CN111443290A - SOP estimation method for power battery of electric vehicle with closed-loop control - Google Patents

SOP estimation method for power battery of electric vehicle with closed-loop control Download PDF

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CN111443290A
CN111443290A CN201911392926.9A CN201911392926A CN111443290A CN 111443290 A CN111443290 A CN 111443290A CN 201911392926 A CN201911392926 A CN 201911392926A CN 111443290 A CN111443290 A CN 111443290A
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李玉芳
徐炳钦
张玉梅
徐国放
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Nanjing University of Aeronautics and Astronautics
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    • G01—MEASURING; TESTING
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    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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    • G—PHYSICS
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    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract

An SOP estimation method for an electric vehicle power battery with closed-loop control. Mainly relates to the technical field of power battery management. The SOP estimation method of the power battery of the electric automobile with the closed-loop control is clear in logic, orderly in steps and high in accuracy. The estimation is carried out according to the following steps: s1, establishing a fitting function of three-dimensional response of capacity loss-temperature-discharge multiplying power and open-circuit voltage-actual capacity-state of charge; s2, identifying parameters online; s3, carrying out online estimation on the SOC by using HF; s4, obtaining a primary limit charging and discharging current; and S5, calculating the corrected SOP of the battery under multiple constraints. The method and the device enable the estimation to be more in line with the real limit working condition, ensure that the power battery can avoid the battery overshoot and over-discharge phenomenon caused by the peak power estimation deviation under the limit power output working condition, and ensure the service life and the working safety of the battery.

Description

SOP estimation method for power battery of electric vehicle with closed-loop control
Technical Field
The invention mainly relates to the technical field of power battery management, in particular to a power battery SOP estimation method.
Background
The sop (state of power) of a power battery, i.e. the power state of the battery, usually uses the peak power as its constant index. The power state of the electric automobile is accurately estimated in the processes of acceleration, regenerative braking and gradient climbing, the optimal matching of the power performance of the whole automobile can be realized on the premise of ensuring the safety of a battery, and the optimization of the control of the whole automobile is achieved. This is one of the metrics for the dynamic performance of an electric vehicle. If the SOP can be accurately estimated, the service efficiency of the lithium battery of the electric automobile can be greatly improved, the battery can be effectively protected from being damaged due to overshoot, the power performance of the automobile is improved, the service life loss of the power battery is reduced, the peak power of the power battery fluctuates along with the discharge temperature, the SOC (State of charge) and the change of the discharge rate, and the real-time high-precision estimation of the peak power is very important.
In the pulse response method (HPPC) in the prior art, a battery is utilized to apply specific pulse excitation under different SOC (state of charge), so that voltage is obtained and then power prediction is carried out, however, the method only considers the static characteristics of the battery and has low precision for dynamic working conditions. In the conventional online SOP estimation method, only characteristic parameters of the electric vehicle under the current stable working condition are considered, and the influence on the parameters caused by working under the limit working condition is ignored. Particularly, when the battery SOP is estimated, the battery discharge multiplying power is under the limit working condition, and the influence of the battery discharge multiplying power on the actual capacity is more prominent. The battery parameter change under the environment is violent in response, and certain challenges are brought to accurate estimation of the SOC, the SOP and the like of the battery.
Disclosure of Invention
Aiming at the problems, the invention provides the SOP estimation method of the power battery of the electric automobile with the closed-loop control, which has clear logic, ordered steps and high accuracy, can enable the estimation to better accord with the real limit working condition, ensures that the power battery can avoid the battery overshoot and over-discharge phenomenon caused by the estimation deviation of the peak power under the working condition of the limit power output, and ensures the service life and the working safety of the battery.
