CN117805647A - Battery detection method, device, terminal equipment and computer readable storage medium - Google Patents
Battery detection method, device, terminal equipment and computer readable storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of batteries, and provides a battery detection method, a device, terminal equipment and a computer readable storage medium, comprising the following steps: acquiring first working condition data of a battery to be detected at a kth detection moment, wherein k is a positive integer, and the first working condition data represents an actual measurement value of a parameter affecting the battery health state of the battery to be detected; determining first battery health state data of the battery to be tested at a kth detection moment according to the first working condition data; and predicting second battery health state data of the battery to be detected at the (k+1) th detection moment according to the first battery health state data. By the method, the detection accuracy of the battery health state can be effectively improved.
Description
Technical Field
The application belongs to the technical field of batteries, and particularly relates to a battery detection method, a device, terminal equipment and a computer readable storage medium.
Background
With the development of electric vehicles, the service life of batteries is also attracting more and more attention. The state of health (SOH), also known as the battery decay factor, is used to reflect the age and health of the battery. The higher the SOH value, the lower the battery aging degree and the higher the health degree; the smaller the SOH value, the higher the battery aging degree and the lower the health degree.
At present, the method for detecting the SOH value mainly obtains the relationship between the SOH value and the battery charge-discharge current/voltage through a charge-discharge test in a laboratory in the early stage, and compares the measured data with the obtained relationship data in the later stage application to obtain the final SOH value. However, a large amount of tests need to be carried out in the earlier stage, so that a large amount of manpower and material resources are consumed; in addition, when the test environment is different from the actual measurement environment (such as temperature, cycle number, etc.), the accuracy of the detection result may be low.
Disclosure of Invention
The embodiment of the application provides a battery detection method, a device, a terminal device and a computer readable storage medium, which can effectively improve the detection accuracy of the battery health state.
In a first aspect, an embodiment of the present application provides a battery detection method, including:
acquiring first working condition data of a battery to be detected at a kth detection moment, wherein k is a positive integer, and the first working condition data represents an actual measurement value of a parameter affecting the battery health state of the battery to be detected;
determining first battery health state data of the battery to be tested at a kth detection moment according to the first working condition data;
and predicting second battery health state data of the battery to be detected at the (k+1) th detection moment according to the first battery health state data.
In the embodiment of the application, the first battery health state data of the battery to be detected at the kth detection moment is obtained according to the working condition data of the battery to be detected at the kth detection moment, and the second battery health state data of the battery to be detected at the kth+1th detection moment is estimated according to the first battery health state data, which is equivalent to the estimation of the current battery health state by using the historical working condition data of the battery to be detected; because the history working condition data is measured in the actual use process of the battery to be measured, the actual use state of the battery to be measured in the actual environment can be reflected, and therefore, the battery health state is predicted by using the history working condition data, the condition that the detection precision is low due to different test environments and actual measurement environments can be effectively avoided, and the accuracy of detecting the battery health state is effectively improved.
In a possible implementation manner of the first aspect, the first battery state of health data includes a first prediction value, where the first prediction value is used to predict a battery state of health of the battery to be tested;
the step of determining the first battery health status data of the battery to be tested at the kth detection time according to the first working condition data comprises the following steps:
acquiring a first cycle life of the battery to be tested at the kth detection moment according to the first working condition data;
Acquiring a first calendar life of the battery to be tested at the kth detection moment according to the first working condition data;
the first predicted value is calculated from the first cycle life and the first calendar life.
In the embodiment of the application, the cycle life of the battery is considered, the calendar life of the battery is considered, and the predicted value of the battery health state calculated by the method can accurately reflect the health degree of the battery.
In a possible implementation manner of the first aspect, the obtaining, according to the first operating condition data, the first cycle life of the battery to be tested at the kth detection time includes:
calculating the cycle times of the battery to be tested at the kth detection moment according to the first working condition data;
and acquiring the first cycle life according to the cycle times.
