CN112213644B - 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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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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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
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- G—PHYSICS
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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
- 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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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 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, polarized internal resistance and polarized capacitance; and determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameter and adopting a second extended Kalman filter EKF algorithm. A corresponding battery management system is also provided. The embodiment of the invention can improve the accuracy of battery charge state estimation.
Description
Technical Field
The present invention relates to the field of battery technologies, and in particular, to a battery state of charge estimation method and a battery management system.
Background
SOC (State of Charge) directly reflects the remaining power of the battery, and is a primary parameter for battery management of the electric vehicle. The prior art SOC estimation method for the battery includes an Extended Kalman Filter (EKF), which is a state estimation algorithm, and regards the power battery as a dynamic system, and SOC is an internal state variable of the system. When estimating the battery SOC (i.e., battery state of charge), the battery model parameters Map are obtained using an offline identification method. The parameters Map identified offline are limited in coverage range, and in order to cover the influence of different currents, temperatures and SOCs on the parameters, a large amount of test data is needed, 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 can greatly influence the accuracy of SOC estimation.
There are many implementation methods of the dual kalman filter algorithm, and in order to simplify the calculation, only the ohmic internal resistance of the battery is estimated online. However, the polarization resistance of the battery also changes along with the change of conditions such as the battery SOC, the external temperature and the like, so that the estimation of the polarization related parameters of the battery is increased, and the estimation accuracy of the SOC can be further improved; initial values of battery parameters are usually required to be obtained based on HPPC test data, the test period is long, and the test steps are complex; however, the parameters of the EKF filter are usually set empirically, so that the optimum design result is not easily obtained.
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-mentioned problems.
To achieve the above object, the present invention provides a battery state of charge estimation method, the estimation method comprising: acquiring load current and terminal voltage of a battery; determining 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, polarized internal resistance and polarized capacitance; and determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameter and adopting a second extended Kalman filter EKF algorithm.
Preferably, the ohmic internal resistance, the polarized internal resistance and the polarized 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 polarized internal resistance and the polarized capacitor are connected in parallel and then connected with the ideal voltage source in series.
Preferably, the determining the state parameter of the battery by using a first extended kalman filter EKF algorithm includes: taking a 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 acquisition value and a voltage acquisition value corresponding to the current acquisition 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 parameters to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining an 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 acquisition value corresponding to the same current acquisition value as an optimization target.
Preferably, said determining the state of charge of said battery using said load current, said terminal voltage and said state parameter using a second extended kalman filter EKF algorithm comprises: obtaining a predicted state of charge based on a state transfer equation of the state of charge, wherein the state transfer equation is as follows:
wherein: x is x k For a kth state vector containing a state of charge, η is coulombic efficiency, Q is the total available capacity of the battery, and T is the algorithmic sampling time; taking the estimated open-circuit voltage calculated by the load current, the state parameter and the equivalent circuit model, and taking the state of charge corresponding to the estimated open-circuit voltage as a measurement state; 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 preset test working conditions at a certain temperature; the second test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition value; based on the current acquisition value and the voltage acquisition value, obtaining a first model estimation value by adopting an ampere-hour integration method, and obtaining a second model estimation value by adopting a second extended Kalman filter EKF algorithm; and adjusting the filter parameters by taking the requirements of algorithm stability and accuracy as targets, so that the mean square error of the first model estimated value and the second model estimated value is minimum.
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 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, polarized internal resistance and polarized capacitance; and the second calculation module is used for determining the charge state of the battery by adopting the load current, the terminal voltage and the state parameter and adopting a second extended Kalman filter EKF algorithm.
Preferably, the ohmic internal resistance, the polarized internal resistance and the polarized 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 polarized internal resistance and the polarized capacitor are connected in parallel and then connected with the ideal voltage source in series.
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 acquisition value and a voltage acquisition value corresponding to the current acquisition 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 parameters to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining an 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 acquisition value corresponding to the same current acquisition 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 preset test working conditions at a certain temperature; the second test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition value; based on the current acquisition value and the voltage acquisition value, obtaining a first model estimation value by adopting an ampere-hour integration method, and obtaining a second model estimation value by adopting a second extended Kalman filter EKF algorithm; and adjusting the filter parameters by taking the requirements of algorithm stability and accuracy as targets, so that the mean square error of the first model estimated value and the second model estimated value is minimum.
