CN116184223A - Method for evaluating accuracy of battery state-of-charge estimation algorithm and electronic equipment - Google Patents
Method for evaluating accuracy of battery state-of-charge estimation algorithm and electronic equipment Download PDFInfo
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- CN116184223A CN116184223A CN202111437846.8A CN202111437846A CN116184223A CN 116184223 A CN116184223 A CN 116184223A CN 202111437846 A CN202111437846 A CN 202111437846A CN 116184223 A CN116184223 A CN 116184223A
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Abstract
The embodiment of the application provides a method for evaluating accuracy of a battery state-of-charge estimation algorithm and electronic equipment, wherein the method comprises the following steps: acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, and taking a position corresponding to the initial state of charge value as a current zone bit; generating a state of charge reference value and a state of charge estimate based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity; calculating to obtain an initial error value based on the charge state reference value and the charge state estimation value; generating a first error value through simulation verification under the condition that the charge state reference value meets a first preset cyclic simulation condition, and obtaining a first error value set; generating cyclic simulation verification information based on the initial error value and the first error value set; and verifying the accuracy of the state of charge estimation algorithm according to the cyclic simulation verification information.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of battery management, in particular to a method for evaluating accuracy of a battery state-of-charge estimation algorithm and electronic equipment.
Background
The current methods for verifying the accuracy Of the State Of Charge (SOC) estimation algorithm are less, and the accuracy Of the SOC estimation algorithm is generally obtained by comparing SOC measurement data obtained by testing with SOC estimation data obtained by simulation. To ensure the validity of the comparison, the current-voltage data of the test procedure must be identical to that used for the simulation. To verify the accuracy of the SOC estimation algorithm when the battery is cycled within a particular SOC interval, current-voltage data for the corresponding cycling conditions is required. The existing method obtains data through actually measuring the circulation working condition, and different SOC intervals need to be independently tested.
However, obtaining the data of the circulation working condition through actual measurement requires a longer time and higher cost, and if the verification requirements of adding capacity bias, current bias, random interval and the like are considered, the workload of the test will rise in geometric progression.
Disclosure of Invention
It is an object of the present disclosure to provide a new solution for accuracy verification of SOC estimation algorithms.
According to a first aspect of the present disclosure, there is provided an embodiment of a method of evaluating accuracy of a battery state of charge estimation algorithm, comprising:
Acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, and taking a position corresponding to the initial state of charge value as a current zone bit;
generating a state of charge reference value and a state of charge estimate based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimate is obtained using a state of charge estimation algorithm to be verified;
calculating to obtain an initial error value based on the charge state reference value and the charge state estimation value;
generating a first error value through simulation verification under the condition that the charge state reference value meets a first preset cyclic simulation condition, and obtaining a first error value set, wherein the first error value is a difference value between the charge state reference value and the charge state estimated value in the simulation verification process;
generating cyclic simulation verification information based on the initial error value and the first error value set;
and verifying the accuracy of the state of charge estimation algorithm according to the cyclic simulation verification information.
Optionally, the generating the first error value through simulation verification includes:
updating the current flag bit according to a preset step value;
Acquiring current data and voltage data corresponding to the updated current flag bit from the first data set as target current data and target voltage data;
generating a target state of charge reference value and a target state of charge estimate based on the target current data, the target voltage data, the preset nominal capacity, and the preset bias capacity;
and calculating a target error value based on the target state of charge reference value and the target state of charge estimated value, and taking the target error value as the first error value.
Optionally, the first preset loop simulation condition includes at least one of the following:
in the case that the first data set is discharge data, the state of charge reference value is greater than a state of charge lower boundary value;
in the case that the first data set is charging data, the state of charge reference value is smaller than a state of charge upper boundary value.
Optionally, the state-of-charge lower boundary value and the state-of-charge upper boundary value are obtained by:
and generating an upper charge state boundary value and a lower charge state boundary value based on the preset nominal capacity and the preset bias capacity.
Optionally, under the condition that the state of charge reference value does not meet a first preset cyclic simulation condition, current data and voltage data corresponding to the state of charge reference value are obtained from a second data set, and a position corresponding to the state of charge reference value is used as a current zone bit;
generating a new state of charge reference value and a new state of charge estimation value based on the current data and the voltage data corresponding to the state of charge reference value, the preset nominal capacity and the preset bias capacity;
calculating a new initial error value based on the new state of charge reference value and the new state of charge estimated value;
generating a second error value through simulation verification under the condition that the new state of charge reference value meets a second preset cyclic simulation condition, and obtaining a second error value set;
generating the cyclic simulation verification information based on the new initial error value and the second set of error values.