In a possible implementation manner of the first aspect, the first working condition data corresponding to the kth detection time includes a state of charge, a charge-discharge current and a sampling time of the kth detection time;
the first battery health state data comprises a first observed value, and the first observed value represents an actual calculated value of the battery health state of the battery to be tested;
The step of determining the first battery health status data of the battery to be tested at the kth detection time according to the first working condition data comprises the following steps:
acquiring rated capacity of the battery to be detected and the state of charge of the (k+1) th detection moment;
calculating the battery capacity of the battery to be detected at the (k+1) th detection moment according to the charge state at the (k+1) th detection moment, the charge state at the (k) th detection moment, the charge-discharge current and the sampling time;
and calculating the first observation value according to the battery capacity of the battery to be detected at the (k+1) th detection moment and the rated capacity.
In a possible implementation manner of the first aspect, the second battery state of health data includes a second predicted value, where the second predicted value is used to predict a battery state of health of the battery to be tested;
the step of predicting and calculating the second battery health status data of the battery to be detected at the kth+1th detection time according to the first battery health status data corresponding to the kth detection time comprises the following steps:
calculating a first difference between the first predicted value and the first observed value;
calculating a first gain of the first difference according to the first working condition data;
And calculating the second predicted value corresponding to the (k+1) th detection moment according to the first predicted value, the first difference value and the first gain.
The first gain is calculated and determined according to the first working condition data, which is equivalent to the influence weight of the observed value and the predicted value on the detection result which is adaptively adjusted according to the historical working condition data. By monitoring the battery working condition data for a long time, the predicted value of the battery health state is more and more close to the real observed value, and therefore the detection precision of the battery is greatly improved.
In a possible implementation manner of the first aspect, the method further includes:
acquiring a reference value of the state of health of the battery;
calculating a second difference between the second predicted value and the reference value;
and determining the health degree of the battery to be tested at the (k+1) th detection moment according to the preset numerical range to which the second difference value belongs.
In a possible implementation manner of the first aspect, the obtaining a reference value of a battery health state includes:
acquiring a sample set, wherein the sample set comprises data of respective battery health states of a plurality of sample batteries, and the working condition data of the sample batteries are matched with the working condition data of the battery to be detected at the (k+1) th detection moment;
Counting the normal distribution of the data in the sample set;
an intermediate value of the normal distribution is determined as the reference value.
In the embodiment of the application, the battery health state of the battery of the other vehicle is counted by utilizing the big data, the battery health state of the battery to be detected is compared and analyzed with the battery health state of the other vehicle, the health degree of the battery to be detected is further detected, and corresponding alarm information is generated. By the method, the reliability of battery health state detection is further improved.
In a second aspect, embodiments of the present application provide a battery detection device, including:
the device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring first working condition data of a battery to be detected at a kth detection moment, k is a positive integer, and the first working condition data represents an actual measurement value of a parameter affecting the battery health state of the battery to be detected;
the calculating unit is used for determining first battery health state data of the battery to be tested at the kth detection moment according to the first working condition data;
and the detection unit is used for predicting the second battery health state data of the battery to be detected at the (k+1) th detection moment according to the first battery health state data.
In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the battery detection method according to any one of the first aspects when the processor executes the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a battery detection method as in any one of the first aspects above.
In a fifth aspect, embodiments of the present application provide a computer program product, which, when run on a terminal device, causes the terminal device to perform the battery detection method according to any one of the first aspects above.
It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a battery detection system provided in an embodiment of the present application;
Fig. 2 is a schematic flow chart of a battery detection method according to an embodiment of the present application;
FIG. 3 is a graph of cycle life provided by an embodiment of the present application;
FIG. 4 is a graph of calendar life provided by an embodiment of the present application;
fig. 5 is a block diagram of a battery detection device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise.
