CN116299015A - Battery state evaluation method, battery state evaluation device, electronic equipment and storage medium - Google Patents
Battery state evaluation method, battery state evaluation device, electronic equipment and storage medium Download PDFInfo
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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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- 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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The application relates to the technical field of energy detection, and provides a battery state evaluation method, a battery state evaluation device, electronic equipment and a storage medium. The method comprises the following steps: according to each historical characteristic data of the battery, obtaining each error value corresponding to each historical characteristic data one by one; carrying out Markov analysis according to at least one target error value in the error values and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery; obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on current battery parameters of the battery; the historical characteristic data comprises historical battery parameters of the battery, historical state predicted values obtained according to the historical battery parameters and ideal state values measured by the battery under the condition of the historical battery parameters. The battery state evaluation method provided by the embodiment of the application can improve the detection precision of the battery state.
Description
Technical Field
The present disclosure relates to the field of energy detection technologies, and in particular, to a battery state evaluation method, a device, an electronic apparatus, and a storage medium.
Background
Currently, batteries have been used as energy storage devices by many renewable energy systems and are applied to new energy automobiles on a large scale, such as new energy automobiles using lithium ion batteries as power sources on a large scale. As one of key parameters of the battery system, an accurate battery state, such as a battery state of charge (SOC), a battery state of health (SOH), or a battery remaining energy (SOE), can help the BMS (Battery Management System ) to perform energy management better, and its variation directly affects the use performance, reliability, and safety of the battery.
In the related art, for battery state estimation, an ampere-hour integration method or an open circuit voltage method is generally used to estimate a battery state to determine a battery state of charge, a battery state of health, or a battery remaining energy of a battery. However, since the battery state detected by using ampere-hour integration and open-circuit voltage fails to accurately characterize the accumulated error existing in the battery state during the estimation process, the detection accuracy is insufficient.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the related art. Therefore, the application provides a battery state evaluation method which can improve the detection precision of the battery state.
The application also provides a battery state evaluation device.
The application also provides electronic equipment.
The present application also proposes a computer-readable storage medium.
The battery state evaluation method according to the embodiment of the first aspect of the application comprises the following steps:
according to each historical characteristic data of the battery, obtaining each error value corresponding to each historical characteristic data one by one;
carrying out Markov analysis according to at least one target error value in the error values and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery;
obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on the current battery parameters of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
After each error value corresponding to each historical characteristic data one by one is obtained by utilizing each historical characteristic data of the battery, markov analysis is carried out according to at least one target error value in each error value and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery, and error compensation is carried out on the current state predicted value of the battery by utilizing the current compensation value. The target error value is determined according to the historical state predicted value and the ideal state value measured by the battery under the condition of the historical battery parameter, so that the confidence coefficient of the target error value can be improved, the current compensation value of the battery is determined by using a Markov analysis mode, the confidence coefficient of the current compensation value for compensating the current state predicted value can be further improved, the accumulated error of the battery state in the estimation process is effectively compensated, and the detection precision of the battery state is improved.
According to one embodiment of the present application, further comprising:
and carrying out prior estimation and posterior estimation on the historical battery parameters in sequence according to the extended Kalman filtering to obtain the estimated value of the historical state.
According to one embodiment of the present application, the performing markov analysis according to at least one target error value in the error values and the estimated historical state value of the battery at the last moment to obtain the current compensation value of the battery includes:
extracting a plurality of target error values with the correlation coefficient larger than a preset coefficient from the error values according to the correlation coefficient corresponding to the error values;
carrying out Markov analysis according to each target error value and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
and the correlation coefficient is determined according to the error value and a historical state predicted value of the historical characteristic data corresponding to the error value.
