CN108445402A - A kind of lithium-ion-power cell state-of-charge method of estimation and system - Google Patents

A kind of lithium-ion-power cell state-of-charge method of estimation and system Download PDF

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Publication number
CN108445402A
CN108445402A CN201810165942.3A CN201810165942A CN108445402A CN 108445402 A CN108445402 A CN 108445402A CN 201810165942 A CN201810165942 A CN 201810165942A CN 108445402 A CN108445402 A CN 108445402A
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battery
state
ocv
time
lithium
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隋宏亮
刘安龙
韩海滨
刘明辉
夏珩
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Guangzhou Xiaopeng Motors Technology Co Ltd
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Guangzhou Xiaopeng Motors Technology Co Ltd
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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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
    • 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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
    • 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
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC

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  • General Physics & Mathematics (AREA)
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Abstract

The invention discloses a kind of lithium-ion-power cell state-of-charge method of estimation and systems, and this approach includes the following steps：The single order RC equivalent-circuit models of battery are established, and establish the state equation of model；The state vector of definition status equation, the state vector include the OCV values of the model parameter and battery of model；According to state vector, the expression formula of the coefficient matrix of solving state equation；Using the real-time working electric current of battery as system incentive, real-time working voltage is as observed quantity, using the model parameter and OCV estimated values of expanded Kalman filtration algorithm on-line identification model；According to OCV estimated values and the OCV SOC relation curves of battery, interpolation obtains the SOC estimation of battery.The present invention can be during battery use, real-time online identification model parameter, and correction model parameter in real time ensure that the precision of battery SOC estimated value, can be widely applied in battery industry to estimate to obtain the OCV estimated values of high accuracy.