With the development of electric vehicles, the service life of batteries is also attracting more and more attention. Taking a lithium battery as an example, the capacity of the lithium battery gradually decreases with the increasing charge and discharge times of the lithium battery, and if the capacity decreases to a certain threshold value, the lithium battery cannot be used normally.
The state of health (SOH), also known as the battery decay factor, is used to reflect the age and health of the battery. The higher the SOH value, the lower the battery aging degree and the higher the health degree; the smaller the SOH value, the higher the battery aging degree and the lower the health degree. Therefore, SOH values are commonly used to characterize battery life.
Battery life includes primarily calendar life and cycle life. Where calendar life refers to the time from the date of production to the end of life of a battery, typically end of life does not refer to the battery being unable to discharge any more, but rather to the battery capacity decaying to some fraction (e.g., 70%) of rated capacity. Due to the chemical nature of the battery, its calendar life may decay over time even if the battery is not charged or discharged. Cycle life refers to the number of cycles that a battery is charged and discharged. The cycle life is usually a theoretical value, and is calculated according to the battery full charge and discharge operation once, but in practical application, the battery is usually not guaranteed to be full charge and discharge every time.
In some related art, an on-board battery management system (battery management system, BMS) controller typically estimates SOH values based only on the cycle life of the battery. However, as described above, battery calendar life is also a major contributor to battery life, and the reliability of estimating SOH values from cycle life alone is low.
In other related technologies, the method for detecting the SOH value mainly includes that a relationship between the SOH value and the battery charge-discharge current/voltage is obtained through a charge-discharge test performed in a laboratory in the early stage, and in the later stage application, measured data are compared with acquired relationship data to obtain a final SOH value. However, a large amount of tests need to be carried out in the earlier stage, so that a large amount of manpower and material resources are consumed; in addition, when the test environment is different from the actual measurement environment (such as temperature, cycle number, etc.), the accuracy of the detection result may be low.
In order to solve the above problems, embodiments of the present application provide a battery detection method, a battery detection apparatus, a terminal device, and a computer-readable storage medium. In the embodiment of the application, the current battery health state is estimated by utilizing the historical working condition data of the battery to be detected, the situation that the detection precision is low due to the fact that the test environment is different from the actual measurement environment is effectively avoided, and the accuracy of battery health state detection is effectively improved. In addition, the battery health states of the battery to be detected and other sample batteries are compared through big data, so that the reliability of battery health state detection is further improved.
In some application scenarios, referring to fig. 1, a schematic diagram of a battery detection system provided in an embodiment of the present application is shown. As shown in fig. 1, the battery detection system may include a cloud platform 11 and a plurality of electric vehicles 12 communicatively connected to the cloud platform, and a battery is mounted on each electric vehicle 12. The central controller of each electric vehicle 12 interacts with the BMS controller to obtain the working condition data of the battery on the electric vehicle, and then the working condition data is uploaded to the cloud platform 11 through a communication device (such as a GPS) installed on the electric vehicle. The cloud platform 11 receives and stores the operating condition data uploaded by each electric vehicle 12.
The cloud platform can be provided with a database for storing a large amount of working condition data. Because the cloud platform storage space is larger, the working condition data of the batteries on a plurality of electric vehicles can be monitored for a long time through the cloud platform, and reliable data basis is provided for subsequent battery detection.
For convenience of description, when it is required to detect a battery on a certain electric vehicle, the electric vehicle in the embodiment of the present application may be referred to as a target vehicle, and the battery on the target vehicle is referred to as a battery to be measured in the embodiment of the present application.
Referring to fig. 2, a flow chart of a battery detection method according to an embodiment of the present application is shown. The battery detection method in the embodiment of fig. 2 may be performed by the cloud platform shown in fig. 1. By way of example, and not limitation, the method may include the steps of:
S101, acquiring first working condition data of a battery to be tested at a kth detection moment, wherein k is a positive integer.
