CN117590256A - Method, device, computer equipment and storage medium for predicting state of power battery - Google Patents
Method, device, computer equipment and storage medium for predicting state of power battery Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000012937 correction Methods 0.000 claims abstract description 208
- 238000004590 computer program Methods 0.000 claims abstract description 30
- 238000007599 discharging Methods 0.000 claims description 10
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
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- 229910001416 lithium ion Inorganic materials 0.000 description 2
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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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
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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Abstract
The present application relates to a state prediction method, apparatus, computer device, storage medium, and computer program product of a power battery. The method comprises the following steps: acquiring a historical predicted value of the battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted; determining an initial predicted value of a battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value; determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment; determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value; and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value. By adopting the method, the prediction accuracy of the battery charge state of the power battery can be improved.
Description
Technical Field
The present disclosure relates to the field of power battery technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for predicting a state of a power battery.
Background
New energy automobiles rapidly develop in recent years, are popular in the market by virtue of the dynamic property, economy and the like, and the sales volume is increased year by year. The lithium ion power battery is widely applied to new energy automobiles by virtue of the advantages of high energy density, long cycle life and the like. However, the lithium ion power battery has poor abuse resistance and risks of overcharge, overdischarge, thermal runaway and the like, and a battery management system (Battery Management System, BMS) is required to be provided to ensure safe and efficient use of the battery. State of Charge (SOC) estimation is the most basic and important State estimation of the BMS, directly affects the energy management strategies of the user and other controllers, and causes great challenges for accurate estimation of the SOC due to non-linearity and rapid time variation of the chemical properties of the battery itself, so that SOC estimation has become an important point and difficulty in BMS research.
In the related art, however, the SOC estimation for the power battery often has the problems of larger error and low accuracy.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a state prediction method, apparatus, computer device, computer-readable storage medium, and computer program product for a power battery that can improve the accuracy of prediction of the state of charge of the battery.
In a first aspect, the present application provides a method for predicting a state of a power battery, including:
acquiring a historical predicted value of a battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted;
determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value;
determining an initial correction value of a battery charge state of the power battery to be predicted at the current moment based on a voltage prediction value of the power battery to be predicted at the current moment;
determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In one embodiment, the determining, based on the historical predicted value, an initial predicted value of a battery state of charge of the power battery to be predicted at a current time includes:
acquiring a current observation value of the power battery to be predicted at the current moment through a current sensor;
and determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value and the current observed value in a first prediction mode.
In one embodiment, determining an initial correction value of a battery state of charge of the power battery to be predicted at a current time based on a voltage prediction value of the power battery to be predicted at the current time includes:
acquiring an equivalent circuit model for a power battery, and determining a voltage predicted value of the power battery to be predicted at the current moment based on the equivalent circuit model;
acquiring a voltage observation value of the power battery to be predicted at the current moment through a voltage sensor;
determining an intermediate correction value of the battery state of charge at the current moment based on the voltage predicted value and the voltage observed value in a second prediction mode;
And determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value, the voltage observed value and the intermediate correction value.
In one embodiment, determining the target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value includes:
comparing the initial correction value with the correction threshold value;
if the comparison result indicates that the initial correction value is smaller than or equal to the correction threshold value, determining the initial correction value as the target correction value;
and if the comparison result indicates that the initial correction value is larger than the correction threshold value, determining the correction threshold value as the target correction value.
In one embodiment, determining the target predicted value of the battery state of charge of the power battery to be predicted at the current time based on the initial predicted value, the historical predicted value, and the target correction value includes:
determining an intermediate predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the initial predicted value and the target corrected value;
and adjusting the intermediate predicted value through the historical predicted value to obtain a target predicted value of the battery charge state of the power battery to be predicted at the current moment.
In one embodiment, the adjusting the intermediate predicted value according to the historical predicted value to obtain the target predicted value of the battery state of charge of the power battery to be predicted at the current moment includes:
acquiring a first weight of the historical predicted value, and determining a second weight of the intermediate predicted value based on the first weight;
and carrying out weighted summation on the historical predicted value and the intermediate predicted value based on the first weight and the second weight to obtain the target predicted value.