The technical scheme of the invention is as follows: the estimation is carried out according to the following steps:
s1, establishing a fitting function of three-dimensional response of capacity loss-temperature-discharge multiplying power and open-circuit voltage-actual capacity-state of charge;
s2, acquiring the voltage and current of the power battery in real time, and identifying parameters on line by using a least square method with forgetting factors based on data driving;
s3, establishing a state space equation suitable for an HF algorithm by using the identified parameters, and carrying out online estimation on the SOC by using HF;
s4, obtaining initial limit charging and discharging current under SOC limit and model voltage limit conditions after obtaining accurate SOC and various parameters;
and S5, returning the initial current to the step S1, correcting the actual available capacity to obtain the corrected limit charging and discharging current, and obtaining the corrected SOP of the battery under multiple constraints.
Step S1 includes the following steps;
s1.1, fitting out the change of capacity loss, namely Ca (T, C) under different working temperatures and discharge rates of the battery so as to cope with the influence caused by the change of the temperature and the discharge rate, wherein T is the temperature, and C is the discharge rate;
and S1.2, fitting the OCV-SOC under different available capacities to obtain corresponding relations, namely OCV (SOC, Ca), under different battery capacities, wherein the SOC is a state of charge and the actual available capacity of the Ca.
The parameters to be identified in step S2 include Rp，Cp，R0(ii) a The parameter equation to be identified is as follows:
Figure BDA0002345496740000021
step S4 includes the following steps;
s4.1, calculating limit current based on SOC limitation;
s4.2, calculating the limiting current based on the model voltage limit;
and S4.3, estimating the limit current based on multiple constraints.
Step S5 includes the following steps;
s5.1, bringing the initially calculated limit charging and discharging current back to the step S1, correcting the actual capacity loss, further correcting the actual available capacity, and correcting the open-circuit voltage curve by using the corrected actual available capacity;
s5.2, obtaining corrected limit current based on the corrected actual available capacity, the corrected open-circuit voltage curve and the corrected SOC;
and S5.3, calculating the SOP based on the corrected limiting current.
The constraints in step S5 include a corrected SOC constraint, a corrected model cutoff voltage constraint, and a factory design constraint.
The invention aims to estimate the SOP of a battery by considering the real-time working environment of a power battery. The Thevenin model (first-order RC model) is adopted to simulate the internal characteristics of the battery, is suitable for charging and discharging analysis of the power type power battery, and is simple and easy in model and low in calculation difficulty. And identifying real-time parameters by adopting a recursive least square method with forgetting factors based on data driving, and then estimating the SOC by adopting an H _ infinity algorithm with strong anti-noise capability. And (4) obtaining the limit discharge current by using the limit of the SOC and the model voltage, bringing back the initial current, and obtaining the actual available capacity under the current to update the constraint condition again. And finally, the SOP estimation after the limit current closed-loop control is finished under the updated multiple constraints. The method ensures that the estimation is more consistent with the real limit working condition, ensures that the power battery can avoid the battery overshoot and over-discharge phenomenon caused by the peak power estimation deviation under the limit power output working condition, and ensures the service life and the working safety of the battery.
The SOP online estimation method with the limit current closed-loop control provided by the invention has the following advantages:
firstly, the selection of the three-dimensional response of the capacity loss-temperature-discharge rate to the reference points of the temperature and the discharge rate considers that the response of the power battery parameters is severe at low temperature and high rate, and the reference points are subdivided in the range, so that the obtained response is more in line with the parameter characteristics of the battery, and the fitting effect of a change sensitive area is better.
And secondly, the parameters are identified on line by using a recursive least square method based on data driving and with forgetting factors, so that the real-time estimation is high in efficiency, and the defect of overlarge estimation deviation caused by error accumulation of a recursive algorithm can be overcome.
And thirdly, aiming at various irregular noises generated in the measuring process and the like, an HF estimation method with strong anti-noise capability is adopted, and the SOC is estimated by utilizing the excellent robustness of the HF estimation method, so that the estimation result is more real and reliable. Considering the influence of the current under the limit condition on the actual capacity, the capacity loss is corrected again by using the obtained initial current, and further the limit current under the model voltage limit and the SOC limit is corrected, so that the SOP estimation is corrected.