In some application scenarios, as described in the embodiment of fig. 1, the BMS controller of the electric vehicle sends the working condition data to the central controller once every preset period, and the central controller uploads the received working condition data to the cloud platform. Or, every preset period, the BMS controller of the electric vehicle sends working condition data to the central controller once, and the central controller packages and uploads the N received working condition data to the cloud platform once receiving the N working condition data.
The preset period may be a time interval between every two adjacent detection moments, or may include a plurality of time intervals. The condition data transmitted to the central controller by the BMS controller each time may include all the condition data from the last detection time to the current detection time.
In this embodiment of the present application, the working condition data represents measured values of parameters affecting the battery state of health of the battery to be measured. For example, the operating condition data may include charge-discharge current, charge-discharge voltage, charge-discharge time, state of charge, temperature, and the like of the battery. The charging and discharging current comprises a charging current and a discharging current, the charging and discharging voltage comprises a charging voltage and a discharging voltage, the charging and discharging time comprises a charging time and a discharging time, and the charging state comprises a maximum charging state and a minimum charging state.
In step S101, the cloud platform acquires first working condition data corresponding to the kth detection time from the database. It should be noted that, the first working condition data corresponding to the kth detection time may include not only the working condition data corresponding to the kth detection time, but also the historical detection times before the kth detection time, which correspond to the historical working condition data. The method can be obtained according to the actual needs of the following steps, and is not particularly limited.
S102, determining first battery health state data of the battery to be tested at a kth detection moment according to the first working condition data.
In one implementation manner, a first observed value of a battery to be tested at a kth detection time can be calculated according to first working condition data, and the first observed value is used as first battery health data, wherein the first observed value represents an actual calculated value of a battery health state of the battery to be tested.
In another implementation manner, a first predicted value of the battery to be tested at the kth detection moment can be calculated according to the first working condition data, and the first predicted value is used as first battery health data, wherein the first predicted value is used for predicting the battery health state of the battery to be tested.
It is understood that in the embodiment of the present application, the observed value represents a value of the battery state of health calculated according to the actual monitored current operating condition data. The predicted value represents a value of the state of health of the battery estimated from the historical operating condition data. For example, when k >2, the first predicted value corresponding to the kth detection time is a value of the battery health state estimated according to the working condition data corresponding to the kth-1 detection time; the first observed value corresponding to the kth detection moment is a value of the battery health state calculated according to the working condition data corresponding to the kth detection moment. For the 1 st detection time, the corresponding predicted value may be a preset value.
In the above implementation, considering the predicted value or the observed value alone, the battery state of health cannot be estimated comprehensively. To solve the above problem, in one embodiment of the present application, the first battery state of health data includes a first observed value and a first predicted value. Correspondingly, calculating the first battery health state data of the battery to be measured at the kth detection moment according to the first working condition data comprises: calculating a first predicted value of the battery to be detected at the kth detection moment according to the first working condition data; and calculating a first observation value of the battery to be tested at the kth detection moment according to the first working condition data.
By the mode, the first battery health state data are determined together according to the predicted value and the observed value, the defect of low battery health state precision caused by the influence of the measurement error of the predicted value and the estimation error of the observed value is overcome, and the calculation precision of the battery health state is effectively improved.
In the related art, the cycle life of a battery is generally utilized as a predicted value of the state of health of the battery. When, as described above, the calendar life of the battery is also a major contributor to battery life, the reliability of estimating SOH values from cycle life alone is low.
To solve the above problem, in some embodiments, the calculating method of the first predicted value includes:
1. and acquiring the first cycle life of the battery to be tested at the kth detection moment according to the first working condition data.
2. And acquiring the first calendar life of the battery to be tested at the kth detection moment according to the first working condition data.
3. The first predicted value is calculated from the first cycle life and the first calendar life.
The first cycle life may be added to the first calendar life to obtain a first predicted value. The first cycle life and the first calendar life may be added according to a preset weight to obtain a first predicted value.
