CN111830418A - SOC estimation method considering external environment influence of soft package battery - Google Patents

SOC estimation method considering external environment influence of soft package battery Download PDF

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CN111830418A
CN111830418A CN202010655623.8A CN202010655623A CN111830418A CN 111830418 A CN111830418 A CN 111830418A CN 202010655623 A CN202010655623 A CN 202010655623A CN 111830418 A CN111830418 A CN 111830418A
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胡犇
赵万忠
邹松春
高犇
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses an SOC estimation method considering the external environment influence of a soft package battery, which comprises the following steps: establishing a state equation and acquiring parameters; carrying out a battery capacity calibration experiment, a constant current pulse experiment and an urban road running condition experiment; obtaining the battery capacity under a single influence factor by using data recorded in a battery capacity calibration experiment, and obtaining a function equation by using battery capacity calibration experiment data fitting; obtaining open-circuit voltage values under different conditions by using constant-current pulse experimental data, fitting power functions under different conditions, and further fitting the coefficient of the same order to influence factors; and substituting the obtained correction of the battery capacity and the battery open-circuit voltage based on the external environment into the adaptive unscented Kalman filtering to obtain an estimation result. The invention increases the influence factor of external mechanical load on the basis of temperature and discharge rate, can be closer to the real scene of the soft package battery in real vehicle application, and simultaneously enables the charge state estimation result of the battery to be more accurate.

Description

SOC estimation method considering external environment influence of soft package battery
Technical Field
The invention belongs to the technical field of power battery management, and particularly relates to an SOC estimation method considering the external environment influence of a soft package battery.
Background
With the rapid development of technology and the strategic national demand for energy structure transformation, new energy automobiles have attracted extensive attention as an important means for alleviating energy crisis and environmental pollution. The most mature products of new energy automobiles are mainly electric automobiles, and the power source of the new energy automobiles is that battery monomers are combined into battery packs in a series-parallel connection mode, and then a plurality of battery packs are combined with hardware required by various systems to form a battery pack. In order to ensure that the power battery supplies power to the whole vehicle in a good state, a Battery Management System (BMS) needs to monitor the environment of each power battery and measure required data by using various sensors, including: charge-discharge current, terminal voltage, temperature, etc.; the possibility of occurrence of an emergency of the battery is greatly reduced, and obtaining an accurate SOC value of the battery is a prerequisite for ensuring that the BMS is in a good operation state.
The two most common methods for current SOC estimation are the ampere-hour integration method and the model-based estimation method, respectively. The ampere-hour integration method is based on an SOC (state of charge) definition formula, and under the condition that the total capacity and the charging and discharging coulombic efficiency of the battery are known, the SOC value of the battery at the current moment can be calculated only by acquiring the charging and discharging current value of the battery. However, this method can only obtain accurate results when the battery is in a constant working environment, the environment of the battery is constantly changed when the battery is in real-vehicle operation, and as the working time of the battery increases, the aging of the battery is inevitable, and various parameters are changed, so this method is not suitable for being used in a real-vehicle BMS system. The model-based estimation method is to finally acquire the SOC value by establishing an accurate battery model and combining different algorithms, and the algorithms can filter data noise and have certain robustness, so the method is more suitable for a dynamic working environment. Batteries can be classified into: cylinder lithium cell, square battery and laminate polymer battery. At present, many electric automobiles represented by Tesla MODEL S adopt a cylindrical lithium battery as a power source, and the main reasons are as follows: good consistency, low cost and good mechanical property of the monomer. However, the battery still has many problems, such as safety of the battery and high complexity of the battery system after the battery is assembled. The soft package battery has the advantages that the soft package battery has different military prominences in recent years, the market share is continuously improved, the soft package battery exceeds other two types of batteries, the packaging material and the structure of the soft package battery enable the soft package battery to have the advantages which are not possessed by a plurality of metal shell batteries, for example, the safety performance is good, the soft package battery is packaged by an aluminum plastic film structurally, and the soft package battery generally expands air and cracks when a safety problem occurs, and the soft package battery is not exploded like a steel shell battery or an aluminum shell battery; the weight of the soft package battery is 40% lighter than that of a steel shell lithium battery with the same capacity, and is 20% lighter than that of an aluminum shell lithium battery; the internal resistance is small, the internal resistance of the soft package battery is smaller than that of a lithium battery, and the self power consumption of the battery can be greatly reduced; the cycle performance is good, the cycle life of the soft package battery is longer, and the cycle attenuation of 100 times is 4-7% less than that of an aluminum shell; the design is flexible, the appearance can be changed into any shape, the thickness can be thinner, and the novel battery cell model can be developed according to the customization of the requirements of customers.