Optionally, the second preset loop simulation condition is obtained by the following steps:
generating a new state of charge upper boundary value and a new state of charge lower boundary value based on the state of charge reference value under the condition that the state of charge reference value meets a preset cyclic simulation condition;
And generating the second preset cyclic simulation condition based on the new state-of-charge upper boundary value and the new state-of-charge lower boundary value.
Optionally, the state of charge reference value is obtained by:
and calculating to obtain a state of charge reference value by using the preset nominal capacity, the current data and the voltage data corresponding to the initial state of charge value through an ampere-hour integration method.
Optionally, the state of charge estimate is obtained by:
adding bias to the current data based on the current data and a preset current bias coefficient in a first experimental process to obtain bias current;
and calculating to obtain a charge state estimated value by using the bias current, the voltage data and the preset bias capacity through an estimation algorithm.
Optionally, the voltage data and the current data of the first experimental process and the voltage data and the current data of the second experimental process obtained by the test are respectively processed to obtain corresponding state of charge values;
and calculating the voltage data and the current data of the first experimental process, the voltage data and the current data of the second experimental process and the corresponding state of charge value respectively by utilizing an interpolation method to obtain a first data set and a second data set corresponding to the simulation step length.
According to a second aspect of the present disclosure, there is provided an embodiment of a state of charge estimation method, comprising:
acquiring voltage data and current data of a target battery;
and obtaining the target state of charge of the target battery based on a target state of charge estimation algorithm according to the voltage data and the current data, wherein the target state of charge estimation algorithm is an algorithm with the corresponding accuracy determined according to the method of the first aspect of the specification meeting a preset condition.
According to a third aspect of the present disclosure, there is provided an embodiment of an electronic device, comprising:
a memory for storing executable instructions;
a processor for executing the method according to the first aspect of the present specification according to the control of the instruction.
According to a fourth aspect of the present disclosure, there is provided an embodiment of a computer-readable storage medium, comprising:
the computer readable storage medium stores a computer program readable by a computer for executing the log processing method according to the first aspect of the present specification when the computer program is read by the computer.
According to the embodiment of the disclosure, by acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, taking a position corresponding to the initial state of charge value as a current flag bit, generating a state of charge reference value and a state of charge estimated value based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimated value is obtained by using a state of charge estimated algorithm to be verified, a relatively accurate state of charge reference value can be obtained, so that an initial error value is calculated based on the state of charge reference value and the state of charge estimated value, a relatively accurate error value can be obtained, a first error value set is generated by simulation verification under the condition that the state of charge reference value meets a first preset cyclic simulation condition, and cyclic simulation verification information is generated based on the initial error value and the first error value set; according to the cyclic simulation verification information, the accuracy of the state of charge estimation algorithm is verified, a large amount of test data can be obtained through data of single charge and discharge, high cost caused by obtaining a large amount of test data during actual measurement is avoided, a large amount of time is saved, and test requirements are greatly reduced.
Other features of the present specification and its advantages will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a flowchart of a method for evaluating accuracy of a battery state of charge estimation algorithm according to an embodiment of the present disclosure.
Fig. 2 is a flow chart of another method of evaluating accuracy of a battery state of charge estimation algorithm provided by an embodiment of the present disclosure.
Fig. 3 is a block diagram of a loop simulation implementation provided by an embodiment of the present disclosure.
Fig. 4 is a flowchart of a state of charge estimation method according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram of a graph illustrating error values.
Fig. 6 is a schematic hardware structure of an electronic device according to an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
< method example >
The state of charge is a basic parameter of the battery management system, is a global variable in the control logic of the battery management system (battery management system, BMS), has direct influence on charge and discharge strategies and energy feedback, and the SOC estimation technology is one of core technologies developed by the battery management system and is a technical difficulty recognized in the industry.