On the other hand, the application also provides a state prediction device of the power battery, which comprises the following components:
the power battery charging and discharging device comprises an acquisition module, a power battery charging and discharging module and a power battery charging and discharging module, wherein the acquisition module is used for acquiring a historical predicted value of a battery charging state of the power battery to be predicted at the last moment in a charging and discharging period of the power battery to be predicted;
the prediction module is used for determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value;
the first determining module is used for determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment;
The comparison module is used for determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
and the second determining module is used for determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In another aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:
acquiring a historical predicted value of a battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted;
determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value;
determining an initial correction value of a battery charge state of the power battery to be predicted at the current moment based on a voltage prediction value of the power battery to be predicted at the current moment;
determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
And determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In another aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a historical predicted value of a battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted;
determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value;
determining an initial correction value of a battery charge state of the power battery to be predicted at the current moment based on a voltage prediction value of the power battery to be predicted at the current moment;
determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In another aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
acquiring a historical predicted value of a battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted;
determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value;
determining an initial correction value of a battery charge state of the power battery to be predicted at the current moment based on a voltage prediction value of the power battery to be predicted at the current moment;
determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
The state prediction method, the state prediction device, the computer equipment, the storage medium and the computer program product of the power battery acquire a historical predicted value of the battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted; determining an initial predicted value of a battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value; determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment; determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value; and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value. By adopting the method, the prediction accuracy of the battery charge state of the power battery can be improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is an application environment diagram of a method of predicting a state of a power battery in one embodiment;
FIG. 2 is a flow chart of a method of predicting a state of a power cell in one embodiment;
FIG. 3 is a flow chart of a method for determining an initial correction value in one embodiment;
FIG. 4 is a flow chart of a method for predicting the state of a power battery according to another embodiment;
FIG. 5 is a block diagram showing a state predicting apparatus of a power battery according to an embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The state prediction method of the power battery provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. Acquiring a historical predicted value of the battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted; determining an initial predicted value of a battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value; determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment; determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value; and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, intelligent vehicle-mounted devices, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In an exemplary embodiment, as shown in fig. 2, a method for predicting a state of a power battery is provided, and the method is applied to the computer device in fig. 1, for example, and includes the following steps 202 to 206. Wherein:
step 202, obtaining a historical predicted value of a battery charge state of the power battery to be predicted at a previous moment in a charge-discharge period of the power battery to be predicted.
In practical implementation, the power battery to be predicted may be a lithium battery, and the charging and discharging cycle of the power battery refers to a complete charging and discharging process of the power battery. The State of Charge (SOC) of a battery is expressed as a percentage, and is used to describe the available State of Charge of the power battery, and its value is defined as the ratio of the remaining capacity of the battery to the rated capacity of the battery. The computer device obtains a historical predicted value of the battery state of charge of the power battery to be predicted at the last moment. The method for estimating the state of charge (SOC) of the power battery mainly comprises the following steps: ampere-hour integration method, open circuit voltage method, neural network prediction, extended kalman filter algorithm, etc. Any charge-discharge period T can comprise a plurality of continuous statistical moments, the current moment is T, the moment immediately before the current moment is T-1, and the computer equipment records the historical predicted value of the battery charge state of the power battery to be predicted at the moment as SOC (T-1).
Step 204, determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value.
In practical implementation, since the historical predicted value of the last time is known and is convenient to obtain, the computer device can select a corresponding estimation mode from the existing estimation modes, and determine the initial predicted value of the battery charge state of the power battery to be predicted at the current time based on the historical predicted value of the last time. According to the characteristics of each estimation mode, on the basis that the historical predicted value of the last moment and the current observed value of the current moment are easy to obtain, the computer equipment can determine the initial predicted value of the current moment to be recorded as SOCah (t) by using an ampere-hour integration method.
Step 206, determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment.