According to one embodiment of the present application, the performing markov analysis according to at least one target error value in the error values and the estimated historical state value of the battery at the last moment to obtain the current compensation value of the battery includes:
acquiring the total distance between any one target error value and each adjacent target error value;
according to a preset ratio of total distances corresponding to the two adjacent target error values, adjusting the target error value which is farther from the numerical center in the two adjacent target error values until the ratio of the total distances corresponding to any two adjacent target error values is smaller than or equal to the preset ratio;
carrying out Markov analysis according to the adjusted target error values and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
wherein the numerical center is determined based on each of the target error values.
According to an embodiment of the present application, according to a preset ratio of total distances corresponding to two adjacent target error values, adjusting the target error value of the two adjacent target error values that is farther from a numerical center includes:
and determining that the ratio of the total distance corresponding to the two adjacent target error values is larger than the preset ratio, and adjusting the target error value which is farther from the numerical center in the two adjacent target error values.
According to one embodiment of the present application, the performing markov analysis according to at least one target error value in the error values and the estimated historical state value of the battery at the last moment to obtain the current compensation value of the battery includes:
obtaining a plurality of state intervals according to the standard deviation of each target error value;
determining a target state interval from each state interval according to the historical state predicted value of the battery at the last moment;
and obtaining the current compensation value of the battery according to the state error matrix obtained by the plurality of state intervals, the initial state matrix obtained by the target state interval and the one-step state transition matrix obtained by each target error value.
According to one embodiment of the application, the target state interval is based on a preset model
Determining; wherein (1)>
Represents a historical state predictive value of the battery at time k,
representing the status interval as +.>
The time history state predictive value is +.>
Is a probability of (2).
The battery state evaluation device according to the embodiment of the second aspect of the present application includes:
the error acquisition module is used for acquiring each error value corresponding to each historical characteristic data one by one according to each historical characteristic data of the battery;
the compensation determining module is used for carrying out Markov analysis according to at least one target error value in the error values and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
the state evaluation module is used for obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on the current battery parameters of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
An electronic device according to an embodiment of a third aspect of the present application includes a processor and a memory storing a computer program, where the processor implements the battery state evaluation method according to any of the above embodiments when executing the computer program.
A computer-readable storage medium according to an embodiment of a fourth aspect of the present application has stored thereon a computer program which, when executed by a processor, implements the battery state evaluation method according to any one of the above embodiments.
A computer program product according to an embodiment of the fifth aspect of the present application, comprising: the computer program, when executed by a processor, implements a battery state evaluation method as described in any one of the embodiments above.
The above technical solutions in the embodiments of the present application have at least one of the following technical effects:
after each error value corresponding to each historical characteristic data one by one is obtained by utilizing each historical characteristic data of the battery, markov analysis is carried out according to at least one target error value in each error value and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery, and error compensation is carried out on the current state predicted value of the battery by utilizing the current compensation value. The target error value is determined according to the historical state predicted value and the ideal state value measured by the battery under the condition of the historical battery parameter, so that the confidence coefficient of the target error value can be improved, the current compensation value of the battery is determined by using a Markov analysis mode, the confidence coefficient of the current compensation value for compensating the current state predicted value can be further improved, the accumulated error of the battery state in the estimation process is effectively compensated, and the detection precision of the battery state is improved.
Drawings
For a clearer description of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a battery state evaluation method according to an embodiment of the present disclosure;
FIG. 2 is an equivalent circuit model diagram of a battery in an embodiment of the present application;
FIG. 3 is a second flow chart of a battery state evaluation method according to an embodiment of the present disclosure;
fig. 4 is a third flow chart of a battery state evaluation method according to an embodiment of the present disclosure;
fig. 5 is a fourth flowchart of a battery state evaluation method according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a battery state evaluation device provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The battery state evaluation method, the battery state evaluation device, the electronic equipment and the storage medium provided by the embodiment of the application will be described and illustrated in detail through several specific embodiments.
In some embodiments, a battery state evaluation method is provided, which is applied to a controller for battery state evaluation. The controller can be a control device such as a singlechip, a control chip or a server, the server can be an independent server or a server cluster formed by a plurality of servers, and the controller can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent sampling point devices.
