CN112213644A - Battery state of charge estimation method and battery management system - Google Patents
Battery state of charge estimation method and battery management system Download PDFInfo
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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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- 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
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Abstract
The invention relates to the technical field of batteries, and provides a battery state of charge estimation method, which comprises the following steps: acquiring load current and terminal voltage of a battery; determining the state parameters of the battery by adopting the load current, the terminal voltage and the state parameters of the battery at the previous moment and adopting a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance; and determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameters and adopting a second Extended Kalman Filter (EKF) algorithm. A corresponding battery management system is also provided. The method and the device can improve the accuracy of battery state of charge estimation.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery state of charge estimation method and a battery management system.
Background
The SOC (State of Charge) directly reflects the remaining battery capacity, and is a primary parameter for battery management of the electric vehicle. The prior art method for estimating the SOC of the battery includes an extended kalman filter algorithm (EKF), which is a state estimation algorithm, and considers the power battery as a dynamic system, and the SOC is an internal state variable of the system. When estimating the battery SOC (i.e., the battery state of charge), the battery model parameter Map is obtained by using an offline identification method. The offline identified parameter Map has a limited coverage range, and needs a large amount of test data in order to cover the influences of different currents, temperatures and SOCs on the parameter, so that the test cost is high, and the identification workload is large. And as the battery ages, the battery parameters change, and the fixed parameter Map inevitably greatly influences the accuracy of SOC estimation.
There are many methods for implementing the dual kalman filter algorithm, and generally, only the ohmic internal resistance of the battery is estimated on line in order to simplify the calculation. But the polarization resistance of the battery can also change along with the change of the conditions such as the SOC of the battery, the external temperature and the like, so that the estimation of the polarization related parameters of the battery is increased, and the estimation precision of the SOC can be further improved; initial values of battery parameters are usually obtained based on HPPC test data, the test period is long, and the test steps are complex; the setting of the parameters of the EKF filter is usually set according to experience, and an optimized design result is not easy to obtain.
Disclosure of Invention
In view of the above, the present invention is directed to a battery state of charge estimation method and a battery management system, so as to at least partially solve the above technical problems.
In order to achieve the above object, the present invention provides a battery state of charge estimation method, including: acquiring load current and terminal voltage of a battery; determining the state parameters of the battery by adopting the load current, the terminal voltage and the state parameters of the battery at the previous moment and adopting a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance; and determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameters and adopting a second Extended Kalman Filter (EKF) algorithm.
Preferably, the ohmic internal resistance, the polarization internal resistance and the polarization capacitance in the state parameters satisfy the following equivalent circuit model: the battery is an ideal voltage source, the ohmic internal resistance of the battery is connected with the ideal voltage source in series, and the polarization internal resistance is connected with the polarization capacitor in series after being connected in parallel.
Preferably, the determining the state parameter of the battery by using the first extended kalman filter EKF algorithm includes: taking the state parameter of the battery at the last moment as a prediction state; taking the state parameters calculated by the load current, the terminal voltage and the equivalent circuit model as measurement states; and obtaining the state parameters of the battery through the first Extended Kalman Filter (EKF) algorithm based on the predicted state and the measured state of the state parameters.
Preferably, the initial value of the state parameter is obtained by: acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current collection value and a voltage collection value corresponding to the current collection value; taking the initial value of the state parameter as a parameter to be calibrated, and inputting a model as the current acquisition value; the model calculation value is a terminal voltage estimation value, and the parameter to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining the optimal value of the initial value of the state parameter by adopting a genetic algorithm function by taking the minimum mean square error of the terminal voltage estimated value and the voltage acquired value corresponding to the same current acquired value as an optimization target.
Preferably, the determining the state of charge of the battery by using the load current, the terminal voltage and the state parameter and using a second Extended Kalman Filter (EKF) algorithm includes: obtaining a predicted state of the state of charge based on a state transition equation of the state of charge, wherein the state transition equation is as follows:
in the formula: x is the number ofkTo comprise a lotusThe kth state vector of the electric state, eta is the coulombic efficiency, Q is the available total capacity of the battery, and T is the algorithm sampling time; the estimated open-circuit voltage calculated by the load current, the state parameters and the equivalent circuit model is used as a measurement state according to the state of charge corresponding to the estimated open-circuit voltage; and obtaining the state of charge of the battery through the second extended Kalman filter EKF algorithm based on the predicted state and the measured state of the state of charge.