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 provided by the invention has the following advantages:
the battery state of charge estimation method of the invention realizes that the technical scheme provides a double EKF (extended Kalman filter) implementation method, and based on a first-order RC equivalent circuit model, two independent EKFs are used for respectively carrying out on-line estimation on all resistance capacitance parameters and SOC of the battery model, so that the test cost and the off-line identification parameter workload are reduced, the calculation precision of the battery model and the SOC is effectively improved, and the operation load is not remarkably improved. The algorithm of the initial value of the battery parameter is provided, the test period is short, and the test steps are simple. And the parameters of the EKF filter are set, so that the simulation method is comprehensively adjusted based on experience and working condition data, and the algorithm is stable and can quickly correct the SOC error.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention, illustrate and explain the invention and are not to be construed as limiting the invention. In the drawings:
fig. 1 is a flowchart of a battery state of charge estimation method according to an embodiment of the present invention;
FIG. 2 is an algorithm block diagram of a battery state of charge estimation method provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of an equivalent circuit model provided by an embodiment of the present 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 collision.
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flowchart 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 (Battery Management System, BMS), the estimation method comprising:
s11, acquiring load current and terminal voltage of the battery;
the state of charge (SOC) of a battery cannot be measured directly, and belongs to an indirect parameter. But the load current and terminal voltage of the battery can be measured by a ammeter and voltmeter, respectively. The state of charge of the battery can be calculated by adopting a corresponding algorithm through the obtained load current and terminal voltage of the battery. In the embodiment of the present invention, the method for obtaining the above-mentioned measurement value is not particularly limited, and the method used in the prior art may be referred to. In the embodiment of the invention, the battery management system can directly obtain the load current of the battery and the terminal voltage of the battery.
S12, determining 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, polarized internal resistance and polarized capacitance;
the battery is used to provide the voltage and current in the circuit, which also has its own state parameters such as: ohmic internal resistance, polarized internal resistance and polarized capacitance. These state parameters affect the operating state of the battery and also have a certain influence 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 facilitated to be improved. In the embodiment of the invention, according to the obtained load current, the terminal voltage and the state parameter of the last moment, the more accurate state parameter of the current moment can be obtained by adopting an extended Kalman filter EKF algorithm.
S13, determining the state of charge of the battery by adopting the load current, the terminal voltage and the state parameter and adopting a second extended Kalman filter EKF algorithm.
Based on the state parameters obtained in the previous step, the measured load current and terminal voltage, respectively calculating the predicted state and the observed state of the charge state of the battery, and obtaining the final charge state of the battery through a corresponding extended Kalman filter EKF algorithm.
Fig. 2 is an algorithm block diagram 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 remarkably increased.
Fig. 3 is a schematic diagram of an equivalent circuit model provided in an embodiment of the present invention, as shown in fig. 3. In an embodiment of the present invention, the ohmic internal resistance, the polarized internal resistance and the polarized capacitance among 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 polarized internal resistance and the polarized capacitor are connected in parallel and then connected with the ideal voltage source in series. The equivalent circuit model in the embodiment of the invention is not limited to 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 illustrated as an example, and as shown in fig. 3, the first-order RC equivalent circuit model of a battery includes an ohmic internal resistance Ro, an RC network unit composed of a polarization resistor Rp and a parallel-connected polarization capacitor Cp, and a voltage source SOC-OCV, which are sequentially connected in series. Wherein Uoc is the battery open circuit voltageUoc=uoc (SOC); ro describes the ohmic internal resistance of the battery, rp and Cp describe the polarization effect of the battery, which are respectively the internal resistance of the battery and the polarization capacitance; i L The total current passing through the battery is positive when discharging and negative when charging; uo is ohmic internal resistance voltage division, up is polarization resistance voltage division, U L Is the battery terminal voltage. In addition, the terminal voltage of each battery is the terminal voltage U of two ends of the circuit after the voltage source SOC-OCV, the ohmic internal resistance R0 and the RC network unit are connected in series L . 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 is adopted, and the operation load can be reduced.
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 a 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, said determining a state of charge of said battery using said load current, said terminal voltage and said state parameter using a second extended kalman filter EKF algorithm, comprising:
obtaining a predicted state of charge based on a state transfer equation of the state of charge, wherein the state transfer equation is as follows:
wherein: x is x k The kth state vector containing the state of charge is specifically: at the k moment, the predicted value of a state vector formed by polarization voltage and state of charge, eta is coulombic efficiency, Q is the total available capacity of the battery, T is algorithm sampling time, and Rp and Cp are battery polarization resistance and polarization capacitance respectively;
taking the estimated open-circuit voltage calculated by the load current, the state parameter and the equivalent circuit model, and taking the state of charge corresponding to the estimated open-circuit voltage as a measurement state; 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. Specific:
the main input of the double EKF algorithm model is the total current I of the battery L And terminal voltage U L . Wherein, the state vector estimated by the EKF parameter identification module (i.e. the first extended kalman filter EKF algorithm) is θ= [ R o R p C p ] T The state vector estimated by the EKF SOC estimation module (i.e., the second extended kalman filter EKF algorithm) is x= [ U p SOC] T 。
The state equation and the output equation of the EKF parameter identification module are respectively:
θ k ＝θ k-1 formula (1)
Wherein, the liquid crystal display device comprises a liquid crystal display device,
an estimated value of the battery terminal voltage at the time k is shown.
