CN112285566A - SOC online estimation method and system based on gas-liquid dynamic model - Google Patents

SOC online estimation method and system based on gas-liquid dynamic model Download PDF

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CN112285566A
CN112285566A CN202011003872.5A CN202011003872A CN112285566A CN 112285566 A CN112285566 A CN 112285566A CN 202011003872 A CN202011003872 A CN 202011003872A CN 112285566 A CN112285566 A CN 112285566A
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CN112285566B (en
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华蕾
江浩斌
陈彪
孙化阳
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Jiangsu University
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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]
    • G01R31/367—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
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements

Abstract

The invention provides an SOC (system on chip) online estimation method and system based on a gas-liquid dynamic model, which comprises the following steps of: reading battery data; defining an initial quantity P matrix, a parameter vector Para, a disturbance r, a count k and an initial open-circuit voltage UOCVThe offline OCV model estimates an open-circuit voltage E _ OCV; estimating terminal voltage E _ U by using terminal voltage model0(ii) a Obtaining an H matrix by performing partial derivation on the online terminal voltage model; calculating an extended Kalman gain K; updating the P matrix and the parameter vector Para; online OCV model estimation open circuit voltage
Figure DDA0002695239160000011
Will be provided with
Figure DDA0002695239160000012
Checking an OCV-SOC relation table to obtain E _ SOC; will obtain the open circuit voltage
Figure DDA0002695239160000013
Updating the initial open circuit voltage UOCV(ii) a The method realizes the coupling of the offline parameter gas-liquid dynamic battery model and the extended Kalman algorithm, fully exerts the characteristics of small calculated amount and high speed of the offline parameter gas-liquid dynamic battery model, high robustness of the extended Kalman algorithm and rapid elimination of the state error of the model, and realizes high-precision SOC estimation under various international working conditions and synthetic working conditions.

Description

SOC online estimation method and system based on gas-liquid dynamic model
Technical Field
The invention belongs to the field of battery management systems, and particularly relates to an SOC (state of charge) online estimation method and system based on a gas-liquid dynamic model.
Background
The new energy automobile mainly comprises a pure electric automobile, a hybrid electric automobile, an electric plug hybrid electric automobile, a fuel cell automobile and the like. However, vehicle manufacturers such as tesla, audi, germany, and byaddi, china, all choose pure electric vehicles as the current major products and research directions.
In order to satisfy the driving power and the driving mileage, the electric vehicle must be equipped with hundreds to thousands of battery cells. In order to ensure the consistency of charging and discharging and safe use of all batteries, a Battery Management System (BMS) is indispensable. Among the various functions of BMS, estimating State of Charge (SOC) and Open Circuit Voltage (OCV) is the most difficult and important task, and battery models often play a dominant role in the estimation process. At present, the existing battery analysis model mainly comprises an electrochemical model and an equivalent circuit model, the two models are difficult to balance between model complexity and SOC estimation accuracy, a state equation of the model cannot directly reflect the influence of temperature on battery characteristics, and a temperature compensation coefficient or an empirical formula needs to be introduced into the model to correct an estimation result, so that the problems of difficulty in model parameter identification and the like are increased, and finally the remaining endurance mileage is difficult to determine. Therefore, the research on a novel battery model and the design of a high-precision SOC estimation algorithm based on the novel battery model have important research significance and application value.
Disclosure of Invention
Aiming at the technical problems, the invention provides an SOC online estimation method and system based on a gas-liquid dynamic model, which realizes the coupling of an offline parameter gas-liquid dynamic battery model and an extended Kalman algorithm, fully exerts the characteristics of less calculation amount, high speed and high robustness of the extended Kalman algorithm and fast elimination of the state error of the model of the offline parameter gas-liquid dynamic battery model, and realizes high-precision SOC estimation under various international working conditions and synthetic working conditions; the invention has low requirement on the sampling period, can obtain good estimation precision within a wide sampling period (1-60 seconds), and can greatly reduce the cost of sampling hardware.