Compared with a metal shell battery, the soft package battery is carried in a real vehicle and is influenced by more factors, the SOC estimation of the battery is indirectly influenced except that the ambient temperature and the discharge rate influence the battery capacity and the open-circuit voltage, and the influence caused by external mechanical load after the soft package battery is grouped is not negligible. Therefore, the invention provides the SOC estimation method for correcting the environment where the soft package battery is located according to the point, so that a more accurate SOC value of the soft package battery in a dynamic environment can be obtained.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide an SOC estimation method considering the external environment influence of a pouch battery, so as to solve the problems of large estimation error, poor robustness and the like caused by insufficient consideration factors for the state estimation of the pouch battery in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention discloses an SOC estimation method considering the external environment influence of a soft package battery, which comprises the following steps of:
step S1: establishing a state equation based on a second-order RC model, and acquiring parameters in the current second-order RC model by using an online parameter identification method in combination with the measured battery current and terminal voltage data;
step S2: selecting a soft package battery to be tested, selecting the test ranges of external mechanical load, temperature and discharge rate, and changing a single variable under a set standard environment to perform a corresponding battery capacity calibration experiment, a constant current pulse experiment and a general urban road driving condition experiment;
step S3: obtaining the battery capacity under a single influence factor by using data recorded in a battery capacity calibration experiment, introducing a capacity correction factor after normalization processing, defining the capacity correction factor as a function related to the three influence factors, and fitting the battery capacity calibration experiment data to obtain a function equation;
step S4: obtaining open-circuit voltage values under different conditions by using the constant-current pulse experimental data in the step S2, defining a fifth-order power function OCV-SOC by combining corresponding SOC values, fitting the power functions under different conditions, and further fitting the coefficient of the same-order term to influence factors to obtain corresponding results;
step S5: under the running conditions of urban roads under different external environments, the correction of the battery capacity and the battery open-circuit voltage based on the external environments, which is obtained by the experimental data, is substituted into the adaptive unscented Kalman filtering, so that the final estimation result is obtained.
Further, the laplace equation of the second-order RC model in step S1 is described as:
Figure BDA0002576639370000021
in the formula of UOCIs the open circuit voltage of the battery, R0Is the ohmic internal resistance of the battery，R1For electrochemical polarization resistance, R2For concentration polarization internal resistance, C1For electrochemical polarization of capacitance, C2Is a concentration polarization resistance, UtIs the terminal voltage of the battery, also called load voltage, I is the current flowing through the battery, i.e. load current;
the corresponding transfer equation is:
Figure BDA0002576639370000031
wherein, tau1＝R1C1，τ2＝R2C2；
The transfer equation is mapped from the S plane to the Z plane by a bilinear transformation method, the transformation equation is
Figure BDA0002576639370000032
The equation after conversion is summarized as follows:
Figure BDA0002576639370000033
then it is converted into a difference equation:
y(k)＝Uoc(k)-Ut(k)＝a1y(k-1)+a2y(k-2)+a3I(k)+a4I(k-1)+a5I(k-2)
in the formula, k, k-1, k-2 are respectively corresponding sampling time, and the open-circuit voltage in the differential equation is simplified as follows:
Uoc(k)＝Uoc(k-1)＝Uoc(k-2)
the difference equation can be finally collated as:
Ut(k)＝(1-a1-a2)Uoc(k)+a1Ut(k-1)+a2Ut(k-2)-a3I(k)-a4I(k-1)-a5I(k-2)
defining a correlation vector:
Figure BDA0002576639370000034
θ(k)＝[(1-a1-a2)Uoc(k) a1a2-a3-a4-a5]T
the final simplified difference equation is expressed as:
Figure BDA0002576639370000035
further, the online parameter identification method in step S1 adopts a least square method with forgetting factor, and its formula is as follows:
Figure BDA0002576639370000036
wherein,
Figure BDA0002576639370000037
the parameter vector estimated at the previous moment, y (k) is the actual observed value of the system at the current moment, K (k) is a gain matrix, P (k) is a covariance matrix of theta (k), and lambda is a forgetting factor;
calculating to obtain theta (k) of each sampling moment by using a least square method with forgetting factors, and further solving to obtain final battery model parameters; order:
Figure BDA0002576639370000041
bringing the above formula into G (z)-1) The following equation is obtained:
Figure BDA0002576639370000042
and (3) the transfer function obtained by reverse deduction and the initial transfer function formula are equal according to the same term coefficient to obtain:
Figure BDA0002576639370000043
further derivation yields:
Figure BDA0002576639370000044
and the parameter value of the second-order RC model can be obtained in real time by using an online parameter identification method in combination with the current and the battery terminal voltage measured by the sensor when the soft package battery actually works.