As shown in fig. 1, the battery state evaluation method provided in this embodiment includes:
102, performing Markov analysis according to at least one target error value in the error values and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
In some embodiments, the historical battery parameters in the historical feature data may be determined based on the current battery state that needs to be detected. The battery state may be a battery state of charge (SOC), a battery state of health (SOH), or a battery remaining energy (SOE), among others. For example, assuming that the current battery state to be detected is a battery state of charge, the temperature, open circuit voltage, ohmic internal resistance, battery terminal voltage, battery current, battery polarization resistance, polarization capacitance, polarization voltage, and the like of the battery at a certain historical time may be obtained as historical battery parameters of the battery at the historical time.
The battery parameters of the battery may be obtained by an equivalent circuit. For example, a suitable equivalent circuit model may be built based on battery characteristics to determine battery parameters based on their spatial state equations. Taking the first-order RC model (Thevenin model) of fig. 2 as an example, the spatial state equation of the first-order RC model is:
wherein U is OC Represents the open circuit voltage of the battery, R 0 Indicating ohmic internal resistance of battery, U t Representing the battery terminal voltage, i L Represents the battery current, R p 、C p 、U p The cell polarization resistance, polarization capacitance and polarization voltage are shown separately.
And processing the space state equation in an offline HPPC test or online parameter identification mode and the like to obtain the battery parameters of the battery.
After the historical battery parameters are obtained by utilizing the space state equation, the historical battery parameters can be processed by using an ampere-hour integration method, an open-circuit voltage method, a particle filtering method, a neural network method and the like to obtain a corresponding historical state predicted value of the battery, such as a historical state of charge predicted value of the battery.
Meanwhile, aiming at any historical battery parameter, special high-precision equipment is adopted in a laboratory, and an ideal state value of the battery under the condition of the historical battery parameter is calculated in advance in real time. Taking battery charge state detection as an example, special high-precision equipment can be adopted in a laboratory aiming at different working conditions, and an ideal SOC value of the battery under the condition of certain historical battery parameters can be obtained by using an AH integration method in real time. Then, according to the historical battery parameter, the historical state predicted value and the corresponding ideal state value, a piece of historical characteristic data comprising battery characteristic data such as the historical battery parameter, the historical state predicted value, the ideal state value and the current variation can be formed. After the history feature data is obtained, an error value corresponding to the history feature data can be obtained according to the ideal state value in the history feature data and the history state predicted value in the history feature data.
In order to further improve the reliability of the error value corresponding to the historical characteristic data, when a certain historical state predicted value is obtained, the rationality of the historical state predicted value can be judged first. If the estimated value of the historical state is located outside the interval, the estimated value of the historical state is unreasonable, and the estimated value of the historical state is removed; otherwise, the historical characteristic data is formed by combining the historical characteristic data with the corresponding historical battery parameters, and the ideal state value and other characteristic data, such as the current variation, of the battery measured under the condition of the historical battery parameters. Or, after obtaining the error value corresponding to the historical characteristic data, judging whether the error value is unreasonable, if so, judging whether the error value is larger than a preset value; if the error value is larger than the preset value, the error value is unreasonable, and the historical characteristic data is removed at the moment, so that the quality of the historical characteristic data is ensured, and the accuracy of the subsequent battery state evaluation is improved.
After each error value is obtained, all the error values can be marked as target error values, and then Markov analysis is carried out by utilizing each target error value and the historical state predicted value of the battery at the last moment so as to predict and obtain the current compensation value of the battery.
After the current compensation value is obtained, the current compensation value is added with the current state estimated value determined based on the current battery parameters of the battery, thus obtaining the current state estimated result of the battery,
after each error value corresponding to each historical characteristic data one by one is obtained by utilizing each historical characteristic data of the battery, markov analysis is carried out according to at least one target error value in each error value and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery, and error compensation is carried out on the current state predicted value of the battery by utilizing the current compensation value. The target error value is determined according to the historical state predicted value and the ideal state value measured by the battery under the condition of the historical battery parameter, so that the confidence coefficient of the target error value can be improved, the current compensation value of the battery is determined by using a Markov analysis mode, the confidence coefficient of the current compensation value for compensating the current state predicted value can be further improved, the accumulated error of the battery state in the estimation process is effectively compensated, and the detection precision of the battery state is improved.