Preferably, the filter parameters in the second extended kalman filter EKF algorithm are determined by: acquiring second test data of a preset test working condition at a certain temperature; the second test data comprises a current collection value and a voltage collection value corresponding to the current collection value; obtaining a first model estimation value by adopting an ampere-hour integration method based on the current acquisition value and the voltage acquisition value, and obtaining a second model estimation value by adopting a second Extended Kalman Filter (EKF) algorithm; and adjusting the filter parameters to minimize the mean square error of the first model estimation value and the second model estimation value by taking the stability and the precision requirement of the algorithm as targets.
In a second aspect of the present invention, there is also provided a battery management system, including: the parameter acquisition module is used for acquiring the load current and the terminal voltage of the battery; the first calculation module is used for determining the state parameters of the battery by adopting the load current, the terminal voltage and the state parameters of the battery at the last moment and adopting a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance; and the second calculation module is used for determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameters and adopting a second Extended Kalman Filter (EKF) algorithm.
Preferably, the ohmic internal resistance, the polarization internal resistance and the polarization capacitance in the state parameters satisfy the following equivalent circuit model: the battery is an ideal voltage source, the ohmic internal resistance of the battery is connected with the ideal voltage source in series, and the polarization internal resistance is connected with the polarization capacitor in series after being connected in parallel.
Preferably, the battery management system includes a battery state initial value storage module, and the initial value of the stored state parameter is obtained by the following steps: acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current collection value and a voltage collection value corresponding to the current collection value; taking the initial value of the state parameter as a parameter to be calibrated, and inputting a model as the current acquisition value; the model calculation value is a terminal voltage estimation value, and the parameter to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining the optimal value of the initial value of the state parameter by adopting a genetic algorithm function by taking the minimum mean square error of the terminal voltage estimated value and the voltage acquired value corresponding to the same current acquired value as an optimization target.
Preferably, the filter parameters in the second extended kalman filter EKF algorithm are determined by: acquiring second test data of a preset test working condition at a certain temperature; the second test data comprises a current collection value and a voltage collection value corresponding to the current collection value; obtaining a first model estimation value by adopting an ampere-hour integration method based on the current acquisition value and the voltage acquisition value, and obtaining a second model estimation value by adopting a second Extended Kalman Filter (EKF) algorithm; and adjusting the filter parameters to minimize the mean square error of the first model estimation value and the second model estimation value by taking the stability and the precision requirement of the algorithm as targets.
In a third aspect of the present invention, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the aforementioned battery state of charge estimation method.
Compared with the prior art, the battery state of charge estimation method has the following advantages:
the technical scheme of the invention provides a double EKF (extended Kalman Filter) implementation method, based on a first-order RC equivalent circuit model, and two independent EKFs are used for respectively carrying out online estimation on all resistance-capacitance parameters and SOC (state of charge) of the battery model, so that the test cost and the workload of offline identification parameters are reduced, the calculation accuracy of the battery model and SOC is effectively improved, and the operation load cannot be obviously improved. The algorithm of the initial value of the battery parameter is short in test period and simple in test steps. And (3) setting parameters of the EKF filter, and adjusting by integrating an experience-based simulation method and a working condition-based simulation method, so that the algorithm is stable and the SOC error can be corrected quickly.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart diagram of a method for estimating state of charge of a battery according to an embodiment of the present invention;
FIG. 2 is a block diagram of an algorithm for a battery state of charge estimation method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an equivalent circuit model provided by an embodiment of the invention;
fig. 4 is a schematic structural diagram of a battery management system according to an embodiment of the present invention.
Detailed Description
In addition, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic flow chart of a battery state of charge estimation method according to an embodiment of the present invention, as shown in fig. 1. A Battery state of charge estimation method applied to a Battery Management System (BMS), the estimation method comprising:
s11, acquiring the load current and terminal voltage of the battery;
the state of charge (SOC) of a battery cannot be measured directly and is an indirect parameter. The load current and terminal voltage of the battery may be measured by an ammeter and a voltmeter, respectively. And calculating the charge state of the battery by adopting a corresponding algorithm according to the acquired load current and terminal voltage of the battery. In the embodiment of the present invention, the method for obtaining the above-described measurement value is not particularly limited, and a method used in the related art may be referred to. In the embodiment of the present invention, the battery management system may directly obtain the load current of the battery and the terminal voltage of the battery.