Further, the step S2 specifically includes:
step S21: the method includes the steps that the influence of external mechanical load on the soft-package battery is researched, an experimental device is designed, steel pressure plates are arranged at the upper portion and the lower portion of the experimental device, the soft-package battery is placed in the middle of the pressure plates, and a layer of plastic pad is arranged between the soft-package battery and the steel pressure plates, so that the battery is prevented from being in direct contact with the pressure plates; the four corners of the two pressure plates are fastened by screws and nuts; selecting an external mechanical load range of 0-1500N, and selecting a group of experimental points every 300N, namely setting 6 groups of comparative experiments; before the experiment is started, a metal resistance wire of a pressure sensor is bonded on the surface of a soft package battery and protected by a covering layer, and a lead-out wire is connected to a display instrument, so that the monitoring of mechanical load applied to the outside of the soft package battery is realized; the soft package battery is charged to the upper cut-off voltage through constant current and constant voltage before the test, then stands for a period of time, and is discharged to the lower cut-off voltage through constant current and constant voltage under the set mechanical load to carry out the capacity calibration test; after each group of experiments is finished, the mechanical load is reset to zero, then the battery is recharged and stands still to prepare for next capacity calibration, and 0N and 25 ℃ are used as standard environments;
step S22: researching the influence of temperature on a soft package battery, selecting a corresponding temperature interval of-10-40 ℃, taking 25 ℃ as a reference and 5 ℃ as a temperature interval, respectively performing a capacity calibration experiment and a constant current pulse experiment, fully charging and standing the battery for a period of time in a standard environment before each group of experiments, then performing constant current and constant voltage discharge to a lower cut-off voltage at the current set temperature to realize capacity calibration, recovering the temperature to 25 ℃ and standing the battery for a period of time, fully charging and standing the battery again, then continuously discharging for 6 minutes at a discharge rate of 1C, standing the battery for 30 minutes, measuring the terminal voltage of the battery at the terminal moment of standing, taking the terminal voltage at the moment as a corresponding open-circuit voltage, and repeating the operation for 10 times, namely, finally emptying the battery; at this moment, capacity calibration and constant current pulse experiments at the same temperature are completed, and then the experiment temperature is changed through a constant temperature box to repeat the operations;
step S23: the method is characterized by researching the influence of discharge rate on the soft package battery, selecting corresponding discharge intervals of 0-5C, performing capacity experiments with 0.5C as an interval between 0C and 1C as an interval between 1C and 5C, fully charging the battery in a standard environment before each experiment, standing for a period of time, and then discharging to a lower cut-off voltage through constant current and constant voltage at a current set temperature to realize capacity calibration.
Further, the step S3 specifically includes: further processing is performed on each set of capacity values obtained in step S2, with the battery capacity at 0N, 25 ℃ and 1C discharge rate as denominator and the capacities measured under other different conditions as numerators, to perform normalization processing, and the obtained result is the capacity correction factor α described in step S3, which is defined as a function of three influencing factors, namely:
α＝f(F T R)
in the formula, F is the external mechanical load of the battery, T is the working environment temperature of the battery, R is the discharge rate, and the specific form of the function is obtained by a fitting method.
Further, the step S4 specifically includes: processing the constant current pulse experimental data at the temperature changed in the step S22, and obtaining a power function formula by using the open-circuit voltage and the corresponding SOC value recorded at each group of temperatures in a fitting manner, wherein the specific form is as follows:
UOC＝k5z5+k4z4+k3z3+k2z2+k1z+k0
in the formula, z is an SOC value, each term coefficient in the above formula is defined as a function of temperature T, and fitting is performed again by using the same term coefficient values obtained by fitting at different temperatures, that is:
ki＝ai,4T4+ai,3T3+ai,2T2+ai,1T+ai,0
therefore, the accurate OCV-SOC relational expression can be obtained under different external environments.