In actual implementation, to improve the accuracy of the SOC prediction values, the computer device may determine the SOC prediction values in combination with a plurality of different SOC estimation methods. Meanwhile, in order to ensure that the variation amplitude of the SOC predicted value is stable and reduce the uncertainty of the SOC predicted value, the computer equipment can estimate the SOC value of the power battery from the voltage level of the power battery. Such as a SOC estimation method based on a Kalman Filter (KF), which is accomplished by establishing a battery equivalent circuit model and using a combination of an ampere-hour integration method. However, in practical application, the SOC predicted value based on the kalman filtering algorithm tends to have a larger variation range, so that the uncertainty of the SOC predicted value is increased, in order to correct the SOC predicted value determined based on the KF algorithm, the computer device determines that the voltage predicted value of the power battery to be predicted at the current moment is denoted as Vmodel, and meanwhile, the initial correction value of the SOC at the current moment of the battery voltage level is denoted as dsoc (t), and taking the kalman filtering algorithm as an example, the initial correction value is the gain correction value in the estimation process.
Step 208, determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value; the correction threshold is determined based on the initial predicted value.
In order to ensure that the fluctuation range of the SOC predicted value is smoother in practical implementation, the computer device may correct the initial correction value by a correction threshold to obtain a target correction value of the power battery to be predicted at the current time, where the correction threshold may be a product of a correction coefficient and the initial predicted value, and the correction coefficient is an empirical value greater than 1, that is, the correction threshold is determined according to the initial predicted value.
Step 210, determining a target predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In practical implementation, the computer device may determine the intermediate predicted value of the SOC at the current time according to the initial predicted value and the target corrected value, and then determine the target predicted value of the battery state of charge of the power battery to be predicted at the current time by combining the historical predicted value of the SOC at the previous time.
In this embodiment, the initial correction value determined from the current level and the target correction value determined from the voltage level determine the target correction value of the battery state of charge at the current time, so that the target correction value can be limited to a reasonable interval, and the change stability of the target correction can be ensured. Then, the historical predicted value of the last moment is combined with the target correction value to determine the target predicted value of the battery state of charge of the power battery to be predicted at the current moment, so that the reasonable target predicted value can be determined by combining the reasonable target correction value on the basis of the historical predicted value of the last moment, and the accuracy of the target predicted value is ensured.
In one exemplary embodiment, determining an initial predicted value of a battery state of charge of a power battery to be predicted at a current time based on historical predicted values includes: acquiring a current observation value of the power battery to be predicted at the current moment through a current sensor; and determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value and the current observed value in a first prediction mode.
In practical implementation, since the historical predicted value of the last moment is known and is convenient to obtain, the computer equipment can determine the initial predicted value of the SOC at the current moment through an ampere-hour integration method, directly observe the current value from the current sensor, and then determine the initial predicted value of the battery charge state of the power battery to be predicted at the current moment through a preset first prediction mode (such as the ampere-hour integration method and the like) based on the current estimated SOC value.
In this embodiment, the accuracy of determining the initial predicted value can be improved by determining the initial predicted value based on the first current prediction method.
In one exemplary embodiment, as shown in fig. 3, an initial correction value of the battery state of charge of the power battery to be predicted at the current time is determined based on the predicted value of the voltage of the power battery to be predicted at the current time, including steps 302 to 306. Wherein:
Step 302, an equivalent circuit model for the power battery is obtained, and a voltage predicted value of the power battery to be predicted at the current moment is determined based on the equivalent circuit model.
In practical implementation, when the computer device performs SOC value prediction of a voltage level for the power battery, an equivalent circuit model for the power battery is constructed first, and then, a voltage predicted value of the power battery to be predicted at the current time t is determined to be marked as Vmodel (t) through the equivalent circuit model.
And step 304, acquiring a voltage observation value of the power battery to be predicted at the current moment through a voltage sensor.
In actual implementation, when the computer device performs SOC value prediction of the voltage level for the power battery, the voltage observed value V (t) of the power battery to be predicted at the current time t can be obtained through the voltage sensor.
Step 306, determining an intermediate correction value of the battery state of charge at the current time based on the voltage predicted value and the voltage observed value by the second prediction mode.
In practical implementation, the SOC prediction method of the preset voltage level is adopted, such as an SOC prediction algorithm based on Kalman filtering. By means of the estimated voltage predicted value and the estimated voltage observed value, a corresponding gain K (t) is determined according to a second prediction method (such as an SOC prediction algorithm based on Kalman filtering), the gain K (t) is generally characterized in a matrix mode, and a value related to an SOC element in the gain is determined as an intermediate correction value of the battery charge state at the current moment and is recorded as Ksoc (t).