In order to improve the accuracy of the obtained estimated historical state value, in some embodiments, the estimated historical state value may be obtained by sequentially performing a priori estimation and a posterior estimation of the battery state on the historical battery parameter by using extended kalman filtering.
As a possible implementation manner, assuming that the battery state to be detected is a battery state of charge, an equivalent model of the battery is shown in fig. 2, then one may choose
For state variables, the following state equation is established:
at this point a state transition matrix is determined:
output matrix:
namely, the observation matrix and the measurement matrix are:
in combination with the extended kalman filter algorithm, the state of charge is estimated as follows:
step 1: initialization of
For the following
Setting P 0 ，Q，R
Step 2: prior estimation
State prior estimation:
state covariance prior estimation:
step 3: posterior estimation
Kalman gain matrix:
state posterior estimation:
state covariance posterior estimation:
step 4: time update
Let k=k+1, return to step 2).
Wherein: x is x k , y k , u k The state quantity, the output quantity and the input quantity of the equivalent circuit model are respectively represented, and Q and R respectively represent covariance matrixes of process noise and measurement noise of the equivalent circuit model.
After the history state predicted value of the battery is obtained through the expansion Kalman filtering, the corresponding error value can be determined by utilizing the history state predicted value and the corresponding ideal state value, so that the influence of unknown non-Gaussian interference on the expansion Kalman filtering algorithm is overcome, the applicability and the robustness of the expansion Kalman filtering algorithm are enhanced, and the accuracy of the obtained error value is improved.
Similarly, the predicted value of the current state of the battery can be obtained in the above manner.
To further improve the confidence level of the obtained current compensation value, in some embodiments, as shown in fig. 3, the performing markov analysis according to at least one target error value in the error values and the estimated historical state value of the battery at the last moment to obtain the current compensation value of the battery includes:
and the correlation coefficient is determined according to the error value and a historical state predicted value of the historical characteristic data corresponding to the error value.
In some embodiments, after each error value is obtained, a correlation coefficient between the error value and the history state estimated value of the corresponding history feature data may be calculated based on a pearson correlation calculation method, so as to select an error value with the correlation coefficient greater than a certain threshold value as the target error value, so that the target error value can effectively reflect the association relationship with the battery state.
The correlation coefficient between the error value and the corresponding estimated historical state value is calculated by the following method:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the i-th history state estimated value, n represents the number of history state estimated values, +.>
Representing the error value corresponding to the i-th history state predicted value,/, for example>
，/>
Respectively->
、/>
Average value of (2).
If the correlation coefficient between the error value and the corresponding history state predicted value is greater than the preset threshold, the error value and the history state predicted value are strongly correlated, and the error value is taken as the target error value. And because all the obtained target error values have a strong association relation with the historical state predicted value, the Markov analysis is performed by using each target error value and the historical state predicted value of the battery at the last moment, so that the accuracy of the obtained current compensation value is higher.
In order to make the obtained target error values conform to one-dimensional gaussian distribution so as to accurately reflect the error interval and the real situation of the distribution, thereby further improving the accuracy of the current compensation value obtained later, in some embodiments, as shown in fig. 4, the markov analysis is performed according to at least one target error value in the error values and the historical state predicted value of the battery at the last moment, so as to obtain the current compensation value of the battery, which includes:
wherein the numerical center is determined based on each of the target error values.