Further, the specific steps of the Adaptive Unscented Kalman Filter (AUKF) in step S5 are as follows:
s51: obtaining 2n +1 sigma points and corresponding weight omega through one-time UT conversion, wherein n is the dimension of the state vector X;
Figure BDA0002576639370000061
in the formula, subscript i is the ith column of the square root of the matrix;
Figure BDA0002576639370000062
is the state vector mean, P is the corresponding covariance matrix;
Figure BDA0002576639370000063
in the formula, subscript m is a mean value, c is a covariance, and superscript is a corresponding sampling point; λ ═ α2(n + k) -n is a scaling parameter, alpha is selected to control the distribution state of sampling points, k and beta are both parameters to be selected, and beta is more than or equal to 0;
establishing a state equation of the soft package battery based on a second-order RC model:
Figure BDA0002576639370000064
wherein x is [ U ═ U1U2z]T，u＝iL，y＝ut，
Figure BDA0002576639370000065
S52: a set of sample points is obtained by the UT transform formula of S51:
Figure BDA0002576639370000066
substituting the prediction into a state equation of S51 to obtain a one-step prediction of each point, wherein i is 1-2 n + 1;
X(i)(k+1|k)＝f[k,X(i)(k|k)]
s53: calculating a one-step prediction and covariance matrix of the state vectors (U1, U2, z), and obtaining by weighted summation of prediction values of the sigma point set:
Figure BDA0002576639370000071
Figure BDA0002576639370000072
s54: and on the basis of the predicted value in the step S53, carrying out UT transformation again to generate a new sigma point set:
Figure BDA0002576639370000073
s55: substituting the point set into an observation equation to obtain a predicted observed quantity:
Z(i)(k+1|k)＝h[X(i)(k+1|k)]；
s56: and obtaining the predicted observed quantity of the sigma point set by the above steps, and obtaining the mean value and covariance of system prediction by weighted summation:
Figure BDA0002576639370000074
Figure BDA0002576639370000075
Figure BDA0002576639370000076
s57: calculating a gain matrix:
Figure BDA0002576639370000077
s58: updating the state vector x and the corresponding covariance using the gain matrix and the one-step predictor of the state vector:
Figure BDA0002576639370000078
Figure BDA0002576639370000079
s59: process noise Q by voltage innovationkAnd measuring the noise RkUpdating:
Figure BDA0002576639370000081
Figure BDA0002576639370000082
in the formula,iis the voltage innovation of the cell model at time k, i.e. the difference between the voltage measurement and the estimate, L is the window size, HkA covariance approximation of the voltage innovation at time k;
the last term z of the state vector x of step S58 is the resulting SOC value.
The invention has the beneficial effects that:
compared with a cylindrical battery, the soft package battery needs to consider more external environmental factors to influence various parameters of the battery, the external mechanical load influencing factor is increased on the basis of temperature and discharge rate, the soft package battery can be closer to a real scene when the soft package battery is applied in a real vehicle, and meanwhile, the estimation result of the charge state of the battery is more accurate.
Drawings
FIG. 1 is a diagram of a second order RC model.
FIG. 2 is a schematic diagram of an experimental apparatus.
Fig. 3 is an overall flow chart of the present invention.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
Referring to fig. 3, the SOC estimation method considering the external environment influence of the pouch battery according to the present invention includes the following steps:
step S1: establishing a state equation based on a second-order RC model, and acquiring parameters in the current second-order RC model by using an online parameter identification method in combination with the measured battery current and terminal voltage data;
referring to FIG. 1, the Laplace equation for the second order RC model is described as:
Figure BDA0002576639370000083
in the formula of UOCIs the open circuit voltage of the battery, R0Is the ohmic internal resistance, R, of the battery1For electrochemical polarization resistance, R2For concentration polarization internal resistance, C1For electrochemical polarization of capacitance, C2Is a concentration polarization resistance, UtIs the terminal voltage of the battery, also called load voltage, I is the current flowing through the battery, i.e. load current;
the corresponding transfer equation is:
Figure BDA0002576639370000091
wherein, tau1＝R1C1，τ2＝R2C2；
The transfer equation is mapped from the S plane to the Z plane by a bilinear transformation method, the transformation equation is
Figure BDA0002576639370000092
The equation after conversion is summarized as follows:
Figure BDA0002576639370000093
then it is converted into a difference equation:
y(k)＝Uoc(k)-Ut(k)＝a1y(k-1)+a2y(k-2)+a3I(k)+a4I(k-1)+a5I(k-2)
in the formula, k, k-1, k-2 are respectively corresponding sampling time, and the open-circuit voltage in the differential equation is simplified as follows:
Uoc(k)＝Uoc(k-1)＝Uoc(k-2)
the difference equation can be finally collated as:
Ut(k)＝(1-a1-a2)Uoc(k)+a1Ut(k-1)+a2Ut(k-2)-a3I(k)-a4I(k-1)-a5I(k-2)
defining a correlation vector:
Figure BDA0002576639370000094
θ(k)＝[(1-a1-a2)Uoc(k) a1a2-a3-a4-a5]T
the final simplified difference equation is expressed as:
Figure BDA0002576639370000095
the online parameter identification method adopts a least square method with forgetting factors, and the formula is as follows:
Figure BDA0002576639370000096
wherein,
Figure BDA0002576639370000097
the parameter vector estimated at the previous moment, y (k) is the actual observed value of the system at the current moment, K (k) is a gain matrix, P (k) is a covariance matrix of theta (k), and lambda is a forgetting factor;
calculating to obtain theta (k) of each sampling moment by using a least square method with forgetting factors, and further solving to obtain final battery model parameters; order:
Figure BDA0002576639370000101
bringing the above formula into G (z)-1) The following equation is obtained:
Figure BDA0002576639370000102
and (3) the transfer function obtained by reverse deduction and the initial transfer function formula are equal according to the same term coefficient to obtain:
Figure BDA0002576639370000103
further derivation yields:
Figure BDA0002576639370000104
and the parameter value of the second-order RC model can be obtained in real time by using an online parameter identification method in combination with the current and the battery terminal voltage measured by the sensor when the soft package battery actually works.