Because of the importance of SOC parameters, a plurality of SOC estimation algorithms are proposed at home and abroad at present, the methods can achieve different precision, and the performance indexes such as resource occupancy rate, operation speed and the like are different. In general, the higher the accuracy of the estimation, the higher the complexity of the estimation algorithm and the more resources are consumed.
In general, the accuracy verification of the SOC estimation algorithm needs to have a relatively accurate SOC reference value, the current and voltage data of each time point can be obtained by performing the working condition test on the battery, and a relatively accurate SOC value can be obtained by processing the current data, so that the correspondence among the current, the voltage and the SOC represents the characteristics of the battery. If the data obtained without passing the actual test can guarantee such correspondence, these data can also be used as a reference value for verification.
The accuracy of the SOC estimation algorithm is generally obtained by comparing SOC measurement data obtained by the test with SOC estimation data obtained by the simulation. To ensure the validity of the comparison, the current-voltage data of the test procedure must be identical to that used for the simulation. To verify the accuracy of the SOC estimation algorithm when the battery is cycled within a particular SOC interval, current-voltage data for the corresponding cycling conditions is required. Longer time and higher cost are needed for obtaining the data of the circulation working condition through actual measurement, and if verification requirements such as capacity bias, current bias and random interval are considered, the workload of the test is increased in geometric progression.
To improve the accuracy of verifying the SOC estimation algorithm, embodiments of the present disclosure provide a method of evaluating the accuracy of a battery state-of-charge estimation algorithm. Referring to fig. 1, a flowchart of a method for evaluating accuracy of a battery state of charge estimation algorithm according to an embodiment of the disclosure is shown, which may be implemented in an electronic device.
As shown in fig. 1, the method of the present embodiment may include the following steps S1100-S1500, which are described in detail below.
Step S1100, current data and voltage data corresponding to the initial state of charge value are obtained from the first data set, and the position corresponding to the initial state of charge value is used as the current zone bit.
In some embodiments, an execution subject (for example, a server) of the method for evaluating accuracy of the battery state-of-charge estimation algorithm may acquire current data and voltage data corresponding to an initial state-of-charge value from the first data set through a wired connection manner or a wireless connection manner, and take a position corresponding to the initial state-of-charge value as a current flag bit. The first data set may be a charging data table or a discharging data table; the data in the first data set are voltage data and current data corresponding to the state of charge value, wherein the time correspondence between the voltage data and the current data, for example, the voltage data and the current data are generated when the same battery is at the voltage at the same moment. The initial state of charge value may be preset, for example, may be 95%. In particular, the initial state of charge value is typically between 80% -100% because when the state of charge value of a device such as a battery reaches below 80%, it is generally considered that the device is not in use, and the state of charge value of the device is generally not 100% for various reasons.
In some optional implementations of some embodiments, the voltage data and the current data of the first experimental process and the voltage data and the current data of the second experimental process obtained by the test are respectively processed to obtain corresponding state of charge values; specifically, the voltage data and the current data of the first experimental process and the voltage data and the current data of the second experimental process may be ordered according to the test time, wherein the voltage data and the current data are in one-to-one correspondence.
And calculating the voltage data and the current data of the first experimental process, the voltage data and the current data of the second experimental process and the corresponding state of charge value respectively by utilizing an interpolation method to obtain a first data set and a second data set corresponding to the simulation step length. Specifically, interpolation generally refers to interpolation, which is an important method of discrete function approximation, and by using the interpolation, the approximation of a function at other points can be estimated through the value condition of the function at a limited number of points. The simulation step length can be preset, the simulation step length cannot be larger than one tenth of the minimum time constant in the circuit, and the smaller the simulation step length is, the more accurate the simulation result is, but the longer the simulation time is.
As an example, the first data set and the second data set may be a charge data table and a discharge data table, respectively, may be as shown in the following table,
| i (Current data) | 10A | 10A | 10A |
| V (Voltage data) | 100V | 110V | 90V |
| SOC (state of charge) | 90％ | 95％ | 85％ |
Step S1200, generating a state of charge reference value and a state of charge estimation value based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimation value is obtained using a state of charge estimation algorithm to be verified.
