CN109061509A - A kind of battery capacity remaining value evaluation method - Google Patents
A kind of battery capacity remaining value evaluation method Download PDFInfo
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Abstract
The present invention relates to a kind of battery capacity remaining value evaluation methods, the corresponding polarizing voltage of three polarization resistances in lithium ion battery equivalent-circuit model and SOC are set as first group of state variable X, another group of battery current capacities are set as second group of state variable Y, using cell voltage as the observational variable in double Extended Kalman filter.According to the currently active capacity of internal impedance state revision battery for updating acquisition in a upper time step, respectively by state equation to the state variable X of current time kk*、Yk*Tentative prediction is carried out, acquisition state variable is a status predication value.Status predication value is theoretical expectation values, to obtain more accurate estimated value, needs to be modified processing to initial predicted value obtained in recursive process using observational variable Z.In the present invention, the recursion value of two state variables is modified simultaneously by observation voltage, the correction value of state variable is by the actual observed value of observational variable and the difference of recursion valueAnd kalman gain K is determined jointly.The present invention can make battery dump energy have self-correction ability to prediction error, improve the accuracy that battery dump energy calculates.
Description
Technical field
The present invention relates to a kind of lithium battery energy storage battery technical fields, and in particular to a kind of battery capacity remaining value evaluation method.
Background technique
With increasingly sharpening for global fossil lack of energy and problem of environmental pollution, new energy revolution is in high gear
Expansion.The renewable and clean energy resources such as water power, wind-powered electricity generation, solar energy, tide energy, nuclear energy and biomass energy are in mankind's energy-consuming
Proportion is increasing.The problems such as to overcome new energy output unstable, and alleviating its caused power grid pressure, usually lead to
Cross battery energy storage technology realize new energy conversion, it is temporary export with stablizing, the various electricity consumptions in human lives are met with this
It needs.
At the same time, in order to realize real-time monitoring, transient discharge pressure limiting early warning and the monomer electricity of cell operating status
The functions such as Balance route, to improve energy-storage system efficiency and reduce security risk, the exploitation of battery management system also just seems ten
Divide important.In battery management system, battery dump energy is a basic deliberated index in cell operations.Including mistake
The various functions filled including overdischarge early warning, monomer electric quantity balancing will be designed dependent on battery dump energy.Therefore,
Electricity estimation is a basic function of battery management system, and the height of electricity estimation precision will affect the usability of whole system
Energy.
Summary of the invention
The purpose of the present invention is to provide a kind of battery capacity remaining value evaluation method based on double Extended Kalman filter.
Battery capacity remaining value evaluation method proposed by the present invention, the battery capacity is lithium ion battery SOC, using double
Extended Kalman filter method, the specific steps are as follows:
(1) the corresponding polarizing voltage of three polarization resistances in lithium ion battery equivalent-circuit model and SOC are set as first
Group state variable, is denoted as, X=[SOC u1 u2 u3]T, another group of battery current capacities of double Extended Kalman filter are as
Two groups of state variables, are denoted as, Y=[RΩ]T, using cell voltage as the observational variable in double Extended Kalman filter, it is denoted as, Z
=uB, shown in observational equation such as formula (3.8):
In formula,ByCharacteristic equation determines；Its
In, fitting coefficient is indicated with matrix G,vkIt makes an uproar for observation
Sound is determined by sensor performance；
(2) it according to battery equivalent circuit model and defined first group of state variable and second group of state variable, builds
Vertical first group of battery status equation (3.9), second group of battery status equation (3.10)；
Yk*=Yk-1+rk-1 (3.10)
In formula,For process variable, ωk-1For process noise, rk-1For random small sample perturbations, the internal resistance of cell is indicated with this
Slowly varying, [X in the whole processk*,Yk*] it is that first group of state variable initial predicted value and second group of state variable are preliminary
Predicted value；
(3) according to the currently active capacity of internal impedance state revision lithium ion battery for updating acquisition in a upper time stepThen pass through first group of state equation and second group of state equation respectively to first group of state variable X of current time kk*、
Second group of state variable Yk*Tentative prediction is carried out, the first group of state variable and second group of state variable obtained at this time is primary
Status predication value；
(4) carrying out the status predication value that recursion obtains to state variable by state equation is theoretical expectation values,
In actual condition, the true SOC of lithium ion battery can be deviated because of the accumulation of process error with theoretical recursion value；And
And state equation can only carry out recurrence calculation according to given initial value, can not identify and correct initial error that may be present；
Therefore, it to obtain more accurate estimated value, needs to carry out initial predicted value obtained in recursive process using observational variable Z
Correcting process, this is the key that Kalman prediction.In the battery SOC estimation of double card Kalman Filtering, by observing voltage
The recursion value of first group of state variable and second group of state variable is modified simultaneously, as follows respectively:
The correction value of two state variables is by the actual observed value of observational variable and the difference of recursion valueAnd card
Germania gain K is determined jointly；Wherein,For the observational variable real-time measurement values obtained by voltage sensor, ZkFor by observing
The kalman gain K difference of the observational variable recursion value that equation obtains, two state variables is as follows:
In formula, RkFor observation noise covariance, size is determined by voltage sensor performance；HkFor the Ya Ke of state variable
Than matrix, value is determined by observational variable and state variable relational expression, as shown in formula (3.15) and formula (3.16)；Pk*For state association
Variance matrix reflects the recursion variation of state variable, as shown in formula (3.17) and formula (3.18):
In formula, Qk-1For state variable process noise covariance.