Step S2: selecting a soft package battery to be tested, selecting the test ranges of external mechanical load, temperature and discharge rate, and changing a single variable under a set standard environment to perform a corresponding battery capacity calibration experiment, a constant current pulse experiment and a general urban road driving condition experiment;
step S21: the influence of external mechanical load on the soft-package battery is researched, an experimental device shown in figure 2 is designed, steel pressure plates are arranged above and below the experimental device, the soft-package battery is placed in the middle of the pressure plates, and a layer of plastic pad is arranged between the soft-package battery and the steel pressure plates, so that the battery is prevented from being in direct contact with the pressure plates; the four corners of the two pressure plates are fastened by screws and nuts; selecting an external mechanical load range of 0-1500N, and selecting a group of experimental points every 300N, namely setting 6 groups of comparative experiments; before the experiment is started, a metal resistance wire of a pressure sensor is bonded on the surface of a soft package battery and protected by a covering layer, and a lead-out wire is connected to a display instrument, so that the monitoring of mechanical load applied to the outside of the soft package battery is realized; the soft package battery is charged to the upper cut-off voltage through constant current and constant voltage before the test, then stands for a period of time, and is discharged to the lower cut-off voltage through constant current and constant voltage under the set mechanical load to carry out the capacity calibration test; after each group of experiments is finished, the mechanical load is reset to zero, then the battery is recharged and stands still to prepare for next capacity calibration, and 0N and 25 ℃ are used as standard environments;
step S22: researching the influence of temperature on a soft package battery, selecting a corresponding temperature interval of-10-40 ℃, taking 25 ℃ as a reference and 5 ℃ as a temperature interval, respectively performing a capacity calibration experiment and a constant current pulse experiment, fully charging and standing the battery for a period of time in a standard environment before each group of experiments, then discharging to a lower cut-off voltage through constant current and constant voltage at the current set temperature to realize capacity calibration, recovering the temperature to 25 ℃ and standing the battery for a period of time, fully charging and standing the battery again, then continuously discharging for 6 minutes (namely discharging for 10% each time) at a discharge rate of 1C, standing the battery for 30 minutes, measuring the terminal voltage of the battery at the terminal moment of standing, taking the terminal voltage as a corresponding open-circuit voltage, and repeating the operation for 10 times, namely obtaining the final emptying of the battery; at this moment, capacity calibration and constant current pulse experiments at the same temperature are completed, and then the experiment temperature is changed through a constant temperature box to repeat the operations;
step S23: the method is characterized by researching the influence of discharge rate on the soft package battery, selecting corresponding discharge intervals of 0-5C, performing capacity experiments with 0.5C as an interval between 0C and 1C as an interval between 1C and 5C, fully charging the battery in a standard environment before each experiment, standing for a period of time, and then discharging to a lower cut-off voltage through constant current and constant voltage at a current set temperature to realize capacity calibration.
Step S3: obtaining the battery capacity under a single influence factor by using data recorded in a battery capacity calibration experiment, introducing a capacity correction factor after normalization processing, defining the capacity correction factor as a function related to the three influence factors, and obtaining a function equation by using battery capacity calibration experiment data fitting;
further processing is performed on each set of capacity values obtained in step S2, with the battery capacity at 0N, 25 ℃ and 1C discharge rate as denominator and the capacities measured under other different conditions as numerators, to perform normalization processing, and the obtained result is the capacity correction factor α described in step S3, which is defined as a function of three influencing factors, namely:
α＝f(F T R)
in the formula, F is the external mechanical load of the battery, T is the working environment temperature of the battery, R is the discharge rate, and the specific form of the function is obtained by a fitting method.
Step S4: obtaining open-circuit voltage values under different conditions by using the constant-current pulse experimental data in the step S2, defining a fifth-order power function OCV-SOC by combining corresponding SOC values, fitting the power functions under different conditions, and further fitting the coefficient of the same-order term to influence factors to obtain corresponding results;
the step S4 specifically includes: processing the constant current pulse experimental data at the temperature changed in the step S22, and obtaining a power function formula by using the open-circuit voltage and the corresponding SOC value recorded at each group of temperatures in a fitting manner, wherein the specific form is as follows:
UOC＝k5z5+k4z4+k3z3+k2z2+k1z+k0
in the formula, z is an SOC value, each term coefficient in the above formula is defined as a function of temperature T, and fitting is performed again by using the same term coefficient values obtained by fitting at different temperatures, that is:
ki＝ai,4T4+ai,3T3+ai,2T2+ai,1T+ai,0
therefore, the accurate OCV-SOC relational expression can be obtained under different external environments.