Drawings
Fig. 1 is Thevenin equivalent circuit model of a power battery;
FIG. 2 is a flow chart of parameter identification and SOC estimation;
FIG. 3 is a flow chart of modified estimation of the SOP estimation.
Detailed Description
The invention is illustrated in FIGS. 1-3, and is described in detail in the following for five aspects:
and S1, establishing a fitting function of the three-dimensional response of the capacity loss-temperature-discharge multiplying power and the open-circuit voltage-actual capacity-charge state.
The invention directly reflects the influence of real-time working temperature and discharge rate on the Curve to the loss of battery capacity, considers the influence capability of a temperature range, selects-20-50 ℃, subdivides the parameters of-20 ℃, -15 ℃, -10 ℃, -5 ℃,0 ℃,5 ℃,10 ℃,20 ℃,30 ℃,40 ℃ and 50 ℃, considers the solving condition of the SOP, and carries out conventional electrical performance test under the condition that the discharge rate is 0.5-3 ℃, the subdivision is 0.5C,1C,1.5C,2C,2.25C,2.5C,2.75C and 3C, and the three-dimensional electrical performance test can be carried out under the condition that the three-dimensional electrical performance test is finished, and the three-dimensional electrical performance test can be used in the CurFITTing in the MAT L, wherein the three-dimensional electrical performance test is carried out under the condition that the three-dimensional electrical performance test is 0.25 ℃ and the three-dimensional electrical performance test is matched with the operation condition that the three-dimensional electrical performance test is 0.5C, and the three-dimensional electrical performance test is matched under the condition that the three-dimensional electrical performance test is 25℃, and the.
S1.1, fitting the change of capacity loss under different working temperatures and discharge rates of the battery, namely Ca (T, C) to cope with the influence brought by the change of temperature and discharge rate, wherein T is temperature, and C is discharge rate. Namely, a three-dimensional change curve of discharge rate, temperature and capacity as shown in fig. 3.
And S1.2, fitting the OCV-SOC under different available capacities to obtain a corresponding relation, namely OCV (SOC, Ca) under different battery capacities, wherein the SOC is a state of charge and the actual available capacity of the Ca. Namely, in the three-dimensional change graphs of the state of charge, the capacity and the open-circuit voltage shown in fig. 3, a plurality of open-circuit voltage graphs under different capacities are intercepted.
And S2, acquiring the voltage and current of the power battery in real time, and identifying parameters on line by using a least square method with forgetting factors based on data driving. Where the parameter to be identified includes Rp，Cp，R0。
The equivalent circuit model according to fig. 1 is based on basic circuit knowledge to build a corresponding mathematical model as shown in equation (1).
Figure BDA0002345496740000041
UpThe voltage, being the polarization voltage,
Figure BDA0002345496740000042
is the corresponding derivative;
Rpis the polarization internal resistance;
Cpis a polarization capacitor;
R0ohmic internal resistance;
ULthe voltage of the line end is obtained by collection;
ILfor controlling the current, the current is obtained by collection;
Uocvis an open circuit voltage, obtained by step S1;
the formula (2) can be obtained by discretizing the formula (1).
Figure BDA0002345496740000043
k represents a discrete time; ts is sampling interval time;
τ is RpAnd CpThe product of (a);
formula (3) can be obtained from formula (2).
UL,k+1＝Uocv,k+1-IL,k+1R0-e-Ts/τUp,k-Rp(1-e-Ts/τ)IL,k(3)
The expression of the polarization voltage at the time k is eliminated by the expression (1) to give the expression (4).
UL,k+1＝Uocv,k+1-IL,k+1R0-e-Ts/τ(Uocv,k-IL,kR0-UL,k)-Rp(1-e-Ts/τ)IL,k(4)
Can be simplified by the formula (4) and can be obtained by variable substitution (5)
ψk+1＝a1ψk+a2IL,k-a3IL,k+1(5)
ψk+1Is UL,k+1-Uocv,k+1，ψkIs UL,k-Uocv,k；
a1Is e-Ts/τ；
a2Is e-Ts/τR0+(e-Ts/τ-1)Rp；
a3Is R0；
To use the R L S parameter identification method with forgetting factor, equation (5) is rewritten to (6):
Figure BDA0002345496740000051
Figure BDA0002345496740000052
a (k) as identified is a parameter matrix,
Figure BDA0002345496740000053
as an updated data matrix.