In some embodiments, the execution body may generate a state of charge reference value and a state of charge estimate based on the current data, the voltage data, a preset nominal capacity, and a preset bias capacity, wherein the state of charge estimate is obtained using a state of charge estimation algorithm to be verified. Since the state of charge value of the device such as the battery in use is 80% or more, the bias capacity=1—the current use time/battery lifetime is 20%. For example, in the case where the service life of the battery is two years, the battery has been used for 1 year, and the bias capacity of the battery is 1-1/2×20% =90%. The offset capacity may be a capacity that is manually set according to experience and is deviated from the nominal capacity due to factors such as environment, and the preset nominal capacity and the preset offset capacity may be preset, for example, the preset nominal capacity may be 100Ah and the preset offset capacity may be 95Ah.
In some alternative implementations of some embodiments, the state of charge reference value is obtained by: and calculating to obtain a charge state reference value by using the preset nominal capacity and current data and voltage data corresponding to the initial charge state value through an ampere-hour integration method. In particular, an ampere-hour integration method lithium battery has been widely used in the fields of industry, daily life and the like, and estimation of the state of charge of the battery has become an important link for battery management. The ampere-hour integration method is the most commonly used SOC estimation method.
In some alternative implementations of some embodiments, the state of charge estimate is obtained by: adding bias to the current data based on the current data and a preset current bias coefficient in a first experimental process to obtain bias current; and calculating a state of charge estimated value by using the bias current, the voltage data and the preset bias capacity through an estimation algorithm. The first experimental process current bias factor may be preset. The estimation algorithm can be an open circuit voltage method, an ampere-hour integration method, an internal resistance method, a neural network, a Kalman filtering method and the like. The first experimental process current bias coefficient may be preset.
As an example, in the case where the first experimental process is a discharging process, the bias current may be calculated by the following formula: ibias=i×i_r_dis, where Ibias represents bias current, I represents current data, and i_r_dis represents a first experimental process current bias coefficient.
When the first experimental procedure is a charging procedure, the bias current may be calculated by the following formula: ibias=i×i_r_ch, where Ibias represents bias current, I represents current data, and i_r_ch represents a first experimental process current bias coefficient.
Step S1300, calculating an initial error value based on the state of charge reference value and the state of charge estimation value.
In some embodiments, the execution body may calculate an initial error value based on the state of charge reference value and the state of charge estimate.
In step S1400, under the condition that the state of charge reference value meets the first preset cyclic simulation condition, a first error value is generated through simulation verification, so as to obtain a first error value set, where the first error value is a difference value between the state of charge reference value and the state of charge estimation value in the simulation verification process.
In some embodiments, the execution body may generate a first error value through simulation verification under the condition that the state of charge reference value meets a first preset loop simulation condition, to obtain a first error value set, where the first error value is a difference value between the state of charge reference value and the state of charge estimation value in the simulation verification process.
In some optional implementations of some embodiments, the first preset loop simulation condition includes at least one of: in the case that the first data set is discharge data, the state of charge reference value is greater than a state of charge lower boundary value; in the case where the first data set is charging data, the state of charge reference value is less than the state of charge upper boundary value. As an example, the state of charge upper boundary value and the state of charge lower boundary value may be randomly generated, for example, the state of charge upper boundary value may be 80% and the state of charge lower boundary value may be 40%. The first preset loop simulation condition may be that the state of charge reference value is greater than 40% when the first data set is the discharge data. In the case where the first data set is charging data, the first preset loop simulation condition may be that the state of charge reference value is less than 80%.
In some alternative implementations of some embodiments, the state of charge lower boundary value and the state of charge upper boundary value are obtained by: and generating an upper state of charge boundary value and a lower state of charge boundary value based on the preset nominal capacity and the preset bias capacity. As an example, the preset nominal capacity may be 100Ah and the preset bias capacity may be 95Ah, then it may be known that the corresponding state of charge value may be 95%, the state of charge lower boundary value should typically be less than 95%, and the state of charge upper boundary value should be greater than the state of charge lower boundary value.
In some alternative implementations of some embodiments, the generating the first error value through simulation verification includes: updating the current flag bit according to a preset step value; acquiring current data and voltage data corresponding to the updated current flag bit from the first data set as target current data and target voltage data; generating a target state of charge reference value and a target state of charge estimate based on the target current data, the target voltage data, the preset nominal capacity and the preset bias capacity; and calculating a target error value based on the target state of charge reference value and the target state of charge estimation value, and taking the target error value as the first error value. As an example, since the current data and the voltage data in the first data set have a correspondence relation with time, it may be that a step value of the position of the current flag bit is 1, and then the step value is updated once by 1. The target error value may be a difference between the target state of charge reference value and the target state of charge estimate.