Step S5: under the urban road running working conditions under different external environments, the correction of the battery capacity and the battery Open Circuit Voltage (OCV) based on the external environment, which is obtained by the experimental data, is substituted into an Adaptive Unscented Kalman Filter (AUKF), so that a final estimation result is obtained;
the specific steps of the Adaptive Unscented Kalman Filtering (AUKF) are as follows:
s51: obtaining 2n +1 sigma points and corresponding weight omega through one-time UT conversion, wherein n is the dimension of the state vector X;
Figure BDA0002576639370000121
in the formula, subscript i is the ith column of the square root of the matrix;
Figure BDA0002576639370000122
is the state vector mean, P is the corresponding covariance matrix;
Figure BDA0002576639370000123
in the formula, subscript m is a mean value, c is a covariance, and superscript is a corresponding sampling point; λ ═ α2(n + k) -n is a scaling parameter, alpha is selected to control the distribution state of sampling points, k and beta are both parameters to be selected, and beta is more than or equal to 0;
establishing a state equation of the soft package battery based on a second-order RC model:
Figure BDA0002576639370000124
wherein x is [ U ═ U1U2z]T，u＝iL，y＝ut，
Figure BDA0002576639370000131
S52: a set of sampling points (i.e., a sigma point set) is obtained by the UT transform formula of S51:
Figure BDA0002576639370000132
substituting the prediction into a state equation of S51 to obtain a one-step prediction of each point, wherein i is 1-2 n + 1;
X(i)(k+1|k)＝f[k,X(i)(k|k)]
s53: calculating a one-step prediction and covariance matrix of the state vectors (U1, U2, z), and obtaining by weighted summation of prediction values of the sigma point set:
Figure BDA0002576639370000133
Figure BDA0002576639370000134
s54: and on the basis of the predicted value in the step S53, carrying out UT transformation again to generate a new sigma point set:
Figure BDA0002576639370000135
s55: substituting the point set into an observation equation to obtain a predicted observed quantity:
Z(i)(k+1|k)＝h[X(i)(k+1|k)]；
s56: and obtaining the predicted observed quantity of the sigma point set by the above steps, and obtaining the mean value and covariance of system prediction by weighted summation:
Figure BDA0002576639370000136
Figure BDA0002576639370000137
Figure BDA0002576639370000138
s57: calculating a gain matrix:
Figure BDA0002576639370000141
s58: updating the state vector x and the corresponding covariance using the gain matrix and the one-step predictor of the state vector:
Figure BDA0002576639370000142
Figure BDA0002576639370000143
s59: process noise Q by voltage innovationkAnd measuring the noise RkUpdating:
Figure BDA0002576639370000144
Figure BDA0002576639370000145
in the formula,iis the voltage innovation of the cell model at time k, i.e. the difference between the voltage measurement and the estimate, L is the window size, HkA covariance approximation of the voltage innovation at time k;
the last term z of the state vector x in step S58 is the finally obtained SOC value.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (7)

1. An SOC estimation method considering external environment influence of a soft package battery is characterized by comprising the following steps:
step S1: establishing a state equation based on a second-order RC model, and acquiring parameters in the current second-order RC model by using an online parameter identification method in combination with the measured battery current and terminal voltage data;
step S2: selecting a soft package battery to be tested, selecting the test ranges of external mechanical load, temperature and discharge rate, and changing a single variable under a set standard environment to perform a corresponding battery capacity calibration experiment, a constant current pulse experiment and a general urban road driving condition experiment;
step S3: obtaining the battery capacity under a single influence factor by using data recorded in a battery capacity calibration experiment, introducing a capacity correction factor after normalization processing, defining the capacity correction factor as a function related to the three influence factors, and fitting the battery capacity calibration experiment data to obtain a function equation;
step S4: obtaining open-circuit voltage values under different conditions by using the constant-current pulse experimental data in the step S2, defining a fifth-order power function OCV-SOC by combining corresponding SOC values, fitting the power functions under different conditions, and further fitting the coefficient of the same-order term to influence factors to obtain corresponding results;
step S5: under the running conditions of urban roads under different external environments, the correction of the battery capacity and the battery open-circuit voltage based on the external environments, which is obtained by the experimental data, is substituted into the adaptive unscented Kalman filtering, so that the final estimation result is obtained.