Step 308, determining an initial correction value of the battery state of charge of the power battery to be predicted at the current time based on the voltage predicted value, the voltage observed value and the intermediate correction value.
In actual implementation, the computer equipment determines a difference value Vmodel (t) -V (t) between the voltage predicted value and the voltage observed value at the current moment and marks beta (t); then, the product Ksoc (t) β (t) of the difference and the intermediate correction value is determined as an initial correction value of the battery state of charge at the current time.
In this embodiment, considering the error of the initial predicted value, the SOC value at the current time is determined again by the second prediction mode of the voltage level, and the initial correction value is determined by the gain information in the determining process, so that timeliness of determining the correction value can be ensured.
In one embodiment, determining a target correction value for the power cell to be predicted at the current time based on the initial correction value and the correction threshold value includes: comparing the initial correction value with the correction threshold value; if the comparison result indicates that the initial correction value is smaller than or equal to the correction threshold value, determining the initial correction value as a target correction value; and if the comparison result indicates that the initial correction value is larger than the correction threshold value, determining the correction threshold value as a target correction value.
In actual implementation, the computer device compares the initial correction value dsoc v (t) to a correction threshold value a dSOCah (t), where a is a correction coefficient, which is an empirical value greater than 1. If dsoc v (t) is equal to or less than a dsoc h (t), determining the initial correction value as a target correction value, i.e., SOCv (t) =dsoc v (t); if dsoc (t) is greater than a dsucah (t), the correction threshold is determined as the target correction value, i.e., SOCv (t) =a dsucah (t).
In the above embodiment, the value range of the target correction threshold is limited to a reasonable section by the correction threshold, so that the rationality of the target correction threshold and the stability of the variation range of the target correction threshold can be ensured.
In one embodiment, determining a target predicted value of a battery state of charge of the power battery to be predicted at a current time based on the initial predicted value, the historical predicted value, and the target correction value includes: based on the initial predicted value and the target corrected value, determining an intermediate predicted value of the battery state of charge of the power battery to be predicted at the current moment; and adjusting the intermediate predicted value through the historical predicted value to obtain a target predicted value of the battery charge state of the power battery to be predicted at the current moment.
In practical implementation, the computer device first determines, through an initial predicted value SOCah (t) at a current time t and a target correction value dsoc (t), an intermediate predicted value of a battery state of charge of the power battery to be predicted at the current time, where the determination may be a weighted summation manner, and sets a weight of the initial predicted value SOCah (t) as a and a weight of the target correction value dsoc (t) as B, where the intermediate predicted value=a+b+dsoc (t). If a=b=1, then the intermediate predictor=socah (t) +dsoc (t). Then, the intermediate predicted value is adjusted through the historical predicted value SOC (t-1) of the last moment (t-1) to obtain a target predicted value of the battery charge state of the power battery to be predicted at the current moment.
In the above embodiment, by determining the target correction value at the current time based on the plurality of correction values obtained from different levels, accuracy of the correction value can be improved, errors can be reduced, and the accuracy of the target prediction correction value can be ensured by determining the target prediction value of the SOC at the current time by combining the historical prediction value at the previous time with the target correction value.
In one embodiment, the adjusting the intermediate predicted value to obtain the target predicted value of the battery state of charge of the power battery to be predicted at the current moment through the historical predicted value includes: acquiring a first weight of a historical predicted value, and determining a second weight of an intermediate predicted value based on the first weight; and based on the first weight and the second weight, carrying out weighted summation on the historical predicted value and the intermediate predicted value to obtain the target predicted value.
In actual implementation, the computer equipment can determine the target predicted value in a weighted summation mode, namely, a first weight of the historical predicted value is obtained, and a second weight of the intermediate predicted value is determined based on the first weight; the historical predictors and the intermediate predictors are weighted summed based on the first weight and the second weight. The first weight is usually marked as b by adopting a low-pass filtering parameter, the second weight is (1-b), the sum of the values of the first weight and the second weight is 1, and the value range of the target predicted value can be effectively limited by adopting the low-pass filtering parameter, so that the target predicted value is in a reasonable value space.