Description

A kind of lithium-ion-power cell state-of-charge method of estimation and system
Technical field
The present invention relates to technical field of lithium ion, estimate more particularly to a kind of lithium-ion-power cell state-of-charge Method and system.
Background technology
SOC, full name are State of Charge, indicate state-of-charge, are also remaining capacity, representative is that battery uses The ratio of residual capacity and the capacity of its fully charged state after lying idle for a period of time or for a long time, commonly uses percentage table Show.The SOC of new-energy automobile lithium ion battery is the key parameter not directly measured, and accurate SOC estimation is to ensure Before the Main Basiss that lithium ion battery uses in working range, and raising lithium ion battery service life and capacity usage ratio It carries.The SOC estimations of lithium ion battery and the important evidence of vehicle progress energy, power match and control, therefore study accurately SOC methods of estimation be of great significance.
Domestic and foreign scholars propose many SOC methods of estimation at present, include mainly：Current integration method, open circuit voltage method, mould Fuzzy logic method, neural network, Kalman filtering method etc., these methods have the respective scope of application.
The advantage of current integration method is simply to be easy to Project Realization.But there are following three disadvantages：First, ampere-hour integrates Method can only solve the situation of change of electricity in a period of time, very high to the dependence of initial value；Secondly as current sensor is smart Accumulated error can not real time correction caused by degree is insufficient；Finally, when battery management system does not work, putting certainly for battery can not be estimated Electrical effect.Open circuit voltage method is the monotonic relationshi according to open-circuit voltage OCV and SOC come the SOC of computation of table lookup battery, and needing will be electric Accurate OCV could be obtained after the sufficient standing of pond, time-consuming, is not suitable for On-line Estimation SOC.Fuzzy logic method is according to a large amount of real Data are tested, the fuzzy thinking of people is simulated with fuzzy logic, finally realize reliable SOC predictions.Disadvantage is that a large amount of real Data are tested, method is complicated, it is difficult to Project Realization.And neural network, due to the nonlinear characteristic of battery SOC, battery model ginseng Number mathematically can not explication, nerual network technique in the estimation of SOC just it is highly useful.The shortcomings that neural network is Lot of experimental data is needed to be trained, evaluated error is influenced very big by training data and training method, and algorithm is complicated, It is difficult Project Realization.Kalman filtering method is by establishing the mapping relations between the state of observation system and observed quantity, realizing Amendment to state or parameter Estimation.The core concept of Kalman filtering method is made in minimum variance meaning to the state of system Optimal estimation, its advantage is that insensitive to initial SOC errors, the disadvantage is that battery performance model accuracy and battery management system Computing capability requires high.
In conclusion there are certain shortcomings and deficiencies in existing SOC methods of estimation, there are estimated accuracies poor, algorithm The problems such as complicated.
Invention content
In order to solve the above technical problems, the object of the present invention is to provide a kind of lithium-ion-power cell state-of-charges to estimate Count method and system.
The technical solution adopted by the present invention to solve the technical problems is：
A kind of lithium-ion-power cell state-of-charge method of estimation, includes the following steps：
S1, the single order RC equivalent-circuit models for establishing battery, and establish the state equation of model；
The state vector of S2, definition status equation, the state vector include the OCV of the model parameter and battery of model Value；
S3, according to state vector, the expression formula of the coefficient matrix of solving state equation, the coefficient matrix includes that state turns Move matrix and observing matrix；
S4, using the real-time working electric current of battery as system incentive, real-time working voltage is as observed quantity, using expansion card The model parameter and OCV estimated values of Kalman Filtering algorithm on-line identification model；
S5, according to the OCV-SOC relation curves of OCV estimated values and battery, interpolation obtains the SOC estimation of battery；
The OCV-SOC relation curves of the battery are tested by carrying out intermittent electric discharge-standing to battery, are tested and are obtained .
Further, the step S4, specifically includes：
S41, using the real-time working electric current of battery as system incentive, in conjunction with the state vector at current time, using extension Kalman filtering algorithm predicts the state vector of subsequent time；
The state vector of S42, the subsequent time obtained according to prediction, obtain the OCV estimated values of model, and calculate acquisition electricity The model parameter of pool model；
S43, the model parameter obtained according to calculating, the coefficient matrix of real-time update state equation, by updated coefficient Matrix is calculated for prediction next time.
Further, further include initialization step before the step S41：
S40, the state vector of init state equation, error co-variance matrix, and made an uproar according to system performance assignment procedure Sound matrix Q, according to the precision assignment measurement noise R of measuring apparatus.
Further, the step S41, specifically includes：
S411, the error co-variance matrix according to current time, predict the error co-variance matrix of subsequent time；
S412, the predicted value according to error co-variance matrix solve the predicted value of the Kalman filtering gain of subsequent time；
S413, according to the predicted value of Kalman filtering gain, calculate the state vector of subsequent time, and update error association side Poor matrix.
Further, the single order RC equivalent-circuit models of the battery include constant pressure source, ohmic internal resistance, polarization resistance and polarization Capacitance, wherein source-series with ohmic internal resistance and constant pressure successively after the polarization resistance and polarization capacity parallel connection.