In the embodiment of the application, the cycle life of the battery is considered, the calendar life of the battery is considered, and the predicted value of the battery health state calculated by the method can accurately reflect the health degree of the battery.
Optionally, one way of calculating the first cycle life in step 1 includes:
calculating the cycle times of the battery to be tested at the kth detection moment according to the first working condition data; and acquiring the first cycle life according to the cycle times.
In this embodiment of the present application, the number of cycles of the battery to be tested at the kth detection time refers to the total number of cycles that the battery to be tested has been cycled up to the kth detection time.
In some application scenarios, the cloud platform may update the cycle number once every time the cloud platform receives the working condition data uploaded once. For example, before receiving the first working condition data of the kth detection time, the cycle number of the battery to be detected counted by the cloud platform is 100; when the cloud platform receives the uploaded first working condition data of the kth detection time, calculating the cycle number (such as 1) of the battery to be detected from the kth-1 detection time to the kth detection time according to the first working condition data, and accumulating the calculated cycle number 1 with 100 to obtain the cycle number 101 corresponding to the kth detection time.
The battery cycle includes a full charge process and a full discharge process once. In practical applications, however, the user will often not perform a full charge at a time. For example, when the remaining battery power is 20%, the first charge is performed until full power is supplied; when the residual capacity of the battery is 80%, carrying out secondary charging until the battery is full; since the first charge amount is 80% and the second charge amount is 20% and the total is 100%, the cycle number of the battery after the second charge can be +1.
The cycle number counting mode can be counted according to the charging process, the discharging process and the charging process.
Taking statistics of the charging process as an example, for each charging process, integrating according to the charging current and the charging time of the charging process in the working condition data to obtain the charging capacity of the charging process; dividing the charge capacity by the full charge capacity of the battery to obtain a capacity percentage of the charge; sequentially accumulating the capacity percentage of each charging process; the sum of the capacity percentages is equal to +1 for every 100 percent of the total capacity.
The manner of statistics according to the discharging process is similar to that according to the charging process, and will not be described here again.
Correspondingly, if the statistics is performed according to the charging process, the first working condition data corresponding to the kth detection time can include the charging time and the charging current of the battery to be tested in each charging process from the kth-1 detection time to the kth detection time. If the statistics is performed according to the discharging process, the first working condition data corresponding to the kth detection time can include the discharging time and the discharging current of the battery to be tested in each discharging process from the kth-1 detection time to the kth detection time.
One implementation way of obtaining the first cycle life according to the cycle number is: acquiring a cycle life table, wherein the table comprises cycle life values corresponding to different cycle times; and searching a first cycle life corresponding to the current cycle times according to the cycle life table.
In the embodiment of the application, the cycle life table may be a relationship table preset according to industry experience or test results. The first cycle life can be rapidly calculated by a table look-up mode, and the time for battery detection is saved.
Exemplary, referring to fig. 3, a graph of cycle life is provided in an embodiment of the present application. As shown in fig. 3, the horizontal axis represents the number of cycles and the vertical axis represents the cycle life (in percent). As can be seen from fig. 3, the cycle life of the battery gradually decreases as the number of cycles increases.
In this embodiment of the present application, one calculation manner of the first calendar life includes: counting the number of interval days from the departure time of the battery to be detected to the kth detection time; searching a first calendar life corresponding to the current interval days according to a preset calendar life table, wherein the calendar life table comprises calendar lives corresponding to different interval days.
The calendar life table may be a pre-established relationship table based on industry experience or test results. The first calendar life can be rapidly calculated in a table look-up mode, and the time for battery detection is saved.
Exemplary, referring to fig. 4, a graph of calendar life is provided in an embodiment of the present application. As shown in fig. 4, the horizontal axis represents days and the vertical axis represents calendar life (in percent). As can be seen from fig. 4, the calendar life of the battery gradually decreases as the number of factory days increases.