In the present invention, lithium ion battery can gradually generate degradation phenomena with the increase of working cycles number, i.e. battery
Available capacity gradually decays from rated value when factory, if the battery capacity in observational equation remains rated capacity, with
The use of battery, the estimated result that will lead to battery SOC be gradually deviated from actual value.Therefore, it is necessary to the currently active appearance of battery
Amount is constantly updated.Battery capacity can only be measured by discharging completely, and in actual operation, which does not have
Standby feasibility.But by lithium-ion electric pool capacity resistance characteristic test result it is found that the ohmic internal resistance R of batteryΩIt is close with its actual capacity
Cut phase is closed, and with the aggravation of deterioration of battery, battery available capacity is gradually reduced, and internal resistance is gradually increased.According to the characteristic, pass through survey
Measure RΩThe available battery current capacities of value.
The beneficial effects of the present invention are:
It is estimated relative to lithium ion battery SOC, there are two advantages more outstanding for this method tool.First, double extension karrs
Graceful filtering ignores high-order term, obtains approximate linearisation mould by the way that state equation and observational equation are carried out Taylor series expansion
Estimation object extension has been arrived non-linear field, can satisfy the electromotive force characteristic of lithium ion battery by type；Secondly, traditional karr
Graceful filtering only has one group of state variable, and state variable is extended to two groups by one group by double Extended Kalman filter, passes through two
The loop iteration amendment of group correlated variables, estimation error caused by improving due to battery capacity decaying improve battery SOC estimation essence
Degree.
Detailed description of the invention
In order to illustrate more clearly of the technical solution in description of the invention, will make below to required in embodiment description
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other
Attached drawing, in which:
Fig. 1 is a kind of battery SOC estimation based on double card Kalman Filtering of battery capacity remaining value evaluation method of the present invention
Flow chart.
Specific embodiment
It is carried out below with reference to power predicating method of the specific embodiment to double Extended Kalman filter provided by the invention
It is described in detail.
Embodiment 1: in view of the practical charge/discharge operation time of lithium ion battery is longer, cost, is mentioned in order to save time
High Efficiency.Battery equivalent circuit model is established using Matlab/Simulink, using the method for operating condition analogue simulation to calculation
The feasibility and validity of method are analyzed.
Under the intermittent electric discharge operating condition that current ratio is 1C, battery SOC is estimated using double Extended Kalman filter
Point counting analysis.Observation noise and process noise are simulated by white Gaussian noise, according to general voltage sensor and current sensor
Working performance sets observation noise covariance respectively as R=2.5 × 10-5, process noise covariance Q=0.039.
It is 0.3s, 0.5s, 1s, 5s and 10s that material calculation, which is respectively set, and remaining capacity initial error is 10%, using double
Extended Kalman filter carries out battery SOC estimation to lithium battery interval simulation electric discharge operating condition.SOC under different step-lengths is obtained to predict
Journey and prediction error.Analyze result it is found that with time step increase, the error between SOC predicted value and true value has
The trend gradually increased.Prediction result stability under 0.3s and 0.5s time step is preferable, and step-length is estimated when being gradually increased
Calculating result will appear Divergent Phenomenon, and especially when Spline smoothing occurs for electric current, error is larger.Therefore, it from the point of view of theoretically, answers
Lesser time step is used as far as possible.But in practice, it is contemplated that when data transmission period delay, sensor acquisition conversion
Between and processor computational load the problems such as, meeting forecast demand, material calculation is also unsuitable too low.