Further, state equation described in the step S1 is：
Wherein, U1,kIndicate the polarizing voltage of current time single order RC equivalent circuit, U1,k+1Indicate subsequent time single order RC etc. The polarizing voltage of circuit is imitated, T indicates sampling period, R1,kIndicate the polarization resistance value at current time, R0,kIndicate current time Ohmic internal resistance value, IkIndicate the real-time working electric current of battery, UkIndicate the real-time working voltage of battery, OCVkIndicate current time The OCV values of battery, τ1,kIndicate time constant, and τ1,k=R1,kC1,k。
Further, state vector x described in the step S2kFor：
xk=[U1,k R1,k τ1,k R0,k OCVk]。
Further, in the step S3, the state-transition matrix A of battery model is：
Further, in the step S3, the observing matrix C of battery model is：
C=[- 10 0-Ik 1]。
The present invention solves another technical solution used by its technical problem：
A kind of lithium-ion-power cell state-of-charge estimating system, including：
At least one processor；
At least one processor, for storing at least one program；
When at least one program is executed by least one processor so that at least one processor is realized The lithium-ion-power cell state-of-charge method of estimation.
The beneficial effects of the invention are as follows：The present invention establishes model by establishing the single order RC equivalent-circuit models of battery State equation, after the state vector of definition status equation, according to state vector, the expression of the coefficient matrix of solving state equation Formula, then using the real-time working electric current of battery as system incentive, real-time working voltage is as observed quantity, using spreading kalman The model parameter and OCV estimated values of filtering algorithm on-line identification model, finally according to OCV estimated values and the OCV- of battery SOC relation curves, interpolation obtain the SOC estimation of battery.The present invention can be during battery use, and real-time online recognizes mould Shape parameter, and correction model parameter in real time ensures battery SOC estimated value to estimate to obtain the OCV estimated values of high accuracy Precision.
Description of the drawings
Fig. 1 is a kind of flow chart of lithium-ion-power cell state-of-charge method of estimation；
Fig. 2 is the OCV-SOC graph of relation of battery in a specific embodiment of the invention；
Fig. 3 is the single order RC equivalent-circuit models that the present invention uses；
Fig. 4 is the power input curve graph of the specific embodiment of the invention；
Fig. 5 is the battery model parameter of the specific embodiment of the invention and the ohmic internal resistance curve graph of OCV on-line identification results；
Fig. 6 is the battery model parameter of the specific embodiment of the invention and the polarization resistance curve graph of OCV on-line identification results；
Fig. 7 is the battery model parameter of the specific embodiment of the invention and the time constant curve graph of OCV on-line identification results；
Fig. 8 is the battery model parameter of the specific embodiment of the invention and the open circuit voltage curve figure of OCV on-line identification results；
Fig. 9 is the SOC estimated result curve graphs of the specific embodiment of the invention；
Figure 10 is the SOC evaluated error curve graphs of the specific embodiment of the invention；
Figure 11 is a kind of electronic block diagrams of lithium-ion-power cell state-of-charge estimating system of the present invention.
Specific implementation mode
Embodiment of the method
Referring to Fig.1, a kind of lithium-ion-power cell state-of-charge method of estimation, includes the following steps：
S1, the single order RC equivalent-circuit models for establishing battery, and establish the state equation of model；
The state vector of S2, definition status equation, the state vector include the OCV of the model parameter and battery of model Value；
S3, according to state vector, the expression formula of the coefficient matrix of solving state equation, the coefficient matrix includes that state turns Move matrix A and observing matrix C；
S4, using the real-time working electric current of battery as system incentive, real-time working voltage is as observed quantity, using expansion card The model parameter and OCV estimated values of Kalman Filtering algorithm on-line identification model；
S5, according to the OCV-SOC relation curves of OCV estimated values and battery, interpolation obtains the SOC estimation of battery；
The OCV-SOC relation curves of the battery are tested by carrying out intermittent electric discharge-standing to battery, are tested and are obtained .In the present embodiment, the OCV-SOC relation curves for testing acquisition are as shown in Figure 2.
This method can be corrected in real time by using the model parameter of expanded Kalman filtration algorithm on-line identification model In automobile actual moving process, model parameter with the factors such as environment temperature, charge-discharge magnification, operating mode duration variation institute The variation of generation, compared with the method for traditional off-line identification parameter, this programme can be during battery use, real-time online Identification model parameter, and correction model parameter in real time ensure that battery SOC to estimate to obtain the OCV estimated values of high accuracy The precision of estimated value.
It is further used as preferred embodiment, the step S4 is specifically included：
S41, using the real-time working electric current of battery as system incentive, in conjunction with the state vector at current time, using extension Kalman filtering algorithm predicts the state vector of subsequent time；
The state vector of S42, the subsequent time obtained according to prediction, obtain the OCV estimated values of model, and calculate acquisition electricity The model parameter of pool model；
S43, the model parameter obtained according to calculating, the coefficient matrix of real-time update state equation, by updated coefficient Matrix is calculated for prediction next time.
It is further used as preferred embodiment, further includes initialization step before the step S41：
The state vector x of S40, init state equationk, error co-variance matrix P, and according to system performance assignment procedure Noise matrix Q, according to the precision assignment measurement noise R of measuring apparatus.