2. The SOC estimation method considering the external environment influence of the soft-package battery according to claim 1, wherein the Laplace equation of the second-order RC model in the step S1 is described as follows:
Figure FDA0002576639360000011
in the formula of UOCIs the open circuit voltage of the battery, R0Is the ohmic internal resistance, R, of the battery1For electrochemical polarization resistance, R2For concentration polarization internal resistance, C1For electrochemical polarization of capacitance, C2Is a concentration polarization resistance, UtIs the terminal voltage of the battery, also called load voltage, I is the current flowing through the battery, i.e. load current;
the corresponding transfer equation is:
Figure FDA0002576639360000012
wherein, tau1＝R1C1，τ2＝R2C2；
The transfer equation is mapped from the S plane to the Z plane by a bilinear transformation method, the transformation equation is
Figure FDA0002576639360000013
The equation after conversion is summarized as follows:
Figure FDA0002576639360000021
then it is converted into a difference equation:
y(k)＝Uoc(k)-Ut(k)＝a1y(k-1)+a2y(k-2)+a3I(k)+a4I(k-1)+a5I(k-2)
in the formula, k, k-1, k-2 are respectively corresponding sampling time, and the open-circuit voltage in the differential equation is simplified as follows:
Uoc(k)＝Uoc(k-1)＝Uoc(k-2)
the difference equation can be finally collated as:
Ut(k)＝(1-a1-a2)Uoc(k)+a1Ut(k-1)+a2Ut(k-2)-a3I(k)-a4I(k-1)-a5I(k-2)
defining a correlation vector:
Figure FDA0002576639360000022
θ(k)＝[(1-a1-a2)Uoc(k) a1a2-a3-a4-a5]T
the final simplified difference equation is expressed as:
Figure FDA0002576639360000023
3. the SOC estimation method considering the external environment influence of the pouch battery as claimed in claim 1, wherein the online parameter identification method in step S1 adopts a least square method with forgetting factor, and the formula is as follows:
Figure FDA0002576639360000024
wherein,
Figure FDA0002576639360000025
the parameter vector estimated at the previous moment, y (k) is the actual observed value of the system at the current moment, K (k) is a gain matrix, P (k) is a covariance matrix of theta (k), and lambda is a forgetting factor;
calculating to obtain theta (k) of each sampling moment by using a least square method with forgetting factors, and further solving to obtain final battery model parameters; order:
Figure FDA0002576639360000026
bringing the above formula into G (z)-1) The following equation is obtained:
Figure FDA0002576639360000031
and (3) the transfer function obtained by reverse deduction and the initial transfer function formula are equal according to the same term coefficient to obtain:
Figure FDA0002576639360000032
further derivation yields:
Figure FDA0002576639360000033
and the parameter value of the second-order RC model can be obtained in real time by using an online parameter identification method in combination with the current and the battery terminal voltage measured by the sensor when the soft package battery actually works.
4. The SOC estimation method considering the external environment influence of the soft package battery according to claim 1, wherein the step S2 specifically comprises:
step S21: the method includes the steps that the influence of external mechanical load on the soft-package battery is researched, an experimental device is designed, steel pressure plates are arranged at the upper portion and the lower portion of the experimental device, the soft-package battery is placed in the middle of the pressure plates, and a layer of plastic pad is arranged between the soft-package battery and the steel pressure plates, so that the battery is prevented from being in direct contact with the pressure plates; the four corners of the two pressure plates are fastened by screws and nuts; selecting an external mechanical load range of 0-1500N, and selecting a group of experimental points every 300N, namely setting 6 groups of comparative experiments; before the experiment is started, a metal resistance wire of a pressure sensor is bonded on the surface of a soft package battery and protected by a covering layer, and a lead-out wire is connected to a display instrument, so that the monitoring of mechanical load applied to the outside of the soft package battery is realized; the soft package battery is charged to the upper cut-off voltage through constant current and constant voltage before the test, then stands for a period of time, and is discharged to the lower cut-off voltage through constant current and constant voltage under the set mechanical load to carry out the capacity calibration test; after each group of experiments is finished, the mechanical load is reset to zero, then the battery is recharged and stands still to prepare for next capacity calibration, and 0N and 25 ℃ are used as standard environments;
step S22: researching the influence of temperature on a soft package battery, selecting a corresponding temperature interval of-10-40 ℃, taking 25 ℃ as a reference and 5 ℃ as a temperature interval, respectively performing a capacity calibration experiment and a constant current pulse experiment, fully charging and standing the battery for a period of time in a standard environment before each group of experiments, then performing constant current and constant voltage discharge to a lower cut-off voltage at the current set temperature to realize capacity calibration, recovering the temperature to 25 ℃ and standing the battery for a period of time, fully charging and standing the battery again, then continuously discharging for 6 minutes at a discharge rate of 1C, standing the battery for 30 minutes, measuring the terminal voltage of the battery at the terminal moment of standing, taking the terminal voltage at the moment as a corresponding open-circuit voltage, and repeating the operation for 10 times, namely, finally emptying the battery; at this moment, capacity calibration and constant current pulse experiments at the same temperature are completed, and then the experiment temperature is changed through a constant temperature box to repeat the operations;
step S23: the method is characterized by researching the influence of discharge rate on the soft package battery, selecting corresponding discharge intervals of 0-5C, performing capacity experiments with 0.5C as an interval between 0C and 1C as an interval between 1C and 5C, fully charging the battery in a standard environment before each experiment, standing for a period of time, and then discharging to a lower cut-off voltage through constant current and constant voltage at a current set temperature to realize capacity calibration.