Step S1500, generating loop simulation verification information based on the initial error value and the first error value set.
In some embodiments, the execution body may generate loop simulation verification information based on the initial error value and the first set of error values. When the initial error value and the error value in the first error value set tend to be stable or differ too much, the execution subject may stop the simulation loop verification. When the error value tends to be stable, the cyclic simulation verification information can be "the accuracy verification result of the SOC estimation algorithm is good, and the verification is stopped", and when the error value is too large, the cyclic simulation verification information can be "the accuracy verification of the SOC estimation algorithm fails, and the verification is stopped".
Step S1600, verifying the accuracy of the state of charge estimation algorithm according to the cyclic simulation verification information.
In some embodiments, the executing body may verify the accuracy of the state of charge estimation algorithm based on the loop simulation verification information. When the verification information is "the accuracy verification result of the SOC estimation algorithm is good, and the verification is stopped" the accuracy verification of the SOC estimation algorithm fails, and the verification is stopped ", the accuracy of the state of charge estimation algorithm corresponding to the former is greater than the accuracy of the state of charge estimation algorithm corresponding to the latter. When the verification information is the verification result of the accuracy of the SOC estimation algorithm, and verification is stopped, the accuracy of the state of charge estimation algorithm can be determined according to the sequence of generating the verification information, and the accuracy of the state of charge estimation algorithm generated first is larger than that of the state of charge estimation algorithm generated later.
Some embodiments of the disclosure disclose a method for evaluating accuracy of a battery state of charge estimation algorithm, by acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, and taking a position corresponding to the initial state of charge value as a current flag bit, generating a state of charge reference value and a state of charge estimation value based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimation value is obtained by using a state of charge estimation algorithm to be verified, a relatively accurate state of charge reference value can be obtained, so that an initial error value is calculated based on the state of charge reference value and the state of charge estimation value, a relatively accurate error value can be obtained, and a first error value set is generated through simulation verification under the condition that the state of charge reference value meets a first preset cycle simulation condition, wherein the first error value is a difference value between the state of charge reference value and the state of charge estimation value in a simulation verification process, and cyclic simulation verification information is generated based on the initial error value and the first error value set; according to the cyclic simulation verification information, the accuracy of the state of charge estimation algorithm is verified, a large amount of test data can be obtained through data of single charge and discharge, high cost caused by obtaining a large amount of test data during actual measurement is avoided, a large amount of time is saved, test requirements are greatly reduced, and algorithm verification efficiency is improved.
Embodiments of the present disclosure provide another method of assessing the accuracy of a battery state of charge estimation algorithm. With continued reference to fig. 2, a flowchart of a method for evaluating accuracy of a battery state of charge estimation algorithm, which may be implemented in an electronic device, is provided in an embodiment of the present disclosure.
As shown in fig. 2, the method of the present embodiment may include the following steps S2100 to S2800, which are described in detail below.
In step S2100, current data and voltage data corresponding to an initial state of charge value are obtained from the first data set, and a position corresponding to the initial state of charge value is used as a current flag bit.
Step S2200, generating a state of charge reference value and a state of charge estimated value based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimated value is obtained by using a state of charge estimated algorithm to be verified.
Step S2300 of calculating an initial error value based on the state of charge reference value and the state of charge estimation value
In some embodiments, the specific implementation of steps S2100-S2300 and the technical effects thereof may refer to steps S1100-S1300 in those embodiments corresponding to fig. 1, which are not described herein.
Step S2400, when the state of charge reference value does not meet the first preset cycle simulation condition, acquiring current data and voltage data corresponding to the state of charge reference value from the second data set, and taking a position corresponding to the state of charge reference value as a current flag bit.
In some embodiments, the executing body may acquire current data and voltage data corresponding to the state of charge reference value from the second data set and use a position corresponding to the state of charge reference value as the current flag bit when the state of charge reference value does not satisfy the first preset loop simulation condition. When the first data set is discharge data, the second data set is charge data. When the first data set is charge data, the second data set is discharge data.
Step S2500, generating a new state of charge reference value and a new state of charge estimation value based on the current data and the voltage data corresponding to the state of charge reference value, the preset nominal capacity and the preset bias capacity.