The technical scheme of the invention is as follows: an SOC online estimation method based on a gas-liquid dynamic model comprises the following steps:
the method comprises the following steps: reading kth set of battery data, k being 1,2,3 …, the battery data including a combination of one or more of voltage, current, temperature, internal resistance;
step two: defining an initial quantity P matrix, a parameter vector Para, a disturbance r, a count k and an initial open-circuit voltage UOCVThe parameter vector Para ═ y1,y2,y3,y4]The initial open circuit voltage UOCVFrom the collected terminal voltage U0Giving;
step three: the kth group of data and the initial open circuit voltage UOCVSubstituting the voltage into an offline OCV model to estimate an open-circuit voltage E _ OCV;
step four: the kth group of data and the initial open circuit voltage U are comparedOCVAnd estimated open circuit voltage E _ OCV, substituting into the on-line voltage model to estimate terminal voltage E _ U0；
Step five: obtaining an H matrix by solving the equation of the online terminal voltage model through partial derivation;
step six: calculating an extended Kalman gain K according to the P matrix, the disturbance r and the H matrix;
step seven: updating the P matrix and the parameter vector Para;
step eight: the kth group of data and the initial open circuit voltage U are comparedOCVAnd step seven parameter vectors Para, substituting the seven parameter vectors Para into the online OCV model to estimate the open-circuit voltage
Figure BDA0002695239140000021
Step nine: will be provided with
Figure BDA0002695239140000022
Checking an OCV-SOC relation table to obtain E _ SOC;
step ten: opening the circuit obtained in the step eightPress and press
Figure BDA0002695239140000023
Updating the initial open circuit voltage UOCV；
And step three, repeating the cycle, and accumulating 1 for k in each cycle to finish the real-time estimation of the SOC of the battery.
In the above scheme, the battery data in the first step is terminal voltage U0Current I and temperature T.
In the above scheme, the P matrix in the initial quantity defined in step two is defined as a 4 × 4 unit matrix, the perturbation r is defined as (0, 0.1) according to engineering experience, and the count k is defined as 1.
In the above scheme, the step three off-line OCV model estimates the open-circuit voltage E _ OCV by estimating equations (1) and (2):
P2＝U0-k3I-k4i | I | charging I > 0, discharging I < 0 (1)
Figure BDA0002695239140000024
Wherein, U0Is terminal voltage, I is current, T is temperature, UOCVIs the open circuit voltage at the previous sampling instant; p2B, c are intermediate variables;
when k is 1,2,3 …, according to U0，I，T，UOCVAnd a set of model offline parameters k1,k2,k3,k4]And E _ OCV is calculated.