In this case, other effective methods are preferred, through the forgetting factor in the R L S method, older data is gradually discarded, so that the latest information is used and therefore a forgetting factor γ is introduced, and γ is taken to be 0.99.
The algorithm with forgetting factor R L S has the following steps:
(1) initializing a (0) and covariance matrix Pa(0)；
(2) Calculating deviation;
Figure BDA0002345496740000061
(3) calculating a gain matrix;
Figure BDA0002345496740000062
(4) updating the next time
Figure BDA0002345496740000063
Figure BDA0002345496740000064
(5) Updating covariance matrix
Figure BDA0002345496740000065
Wherein
Figure BDA0002345496740000066
Is an estimate of the system at the previous time;
Figure BDA0002345496740000067
is a current observed value obtained on the basis of an estimated value at the last moment; y isa(k +1) is the actual observed value; using difference ea(k +1) as a prediction error. The initialization a (0) can be selected empirically, Pa(0) α I can be taken, wherein α can be 106I.e., relatively large, I is the identity matrix.
And (3) continuously updating the push circulation (2) to (5) based on the data to obtain a real-time coefficient matrix a (k), and performing reverse push according to a recursion relation to obtain real-time parameters needing to be identified as a formula (12).
Figure BDA0002345496740000068
And substituting the obtained real-time parameters into an HF algorithm to carry out online estimation on the SOC.
And S3, establishing a state space equation suitable for the HF algorithm by using the identified parameters, and carrying out online estimation on the SOC by using the HF.
The identified parameters are obtained through the steps, and the SOC can be estimated on line through an HF algorithm. The HF filtering algorithm is able to handle systems with modeling errors while also allowing parameter control in uncertain noise environments. This type of capability to tolerate uncertainties and to maintain as stable an operation as possible in noisy environments is called system robustness.
The on-time integral calculation of the power battery SOC is as shown in the formula (13).
Figure BDA0002345496740000071
The nonlinear and linear processing of the HF estimation algorithm is as in equation (14).
Figure BDA0002345496740000072
Wherein:
Figure BDA0002345496740000073
the Jacobian matrix in equation (14) can be represented by f (x)k,uk)，h(xk,uk) For xkThe partial derivatives of (d) are obtained as follows:
Figure BDA0002345496740000074
f(xk,uk) A state function representing the model;
h(xk,uk) An observation function representing the model;
wk～(0,Qk)，υk～(0,Rk) System noise and observation noise, respectively, where QkIs the covariance matrix of the system noise, RkIs a covariance matrix of observed noise;
ukfor input vector, i.e. control of current IL；
zkTo represent a state vector xkThe parameter of interest (c);
l is zkAnd xkThe transformation matrix of (2);
the HF algorithm steps are as follows:
(1) initialization
Figure BDA0002345496740000081
λ，Q0，R0，S0。
Figure BDA0002345496740000082
Lambda is a performance boundary, if the performance boundary is set to be 0, the algorithm is degraded to be a Kalman filtering algorithm;
Figure BDA0002345496740000083
an initial state error covariance matrix;
R0is RkAn initial value of (1);
S0a symmetric positive array set based on the degree of interest of each component in the state vector;
(2) and (3) a priori estimation:
Figure BDA0002345496740000084
(3) and (3) posterior estimation:
Figure BDA0002345496740000085
the real-time estimated SOC value is stored in zkFor constraining the limiting current.
And S4, obtaining initial limit charging and discharging current under the SOC limit and model voltage limit conditions after obtaining the accurate SOC and various parameters.