In one embodiment, one way of calculating the first observation includes:
acquiring rated capacity of the battery to be detected and the state of charge of the (k+1) th detection moment; calculating the battery capacity of the battery to be detected at the (k+1) th detection moment according to the charge state at the (k+1) th detection moment, the charge state at the (k) th detection moment, the charge-discharge current and the sampling time; and calculating the first observation value according to the battery capacity of the battery to be detected at the (k+1) th detection moment and the rated capacity.
The state of charge is the ratio of the remaining capacity of the battery to the full charge capacity, and is also known as the residual charge, and is usually expressed in terms of percentage.
The process of acquiring the working condition data by each detection time BMS controller corresponds to a sampling time, and the sampling time can be a sampling time or a sampling time period.
Specifically, the formula can be usedAnd calculating an observed value. Wherein SOC (k) is the state of charge at the kth detection time, SOC (k+1) is the state of charge at the kth+1th detection time, i (k) is the charge-discharge current at the kth detection time, deltaT is the sampling time, cap Forehead (forehead) SOH_observe (k) is the first observed value at the kth detection time, which is the rated capacity of the battery.
S103, predicting second battery health state data of the battery to be detected at the (k+1) th detection moment according to the first battery health state data.
In this embodiment of the present application, the second battery health status data includes a second predicted value, where the second predicted value is used to predict a battery health status of the battery to be measured. Correspondingly, predicting the second battery health status data of the battery to be detected at the (k+1) th detection moment according to the first battery health status data comprises: and predicting a second predicted value of the battery to be detected at the (k+1) th detection moment according to the first predicted value and the second observed value.
Alternatively, in one implementation, the first observed value and the first predicted value may be weighted and summed to obtain the second predicted value. Wherein, the weight value can be preset.
However, in this way, since the weights are preset, the contributions of the first predicted value and the first observed value to the second predicted value are fixed, and cannot be adaptively adjusted, which may result in inaccurate final second predicted value.
To address the above issues, in some embodiments, another implementation may include:
calculating a first difference between the first predicted value and the first observed value; calculating a first gain of the first difference according to the first working condition data; and calculating the second predicted value corresponding to the (k+1) th detection moment according to the first predicted value, the first difference value and the first gain.
Specifically, the second predicted value is calculated according to the formula soh_feature (k+1) =soh_feature (K) +k (k+1) Err (k+1). Wherein soh_feature (k+1) is a second predicted value corresponding to the kth+1th detection time, soh_feature (K) is a first predicted value corresponding to the kth detection time, K (k+1) is a first gain, err (k+1) is a first difference value, err (k+1) =soh_feature (K) -soh_observe (K).
Alternatively, a kalman filter algorithm may be used to implement the calculation of the second predicted value at the (k+1) th detection time. Specifically, a first gainWherein (1)>SOH _forecast (k+ 1|k) is a bayesian probability, P (k+1) is a covariance matrix corresponding to the kth detection time, P (k+1) =p (k) +q, Q is a systematic error covariance, and R is a measurement error covariance.
After calculating the second predicted value of the (k+1) th detection time, updating the Kalman filtering parameter, including: the covariance matrix is updated by the formula P (k+1) = (I-K (k+1) C) P (K).
It should be noted that, for the 1 st detection time, parameters such as covariance matrix, systematic error covariance, and measurement error covariance in the algorithm are preset initial values.
With the above-mentioned kalman filter algorithm, the kalman gain changes with one detection (increase in k value). As shown in the formula, the Kalman gain is determined according to the historical working condition data, which is equivalent to the influence weight of the observed value and the predicted value on the detection result, which is adaptively adjusted according to the historical working condition data. By monitoring the battery working condition data for a long time, the predicted value of the battery health state is more and more close to the real observed value, and therefore the detection precision of the battery is greatly improved.