S12, determining the state parameters of the battery by adopting the load current, the terminal voltage and the state parameters of the battery at the previous moment and adopting a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance;
batteries are used to provide voltage and current in the circuit, which also have their own state parameters, such as: ohmic internal resistance, polarization internal resistance and polarization capacitance. These state parameters affect the operating state of the battery and also have some effect on the estimation of the state of charge of the battery. Therefore, the accurate state parameters are acquired, and the estimation accuracy of the state of charge is improved. In the embodiment of the invention, according to the obtained load current and the terminal voltage and the state parameter at the previous moment, the EKF algorithm is adopted, so that the more accurate state parameter at the current moment can be obtained.
And S13, determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameters and adopting a second Extended Kalman Filter (EKF) algorithm.
And respectively calculating the predicted state and the observed state of the charge state of the battery based on the state parameters obtained by calculation in the last step and the measured load current and terminal voltage, and obtaining the final charge state of the battery through a corresponding Extended Kalman Filter (EKF) algorithm.
Fig. 2 is a block diagram of an algorithm of a battery state of charge estimation method according to an embodiment of the present invention, as shown in fig. 2. By the estimation method provided by the embodiment of the invention, the calculation accuracy of the state of charge can be further improved, and the calculation complexity is not increased obviously.
Fig. 3 is a schematic diagram of an equivalent circuit model provided by the embodiment of the invention, as shown in fig. 3. In an embodiment of the present invention, the ohmic internal resistance, the polarization internal resistance and the polarization capacitance in the state parameters satisfy the following equivalent circuit model: the battery is an ideal voltage source, the ohmic internal resistance of the battery is connected with the ideal voltage source in series, and the polarization internal resistance is connected with the polarization capacitor in series after being connected in parallel. The equivalent circuit model in the embodiment of the present invention does not limit the order of the RC equivalent circuit model, and may be a first-order RC equivalent circuit model, a second-order RC equivalent circuit model, or a multi-order RC equivalent circuit model. In the embodiment of the present invention, a first-order RC equivalent circuit model is taken as an example, and as shown in fig. 3, the first-order RC equivalent circuit model is a first-order RC equivalent circuit model of a battery, which includes an ohmic internal resistance Ro, an RC network unit composed of a polarization resistance Rp and a polarization capacitor Cp connected in parallel, and a voltage source SOC-OCV, which are connected in series in sequence. Wherein, Uoc is the open circuit voltage of the battery, Uoc ═ Uoc (soc); ro describes ohmic internal resistance of the battery, Rp and Cp describe polarization effect of the battery, namely polarization internal resistance and polarization capacitance of the battery respectively; i isLThe total current through the battery is made positive during discharging and negative during charging; uo is ohmic internal resistance voltage division, Up is polarization resistance voltage division, ULIs the battery terminal voltage. In addition, the terminal voltage of each battery is the terminal voltage U at two ends of the circuit after the voltage source SOC-OCV, the ohmic internal resistance R0 and the RC network unit are connected in seriesL. The above equivalent circuit model is used to determine the voltage and current relationships between the elements in the circuit. Compared with other models, the first-order equivalent circuit model can reduce the operation load.