The battery is a highly nonlinear system, and the SOP estimation under the single parameter constraint often causes a large error of the peak power estimation due to incomplete consideration, thereby causing permanent damage to the battery. Therefore, the estimation of the SOP under multiple constraints is necessary to ensure the safety and performance of the battery during operation.
And S4.1, limiting current based on SOC limit.
In order to ensure that the battery avoids the phenomenon of over-discharge under the limit discharge working condition, the current is controlled to avoid over-discharge when the SOC value is close to a limited design value. The design constraint considering the SOC is as in equation (31).
Figure BDA0002345496740000091
Recording the estimated charging and discharging current as a primary limit charging and discharging current based on the estimation carried out under the current running condition of the automobile, and selecting the duration time for 8s, 20s and 35s according to the continuous high-power output requirement of the automobile; j is the duration discharge time, and can be 8s, 20s and 35 s;
Figure BDA0002345496740000092
the limit discharge current is continuously within J sampling time under the SOC limit;
Figure BDA0002345496740000093
limiting charging current for continuous J sampling time under SOC limit
zminIs a set SOC lower limit value;
zmaxis a set SOC upper limit value;
and S4.2, limiting current under the voltage limit based on the model.
In order to ensure that the battery meets the battery safety requirement of cut-off voltage under the model, reference time J and sampling time k-k + J are selected, the control variable current is assumed to be constant in the process, and a formula (21) of k-k + J can be obtained by a formula (14) and a formula (16).
Figure BDA0002345496740000094
The polarization voltage obtained from equation (2) is as shown in equation (22).
Figure BDA0002345496740000095
Considering that the open circuit voltage is a nonlinear function of Ca and SOC, and SOC is a function of current, taylor expansion processing is performed on the open circuit voltage as shown in equation (23).
Figure BDA0002345496740000096
The higher order small terms have been ignored.
The terminal voltage expression (24) can be obtained by substituting the formula (22) and the formula (23) for the formula (2).
Figure BDA0002345496740000097
The limit charging and discharging current can be obtained according to the formula (25) based on the limit considering that the terminal voltage has upper and lower cut-off voltages.
Figure BDA0002345496740000101
Figure BDA0002345496740000102
Based on the lower limit of model cut-off voltage, the lower limit discharge is continuously performed at J sampling momentsCurrent flow;
Figure BDA0002345496740000103
the lower limit charging current is continuously obtained at J sampling moments under the limit of the lower limit cut-off voltage of the model;
and S4.3, estimating the limiting current based on the multi-constraint lower limit current.
The maximum square current under multiple constraints is as equation (26).
Figure BDA0002345496740000104
Figure BDA0002345496740000105
Is the initial maximum discharge current under constraint;
Figure BDA0002345496740000106
is the initial minimum charging current under the constraint.
And S5, returning the initial current to the step S1, correcting the actual available capacity to obtain the corrected limit charging and discharging current, and obtaining the corrected SOP of the battery under multiple constraints.
The change of various characteristics of the power battery can respond to the change of the discharge rate, and particularly, the loss of the practical available capacity is greatly changed when the power battery is operated under the limit discharge rate. The SOP estimation of the power state of the battery is just solved under the limit working condition, so that the correction of the limit current under the preset working condition has important significance for the estimation of the SOP.
S5.1, correction of actually available capacity and open-circuit voltage curve
And (3) correcting the actual available capacity:
Figure BDA0002345496740000107
and (3) correcting an open circuit voltage curve:
Figure BDA0002345496740000108
Figure BDA0002345496740000111
is the corrected actual available amount;
Figure BDA0002345496740000112
the limit current is calculated initially;
s5.2, correcting the current under the limit working condition
The corrected SOC is obtained by combining the step S5.1 according to the corrected actual available quantity and the open-circuit voltage curve
Figure BDA0002345496740000113
Obtaining the limiting current under the corrected SOC limit
Figure BDA0002345496740000114
As shown in formula (29).