In the embodiment shown in S101-S103, the observed value and the predicted value of the battery health state of the battery to be detected at the kth detection moment are obtained according to the working condition data of the battery to be detected at the kth detection moment, and the predicted value of the battery health state of the battery to be detected at the kth+1th detection moment is estimated according to the observed value and the predicted value, which is equivalent to estimating the current battery health state by using the historical working condition data of the battery to be detected; because the history working condition data is measured in the actual use process of the battery to be measured, the actual use state of the battery to be measured in the actual environment can be reflected, and therefore, the battery health state is predicted by using the history working condition data, the condition that the detection precision is low due to different test environments and actual measurement environments can be effectively avoided, and the accuracy of detecting the battery health state is effectively improved. In addition, the observation value and the prediction value corresponding to the historical detection moment are considered, so that the detection error caused by inaccurate data of one side of the observation value or the prediction value is effectively reduced, and the accuracy of battery health state detection is further ensured.
In some embodiments, the detection method further comprises:
acquiring a reference value of the state of health of the battery; calculating a second difference between the second predicted value and the reference value; and determining the health degree of the battery to be tested at the (k+1) th detection moment according to the preset numerical range to which the second difference value belongs.
Optionally, different alarm information is set for different preset numerical ranges. Exemplary, as shown in the following table:
alternatively, the reference value may be a value predetermined based on industry experience or test data. However, this method often affects the reliability of the reference value due to subjective judgment errors or experimental environmental differences.
To solve the above problem, alternatively, one way to obtain the reference value may be:
acquiring a sample set, wherein the sample set comprises data of respective battery health states of a plurality of sample batteries, and the working condition data of the sample batteries are matched with the working condition data of the battery to be detected at the (k+1) th detection moment; counting the normal distribution of the data in the sample set; an intermediate value of the normal distribution is determined as the reference value.
The sample battery may be a battery on a plurality of electric vehicles in the battery detection system as described in the embodiment of fig. 1.
In the implementation of the application, the condition data is matched, namely, the service environments, the circulation times and the charging and discharging processes of the sample battery and the battery to be tested are similar. This ensures the reliability of the reference value. For example, the ambient temperature in the operating mode data of the sample battery is within the same threshold range as the ambient temperature in the operating mode data of the battery to be tested; the charge and discharge current of the sample battery in the charge and discharge process is the same as that of the battery to be tested; the cycle times of the sample battery and the cycle times of the battery to be tested are in the same numerical range; etc.
In the embodiment of the application, the battery health state of the battery of the other vehicle is counted by utilizing the big data, the battery health state of the battery to be detected is compared and analyzed with the battery health state of the other vehicle, the health degree of the battery to be detected is further detected, and corresponding alarm information is generated. By the method, the reliability of battery health state detection is further improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Fig. 5 is a block diagram of the battery detection device according to the embodiment of the present application, corresponding to the battery detection method described in the above embodiment, and only the portion related to the embodiment of the present application is shown for convenience of explanation.
Referring to fig. 5, the apparatus includes:
the obtaining unit 51 is configured to obtain first working condition data of the battery to be tested at a kth detection time, where k is a positive integer, and the first working condition data represents an actual measurement value of a parameter affecting a battery health state of the battery to be tested.
And the calculating unit 52 is configured to determine first battery health status data of the battery to be tested at a kth detection time according to the first working condition data.
And the detecting unit 53 is configured to predict the second battery state of health data of the battery to be tested at the (k+1) th detection time according to the first battery state of health data.
Optionally, the first battery state of health data includes a first predicted value, where the first predicted value is used to predict a battery state of health of the battery to be tested. Correspondingly, the computing unit 52 is also configured to:
acquiring a first cycle life of the battery to be tested at the kth detection moment according to the first working condition data;
acquiring a first calendar life of the battery to be tested at the kth detection moment according to the first working condition data;
the first predicted value is calculated from the first cycle life and the first calendar life.
Optionally, the computing unit 52 is further configured to:
calculating the cycle times of the battery to be tested at the kth detection moment according to the first working condition data;
and acquiring the first cycle life according to the cycle times.