In some embodiments, to accurately reflect the reality of the error interval and distribution, it is necessary to deal with outliers that are far from other sample points due to randomness or data errors. For this purpose, the target error value may be preprocessed based on the K-nearest neighbor idea. For example, an average value of each target error value may be calculated first, and the average value may be taken as a numerical center. Alternatively, the mode of each target error value is taken as the numerical center. Or forming a circumscribed circle according to each target error value, and calculating the gravity center of the circumscribed circle to take the gravity center of the circumscribed circle as a numerical center. Then, for any target error value, the sum of the distances of that target error value and its k adjacent target error values, i.e., the total distance, may be calculated. The distance calculation formula may be:
wherein X, Y is any two target error values.
After the total distance corresponding to each target error value is obtained, the target error value which is farther from the numerical center in the two adjacent target error values can be adjusted according to the preset ratio of the total distance corresponding to the two adjacent target error values until the ratio of the total distance corresponding to any two adjacent target error values is smaller than or equal to the preset ratio.
In order to improve the adjustment efficiency of the target error values, in some embodiments, it may be determined that the ratio of the total distances corresponding to the two adjacent target error values is greater than a preset ratio. For example, assuming that the total distances corresponding to two adjacent target error values are the first total distance and the second total distance, respectively, it is determined whether the ratio of the first total distance to the second total distance is greater than a preset ratio. If so, one of the two target error values is an outlier far away from other target error values, and at the moment, the target error value which is farther away from the numerical center in the two target error values is adjusted, so that the ratio of the total distance corresponding to the two target error values is smaller than or equal to a preset ratio. Otherwise, the two target error values are not outliers, and the values of the two target error values before and after adjustment are unchanged.
The ratio of the total distances corresponding to the adjacent two target error values is as follows:
And when the ratio of the total distances corresponding to all the two adjacent target error values meets the condition of being smaller than or equal to the preset ratio, the adjustment of each target error value is completed, and at the moment, all the target error values are used as adjusted target error values, so that the target error values for carrying out Markov analysis can accurately reflect the actual conditions of error intervals and distribution, and the reliability of the current compensation value obtained through Markov analysis is improved.
To further improve the reliability of the current compensation value obtained by markov analysis, in some embodiments, as shown in fig. 5, performing markov analysis according to at least one target error value of the error values and a historical state predicted value of the battery at a previous moment to obtain the current compensation value of the battery, where the method includes:
For example, after each target error value is obtained, the state of the battery may be divided into a plurality of state intervals, such as S1[0,
-0.5s]，S2 [/>
- 0.5s，/>
+0.5s]s3 [ ] and>
+0.5s，1]. Wherein s represents the standard deviation of each target error value normalized,/for>
The average value normalized for each target error value.
After determining a plurality of state intervals, according to the historical state predicted value of the battery at the last moment, a target error value corresponding to the battery at the last moment can be determined, and then the state interval to which the target error value corresponding to the battery at the last moment belongs is taken as the target state interval.
After the target state interval is obtained, the state error matrix obtained from the multiple state intervals, the initial state matrix obtained from the target state interval and the one-step state transition matrix obtained from each target error value can be used for calculating the current compensation value of the battery, for example:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the current compensation value of the battery,/->
Represents the initial state matrix corresponding to the previous time k, P 1 The state transition matrix is one-step, and Q is a state error matrix.
Exemplary, P 1 Is that
A matrix of dimensions may be derived from the time-ordered target error values. Q is
The dimensional matrix is obtained by inversely normalizing the interval median value of each state interval. P (P) k Is->
The matrix of dimension is determined by the state interval corresponding to the historical state estimated value of the battery at the moment k. If the state interval corresponding to the history state predicted value of the battery at the time k is S3, P k =[0 0 1]。
In order to increase the accuracy of the obtained target state interval and further increase the reliability of the current compensation value obtained later, in some embodiments, the state interval corresponding to the maximum probability of selection may be determined to be the target state interval according to the maximum similarity criterion, i.e. the target state interval may be determined to be the target state interval according to a preset model
Determining; wherein (1)>
Represents a history state predictive value of the battery at time k, < >>
Representing the status interval as +.>
The time history state predictive value is +.>
Is a probability of (2).
