrow>
<mrow>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<msup>
<mi>A</mi>
<mi>a</mi>
</msup>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mi>T</mi>
<mi>K</mi>
</msub>
<msup>
<mrow>
<mo>(</mo>
<msup>
<mi>A</mi>
<mi>a</mi>
</msup>
<mo>)</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>+</mo>
<msubsup>
<mi>F</mi>
<mi>k</mi>
<mi>a</mi>
</msubsup>
<mi>W</mi>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>F</mi>
<mi>k</mi>
<mi>a</mi>
</msubsup>
<mo>)</mo>
</mrow>
<mi>T</mi>
</msup>
<mo>.</mo>
</mrow>
7. the method that a kind of enhancing battery status according to claim 1 estimates robustness, it is characterised in that：The redundancy
State variable is one or more, to have the variable of specific physical significance or variable without specific physical significance.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710239112.6A CN107064816A (en) | 2017-04-13 | 2017-04-13 | It is a kind of to strengthen the method that battery status estimates robustness |
| PCT/CN2017/108286 WO2018188321A1 (en) | 2017-04-13 | 2017-10-30 | Method for enhancing battery state estimation robustness |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710239112.6A CN107064816A (en) | 2017-04-13 | 2017-04-13 | It is a kind of to strengthen the method that battery status estimates robustness |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107064816A true CN107064816A (en) | 2017-08-18 |
Family
ID=59600219
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710239112.6A Pending CN107064816A (en) | 2017-04-13 | 2017-04-13 | It is a kind of to strengthen the method that battery status estimates robustness |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107064816A (en) |
| WO (1) | WO2018188321A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107255786A (en) * | 2017-05-18 | 2017-10-17 | 中山职业技术学院 | A kind of ferric phosphate lithium cell LOC models |
| WO2018188321A1 (en) * | 2017-04-13 | 2018-10-18 | 绵阳世睿科技有限公司 | Method for enhancing battery state estimation robustness |
| CN109001640A (en) * | 2018-06-29 | 2018-12-14 | 深圳市科列技术股份有限公司 | A kind of data processing method and device of power battery |
| CN109633473A (en) * | 2019-01-23 | 2019-04-16 | 刘平 | A kind of distributed battery group state-of-charge algorithm for estimating |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111220920B (en) * | 2019-11-22 | 2023-04-25 | 国网浙江省电力有限公司台州供电公司 | Retired lithium ion battery state of charge calculation method based on H-infinity unscented Kalman filtering algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110224928A1 (en) * | 2010-03-10 | 2011-09-15 | Gm Global Technology Operations, Inc. | Battery state estimator using multiple sampling rates |
| CN103116136A (en) * | 2013-01-21 | 2013-05-22 | 天津大学 | Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm |
| US20130300377A1 (en) * | 2012-05-08 | 2013-11-14 | GM Global Technology Operations LLC | Battery state-of-charge estimator using robust h(infinity) observer |
| CN104977545A (en) * | 2015-08-03 | 2015-10-14 | 电子科技大学 | Power battery charge state estimation method and system |
| CN105203826A (en) * | 2015-09-11 | 2015-12-30 | 同济大学 | Current detection method for redundant current sensor power battery system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3714330B2 (en) * | 2003-02-12 | 2005-11-09 | 日産自動車株式会社 | Secondary battery charge rate estimation device |
| CN104007390B (en) * | 2013-02-24 | 2018-03-20 | 快捷半导体(苏州)有限公司 | Battery state of charge tracking, equivalent circuit selection and reference test method and system |
| CN103558556B (en) * | 2013-10-31 | 2016-02-03 | 重庆长安汽车股份有限公司 | A kind of power battery SOH estimation method |
| CN104181470B (en) * | 2014-09-10 | 2017-04-26 | 山东大学 | Battery state-of-charge (SOC) estimation method based on nonlinear prediction extended Kalman filtering |
| GB2532726A (en) * | 2014-11-24 | 2016-06-01 | Thunot Andre | Cell internal impedance diagnostic system |
| CN107064816A (en) * | 2017-04-13 | 2017-08-18 | 绵阳世睿科技有限公司 | It is a kind of to strengthen the method that battery status estimates robustness |
-
2017
- 2017-04-13 CN CN201710239112.6A patent/CN107064816A/en active Pending