The state equation of the EKF SOC estimation module is:
where η is coulombic efficiency, Q is total battery capacity available, and T is 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 formula (2).
Furthermore, an EKF parameter identification module may be obtained, where the system state matrix is:
the output matrix is:
with respect to the output matrix C θ Considering the correlation of the state vector x and the parameter vector θ, it is necessary to calculate the full differential of the terminal voltage to the parameter vector, i.e., it is necessary to include
The terms, specific calculations, are further described below.
The system state matrix in the EKF estimation module is:
the output matrix is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
can be calculated according to a map table or a relation function of the SOC-OCV.
Based on the state matrix and the output matrix result, the iterative equations of the algorithm are as shown in (8) - (17):
combining the double EKF iterative equations to obtain the parameter identification module output matrix C θ The iterative solving process of (a) is as follows:
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 acquisition value and a voltage acquisition value corresponding to the current acquisition 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 parameters to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining an 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 acquisition value corresponding to the same current acquisition value as an optimization target. The method comprises the following specific steps: NEDC (New Standard European test cycle) or WLTC (Global light vehicle test cycle) working condition test data of the battery at 25 ℃ are obtained and imported into Matlab working space. And (3) constructing a model of an equivalent circuit shown in fig. 1 in Simulink, and setting Ro/Rp/Cp as parameters to be calibrated. The model input is a current acquisition value; the model output is the terminal voltage estimate v_mdl. And calling MATLAB genetic algorithm function GA to search Ro/Rp/Cp optimal values. Wherein the genetic algorithm optimization target is that the mean square error of the estimated voltage V_mdl and the acquired voltage V_meas of the equivalent circuit model is minimum. By adopting the steps, the state parameters of the battery model can be obtained accurately.
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 preset test working conditions at a certain temperature; the second test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition value; based on the current acquisition value and the voltage acquisition value, obtaining a first model estimation value by adopting an ampere-hour integration method, and obtaining a second model estimation value by adopting a second extended Kalman filter EKF algorithm; and adjusting the filter parameters by taking the requirements of algorithm stability and accuracy as targets, so that the mean square error of the first model estimated value and the second model estimated value is minimum. The method comprises the following specific steps: NEDC or WLTC working condition test data of the battery at 25 ℃ are obtained and imported into a Matlab working space. Constructing an algorithm model in the Simulink based on the formulas (8) to (21), wherein the model is input into a voltage and current acquisition value, and the model is output into 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, the parameters of the filter are adjusted and optimized, and the parameters are determined by comprehensively considering the following performance indexes: 1) Algorithm stability, convergence of calculation results, and no abnormal jump; 2) The SOC precision meets the requirement; 3) The estimated voltage and current noise can be filtered; on the premise of meeting 1), 2) and 3), the initial error of the SOC can be quickly corrected.
Accordingly, 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. There is also provided a battery management system in the present embodiment, the battery management system 30 including: a parameter obtaining module 31, configured to obtain a load current and a terminal voltage of the battery; a first calculation module 32, configured to determine a state parameter of the battery using the load current, the terminal voltage, and a state parameter of the battery at a previous time, and using a first extended kalman filter EKF algorithm; the state parameters include: ohmic internal resistance, polarized internal resistance and polarized capacitance; and a second calculation module 33 for determining the state of charge of the battery using a second extended kalman filter EKF algorithm using the load current, the terminal voltage and the state parameter.
Further, the ohmic internal resistance, the polarized internal resistance and the polarized 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 polarized internal resistance and the polarized capacitor are connected in parallel and then connected with the ideal voltage source in series.
Further, 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 acquisition value and a voltage acquisition value corresponding to the current acquisition 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 parameters to be calibrated, the model input and the model calculation value meet the equivalent circuit model; and determining an 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 acquisition value corresponding to the same current acquisition 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 preset test working conditions at a certain temperature; the second test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition value; the second test data may be the same as or different from the first test data. Based on the current acquisition value and the voltage acquisition value, obtaining a first model estimation value by adopting an ampere-hour integration method, and obtaining a second model estimation value by adopting a second extended Kalman filter EKF algorithm; and adjusting the filter parameters by taking the requirements of algorithm stability and accuracy as targets, so that the mean square error of the first model estimated value and the second model estimated value is minimum.