It is further used as preferred embodiment, the step S41 is specifically included：
S411, the error co-variance matrix according to current time, predict the error co-variance matrix of subsequent time；Specifically adopt With the error co-variance matrix predicted value of following formula predicted state equation：
Pk+1|k=APkAT+Q
Wherein, PkIndicate the error co-variance matrix at current time, Pk+1|kIndicate the error co-variance matrix of subsequent time Predicted value, A indicate battery model state-transition matrix, ATIndicate that the transposed matrix of A, C indicate the observation square of battery model Battle array, Q indicate process noise matrix；
S412, the predicted value according to error co-variance matrix solve the Kalman filtering gain of subsequent time using following formula Predicted value：
Kk+1=Pk+1|kCT[CPk+1|kCT+R]-1
S413, according to the predicted value of Kalman filtering gain, calculate the state vector of subsequent time, and update error association side Poor matrix.
State vector is updated with specific reference to following formula：
Wherein,WithIt indicates to update preceding and updated state vector, U respectivelyk+1Indicate the battery of subsequent time Real-time working voltage；
The error co-variance matrix of state equation is updated according to the following formula：
Pk+1=Pk+1|k-Kk+1CPk+1|k
Wherein, Pk+1Indicate updated error co-variance matrix.
It can be seen that the identification of Model Parameters process of this programme, using the method that online recognition recognizes, without realizing Acquisition batch data obtains fixed model parameter to calculate, but battery in actual use, according to the reality of battery When operating current and real-time working voltage on-line identification obtain real-time model parameter, can be to the battery model of this method When the factors such as environment temperature, charge-discharge magnification, operating mode duration change, high-precision is still kept, to ensure that this The precision for the SOC value that method estimation obtains.
It is further used as preferred embodiment, with reference to shown in Fig. 3, the single order RC equivalent-circuit models of the battery include Constant pressure source, ohmic internal resistance R0, polarization resistance R1With polarization capacity C1, wherein the polarization resistance R1With polarization capacity C1After parallel connection, Successively with ohmic internal resistance R0It is source-series with constant pressure.Constant pressure source both ends are that open-circuit voltage OCV, U are battery terminal voltage, R0For electrode material The ohmic internal resistance of material, electrolyte, diaphragm, R1、C1For simulated battery polarization reaction, work in battery dynamic change, battery The direction of mode input electric current I is as shown in Figure 3.
It is further used as preferred embodiment, state equation is described in the step S1：
Wherein, U1,kIndicate the polarizing voltage of current time single order RC equivalent circuit, U1,k+1Indicate subsequent time single order RC etc. The polarizing voltage of circuit is imitated, T indicates sampling period, R1,kIndicate the polarization resistance value at current time, R0,kIndicate current time Ohmic internal resistance value, IkIndicate the real-time working electric current of battery, UkIndicate the real-time working voltage of battery, OCVkIndicate current time The OCV values of battery, τ1,kIndicate time constant, and τ1,k=R1,kC1,k, C1,kIndicate the polarization capacity value at current time.
It is further used as preferred embodiment, state vector x described in the step S2kFor：
xk=[U1,k R1,k τ1,k R0,k OCVk]。
It is further used as preferred embodiment, in the step S3, the state-transition matrix A of battery model is：
It is further used as preferred embodiment, in the step S3, the observing matrix C of battery model is：
C=[- 10 0-Ik 1]。
In the present embodiment, power input curve graph in step S4 as shown in figure 4, the curve current component IkAs being System excitation, component of voltage UkAs observed quantity.
For corresponding identification of Model Parameters result as shown in Fig. 5~Fig. 7, corresponding OCV estimated values are as shown in Figure 8, wherein figure 5 be the battery model parameter of the specific embodiment of the invention and the ohmic internal resistance curve graph of OCV on-line identification results, and Fig. 6 is this hair The battery model parameter of bright specific embodiment and the polarization resistance curve graph of OCV on-line identification results, Fig. 7 are of the invention specific real It is the electricity of the specific embodiment of the invention to apply the battery model parameter of example and the time constant curve graph of OCV on-line identification results, Fig. 8 The open circuit voltage curve figure of pool model parameter and OCV on-line identification results.In addition, the SOC that Fig. 9 is the specific embodiment of the invention estimates Count result curve figure, as shown in Figure 9, the SOC curves that this method estimation obtains with it is practical measure the SOC curves degree that obtains compared with Height, Figure 10 are the SOC evaluated error curve graphs of the specific embodiment of the invention, according to diagram it is found that the SOC evaluated errors of this method Within ± 2%, precision is higher.
System embodiment
Referring to Fig.1 1, a kind of lithium-ion-power cell state-of-charge estimating system is present embodiments provided, including：
At least one processor 200；
At least one processor 100, for storing at least one program；
When at least one program is executed by least one processor 200 so that at least one processor 200 realize the lithium-ion-power cell state-of-charge method of estimation.
The lithium-ion-power cell state-of-charge estimating system of the present embodiment, executable the method for the present invention embodiment are provided Lithium-ion-power cell state-of-charge method of estimation, the arbitrary combination implementation steps of executing method embodiment have the party The corresponding function of method and advantageous effect.
It is to be illustrated to the preferable implementation of the present invention, but the invention is not limited to the implementation above Example, those skilled in the art can also make various equivalent variations or be replaced under the premise of without prejudice to spirit of that invention It changes, these equivalent modifications or replacement are all contained in the application claim limited range.