In the above scheme, the fourth step estimates the terminal voltage E _ U by the terminal voltage model in the estimation equation (3)0，
Figure BDA0002695239140000025
In the above scheme, in the fifth step
Figure BDA0002695239140000026
In the above scheme, in the sixth step, K ═ P × H '× (H × P × H' + r)-1。
In the above scheme, the step seven updates the P matrix and the parameter vector Para, where P ═ E (4) -K × H) × P, Para ═ Para + K × (U)0–E_U0)。
In the above scheme, the eight-step online OCV model estimates the open-circuit voltage by estimating equations (4) and (5)
Figure BDA0002695239140000027
P2＝U0-y3I-y4I | I | charging I > 0, discharging I < 0 (4)
Figure BDA0002695239140000031
A system for realizing the online SOC estimation method based on the gas-liquid dynamic model comprises a signal acquisition module, an SOC estimation module and a display module;
the signal acquisition module comprises a current sensor, a temperature sensor and a voltage sensor, is used for acquiring the current, the temperature and the voltage of the battery, is connected with the SOC estimation module, and transmits the acquired current, temperature and voltage signals to the SOC estimation module;
the SOC estimation module comprises a single chip microcomputer, and an initial quantity P matrix, a parameter vector Para, a disturbance r, a count k and an initial open-circuit voltage U are definedOCVThe kth group of data and the initial open circuit voltage UOCVSubstituting the voltage into an offline OCV model to estimate an open-circuit voltage E _ OCV; the kth group of data and the initial open circuit voltage U are comparedOCVAnd estimated E _ OCV substituted into the on-line voltage model to estimate terminal voltage E _ U0(ii) a Obtaining an H matrix by performing partial derivation on the online terminal voltage model; calculating an extended Kalman gain K according to the P matrix, the disturbance r and the H matrix; updating the P matrix and the parameter vector Para; the kth group of data and the initial open circuit voltage U are comparedOCVAnd an updated parameter vector Para substituted into the online OCV model to estimate the open circuit voltage
Figure BDA0002695239140000032
Will be provided with
Figure BDA0002695239140000033
Checking an OCV-SOC relation table to obtain E _ SOC; will obtain the open circuit voltage
Figure BDA0002695239140000034
Updating the initial open circuit voltage UOCVCircularly repeating, accumulating 1 for k in each circulation, and finishing the real-time estimation of the SOC of the battery;
the SOC estimation module is connected with the display module and sends the battery data and the SOC value to the display module for display.
Compared with the prior art, the invention has the beneficial effects that:
1. the method realizes the coupling of the offline parameter gas-liquid dynamic battery model and the extended Kalman algorithm, and fully exerts the characteristics of small calculated amount and high speed of the offline parameter gas-liquid dynamic battery model, high robustness of the extended Kalman algorithm and rapid elimination of the state error of the model.
2. The invention realizes high-precision SOC estimation under various international working conditions and synthetic working conditions, and can improve the existing estimation precision by about one time.
3. The invention has low requirement on the sampling period, can obtain good estimation precision within a wide sampling period (1-90 seconds), and can greatly reduce the cost of sampling hardware.
Drawings
FIG. 1: is an implementation flow diagram of one embodiment of the present invention;
FIG. 2: is a flow chart of an embodiment of the invention for identifying model offline parameters;
FIG. 3: is an OCV-SOC relationship diagram according to an embodiment of the present invention;
FIG. 4: is an estimation effect diagram of four working conditions of one embodiment of the invention;
FIG. 5: is a CC working condition estimation error diagram of an embodiment of the invention;
FIG. 6: is an estimation error diagram of the FUDS operating condition according to an embodiment of the present invention;
FIG. 7: the UDDS working condition estimation error map is an embodiment of the invention;
FIG. 8: is a synthetic condition estimation error map of an embodiment of the invention;
FIG. 9: is the effect of different sampling period estimation according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention. In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Fig. 1 shows a preferred embodiment of the online SOC estimation method based on a gas-liquid dynamic model according to the present invention, which includes the following steps:
the method comprises the following steps: reading kth set of battery data, k being 1,2,3 …, the battery data including a combination of one or more of voltage, current, temperature, internal resistance;
step two: defining an initial quantity P matrix, a parameter vector Para, a disturbance r, a count k and an initial open-circuit voltage UOCVThe parameter vector Para ═ y1,y2,y3,y4]The initial open circuit voltage UOCVFrom the collected terminal voltage U0Giving;
step three: the kth group of data and the initial open circuit voltage UOCVSubstituted into off-line OThe CV model estimates the open circuit voltage E _ OCV;
step four: the kth group of data and the initial open circuit voltage U are comparedOCVAnd estimated open circuit voltage E _ OCV, substituting into the on-line voltage model to estimate terminal voltage E _ U0；
Step five: obtaining an H matrix by performing partial derivation on the online terminal voltage model;
step six: calculating an extended Kalman gain K according to the P matrix, the disturbance r and the H matrix;
step seven: updating the P matrix and the parameter vector Para;
step eight: the kth group of data and the initial open circuit voltage U are comparedOCVAnd step seven parameter vectors Para, substituting the seven parameter vectors Para into the online OCV model to estimate the open-circuit voltage
Figure BDA0002695239140000041
Step nine: will be provided with
Figure BDA0002695239140000042
Checking an OCV-SOC relation table to obtain E _ SOC;
step ten: the open circuit voltage obtained in the step eight
Figure BDA0002695239140000043
Updating the initial open circuit voltage UOCV；
And step three, repeating the cycle, and accumulating 1 for k in each cycle to finish the real-time estimation of the SOC of the battery.