5. The SOC estimation method considering the external environment influence of the soft package battery according to claim 4, wherein the step S3 specifically comprises: further processing is performed on each set of capacity values obtained in step S2, with the battery capacity at 0N, 25 ℃ and 1C discharge rate as denominator and the capacities measured under other different conditions as numerators, to perform normalization processing, and the obtained result is the capacity correction factor α described in step S3, which is defined as a function of three influencing factors, namely:
α＝f(F T R)
in the formula, F is the external mechanical load of the battery, T is the working environment temperature of the battery, R is the discharge rate, and the specific form of the function is obtained by a fitting method.
6. The SOC estimation method considering the external environment influence of the soft package battery according to claim 4, wherein the step S4 specifically comprises: processing the constant current pulse experimental data at the temperature changed in the step S22, and obtaining a power function formula by using the open-circuit voltage and the corresponding SOC value recorded at each group of temperatures in a fitting manner, wherein the specific form is as follows:
UOC＝k5z5+k4z4+k3z3+k2z2+k1z+k0
in the formula, z is an SOC value, each term coefficient in the above formula is defined as a function of temperature T, and fitting is performed again by using the same term coefficient values obtained by fitting at different temperatures, that is:
ki＝ai,4T4+ai,3T3+ai,2T2+ai,1T+ai,0
therefore, the accurate OCV-SOC relational expression can be obtained under different external environments.
7. The SOC estimation method considering the external environment influence of the soft package battery according to claim 1, wherein the adaptive unscented Kalman filtering in the step S5 specifically comprises the following steps:
s51: obtaining 2n +1 sigma points and corresponding weight omega through one-time UT conversion, wherein n is the dimension of the state vector X;
Figure FDA0002576639360000051
in the formula, subscript i is the ith column of the square root of the matrix;
Figure FDA0002576639360000052
is the state vector mean, P is the corresponding covariance matrix;
Figure FDA0002576639360000053
in the formula, subscript m is a mean value, c is a covariance, and superscript is a corresponding sampling point; λ ═ α2(n + k) -n is a scaling parameter, alpha is selected to control the distribution state of sampling points, k and beta are both parameters to be selected, and beta is more than or equal to 0;
establishing a state equation of the soft package battery based on a second-order RC model:
Figure FDA0002576639360000054
wherein x is [ U ═ U1U2z]T，u＝iL，y＝ut，
Figure FDA0002576639360000055
S52: a set of sample points is obtained by the UT transform formula of S51:
Figure FDA0002576639360000056
substituting the prediction into a state equation of S51 to obtain a one-step prediction of each point, wherein i is 1-2 n + 1;
X(i)(k+1|k)＝f[k,X(i)(k|k)]；
s53: calculating a one-step prediction and covariance matrix of the state vectors (U1, U2, z), and obtaining by weighted summation of prediction values of the sigma point set:
Figure FDA0002576639360000061
Figure FDA0002576639360000062
s54: and on the basis of the predicted value in the step S53, carrying out UT transformation again to generate a new sigma point set:
Figure FDA0002576639360000063
s55: substituting the point set into an observation equation to obtain a predicted observed quantity:
Z(i)(k+1|k)＝h[X(i)(k+1|k)]；
s56: and obtaining the predicted observed quantity of the sigma point set by the above steps, and obtaining the mean value and covariance of system prediction by weighted summation:
Figure FDA0002576639360000064
Figure FDA0002576639360000065
Figure FDA0002576639360000066
s57: calculating a gain matrix:
Figure FDA0002576639360000067
s58: updating the state vector x and the corresponding covariance using the gain matrix and the one-step predictor of the state vector:
Figure FDA0002576639360000068
Figure FDA0002576639360000069
s59: process noise Q by voltage innovationkAnd measuring the noise RkUpdating:
Figure FDA00025766393600000610
Figure FDA00025766393600000611
in the formula,iis the voltage innovation of the cell model at time k, i.e. the difference between the voltage measurement and the estimate, L is the window size, HkA covariance approximation of the voltage innovation at time k;
the last term z of the state vector x in step S58 is the finally obtained SOC value.
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