The operation process of the battery management system refers to the implementation process of the battery state-of-charge estimation method.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (9)
1. A battery state of charge estimation method, the estimation method comprising:
acquiring load current and terminal voltage of a battery;
determining 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, polarized internal resistance and polarized capacitance;
determining the state of charge of the battery using a second extended kalman filter EKF algorithm using the load current, the terminal voltage, and the state parameter, comprising:
obtaining a predicted state of charge based on a state transfer equation of the state of charge, wherein the state transfer equation is as follows:
wherein: x is x k 、x k-1 The method comprises the steps of respectively obtaining a kth state vector and a kth-1 th state vector containing a state of charge, wherein eta is coulombic efficiency, Q is the total available capacity of the battery, T is algorithm sampling time, rp is the polarization resistance of the battery, and Cp is the polarization capacitance of the battery; i L，k-1 Is the k-1 th cell total current;
the load current, the state parameter and the estimated open-circuit voltage calculated by the equivalent circuit model are taken as measurement states 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.
2. The battery state of charge estimation method according to claim 1, wherein the ohmic internal resistance, the polarized internal resistance, and the polarized capacitance among 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 polarized internal resistance and the polarized capacitor are connected in parallel and then connected with the ideal voltage source in series.
3. The method of claim 2, wherein determining the state parameter of the battery using a first extended kalman filter EKF algorithm comprises:
taking a 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. A battery state of charge estimation method according to 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 acquisition value and a voltage acquisition value corresponding to the current acquisition 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 parameters to be calibrated, the model input and the model calculation value meet the equivalent circuit model;
and taking the minimum mean square error between the terminal voltage estimated value corresponding to the same current acquisition value and the voltage acquisition value as an optimization target, and taking the optimal value determined by the genetic algorithm function as the initial value of the state parameter.
5. The battery state of charge estimation method of claim 1, wherein the filter parameters in the second extended kalman filter EKF algorithm are determined by:
acquiring second test data of preset test working conditions at a certain temperature; the second test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition value;
based on the current acquisition value and the voltage acquisition value, obtaining a first model estimation value by adopting an ampere-hour integration method, and obtaining a second model estimation value by adopting a second extended Kalman filter EKF algorithm;
and adjusting the filter parameters by taking the requirements of algorithm stability and accuracy as targets, so that the mean square error of the first model estimated value and the second model estimated value is minimum.
6. A battery management system, the battery management system comprising:
the parameter acquisition module is used for acquiring the load current and 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, polarized internal resistance and polarized capacitance; and
a second calculation module, configured to determine a state of charge of the battery using the load current, the terminal voltage, and the state parameter using a second extended kalman filter EKF algorithm, including: obtaining a predicted state of charge based on a state transfer equation of the state of charge, wherein the state transfer equation is as follows:
wherein: x is x k 、x k-1 The method comprises the steps of respectively obtaining a kth state vector and a kth-1 th state vector containing a state of charge, wherein eta is coulombic efficiency, Q is the total available capacity of the battery, T is algorithm sampling time, rp is the polarization resistance of the battery, and Cp is the polarization capacitance of the battery; i L，k-1 Is the k-1 th cell total current;
the load current, the state parameter and the estimated open-circuit voltage calculated by the equivalent circuit model are taken as measurement states 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.
7. The battery management system of claim 6 wherein the ohmic internal resistance, the polarized internal resistance, and the polarized capacitance of 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 polarized internal resistance and the polarized capacitor are connected in parallel and then connected with the ideal voltage source in series.
8. The battery management system of claim 7, wherein the battery management system includes a battery state initial value storage module that stores initial values of state parameters obtained by:
acquiring first test data of a preset test working condition at a certain temperature; the first test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition 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 parameters to be calibrated, the model input and the model calculation value meet the equivalent circuit model;
and taking the minimum mean square error between the terminal voltage estimated value corresponding to the same current acquisition value and the voltage acquisition value as an optimization target, and taking the optimal value determined by the genetic algorithm function as the initial value of the state parameter.
9. The battery management system of claim 7, wherein the filter parameters in the second extended kalman filter EKF algorithm are determined by:
acquiring second test data of preset test working conditions at a certain temperature; the second test data comprises a current acquisition value and a voltage acquisition value corresponding to the current acquisition value;
based on the current acquisition value and the voltage acquisition value, obtaining a first model estimation value by adopting an ampere-hour integration method, and obtaining a second model estimation value by adopting a second extended Kalman filter EKF algorithm;
and adjusting the filter parameters by taking the requirements of algorithm stability and accuracy as targets, so that the mean square error of the first model estimated value and the second model estimated value is minimum.
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| CN110395141A (en) * | 2019-06-27 | 2019-11-01 | 武汉理工大学 | Dynamic lithium battery SOC estimation method based on adaptive Kalman filter method |
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| CN110395141A (en) * | 2019-06-27 | 2019-11-01 | 武汉理工大学 | Dynamic lithium battery SOC estimation method based on adaptive Kalman filter method |
| CN111007400A (en) * | 2019-11-22 | 2020-04-14 | 西安工程大学 | Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method |
| CN111337832A (en) * | 2019-12-30 | 2020-06-26 | 南京航空航天大学 | Power battery multidimensional fusion SOC and SOH online joint estimation method |
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