Preferably, the battery data in the first step is terminal voltage U0Current I and temperature T.
Preferably, in the step two, the P matrix in the initial quantity is defined as a 4 × 4 unit matrix, the perturbation r is defined as (0, 0.1) according to engineering experience, and the count k is defined as 1.
The step three off-line OCV model estimates the open-circuit voltage E _ OCV by estimating equations (1) and (2):
P2＝U0-k3I-k4i | I | charging I > 0, discharging I < 0 (1)
Figure BDA0002695239140000051
Wherein, U0Is terminal voltage, I is current, T is temperature, UOCVIs the open circuit voltage at the previous sampling instant; p2B, c are intermediate variables;
when k is 1,2,3 …, according to U0，I，T，UOCVAnd a set of model offline parameters k1,k2,k3,k4]And E _ OCV is calculated.
The fourth step estimates the terminal voltage E _ U through an estimation equation (3) by using the terminal voltage model0，
Figure BDA0002695239140000052
In the fifth step
Figure BDA0002695239140000053
In step six, K ═ P × H '× (H × P × H' + r)-1。
The seventh step updates the P matrix and the parameter vector Para, where P ═ E (4) -K × H) × P, Para ═ Para + K × (U)0–E_U0)。
The step eight online OCV model estimates the open circuit voltage by estimating equations (4) and (5)
Figure BDA0002695239140000054
P2＝U0-y3I-y4I | I | charging I > 0, discharging I < 0 (4)
Figure BDA0002695239140000055
The specific embodiment is as follows:
carrying out HPPC (high Power Per cell) experiment on the lithium ion battery under variable temperature working condition, and recording the terminal voltage, the temperature, the current and the corresponding open-circuit voltage number of the batteryThe method is used for determining the off-line parameters of a group of models, so that the lithium ion battery is subjected to dynamic condition test to verify the effectiveness of the SOC estimation method. The method for determining the offline parameters of the model is shown in fig. 2, and the process of identifying the offline parameters is disclosed in the patent application 201910217008.6, so as to obtain a set of offline parameters k1,k2,k3,k4]＝[0.4000,0.0295,0.0028,1.23×10-5]The dynamic working conditions are CC (constant Current), FUDS (coal governing scheduling), UDDS (coal dynamics governing scheduling) and synthetic working conditions, and the maximum discharge multiplying power of all the working conditions is 2.5C;
the SOC online estimation method based on the gas-liquid dynamic model comprises the following steps:
step one, reading kth group of battery data, wherein k is 1,2,3 …, and the battery data comprises terminal voltage U0Current I and temperature T;
step two, defining an initial quantity, wherein the initial quantity P is equal to an E (4) matrix, and a parameter vector Para is equal to [ y1,y2,y3,y4]The disturbance r is 0.003, the count k is 1, and the initial open circuit voltage U is set to zeroOCV＝U0(ii) a The P matrix is defined as a 4 multiplied by 4 unit matrix, the parameter vector Para is defined as a zero vector, the disturbance r is very small, the disturbance r is defined as (0, 0.1) according to engineering experience, and the initial open-circuit voltage U is obtainedOCVFrom the collected terminal voltage U0Giving;