In some embodiments, the execution body may generate a new state of charge reference value and a new state of charge estimate based on the current data and the voltage data corresponding to the state of charge reference value, the preset nominal capacity, and the preset bias capacity. The method for generating the new state of charge reference value and the new state of charge estimation value may be the same as the method for calculating the state of charge reference value and the state of charge estimation value described above, and will not be described herein.
Step S2600, calculating a new initial error value based on the new state of charge reference value and the new state of charge estimation value.
In some embodiments, the execution body may calculate a new initial error value based on the new state of charge reference value and the new state of charge estimate. The new initial error value may be a difference between the new state of charge reference value and the new state of charge estimate.
Step S2700, generating a second error value through simulation verification under the condition that the new state of charge reference value meets a second preset loop simulation condition, so as to obtain a second error value set.
In some embodiments, the executing body may generate a second error value through simulation verification under the condition that the new state of charge reference value meets a second preset loop simulation condition, to obtain a second error value set.
In some alternative implementations of some embodiments, the second preset loop simulation condition is obtained by: generating a new state of charge upper boundary value and a new state of charge lower boundary value based on the state of charge reference value under the condition that the state of charge reference value meets a preset cyclic simulation condition; and generating the second preset loop simulation condition based on the new state-of-charge upper boundary value and the new state-of-charge lower boundary value.
Step S2800, generating the cyclic simulation verification information based on the new initial error value and the second error value set.
In some embodiments, the execution body may generate the loop simulation verification information based on the new initial error value and the second set of error values.
Some embodiments of the disclosure disclose a method for evaluating accuracy of a state of charge estimation algorithm of a battery, by acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, taking a position corresponding to the initial state of charge value as a current flag bit, generating a state of charge reference value and a state of charge estimation value based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimation value is obtained using the state of charge estimation algorithm to be verified, a relatively accurate state of charge reference value can be obtained, so that an initial error value is calculated based on the state of charge reference value and the state of charge estimation value, a relatively accurate error value can be obtained, and under the condition that the state of charge reference value does not meet a first preset cycle simulation condition, current data and voltage data corresponding to the state of charge reference value are acquired from a second data set, and a new state of charge value are generated based on the current data and the voltage data corresponding to the state of charge reference value, the preset nominal capacity and the preset bias capacity; determining a corresponding data set according to the state of charge reference value, calculating based on the new state of charge reference value and the new state of charge estimated value to obtain a new initial error value, generating a second error value through simulation verification to obtain a second error value set under the condition that the new state of charge reference value meets a second preset cycle simulation condition, generating the cycle simulation verification information based on the new initial error value and the second error value set, and obtaining a large amount of test data through data of single charge and discharge, thereby greatly reducing test requirements and improving algorithm verification efficiency.
Embodiments of the present disclosure provide another method of assessing the accuracy of a battery state of charge estimation algorithm. With continued reference to fig. 3, a block diagram of a cyclic simulation implementation provided by an embodiment of the present disclosure, the method may be implemented in an electronic device.
As shown in fig. 3, the method of the present embodiment may include the following steps S301 to S311, which are described in detail below.
Step S301, generating an upper boundary value of the state of charge and a lower boundary value of the state of charge based on the preset nominal capacity and the preset bias capacity.
Step S302, current data and voltage data corresponding to the initial state of charge value are obtained from the first data set, and the position corresponding to the initial state of charge value is used as a current zone bit.
Step S307 includes steps S303 to S306.
Step S303, calculating a state of charge reference value by using the preset nominal capacity, the current data and the voltage data corresponding to the initial state of charge value through an ampere-hour integration method.
And step S304, adding bias to the current data based on the current data and a preset current bias coefficient in the first experimental process to obtain bias current.
In step S305, a state of charge estimated value is calculated by using the bias current, the voltage data and the preset bias capacity through an estimation algorithm.
Step S306, calculating an initial error value based on the state of charge reference value and the state of charge estimation value.
Step S308, judging whether state conversion is needed according to the upper boundary value of the state of charge and the lower boundary value of the state of charge.
In some embodiments, it is first determined whether the first data set is a discharge data set or a charge data set, where in the case that the first data set is discharge data, it is determined whether the state of charge reference value is greater than the state of charge lower boundary value, and where in the case that the state of charge reference value is greater than the state of charge lower boundary value, a state transition is required, and step S311 is performed.