In the above embodiment, the target SOC value is determined by means of weighted summation, and not only the SOC value at the previous moment is combined, but also the target SOC value is adjusted by means of low-pass filtering, so that the value range of the target predicted value can be effectively limited, and the target predicted value is in a reasonable value space.
To explain the state prediction method of the power battery in detail, an embodiment will be described below, in which the first prediction method for SOC estimation of the power battery is an ampere-hour integration method, and the second prediction method is an SOC estimation method based on a kalman filter type. Because of the SOC estimation method based on Kalman filtering, the variation amplitude of the gain correction value is large, the accuracy of SOC estimation is affected, and the estimated SOC fluctuation is large. Based on this, the state prediction method of the power battery provided in this embodiment actually uses adaptive filtering to limit the gain correction value in the SOC estimation process based on kalman filtering, and performs low-pass filtering processing on the SOC estimated at each moment to obtain the SOC with a smooth variation process. As shown in fig. 4, the specific implementation procedure is as follows:
Step 402, using the SOC value at the previous time and the current observation value at the current time, obtaining an SOC estimated value SOCah (t) at the current time by an ampere-hour integration method.
The current charge and discharge period of the power battery is T, the current time T is a statistical time within the current charge and discharge period T, the SOC value of the last time (T-1) is recorded as SOC (T-1), a current observation value i (T) of the current time is obtained through a current sensor, and based on an ampere-hour integration method, an SOC estimated value (namely a historical predicted value in the previous) of the power battery at the current time is estimated and recorded as SOCah (T).
Step 404, determining the difference between the SOC estimation value at the current time and the SOC value at the previous time.
Wherein, the difference solving process is as follows: dSOCah (t) =socah (t) -SOC (t-1). dSOCah (t) is the difference between the two, and can be used for representing the variation amplitude of the SOC value of the power battery estimated based on the current level in unit time interval.
And step 406, calculating a terminal voltage predicted value of the battery equivalent circuit model.
When estimating the SOC value of the power battery based on the kalman filter, a battery equivalent circuit model is generally constructed, and a terminal voltage predicted value (i.e., the voltage predicted value in the foregoing) of the power battery at the current moment can be predicted by using the battery equivalent circuit model, where the terminal voltage predicted value is denoted as Vmodel (t).
Step 408, determining a difference between the terminal voltage predicted value and the voltage observed value measured value at the current time.
Wherein, the difference solving process is as follows: beta (t) =vmodel (t) -V (t); the voltage observation V (t) is obtained by a voltage sensor.
In step 410, the gain of the Kalman filtering algorithm used is calculated, and the element corresponding to the SOC correction is determined based on the gain.
In actual implementation, the gain K (t) in the estimation process of the Kalman filtering algorithm is determined, and an element corresponding to the SOC correction is selected as Ksoc (t).
Step 412, determining a gain correction value during the Kalman filtering algorithm estimation process.
The gain correction value is determined as follows: dsoc (t) =ksoc (t) ×β (t); i.e., the product β (t) of the element Ksoc (t) corresponding to the SOC correction in the determining step 410 and the voltage difference in the step 708, as a gain correction value (i.e., the initial correction value in the foregoing).
Step 414, obtaining the correction coefficient, determining the correction threshold of the gain correction value, comparing the gain correction value with the correction threshold, and determining the target gain correction value based on the comparison result.
The correction coefficient a is an empirical value, and is typically a number greater than 1, and the product a×dsocah (t) of the correction coefficient a and the SOC estimated value SOCah (t) at the current time determined in step 402 is taken as the correction threshold. The gain correction value dsoc (t) and the correction threshold value a×dsocah (t) in step 412 are compared, and based on the comparison result, the process of determining the target gain correction value is as follows:
If dsoc (t) is equal to or less than a dsoc h (t), dsoc (t) =dsoc (t);
if dsoc (t) is greater than a dSOCah (t), dsoc (t) =a dSOCah (t).
Step 416, determining a target predicted value of the SOC of the power battery at the current time based on the SOC value at the previous time, the target gain correction value at the current time, and the SOC predicted value at the current time.