step three, an off-line OCV model estimates the open-circuit voltage E _ OCV as 4.1987, wherein the estimation equations are shown as equations (1) and (2), as shown in table 1, and when k is 1, U is0＝4.190，I＝-3.428，T＝298.15，UOCV4.190 and k1,k2,k3,k4]＝[0.4000,0.0295,0.0028,1.23×10-5]；
P2＝U0-k3I-k4I | I | charging I > 0, discharging I < 0 (1)
Figure BDA0002695239140000061
Equations (1) and (2) are derived from the gas-liquid dynamic cell model:
the formula I is as follows: p2V＝n2RT, formula two: p3V＝n3RT, formula three:
Figure BDA0002695239140000062
the formula four is as follows:
Figure BDA0002695239140000063
the formula five is as follows:
Figure BDA0002695239140000064
wherein, P1: gas pressure in the vessel, P, at steady state2: pressure of gas in the container, P, during inflation/deflation3: pressure in the vessel at steady state, n, to be estimated2: amount of gaseous material, n, in the container during inflation/deflation3: the amount of gaseous material in the vessel at steady state to be estimated, T: gas temperature in the container, V: gas volume, R: thermodynamic constant, VW: volume of liquid, bmThe van der waals volume of the gas molecules,
Figure BDA0002695239140000069
effective gap, nj1: amount of substance dissolved in gas in liquid at steady state, nj3: the amount of material dissolved in the liquid at steady state to be estimated;
wherein the amount of substance n2、n3、nj1And nj3The satisfied control relationship is as follows: n is3＝n2+nj1-nj3；
Substituting the formula I to the formula IV into a formula VI, wherein the formula VII is as follows:
Figure BDA0002695239140000065
is provided with
Figure BDA0002695239140000066
Formula seven is rewritten as formula eight:
Figure BDA0002695239140000067
in respect of P3The quadratic equation of (a) is 1,
Figure BDA0002695239140000068
because of ac<0 and Δ ═ b24ac > 0, so that
Figure BDA0002695239140000071
In order to satisfy the electricity expression habit, P in the formula V0Corresponding terminal voltage U0V corresponds to the current I, T represents the temperature, 1/2 μ ρ ═ k3And 1/2 ρ ═ k4，P3Corresponding to E _ OCV, P1Corresponding UOCVThus, equations (1) and (2) are obtained.
TABLE 1 sample estimation results
Figure BDA0002695239140000072
Step four: estimating terminal voltage E _ U by using terminal voltage model0Wherein the estimation equation is shown in equation (3):
Figure BDA0002695239140000073
calculated E _ U0＝4.1987；
Equation (3) is derived from the gas-liquid dynamic battery model:
is provided with
Figure BDA0002695239140000074
Substituting formula seven, let 1/2 μ ρ ═ y3，1/2ρ＝y4Substituting the formula into a formula V, and substituting the formula seven into the formula V to obtain a formula nine:
Figure BDA0002695239140000075
to satisfy the electricity expression habit, P0Corresponding to E _ U0V corresponds to the current I, T represents the temperature, P3Corresponding to E _ OCV, P1Corresponding UOCVThus, equation (3) is obtained.