If the first data set is the charge data, it is determined whether the state of charge reference value is smaller than the state of charge upper boundary value, and if the state of charge reference value is smaller than the state of charge upper boundary value, the state transition is not necessary, and step S309 is executed.
Step S309, updating the current flag bit according to a preset step value.
Step S310, obtaining, from the first data set, current data and voltage data corresponding to the updated current flag bit as target current data and target voltage data.
Step S311, a new state of charge reference value and a new state of charge estimation value are generated.
In some embodiments, the execution body may randomly generate the new state of charge reference value and the new state of charge estimate.
In some embodiments of the present disclosure, data is obtained by actually measuring the cycle conditions, and independent tests are required for different SOC intervals.
Embodiments of the present disclosure provide another state of charge estimation method. Please continue to refer to fig. 4, which is a flowchart illustrating a state of charge estimation method according to an embodiment of the present disclosure, the method may be implemented in an electronic device.
As shown in fig. 4, the method of the present embodiment may include the following steps S4100-S4200, which are described in detail below.
In step S4100, voltage data and current data of the target battery are acquired.
In some embodiments, the execution subject of the state of charge estimation method may acquire the voltage data and the current data of the target battery in a wired or wireless manner.
Step S4200 obtains a target state of charge of the target battery based on a target state of charge estimation algorithm according to the voltage data and the current data.
In some embodiments, the executing body may obtain the target state of charge of the target battery based on a target state of charge estimation algorithm according to the voltage data and the current data, where the target state of charge estimation algorithm is an algorithm whose corresponding accuracy meets a preset condition. The preset condition may be preset, for example, may be "a state of charge estimation algorithm with a selection accuracy of greater than 95%, or may be" a state of charge estimation algorithm with a maximum selection accuracy ".
Some embodiments of the disclosure disclose a state of charge estimation method, which includes obtaining voltage data and current data of a target battery, obtaining a target state of charge of the target battery based on a target state of charge estimation algorithm according to the voltage data and the current data, wherein the target state of charge estimation algorithm is an algorithm with corresponding accuracy meeting a preset condition, and obtaining a more accurate state of charge by selecting an algorithm with higher accuracy.
With continued reference to fig. 5, which is a schematic diagram illustrating a plot of error values, the method may be implemented in an electronic device.
In fig. 5, SOC-REF represents a state of charge reference value, SOC-AhBias represents a state of charge bias value calculated by an ampere-hour integration method, and SOC-far represents a state of charge value calculated by the SOC estimation algorithm.
SOC-error-AhBias represents the difference between SOC-FUR and SOC-REF, and SOC-error-AhBias represents the difference between SOC-AhBias and SOC-REF, wherein the smoother the curve of the SOC-error-AhBias is, the better the accuracy of the verified estimation algorithm is.
< device example >
In this embodiment, referring to fig. 6, a schematic structural diagram of an electronic device is provided.
As shown in fig. 6, the electronic device 600 may include a processor 620 and a memory 610, the memory 610 for storing executable instructions; the processor 620 is configured to operate the electronic device according to control of the instructions to perform a method according to any embodiment of the present disclosure.
It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, and taking a position corresponding to the initial state of charge value as a current zone bit; generating a state of charge reference value and a state of charge estimate based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity; calculating to obtain an initial error value based on the charge state reference value and the charge state estimation value; generating a first error value through simulation verification under the condition that the charge state reference value meets a first preset cyclic simulation condition, and obtaining a first error value set; generating loop simulation verification information based on the initial error value and the first error value set.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes a first acquisition unit, a determination unit, a second acquisition unit, a replacement unit, and a completion unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the first acquisition unit may also be described as "a unit that acquires the current version information of the application in response to detection of the first user operation for the application".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (12)
1. A method of evaluating accuracy of a battery state of charge estimation algorithm, comprising:
acquiring current data and voltage data corresponding to an initial state of charge value from a first data set, and taking a position corresponding to the initial state of charge value as a current zone bit;
generating a state of charge reference value and a state of charge estimate based on the current data, the voltage data, a preset nominal capacity and a preset bias capacity, wherein the state of charge estimate is obtained using a state of charge estimation algorithm to be verified;
calculating to obtain an initial error value based on the charge state reference value and the charge state estimation value;
generating a first error value through simulation verification under the condition that the charge state reference value meets a first preset cyclic simulation condition, and obtaining a first error value set, wherein the first error value is a difference value between the charge state reference value and the charge state estimated value in the simulation verification process;
generating cyclic simulation verification information based on the initial error value and the first error value set;
and verifying the accuracy of the state of charge estimation algorithm according to the cyclic simulation verification information.