After the current compensation value of the battery is obtained, the current state compensation value can be added with the current state predicted value of the battery, so that the current state evaluation result of the battery is obtained.
The battery state evaluation device provided in the present application is described below, and the battery state evaluation device described below and the battery state evaluation method described above may be referred to correspondingly to each other.
In some embodiments, as shown in fig. 6, there is provided a battery state evaluation device including:
the error obtaining module 210 is configured to obtain, according to each historical feature data of the battery, each error value corresponding to each historical feature data one by one;
the compensation determining module 220 is configured to perform markov analysis according to at least one target error value of the error values and a historical state predicted value of the battery at a previous time to obtain a current compensation value of the battery;
a state evaluation module 230, configured to obtain a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on a current battery parameter of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
After each error value corresponding to each historical characteristic data one by one is obtained by utilizing each historical characteristic data of the battery, markov analysis is carried out according to at least one target error value in each error value and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery, and error compensation is carried out on the current state predicted value of the battery by utilizing the current compensation value. The target error value is determined according to the historical state predicted value and the ideal state value measured by the battery under the condition of the historical battery parameter, so that the confidence coefficient of the target error value can be improved, the current compensation value of the battery is determined by using a Markov analysis mode, the confidence coefficient of the current compensation value for compensating the current state predicted value can be further improved, the accumulated error of the battery state in the estimation process is effectively compensated, and the detection precision of the battery state is improved.
In some embodiments, the error acquisition module 210 is further configured to:
and carrying out prior estimation and posterior estimation on the historical battery parameters in sequence according to the extended Kalman filtering to obtain the estimated value of the historical state.
In some embodiments, the compensation determination module 220 is specifically configured to:
extracting a plurality of target error values with the correlation coefficient larger than a preset coefficient from the error values according to the correlation coefficient corresponding to the error values;
carrying out Markov analysis according to each target error value and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
and the correlation coefficient is determined according to the error value and a historical state predicted value of the historical characteristic data corresponding to the error value.
In some embodiments, the compensation determination module 220 is specifically configured to:
acquiring the total distance between any one target error value and each adjacent target error value;
according to a preset ratio of total distances corresponding to the two adjacent target error values, adjusting the target error value which is farther from the numerical center in the two adjacent target error values until the ratio of the total distances corresponding to any two adjacent target error values is smaller than or equal to the preset ratio;
carrying out Markov analysis according to the adjusted target error values and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
wherein the numerical center is determined based on each of the target error values.
In some embodiments, the compensation determination module 220 is specifically configured to:
and determining that the ratio of the total distance corresponding to the two adjacent target error values is larger than the preset ratio, and adjusting the target error value which is farther from the numerical center in the two adjacent target error values.
In some embodiments, the compensation determination module 220 is specifically configured to:
obtaining a plurality of state intervals according to the standard deviation of each target error value;
determining a target state interval from each state interval according to the historical state predicted value of the battery at the last moment;
and obtaining the current compensation value of the battery according to the state error matrix obtained by the plurality of state intervals, the initial state matrix obtained by the target state interval and the one-step state transition matrix obtained by each target error value.
In some embodiments, the target state interval is based on a preset model
Determining; wherein (1)>
Represents a historical state predictive value of the battery at time k,
representing the status interval as +.>
The time history state predictive value is +.>
Is a probability of (2).