Step five: an H matrix of the terminal voltage model at the line is calculated,
Figure BDA0002695239140000076
step six: calculating extended kalman gain K, K ═ P × H '× (H × P × H' + r)-1；
Step seven: updating a P matrix and a parameter vector Para, wherein P (E (4) -KxH) xP, Para + K (U)0–E_U0)；
Step eight: online OCV model estimation open circuit voltage
Figure BDA0002695239140000077
Wherein the estimation equations are shown in equations (4) and (5);
P2＝U0-y3I-y4i | I | charging I > 0, discharging I < 0 (4)
Figure BDA0002695239140000081
Step nine: will be provided with
Figure BDA0002695239140000082
Looking up an OCV-SOC relation table to obtain that E _ SOC is 100.21%, wherein the OCV-SOC relation table is shown in FIG. 3;
step ten: updating the initial open circuit voltage UOCVAnd a count k of the number of bits, wherein,
Figure BDA0002695239140000083
k＝k+1；
turning to the third step, repeating the steps in a circulating manner to complete the real-time estimation of the SOC of the battery, wherein k is the estimation results of 1-8 steps as shown in table 1, the estimation results under the FUDS working condition and the UDDS working condition are shown in table 2 and fig. 4 to 8, and in order to quantitatively evaluate the SOC estimation results, defining the Mean Absolute Error (MAE) and the Maximum Error (ME) as shown in equations (6) and (7);
Figure BDA0002695239140000084
Figure BDA0002695239140000085
wherein, N represents the total number of data, l represents the count, and the number of data;
TABLE 2 estimation accuracy for four conditions
Figure BDA0002695239140000086
Fig. 4 shows the estimation effect of the present invention under CC, FUDS, UDDS and synthesis conditions, respectively, and the solid line (estimated value) and the dotted line (experimental value) are highly overlapped under four conditions, indicating that the present invention obtains a good estimation effect in the whole condition SOC of 0-100% in the discharge process; fig. 5 to 8 show the estimated errors of the model under CC, FUDS, UDDS and synthesis conditions, respectively, and table 2 shows that the minimum mean absolute error of the present invention is 0.36% under the UDDS condition, the optimum maximum estimated error is 1.59% under the constant current condition, and the maximum estimated error and the mean absolute error under the synthesis condition are 2.25% and 0.49% respectively; the invention realizes the precision that the maximum estimation error is not more than 2.5 percent and the maximum average absolute error is not more than 0.5 percent under the condition that the maximum discharge rate reaches more than 2.5C, and the estimation effect of the invention is obviously better than that of the existing 5 percent estimation technology. Fig. 9 shows the estimation effect of the present invention under different sampling periods of the FUDS condition, the present invention achieves good estimation effect in the sampling period of 1s to 60s, table 3 shows the estimation accuracy, the average absolute error is gradually increased as the sampling period increases, but the maximum error reaches a lower level at 15s and 30s and increases thereafter, and the maximum error and the average absolute error reach 2.24% and 0.54% respectively at the sampling period of 60 s.
TABLE 3 estimation results for different sampling periods
Figure BDA0002695239140000091
A system for realizing the online SOC estimation method based on the gas-liquid dynamic model comprises a signal acquisition module, an SOC estimation module and a display module;
the signal acquisition module comprises a current sensor, a temperature sensor and a voltage sensor, is used for acquiring the current, the temperature and the voltage of the battery, is connected with the SOC estimation module, and transmits the acquired current, temperature and voltage signals to the SOC estimation module;
the SOC estimation module comprises a single chip microcomputer, an initial quantity P matrix, a parameter vector Para, disturbance r, a count k and an initial open-circuit voltage U are definedOCVThe kth group of data and the initial open circuit voltage UOCVSubstituting the voltage into an offline OCV model to estimate an open-circuit voltage E _ OCV; the kth group of data and the initial open circuit voltage U are comparedOCVAnd estimated E _ OCV substituted into the on-line voltage model to estimate terminal voltage E _ U0(ii) a Obtaining an H matrix by performing partial derivation on the online terminal voltage model; calculating an extended Kalman gain K according to the P matrix, the disturbance r and the H matrix; updating the P matrix and the parameter vector Para; the kth group of data and the initial open circuit voltage U are comparedOCVAnd an updated parameter vector Para substituted into the online OCV model to estimate the open circuit voltage
Figure BDA0002695239140000092
Will be provided with
Figure BDA0002695239140000093
Checking an OCV-SOC relation table to obtain E _ SOC; will obtain the open circuit voltage
Figure BDA0002695239140000094
Updating the initial open circuit voltage UOCVCircularly repeating, accumulating 1 for k in each circulation, and finishing the real-time estimation of the SOC of the battery;
the SOC estimation module is connected with the display module and sends the battery data and the SOC value to the display module for display.