The specific determination process is as follows:
SOC(t) = b*SOC(t-1) +(1-b)*(SOCah(t) + dSOCv(t))；
where b is a low pass filter constant and b is an empirical value.
And finally, repeating the calculation steps at each moment in any charge-discharge cycle of the power battery to finish the updating calculation of the SOC of the power battery.
In the embodiment of the application, the gain correction value of the SOC value determined based on the Kalman filtering algorithm is limited by the SOC value estimated by the ampere-hour integration method, so that the target predicted value of the proper SOC is determined, and the gain correction variation amplitude can be controlled within a reasonable interval range, the SOC is accurately estimated, and the estimated fluctuation amplitude of the SOC is smooth.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a state prediction device of the power battery for realizing the state prediction method of the power battery. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the state prediction device for one or more power batteries provided below may refer to the limitation of the state prediction method for a power battery hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 5, there is provided a state prediction apparatus of a power battery, including: an acquisition module 510, a prediction module 520, a first determination module 530, a comparison module 540, and a second determination module 550, wherein:
and the acquisition module is used for acquiring a historical predicted value of the battery charge state of the power battery to be predicted at the last moment in the charge-discharge period of the power battery to be predicted.
And the prediction module is used for determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value.
The first determining module is used for determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment.
The comparison module is used for determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value; a correction threshold is determined based on the initial predicted value.
And the second determining module is used for determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In one embodiment, the prediction module is further configured to obtain, through the current sensor, a current observed value of the power battery to be predicted at a current moment; and determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value and the current observed value through a first prediction mode.
In one embodiment, the first determining module is further configured to obtain an equivalent circuit model for the power battery, and determine a voltage prediction value of the power battery to be predicted at the current moment based on the equivalent circuit model; acquiring a voltage observation value of the power battery to be predicted at the current moment through a voltage sensor; determining an intermediate correction value of the battery state of charge at the current moment based on the voltage predicted value and the voltage observed value in a second prediction mode; and determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value, the voltage observed value and the intermediate correction value.
In one embodiment, the comparing module is further configured to compare the initial correction value to a correction threshold value; if the comparison result indicates that the initial correction value is smaller than or equal to the correction threshold value, determining the initial correction value as a target correction value; and if the comparison result indicates that the initial correction value is larger than the correction threshold value, determining the correction threshold value as a target correction value.
In one embodiment, the second determining module is further configured to determine an intermediate predicted value of the battery state of charge of the power battery to be predicted at the current time based on the initial predicted value and the target correction value; and adjusting the intermediate predicted value through the historical predicted value to obtain a target predicted value of the battery charge state of the power battery to be predicted at the current moment.
In one embodiment, the second determining module is further configured to obtain a first weight of the historical predicted value, and determine a second weight of the intermediate predicted value based on the first weight; and based on the first weight and the second weight, carrying out weighted summation on the historical predicted value and the intermediate predicted value to obtain the target predicted value.
The respective modules in the state prediction apparatus of the power battery described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing the operation data of the power battery to be predicted. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method of predicting a state of a power cell.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: acquiring a historical predicted value of the battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted; determining an initial predicted value of a battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value; determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment; determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and the correction threshold value; a correction threshold is determined based on the initial predicted value; and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a current observation value of the power battery to be predicted at the current moment through a current sensor; and determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value and the current observed value through a first prediction mode.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring an equivalent circuit model aiming at the power battery, and determining a voltage predicted value of the power battery to be predicted at the current moment based on the equivalent circuit model; acquiring a voltage observation value of the power battery to be predicted at the current moment through a voltage sensor; determining an intermediate correction value of the battery state of charge at the current moment based on the voltage predicted value and the voltage observed value in a second prediction mode; and determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value, the voltage observed value and the intermediate correction value.
In one embodiment, the processor when executing the computer program further performs the steps of: comparing the initial correction value with the correction threshold value; if the comparison result indicates that the initial correction value is smaller than or equal to the correction threshold value, determining the initial correction value as a target correction value; and if the comparison result indicates that the initial correction value is larger than the correction threshold value, determining the correction threshold value as a target correction value.