It is 0%, 5%, 10% and 15% 4 kind of situation that initial error, which is respectively set,.With being incremented by for time, initial error
In the case where 0, then it is able to maintain stablizes near actual value in the whole process.For with initial error 5%, 10% and
15% three kinds of situations, battery SOC predicted value are approached to its virtual condition.By 5% to 15%, error correction rate gradually increases
Greatly.Illustrate that double Extended Kalman filter are a kind of evaluation methods that error is corrected with error.Initial error is bigger, estimation amendment
Rate it is also bigger.
The beneficial effect of battery capacity remaining value evaluation method of the present invention is:
This method has self-correction ability to prediction error, and material calculation is smaller, and estimation accuracy is higher, calculates the time
Expense is also bigger.
For those skilled in the art, the present invention is not limited to the details of above-described embodiment, without departing substantially from essence of the invention
In the case where mind and range, the present invention can be realized in other specific forms.In addition, those skilled in the art can be to this hair
Bright to carry out various modification and variations without departing from the spirit and scope of the present invention, these improvements and modifications also should be regarded as of the invention
Protection scope.Therefore, it includes preferred embodiment and all changes for falling into the scope of the invention that the following claims are intended to be interpreted as
More and modify.
Claims (1)
1. battery capacity remaining value evaluation method, it is characterised in that: the battery capacity is lithium ion battery SOC, using double expansions
Open up kalman filter method, the specific steps are as follows:
(1) the corresponding polarizing voltage of three polarization resistances in lithium ion battery equivalent-circuit model and SOC are set as first group of shape
State variable is denoted as, X=[SOC u1 u2 u3]T, another group of battery current capacities of double Extended Kalman filter are as second group
State variable is denoted as, Y=[RΩ]T；Using cell voltage as the observational variable in double Extended Kalman filter, it is denoted as, Z=uB,
Shown in observational equation such as formula (3.8):
In formula,The E as shown in formula (2.11)B- SOC characteristic equation determines；vkFor observation noise, determined by sensor performance
It is fixed；
(2) according to battery equivalent circuit model and defined first group of state variable and second group of state variable, is established
One group of battery status equation (3.9), second group of battery status equation (3.10)；
Yk*=Yk-1+rk-1 (3.10)
In formula,For process variable, ωk-1For process noise, rk-1For random small sample perturbations, indicate the internal resistance of cell whole with this
It is slowly varying during a, [Xk*,Yk*] it is first group of state variable initial predicted value and second group of state variable tentative prediction
Value；
(3) according to the currently active capacity of internal impedance state revision lithium ion battery for updating acquisition in a upper time stepSo
Pass through first group of state equation and second group of state equation respectively afterwards to first group of state variable X of current time kk*, second group
State variable Yk*Tentative prediction is carried out, the first group of state variable and second group of state variable obtained at this time is that a next state is pre-
Measured value；
(4) carrying out the status predication value that recursion obtains to state variable by state equation is theoretical expectation values, in reality
In operating condition, the true SOC of lithium ion battery can be deviated because of the accumulation of process error with theoretical recursion value；Moreover, shape
State equation can only carry out recurrence calculation according to given initial value, can not identify and correct initial error that may be present；Therefore,
To obtain more accurate estimated value, need to be modified initial predicted value obtained in recursive process using observational variable Z
Processing, this is the key that Kalman prediction；In the battery SOC estimation of double card Kalman Filtering, simultaneously by observation voltage
The recursion value of first group of state variable and second group of state variable is modified, as follows respectively:
The correction value of two state variables is by the actual observed value of observational variable and the difference of recursion valueAnd Kalman
Gain K is determined jointly；Wherein,For the observational variable real-time measurement values obtained by voltage sensor, ZkFor by observational equation
The kalman gain K difference of the observational variable recursion value of acquisition, two state variables is as follows:
In formula, RkFor observation noise covariance, size is determined by voltage sensor performance；HkFor the Jacobi square of state variable
Battle array, value is determined by observational variable and state variable relational expression, as shown in formula (3.15) and formula (3.16)；Pk*For state covariance
Matrix reflects the recursion variation of state variable, as shown in formula (3.17) and formula (3.18):
In formula, Qk-1For state variable process noise covariance.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113238154A (en) * | 2021-03-02 | 2021-08-10 | 翱捷科技股份有限公司 | Method and system for measuring residual electric quantity of battery based on coulometer |
| CN116879763A (en) * | 2023-09-07 | 2023-10-13 | 上海融和元储能源有限公司 | Battery fault early warning method based on Kalman filtering algorithm |
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| CN116879763A (en) * | 2023-09-07 | 2023-10-13 | 上海融和元储能源有限公司 | Battery fault early warning method based on Kalman filtering algorithm |
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