In an embodiment of the present invention, the determining the state parameter of the battery by using a first extended kalman filter EKF algorithm includes: taking the state parameter of the battery at the last moment as a prediction state; taking the state parameters calculated by the load current, the terminal voltage and the equivalent circuit model as measurement states; and obtaining the state parameters of the battery through the first Extended Kalman Filter (EKF) algorithm based on the predicted state and the measured state of the state parameters. And further, the determining the state of charge of the battery by using the load current, the terminal voltage and the state parameters and using a second Extended Kalman Filter (EKF) algorithm comprises:
obtaining a predicted state of the state of charge based on a state transition equation of the state of charge, wherein the state transition equation is as follows:
in the formula: x is the number ofkA kth state vector containing a state of charge specifically: at the kth moment, the predicted value of a state vector consisting of polarization voltage and a charge state is obtained, eta is the coulombic efficiency, Q is the available total capacity of the battery, T is the algorithm sampling time, and Rp and Cp are respectively the polarization resistance and the polarization capacitance of the battery;
the estimated open-circuit voltage calculated by the load current, the state parameters and the equivalent circuit model is used as a measurement state according to the state of charge corresponding to the estimated open-circuit voltage; and obtaining the state of charge of the battery through the second extended Kalman filter EKF algorithm based on the predicted state and the measured state of the state of charge. Specifically, the method comprises the following steps:
the main input of the double EKF algorithm model is total battery current ILAnd terminal voltage UL. Wherein, the state vector estimated by the EKF parameter identification module (i.e. the first extended kalman filter EKF algorithm) is θ ═ RoRpCp]TThe state vector estimated by the EKF SOC estimation module (i.e. the second extended kalman filter EKF algorithm) is x ═ Up SOC]T。
The state equation and the output equation of the EKF parameter identification module are respectively:
θk＝θk-1formula (1)
Wherein the content of the first and second substances,
representing an estimate of the battery terminal voltage at time k.
The state equation of the EKF SOC estimation module is as follows:
wherein eta is the coulombic efficiency, Q is the total available capacity of the battery, and T is the algorithm sampling time.
The output equation of the EKF SOC estimation module is the same as the output equation of the parameter identification module, namely the equation (2).
Further, an EKF parameter identification module is obtained, and the system state matrix is:
the output matrix is:
with respect to the output matrix CθConsidering the correlation between the state vector x and the parameter vector theta, the calculation of the full differential of the terminal voltage to the parameter vector is required, that is, the calculation process includes
The specific calculation process will be further described below.
The system state matrix in the EKF estimation module is:
the output matrix is:
wherein the content of the first and second substances,
can be calculated according to a map table or a relation function of SOC-OCV.
Based on the state matrix and the output matrix result, the iterative equations of the algorithm are shown in (8) - (17):
combining the dual EKF iterative equations to obtain the output matrix C of the parameter identification moduleθThe iterative solution process of (a) is:
in an embodiment of the present invention, the initial value of the state parameter is obtained by:
acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current collection value and a voltage collection value corresponding to the current collection value; taking the initial value of the state parameter as a parameter to be calibrated, and inputting a model as the current acquisition value; the model calculation value is a terminal voltage estimation value, and the parameter to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining the optimal value of the initial value of the state parameter by adopting a genetic algorithm function by taking the minimum mean square error of the terminal voltage estimated value and the voltage acquired value corresponding to the same current acquired value as an optimization target. The method comprises the following specific steps: and acquiring NEDC (New European test cycle) or WLTC (global light automobile test cycle) working condition test data of the battery at 25 ℃, and importing the working condition test data into a Matlab working space. A model of the equivalent circuit shown in FIG. 1 is built on Simulink, and Ro/Rp/Cp is set as a parameter to be calibrated. The model input is a current acquisition value; the model output is the terminal voltage estimate V mdl. The MATLAB genetic algorithm function GA is called to search for the optimal value of Ro/Rp/Cp. The genetic algorithm optimization target is that the mean square error of the equivalent circuit model estimated voltage V _ mdl and the acquired voltage V _ meas is minimum. By adopting the steps, the more accurate state parameters of the battery model can be obtained.
In an embodiment of the invention, the filter parameters in the second extended kalman filter EKF algorithm are determined by: acquiring second test data of a preset test working condition at a certain temperature; the second test data comprises a current collection value and a voltage collection value corresponding to the current collection value; obtaining a first model estimation value by adopting an ampere-hour integration method based on the current acquisition value and the voltage acquisition value, and obtaining a second model estimation value by adopting a second Extended Kalman Filter (EKF) algorithm; and adjusting the filter parameters to minimize the mean square error of the first model estimation value and the second model estimation value by taking the stability and the precision requirement of the algorithm as targets. The method comprises the following specific steps: and acquiring NEDC or WLTC working condition test data of the battery at 25 ℃ and introducing the NEDC or WLTC working condition test data into a Matlab working space. An algorithm model is built on the Simulink based on the formulas (8) to (21), the input of the model is a voltage and current acquisition value, and the output of the model is an SOC estimation value; and constructing an SOC reference value calculation model (such as an ampere-hour integration method). According to the Simulink simulation result, filter parameters are adjusted and optimized, and the following performance indexes are comprehensively considered for parameter determination: 1) the algorithm is stable, the calculation result is converged, and abnormal jump does not occur; 2) the SOC precision meets the requirement; 3) the noise of the estimated voltage and current can be filtered; under the premise of meeting 1), 2) and 3), the initial error of the SOC can be quickly corrected.