In one embodiment, the processor when executing the computer program further performs the steps of: based on the initial predicted value and the target corrected value, determining an intermediate predicted value of the battery state of charge of the power battery to be predicted at the current moment; and adjusting the intermediate predicted value through the historical predicted value to obtain a target predicted value of the battery charge state of the power battery to be predicted at the current moment.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a first weight of a historical predicted value, and determining a second weight of an intermediate predicted value based on the first weight; and based on the first weight and the second weight, carrying out weighted summation on the historical predicted value and the intermediate predicted value to obtain the target predicted value.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, in the operation data of the power battery related to the present application, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) related to the user are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of predicting a state of a power battery, the method comprising:
acquiring a historical predicted value of a battery charge state of the power battery to be predicted at the last moment in a charge-discharge period of the power battery to be predicted;
determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the historical predicted value;
Determining an initial correction value of a battery charge state of the power battery to be predicted at the current moment based on a voltage prediction value of the power battery to be predicted at the current moment;
determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
and determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
2. The method of claim 1, wherein the determining an initial predicted value of the battery state of charge of the power battery to be predicted at the current time based on the historical predicted value comprises:
acquiring a current observation value of the power battery to be predicted at the current moment through a current sensor;
and determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value and the current observed value in a first prediction mode.
3. The method of claim 1, wherein the determining an initial correction value for the battery state of charge of the power battery to be predicted at the current time based on the predicted value of the voltage of the power battery to be predicted at the current time comprises:
Acquiring an equivalent circuit model for a power battery, and determining a voltage predicted value of the power battery to be predicted at the current moment based on the equivalent circuit model;
acquiring a voltage observation value of the power battery to be predicted at the current moment through a voltage sensor;
determining an intermediate correction value of the battery state of charge at the current moment based on the voltage predicted value and the voltage observed value in a second prediction mode;
and determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value, the voltage observed value and the intermediate correction value.
4. The method of claim 1, wherein determining a target correction value for the power cell to be predicted at a current time based on the initial correction value and a correction threshold value comprises:
comparing the initial correction value with the correction threshold value;
if the comparison result indicates that the initial correction value is smaller than or equal to the correction threshold value, determining the initial correction value as the target correction value;
and if the comparison result indicates that the initial correction value is larger than the correction threshold value, determining the correction threshold value as the target correction value.
5. The method of claim 1, wherein the determining a target predicted value of the battery state of charge of the power battery to be predicted at the current time based on the initial predicted value, the historical predicted value, and the target correction value comprises:
determining an intermediate predicted value of the battery state of charge of the power battery to be predicted at the current moment based on the initial predicted value and the target corrected value;
and adjusting the intermediate predicted value through the historical predicted value to obtain a target predicted value of the battery charge state of the power battery to be predicted at the current moment.
6. The method according to claim 5, wherein the adjusting the intermediate predicted value by the historical predicted value to obtain the target predicted value of the battery state of charge of the power battery to be predicted at the current time includes:
acquiring a first weight of the historical predicted value, and determining a second weight of the intermediate predicted value based on the first weight;
and carrying out weighted summation on the historical predicted value and the intermediate predicted value based on the first weight and the second weight to obtain the target predicted value.
7. A state prediction apparatus of a power battery, characterized by comprising:
the power battery charging and discharging device comprises an acquisition module, a power battery charging and discharging module and a power battery charging and discharging module, wherein the acquisition module is used for acquiring a historical predicted value of a battery charging state of the power battery to be predicted at the last moment in a charging and discharging period of the power battery to be predicted;
the prediction module is used for determining an initial predicted value of the battery charge state of the power battery to be predicted at the current moment based on the historical predicted value;
the first determining module is used for determining an initial correction value of the battery charge state of the power battery to be predicted at the current moment based on the voltage predicted value of the power battery to be predicted at the current moment;
the comparison module is used for determining a target correction value of the power battery to be predicted at the current moment based on the initial correction value and a correction threshold value; the correction threshold is determined based on the initial predicted value;
and the second determining module is used for determining a target predicted value of the battery charge state of the power battery to be predicted at the current moment based on the initial predicted value, the historical predicted value and the target corrected value.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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