According to the present embodiment, preferably, the signal acquisition module includes a current sensor, a temperature sensor and a voltage sensor.
The SOC estimation module comprises a single chip microcomputer, preferably STM 32. The SOC online estimation method based on the gas-liquid dynamic model is realized on hardware, and can be realized on an STM32 single chip microcomputer by using codes written in C language on a Keil uVision5 development platform.
The SOC estimation module specifically comprises:
firstly, loading an STM32 single chip microcomputer library function file, configuring an STM32 single chip microcomputer register by using a library function, and compiling a clock function, a timer function, a delay function, a storage function, a data verification function, an open-circuit voltage estimation function, a main function and the like;
firstly, connecting a current sensor and a temperature sensor to a signal acquisition card, wherein the acquisition card can directly acquire the voltage of a single battery, and preferably, the voltage range of the single battery is within 0-5V;
secondly, the acquisition card is connected with a serial port of the STM singlechip, RS-232 is selected as a communication mode, and current, voltage and temperature signals of the battery are transmitted to the singlechip;
STM32 single chip microcomputer main function reads current, voltage and temperature signals of the battery, calls an OCV estimation function to calculate an open-circuit voltage value under current input, and obtains an SOC value through an OCV-SOC relationship; writing the battery current, the voltage, the temperature and the calculated SOC value into a memory card, and sending the battery current, the voltage, the temperature and the calculated SOC value to a display module of an upper computer for display;
and fourthly, circulating the steps from the first step to the third step to finish the real-time SOC estimation of the battery pack.
The upper computer is developed based on a Microsoft Visual Studio platform and is used for displaying the terminal voltage and the SOC of the battery pack, the SOC of all the series single batteries and the fitted lowest SOC of the batteries;
the singlechip includes: 2nA single-chip microcomputer, n is 1,2,3, and various arithmetic units of ARM cores;
the signal communication protocol used includes: RS-485, CAN, TCP, modbus, MPI, serial port communication and the like.
It should be understood that although the present description has been described in terms of various embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and those skilled in the art will recognize that the embodiments described herein may be combined as suitable to form other embodiments, as will be appreciated by those skilled in the art.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.

Claims (10)

1. An SOC online estimation method based on a gas-liquid dynamic model is characterized by comprising the following steps:
the method comprises the following steps: reading kth set of battery data, k being 1,2,3 …, the battery data including a combination of one or more of voltage, current, temperature, internal resistance;
step two: defining an initial quantity P matrix, a parameter vector Para, a disturbance r, a count k and an initial open-circuit voltage UOCVThe parameter vector Para ═ y1,y2,y3,y4]The initial open circuit voltage UOCVFrom the collected terminal voltage U0Giving;
step three: the kth group of data and the initial open circuit voltage UOCVSubstituting the voltage into an offline OCV model to estimate an open-circuit voltage E _ OCV;
step four: the kth group of data and the initial open circuit voltage U are comparedOCVAnd estimated open circuit voltage E _ OCV, substituting into the on-line voltage model to estimate terminal voltage E _ U0；
Step five: obtaining an H matrix by solving the equation of the online terminal voltage model through partial derivation;
step six: calculating an extended Kalman gain K according to the P matrix, the disturbance r and the H matrix;
step seven: updating the P matrix and the parameter vector Para;
step eight: the kth group of data and the initial open circuit voltageUOCVAnd step seven parameter vectors Para, substituting the seven parameter vectors Para into the online OCV model to estimate the open-circuit voltage
Figure FDA0002695239130000012
Step nine: will be provided with
Figure FDA0002695239130000013
Checking an OCV-SOC relation table to obtain E _ SOC;
step ten: open circuit voltage obtained in step eight
Figure FDA0002695239130000014
Updating the initial open circuit voltage UOCV；
And step three, repeating the cycle, and accumulating 1 for k in each cycle to finish the real-time estimation of the SOC of the battery.