Fig. 7 illustrates a physical schematic diagram of an electronic device, as shown in fig. 7, which may include: processor 810, communication interface (Communication Interface) 820, memory 830, and communication bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through communication bus 840. Processor 810 may invoke a computer program in memory 830 to perform a battery state assessment method, including, for example:
according to each historical characteristic data of the battery, obtaining each error value corresponding to each historical characteristic data one by one;
carrying out Markov analysis according to at least one target error value in the error values and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery;
obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on the current battery parameters of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
Further, the logic instructions in the memory 830 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present application further provide a storage medium, where the storage medium includes a computer program, where the computer program may be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer is capable of executing the battery state evaluation method provided in the foregoing embodiments, for example, including:
according to each historical characteristic data of the battery, obtaining each error value corresponding to each historical characteristic data one by one;
carrying out Markov analysis according to at least one target error value in the error values and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery;
obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on the current battery parameters of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A battery state evaluation method, characterized by comprising:
according to each historical characteristic data of the battery, obtaining each error value corresponding to each historical characteristic data one by one;
carrying out Markov analysis according to at least one target error value in the error values and a historical state predicted value of the battery at the last moment to obtain a current compensation value of the battery;
obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on the current battery parameters of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
2. The battery state evaluation method according to claim 1, characterized by further comprising:
and carrying out prior estimation and posterior estimation on the historical battery parameters in sequence according to the extended Kalman filtering to obtain the estimated value of the historical state.
3. The battery state evaluation method according to claim 1 or 2, wherein performing markov analysis based on at least one target error value of the error values and a history state estimated value of the battery at a previous time to obtain a current compensation value of the battery, comprises:
extracting a plurality of target error values with the correlation coefficient larger than a preset coefficient from the error values according to the correlation coefficient corresponding to the error values;
carrying out Markov analysis according to each target error value and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
and the correlation coefficient is determined according to the error value and a historical state predicted value of the historical characteristic data corresponding to the error value.
4. The battery state evaluation method according to claim 1 or 2, wherein performing markov analysis based on at least one target error value of the error values and a history state estimated value of the battery at a previous time to obtain a current compensation value of the battery, comprises:
acquiring the total distance between any one target error value and each adjacent target error value;
according to a preset ratio of total distances corresponding to the two adjacent target error values, adjusting the target error value which is farther from the numerical center in the two adjacent target error values until the ratio of the total distances corresponding to any two adjacent target error values is smaller than or equal to the preset ratio;
carrying out Markov analysis according to the adjusted target error values and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
wherein the numerical center is determined based on each of the target error values.
5. The battery state evaluation method according to claim 4, wherein adjusting the target error value of the two adjacent target error values farther from the numerical center according to a preset ratio of total distances corresponding to the two adjacent target error values comprises:
and determining that the ratio of the total distance corresponding to the two adjacent target error values is larger than the preset ratio, and adjusting the target error value which is farther from the numerical center in the two adjacent target error values.
6. The battery state evaluation method according to claim 1, 2 or 5, wherein performing markov analysis based on at least one target error value of the error values and a historical state predicted value of the battery at a previous time to obtain a current compensation value of the battery, comprises:
obtaining a plurality of state intervals according to the standard deviation of each target error value;
determining a target state interval from each state interval according to the historical state predicted value of the battery at the last moment;
and obtaining the current compensation value of the battery according to the state error matrix obtained by the plurality of state intervals, the initial state matrix obtained by the target state interval and the one-step state transition matrix obtained by each target error value.
7. The battery state evaluation method according to claim 6, wherein the target state interval is based on a preset model
Determining; wherein (1)>
Represents a history state predictive value of the battery at time k, < >>
Representing the status interval as +.>
The time history state predictive value is +.>
Is a probability of (2).
8. A battery state evaluation device, characterized by comprising:
the error acquisition module is used for acquiring each error value corresponding to each historical characteristic data one by one according to each historical characteristic data of the battery;
the compensation determining module is used for carrying out Markov analysis according to at least one target error value in the error values and the historical state predicted value of the battery at the last moment to obtain the current compensation value of the battery;
the state evaluation module is used for obtaining a current state evaluation result of the battery according to the current compensation value and a current state predicted value determined based on the current battery parameters of the battery;
wherein the historical characteristic data comprises historical battery parameters of the battery, a historical state predicted value obtained according to the historical battery parameters, and an ideal state value measured by the battery under the condition of the historical battery parameters.
9. An electronic device comprising a processor and a memory storing a computer program, characterized in that the processor implements the battery state assessment method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the battery state evaluation method of any one of claims 1 to 7.
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