Correspondingly, fig. 4 is a schematic structural diagram of a battery management system according to an embodiment of the present invention, as shown in fig. 4. In the present embodiment, there is also provided a battery management system, where the battery management system 30 includes: a parameter obtaining module 31, configured to obtain a load current and a terminal voltage of the battery; the first calculation module 32 is configured to determine the state parameter of the battery by using the load current, the terminal voltage, and the state parameter of the battery at the previous moment and by using a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance; and a second calculation module 33, configured to determine the state of charge of the battery by using the load current, the terminal voltage, and the state parameter and using a second extended kalman filter EKF algorithm.
Further, the ohmic internal resistance, the polarization internal resistance and the polarization capacitance in the state parameters satisfy the following equivalent circuit model: the battery is an ideal voltage source, the ohmic internal resistance of the battery is connected with the ideal voltage source in series, and the polarization internal resistance is connected with the polarization capacitor in series after being connected in parallel.
Further, the battery management system comprises a battery state initial value storage module, and the initial value of the stored state parameter is obtained by adopting the following steps: acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current collection value and a voltage collection value corresponding to the current collection value; taking the initial value of the state parameter as a parameter to be calibrated, and inputting a model as the current acquisition value; the model calculation value is a terminal voltage estimation value, and the parameter to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining the optimal value of the initial value of the state parameter by adopting a genetic algorithm function by taking the minimum mean square error of the terminal voltage estimated value and the voltage acquired value corresponding to the same current acquired value as an optimization target.
Further, the filter parameters in the second extended kalman filter EKF algorithm are determined by: acquiring second test data of a preset test working condition at a certain temperature; the second test data comprises a current collection value and a voltage collection value corresponding to the current collection value; the second test data may be the same as or different from the first test data. Obtaining a first model estimation value by adopting an ampere-hour integration method based on the current acquisition value and the voltage acquisition value, and obtaining a second model estimation value by adopting a second Extended Kalman Filter (EKF) algorithm; and adjusting the filter parameters to minimize the mean square error of the first model estimation value and the second model estimation value by taking the stability and the precision requirement of the algorithm as targets.
The operation process of the battery management system is as described in the implementation process of the battery state of charge estimation method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A battery state of charge estimation method, comprising:
acquiring load current and terminal voltage of a battery;
determining the state parameters of the battery by adopting the load current, the terminal voltage and the state parameters of the battery at the previous moment and adopting a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance;
and determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameters and adopting a second Extended Kalman Filter (EKF) algorithm.
2. The battery state of charge estimation method of claim 1, wherein the ohmic internal resistance, the polarization internal resistance and the polarization capacitance in the state parameters satisfy the following equivalent circuit model: the battery is an ideal voltage source, the ohmic internal resistance of the battery is connected with the ideal voltage source in series, and the polarization internal resistance is connected with the polarization capacitor in series after being connected in parallel.
3. The method according to claim 2, wherein said determining the state parameter of the battery using a first Extended Kalman Filter (EKF) algorithm comprises:
taking the state parameter of the battery at the last moment as a prediction state;
taking the state parameters calculated by the load current, the terminal voltage and the equivalent circuit model as measurement states;
and obtaining the state parameters of the battery through the first Extended Kalman Filter (EKF) algorithm based on the predicted state and the measured state of the state parameters.
4. The battery state of charge estimation method of claim 3, wherein the initial value of the state parameter is obtained by:
acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current collection value and a voltage collection value corresponding to the current collection value;
taking the initial value of the state parameter as a parameter to be calibrated, and inputting a model as the current acquisition value; the model calculation value is a terminal voltage estimation value, and the parameter to be calibrated, the model input and the model calculation value meet the equivalent circuit model;
and taking the minimum mean square error of the terminal voltage estimated value and the voltage acquired value corresponding to the same current acquired value as an optimization target, and taking an optimal value determined by a genetic algorithm function as an initial value of the state parameter.