2. The method for online estimation of SOC as claimed in claim 1, wherein the battery data in the first step is terminal voltage U0Current I and temperature T.
3. The method for online estimation of SOC based on the aerodynamic model of claim 1, wherein the P matrix in the initial quantity defined in step two is defined as a 4 × 4 unit matrix, the disturbance r is defined as (0, 0.1) according to engineering experience, and the count k is defined as 1.
4. The online estimation method for SOC based on gas-liquid dynamic model according to claim 1, characterized in that the step three off-line OCV model estimates the open-circuit voltage E _ OCV by estimating equations (1) and (2):
P2＝U0-k3I-k4i | I | charging I > 0, discharging I < 0 (1)
Figure FDA0002695239130000011
Wherein, U0Is terminal voltage, I is current, T is temperature, UOCVIs the open circuit voltage at the previous sampling instant; p2B, c are intermediate variables;
when k is 1,2,3 …, according to U0，I，T，UOCVAnd a set of model offline parameters k1,k2,k3,k4]And E _ OCV is calculated.
5. The method for online estimation of SOC as claimed in claim 1, wherein the step four online voltage model estimates the terminal voltage E _ U by estimating equation (3)0，
Figure FDA0002695239130000021
6. The on-line SOC estimation method based on the gas-liquid dynamic model as claimed in claim 1, wherein in the fifth step
Figure FDA0002695239130000022
7. The method of claim 1, wherein K ═ P × H '× (H × P × H' + r) in the sixth step-1。
8. The method of claim 1, wherein the step seven updates a P matrix and a parameter vector Para, wherein P (E (4) -KxH) xP, Para + Kx (U) is provided0–E_U0)。
9. The method for estimating the SOC on line based on the hydro-pneumatic model according to claim 1, wherein the step eight online OCV model is obtained by estimating equation (A)4) And (5) estimating the open circuit voltage
Figure FDA0002695239130000023
P2＝U0-y3I-y4I | I | charging I > 0, discharging I < 0 (4)
Figure FDA0002695239130000024
10. A system for realizing the online SOC estimation method based on the gas-liquid dynamic model is characterized by comprising a signal acquisition module, an SOC estimation module and a display module;
the signal acquisition module comprises a current sensor, a temperature sensor and a voltage sensor, is used for acquiring the current, the temperature and the voltage of the battery, is connected with the SOC estimation module, and transmits the acquired current, temperature and voltage signals to the SOC estimation module;
the SOC estimation module comprises a single chip microcomputer, and an initial quantity P matrix, a parameter vector Para, a disturbance r, a count k and an initial open-circuit voltage U are definedOCVThe kth group of data and the initial open circuit voltage UOCVSubstituting the voltage into an offline OCV model to estimate an open-circuit voltage E _ OCV; the kth group of data and the initial open circuit voltage U are comparedOCVAnd estimated E _ OCV substituted into the on-line voltage model to estimate terminal voltage E _ U0(ii) a Obtaining an H matrix by performing partial derivation on the online terminal voltage model; calculating an extended Kalman gain K according to the P matrix, the disturbance r and the H matrix; updating the P matrix and the parameter vector Para; the kth group of data and the initial open circuit voltage U are comparedOCVAnd an updated parameter vector Para substituted into the online OCV model to estimate the open circuit voltage
Figure FDA0002695239130000025
Will be provided with
Figure FDA0002695239130000026
Looking up the OCV-SOC relation table to obtain E _ SOC；
Will obtain the open circuit voltage
Figure FDA0002695239130000027
Updating the initial open circuit voltage UOCVCircularly repeating, accumulating 1 for k in each circulation, and finishing the real-time estimation of the SOC of the battery;
the SOC estimation module is connected with the display module and sends the battery data and the SOC value to the display module for display.
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