- 2017-10-30 WO PCT/CN2017/108286 patent/WO2018188321A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110224928A1 (en) * | 2010-03-10 | 2011-09-15 | Gm Global Technology Operations, Inc. | Battery state estimator using multiple sampling rates |
| US20130300377A1 (en) * | 2012-05-08 | 2013-11-14 | GM Global Technology Operations LLC | Battery state-of-charge estimator using robust h(infinity) observer |
| CN103116136A (en) * | 2013-01-21 | 2013-05-22 | 天津大学 | Lithium battery charge state assessment method based on finite difference expansion Kalman algorithm |
| CN104977545A (en) * | 2015-08-03 | 2015-10-14 | 电子科技大学 | Power battery charge state estimation method and system |
| CN105203826A (en) * | 2015-09-11 | 2015-12-30 | 同济大学 | Current detection method for redundant current sensor power battery system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018188321A1 (en) * | 2017-04-13 | 2018-10-18 | 绵阳世睿科技有限公司 | Method for enhancing battery state estimation robustness |
| CN107255786A (en) * | 2017-05-18 | 2017-10-17 | 中山职业技术学院 | A kind of ferric phosphate lithium cell LOC models |
| CN109001640A (en) * | 2018-06-29 | 2018-12-14 | 深圳市科列技术股份有限公司 | A kind of data processing method and device of power battery |
| CN109633473A (en) * | 2019-01-23 | 2019-04-16 | 刘平 | A kind of distributed battery group state-of-charge algorithm for estimating |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2018188321A1 (en) | 2018-10-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Peng et al. | An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries | |
| CN107064816A (en) | It is a kind of to strengthen the method that battery status estimates robustness | |
| CN103941195B (en) | Method for battery SOC estimation based on small model error criterion expanding Kalman filter | |
| Meng et al. | Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman filter and support vector machine | |
| CN110441694B (en) | Lithium battery state-of-charge estimation method based on multiple fading factors Kalman filtering | |
| EP3207388B1 (en) | Battery condition monitoring | |
| CN111581904B (en) | Lithium battery SOC and SOH collaborative estimation method considering cycle number influence | |
| CN108594135A (en) | A kind of SOC estimation method for the control of lithium battery balance charge/discharge | |
| CN105116343B (en) | The electrokinetic cell state of charge method of estimation and system of least square method supporting vector machine | |
| CN104502858B (en) | Electrokinetic cell SOC methods of estimation and system based on backward difference discrete model | |
| CN103003709B (en) | Battery status estimation unit and battery status method of estimation | |
| CN104267261B (en) | On-line secondary battery simplified impedance spectroscopy model parameter estimating method based on fractional order united Kalman filtering | |
| CN105510829B (en) | A kind of Novel lithium ion power battery SOC methods of estimation | |
| CN104617623B (en) | A kind of electric automobile power battery group balance control method | |
| Seo et al. | Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures | |
| CN109061506A (en) | Lithium-ion-power cell SOC estimation method based on Neural Network Optimization EKF | |
| CN103529398A (en) | Online lithium ion battery SOC (state of charge) estimation method based on extended Kalman filter | |
| CN113156321B (en) | Estimation method of lithium ion battery state of charge (SOC) | |
| CN110824363B (en) | Lithium battery SOC and SOE joint estimation method based on improved CKF | |
| Ge et al. | State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm | |
| CN105203963A (en) | Charge state estimation method based on open-circuit voltage hysteretic characteristics | |
| CN110457789A (en) | A kind of lithium ion battery residual life prediction technique merged based on improvement particle filter with double exponential decay experience physical models | |
| CN106772094A (en) | A kind of SOC methods of estimation of the battery model based on parameter adaptive | |
| CN105717460A (en) | Power battery SOC estimation method and system based on nonlinear observer | |
| CN109633472A (en) | A kind of state-of-charge algorithm for estimating of lithium battery monomer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170818 |
|
| RJ01 | Rejection of invention patent application after publication |