2. The method of claim 1, wherein generating the first error value through simulation verification comprises:
updating the current flag bit according to a preset step value;
acquiring current data and voltage data corresponding to the updated current flag bit from the first data set as target current data and target voltage data;
generating a target state of charge reference value and a target state of charge estimate based on the target current data, the target voltage data, the preset nominal capacity, and the preset bias capacity;
and calculating a target error value based on the target state of charge reference value and the target state of charge estimated value, and taking the target error value as the first error value.
3. The method of claim 1, wherein the first preset loop simulation conditions comprise at least one of:
in the case that the first data set is discharge data, the state of charge reference value is greater than a state of charge lower boundary value;
in the case that the first data set is charging data, the state of charge reference value is smaller than a state of charge upper boundary value.
4. A method according to claim 3, wherein the lower state of charge boundary value and the upper state of charge boundary value are obtained by:
And generating an upper charge state boundary value and a lower charge state boundary value based on the preset nominal capacity and the preset bias capacity.
5. The method according to claim 1, wherein the method further comprises:
under the condition that the state of charge reference value does not meet a first preset cyclic simulation condition, current data and voltage data corresponding to the state of charge reference value are obtained from a second data set, and the position corresponding to the state of charge reference value is used as a current zone bit;
generating a new state of charge reference value and a new state of charge estimation value based on the current data and the voltage data corresponding to the state of charge reference value, the preset nominal capacity and the preset bias capacity;
calculating a new initial error value based on the new state of charge reference value and the new state of charge estimated value;
generating a second error value through simulation verification under the condition that the new state of charge reference value meets a second preset cyclic simulation condition, and obtaining a second error value set;
generating the cyclic simulation verification information based on the new initial error value and the second set of error values.
6. The method according to claim 5, wherein the second preset loop simulation conditions are obtained by:
Generating a new state of charge upper boundary value and a new state of charge lower boundary value based on the state of charge reference value under the condition that the state of charge reference value meets a preset cyclic simulation condition;
and generating the second preset cyclic simulation condition based on the new state-of-charge upper boundary value and the new state-of-charge lower boundary value.
7. The method according to claim 1, characterized in that the state of charge reference value is obtained by:
and calculating to obtain a state of charge reference value by using the preset nominal capacity, the current data and the voltage data corresponding to the initial state of charge value through an ampere-hour integration method.
8. The method of claim 1, wherein the state of charge estimate is obtained by:
adding bias to the current data based on the current data and a preset current bias coefficient in a first experimental process to obtain bias current;
and calculating to obtain a charge state estimated value by using the bias current, the voltage data and the preset bias capacity through an estimation algorithm.
9. The method according to claim 1, wherein the method further comprises:
Respectively processing the voltage data and the current data of the first experimental process and the voltage data and the current data of the second experimental process obtained through testing to respectively obtain corresponding state of charge values;
and calculating the voltage data and the current data of the first experimental process, the voltage data and the current data of the second experimental process and the corresponding state of charge value respectively by utilizing an interpolation method to obtain a first data set and a second data set corresponding to the simulation step length.
10. A state of charge estimation method, comprising:
acquiring voltage data and current data of a target battery;
and obtaining the target state of charge of the target battery based on a target state of charge estimation algorithm according to the voltage data and the current data, wherein the target state of charge estimation algorithm is an algorithm with the corresponding accuracy determined according to the method of any one of claims 1-9 meeting preset conditions.
11. An electronic device, comprising:
a memory for storing executable instructions;
a processor for executing the method according to any of claims 1-9, operating the electronic device according to the control of the instructions.
12. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program readable for execution by a computer for performing the method according to any one of claims 1-9 when being read by the computer.
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| CN116449221B (en) * | 2023-06-14 | 2023-09-29 | 浙江天能新材料有限公司 | Lithium battery state of charge prediction method, device, equipment and storage medium |
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