Claims (10)

1. a kind of lithium-ion-power cell state-of-charge method of estimation, which is characterized in that include the following steps：
S1, the single order RC equivalent-circuit models for establishing battery, and establish the state equation of model；
The state vector of S2, definition status equation, the state vector include the OCV values of the model parameter and battery of model；
S3, according to state vector, the expression formula of the coefficient matrix of solving state equation, the coefficient matrix includes state transfer square Battle array and observing matrix；
S4, using the real-time working electric current of battery as system incentive, real-time working voltage is as observed quantity, using spreading kalman The model parameter and OCV estimated values of filtering algorithm on-line identification model；
S5, according to the OCV-SOC relation curves of OCV estimated values and battery, interpolation obtains the SOC estimation of battery；
The OCV-SOC relation curves of the battery are tested by carrying out intermittent electric discharge-standing to battery, and acquisition is tested 's.
2. lithium-ion-power cell state-of-charge method of estimation according to claim 1, which is characterized in that the step S4 is specifically included：
S41, using the real-time working electric current of battery as system incentive, in conjunction with the state vector at current time, using extension karr The state vector of graceful filtering algorithm prediction subsequent time；
The state vector of S42, the subsequent time obtained according to prediction, obtain the OCV estimated values of model, and calculate and obtain battery mould The model parameter of type；
S43, the model parameter obtained according to calculating, the coefficient matrix of real-time update state equation, by updated coefficient matrix For predicting to calculate next time.
3. lithium-ion-power cell state-of-charge method of estimation according to claim 2, which is characterized in that the step Further include initialization step before S41：
S40, the state vector of init state equation, error co-variance matrix, and according to system performance assignment procedure noise square Battle array Q, according to the precision assignment measurement noise R of measuring apparatus.
4. lithium-ion-power cell state-of-charge method of estimation according to claim 2, which is characterized in that the step S41 is specifically included：
S411, the error co-variance matrix according to current time, predict the error co-variance matrix of subsequent time；
S412, the predicted value according to error co-variance matrix solve the predicted value of the Kalman filtering gain of subsequent time；
S413, according to the predicted value of Kalman filtering gain, calculate the state vector of subsequent time, and update error covariance square Battle array.
5. lithium-ion-power cell state-of-charge method of estimation according to claim 1, which is characterized in that the battery Single order RC equivalent-circuit models include constant pressure source, ohmic internal resistance, polarization resistance and polarization capacity, wherein the polarization resistance and It is source-series with ohmic internal resistance and constant pressure successively after polarization capacity parallel connection.
6. lithium-ion-power cell state-of-charge method of estimation according to claim 1, which is characterized in that the step S1 Described in state equation be：
Wherein, U1,kIndicate the polarizing voltage of current time single order RC equivalent circuit, U1,k+1Indicate the equivalent electricity of subsequent time single order RC The polarizing voltage on road, T indicate sampling period, R1,kIndicate the polarization resistance value at current time, R0,kIndicate the ohm at current time Internal resistance value, IkIndicate the real-time working electric current of battery, UkIndicate the real-time working voltage of battery, OCVkIndicate current time battery OCV values, τ1,kIndicate time constant, and τ1,k=R1,kC1,k。
7. lithium-ion-power cell state-of-charge method of estimation according to claim 6, which is characterized in that the step S2 Described in state vector xkFor：
xk=[U1,k R1,k τ1,k R0,k OCVk]。
8. lithium-ion-power cell state-of-charge method of estimation according to claim 7, which is characterized in that the step S3 In, the state-transition matrix A of battery model is：
9. lithium-ion-power cell state-of-charge method of estimation according to claim 7, which is characterized in that the step S3 In, the observing matrix C of battery model is：
C=[- 10 0-Ik 1]。
10. a kind of lithium-ion-power cell state-of-charge estimating system, which is characterized in that including：
At least one processor；
At least one processor, for storing at least one program；
When at least one program is executed by least one processor so that at least one processor is realized as weighed Profit requires 1-9 any one of them lithium-ion-power cell state-of-charge methods of estimation.
CN201810165942.3A 2018-02-28 2018-02-28 A kind of lithium-ion-power cell state-of-charge method of estimation and system Pending CN108445402A (en)

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