5. The battery state of charge estimation method of claim 1, wherein said determining the state of charge of the battery using the load current, the terminal voltage and the state parameters using a second Extended Kalman Filter (EKF) algorithm comprises:
obtaining a predicted state of the state of charge based on a state transition equation of the state of charge, wherein the state transition equation is as follows:
in the formula: x is the number ofkIs the kth state including the state of chargeVector quantity, wherein eta is coulombic efficiency, Q is total available capacity of the battery, T is algorithm sampling time, Rp is polarization resistance of the battery, and Cp is polarization capacitance of the battery;
the estimated open-circuit voltage calculated by the load current, the state parameters and the equivalent circuit model is used as a measurement state according to the state of charge corresponding to the estimated open-circuit voltage;
and obtaining the state of charge of the battery through the second extended Kalman filter EKF algorithm based on the predicted state and the measured state of the state of charge.
6. The battery state of charge estimation method of claim 5, wherein the filter parameters in the second extended Kalman Filter EKF algorithm are determined by:
acquiring second test data of a preset test working condition at a certain temperature; the second test data comprises a current collection value and a voltage collection value corresponding to the current collection value;
obtaining a first model estimation value by adopting an ampere-hour integration method based on the current acquisition value and the voltage acquisition value, and obtaining a second model estimation value by adopting a second Extended Kalman Filter (EKF) algorithm;
and adjusting the filter parameters to minimize the mean square error of the first model estimation value and the second model estimation value by taking the stability and the precision requirement of the algorithm as targets.
7. A battery management system, characterized in that the battery management system comprises:
the parameter acquisition module is used for acquiring the load current and the terminal voltage of the battery;
the first calculation module is used for determining the state parameters of the battery by adopting the load current, the terminal voltage and the state parameters of the battery at the last moment and adopting a first Extended Kalman Filter (EKF) algorithm; the state parameters include: ohmic internal resistance, polarization internal resistance and polarization capacitance; and
and the second calculation module is used for determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameters and adopting a second Extended Kalman Filter (EKF) algorithm.
8. The battery management system according to claim 7, wherein the ohmic internal resistance, the polarization internal resistance, and the polarization capacitance in the state parameters satisfy the following equivalent circuit model: the battery is an ideal voltage source, the ohmic internal resistance of the battery is connected with the ideal voltage source in series, and the polarization internal resistance is connected with the polarization capacitor in series after being connected in parallel.
9. The battery management system according to claim 8, wherein the battery management system comprises a battery state initial value storage module, and the initial value of the stored state parameter is obtained by the following steps:
acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current collection value and a voltage collection value corresponding to the current collection value;
taking the initial value of the state parameter as a parameter to be calibrated, and inputting a model as the current acquisition value; the model calculation value is a terminal voltage estimation value, and the parameter to be calibrated, the model input and the model calculation value meet the equivalent circuit model;
and taking the minimum mean square error of the terminal voltage estimated value and the voltage acquired value corresponding to the same current acquired value as an optimization target, and taking an optimal value determined by a genetic algorithm function as an initial value of the state parameter.
10. The battery management system of claim 8, wherein the filter parameters in the second extended kalman filter EKF algorithm are determined by:
acquiring second test data of a preset test working condition at a certain temperature; the second test data comprises a current collection value and a voltage collection value corresponding to the current collection value;
obtaining a first model estimation value by adopting an ampere-hour integration method based on the current acquisition value and the voltage acquisition value, and obtaining a second model estimation value by adopting a second Extended Kalman Filter (EKF) algorithm;
and adjusting the filter parameters to minimize the mean square error of the first model estimation value and the second model estimation value by taking the stability and the precision requirement of the algorithm as targets.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115453376A (en) * | 2022-10-26 | 2022-12-09 | 国网浙江省电力有限公司宁波市北仑区供电公司 | State monitoring method and system for power battery of electric automobile and electric automobile |
| CN116699415A (en) * | 2023-05-26 | 2023-09-05 | 云储新能源科技有限公司 | Method and system for estimating electric quantity of dynamic reconfigurable battery system and electronic equipment |
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