Figure BDA0002345496740000115
Obtaining the limiting current under the limitation of the corrected model cut-off voltage according to the step S4
Figure BDA0002345496740000116
As shown in equation (30).
Figure BDA0002345496740000117
The corrected limiting current can be obtained according to the formula (26), the formula (29) and the formula (30)
Figure BDA0002345496740000118
As shown in equation (31).
Figure BDA0002345496740000119
S5.3, calculating the SOP based on the corrected limiting current
Taking into account the constraints of factory design
Figure BDA00023454967400001110
Continuous-time limit power based on SOC and model limiting condition
Figure BDA00023454967400001111
The estimate is as in equation (32).
Figure BDA00023454967400001112
The present invention is also directed to other embodiments, and various changes and modifications, and any combination of features, which may be included in the present invention, may be made by those skilled in the art without departing from the spirit of the present invention, and the scope of the present invention is defined by the appended claims.

Claims (6)

1. An SOP estimation method for an electric vehicle power battery with closed-loop control is characterized by comprising the following steps of:
s1, establishing a fitting function of three-dimensional response of capacity loss-temperature-discharge multiplying power and open-circuit voltage-actual capacity-state of charge;
s2, acquiring the voltage and current of the power battery in real time, and identifying parameters on line by using a least square method with forgetting factors based on data driving;
s3, establishing a state space equation suitable for an HF algorithm by using the identified parameters, and carrying out online estimation on the SOC by using HF;
s4, obtaining initial limit charging and discharging current under SOC limit and model voltage limit conditions after obtaining accurate SOC and various parameters;
and S5, returning the initial current to the step S1, correcting the actual available capacity to obtain the corrected limit charging and discharging current, and obtaining the corrected SOP of the battery under multiple constraints.
2. The method for estimating the power battery SOP of the electric vehicle with the closed-loop control as claimed in claim 1, wherein the step S1 comprises the following steps;
s1.1, fitting out the change of capacity loss, namely Ca (T, C) under different working temperatures and discharge rates of the battery so as to cope with the influence caused by the change of the temperature and the discharge rate, wherein T is the temperature, and C is the discharge rate;
and S1.2, fitting the OCV-SOC under different available capacities to obtain corresponding relations, namely OCV (SOC, Ca), under different battery capacities, wherein the SOC is a state of charge and the actual available capacity of the Ca.
3. The method for estimating SOP of power battery of electric vehicle with closed-loop control as claimed in claim 1, wherein the parameter to be identified in step S2 includes Rp，Cp，R0(ii) a The parameter equation to be identified is as follows:
Figure FDA0002345496730000011
4. the method for estimating the power battery SOP of the electric vehicle with the closed-loop control as claimed in claim 1, wherein the step S4 comprises the following steps;
s4.1, calculating limit current based on SOC limitation;
s4.2, calculating the limiting current based on the model voltage limit;
and S4.3, estimating the limit current based on multiple constraints.
5. The method for estimating the power battery SOP of the electric vehicle with the closed-loop control as claimed in claim 1, wherein the step S5 comprises the following steps;
s5.1, bringing the initially calculated limit charging and discharging current back to the step S1, correcting the actual capacity loss, further correcting the actual available capacity, and correcting the open-circuit voltage curve by using the corrected actual available capacity;
s5.2, obtaining corrected limit current based on the corrected actual available capacity, the corrected open-circuit voltage curve and the corrected SOC;
and S5.3, calculating the SOP based on the corrected limiting current.
6. The method for estimating the SOP of the power battery of the electric vehicle with the closed-loop control as claimed in claim 1, wherein the constraints in the step S5 include a modified SOC constraint, a modified model cut-off voltage constraint and a factory design constraint.
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CN114167289A (en) * 2021-12-01 2022-03-11 中国第一汽车股份有限公司 Method and device for determining output power of battery and vehicle
CN117686936A (en) * 2024-02-01 2024-03-12 杭州高特电子设备股份有限公司 Online detection method and system for residual service life of battery system

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