Optionally, the first working condition data corresponding to the kth detection time includes a state of charge, a charge-discharge current and a sampling time of the kth detection time; the first battery state of health data includes a first observed value representing an actual calculated value of a battery state of health of the battery under test.
Correspondingly, the computing unit 52 is also configured to:
acquiring rated capacity of the battery to be detected and the state of charge of the (k+1) th detection moment;
calculating the battery capacity of the battery to be detected at the (k+1) th detection moment according to the charge state at the (k+1) th detection moment, the charge state at the (k) th detection moment, the charge-discharge current and the sampling time;
and calculating the first observation value according to the battery capacity of the battery to be detected at the (k+1) th detection moment and the rated capacity.
Optionally, the second battery state of health data includes a second predicted value, where the second predicted value is used to predict a battery state of health of the battery to be tested. Accordingly, the detection unit 53 is further configured to:
calculating a first difference between the first predicted value and the first observed value;
calculating a first gain of the first difference according to the first working condition data;
and calculating the second predicted value corresponding to the (k+1) th detection moment according to the first predicted value, the first difference value and the first gain.
Optionally, the apparatus 5 further comprises:
an alarm unit 54 for acquiring a reference value of the battery state of health; calculating a second difference between the second predicted value and the reference value; and determining the health degree of the battery to be tested at the (k+1) th detection moment according to the preset numerical range to which the second difference value belongs.
Optionally, the alarm unit 54 is further configured to:
acquiring a sample set, wherein the sample set comprises data of respective battery health states of a plurality of sample batteries, and the working condition data of the sample batteries are matched with the working condition data of the battery to be detected at the (k+1) th detection moment;
counting the normal distribution of the data in the sample set;
an intermediate value of the normal distribution is determined as the reference value.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.
In addition, the battery detection device shown in fig. 5 may be a software unit, a hardware unit, or a unit combining soft and hard, which are built in an existing terminal device, or may be integrated into the terminal device as an independent pendant, or may exist as an independent terminal device.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Fig. 6 is a schematic structural diagram of a terminal device provided in an embodiment of the present application. As shown in fig. 6, the terminal device 6 of this embodiment includes: at least one processor 60 (only one shown in fig. 6), a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, the processor 60 implementing the steps in any of the various battery detection method embodiments described above when executing the computer program 62.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that fig. 6 is merely an example of the terminal device 6 and is not meant to be limiting as to the terminal device 6, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 60 may be a central processing unit (Central Processing Unit, CPU), the processor 60 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may in some embodiments be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6. The memory 61 may in other embodiments also be an external storage device of the terminal device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal device 6. The memory 61 is used for storing an operating system, an application program, a Boot Loader (Boot Loader), data, other programs, etc., such as program codes of the computer program. The memory 61 may also be used for temporarily storing data that has been output or is to be output.
Embodiments of the present application also provide a computer readable storage medium storing a computer program, which when executed by a processor, may implement the steps in the above-described method embodiments.
The embodiments of the present application provide a computer program product which, when run on a terminal device, causes the terminal device to perform the steps of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to an apparatus/terminal device, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; 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 technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A battery detection method, characterized by comprising:
acquiring first working condition data of a battery to be detected at a kth detection moment, wherein k is a positive integer, and the first working condition data represents an actual measurement value of a parameter affecting the battery health state of the battery to be detected;
Determining first battery health state data of the battery to be tested at a kth detection moment according to the first working condition data;
and predicting second battery health state data of the battery to be detected at the (k+1) th detection moment according to the first battery health state data.
2. The battery detection method according to claim 1, wherein the first battery state of health data includes a first predicted value for predicting a battery state of health of the battery to be detected;
the step of determining the first battery health status data of the battery to be tested at the kth detection time according to the first working condition data comprises the following steps:
acquiring a first cycle life of the battery to be tested at the kth detection moment according to the first working condition data;
acquiring a first calendar life of the battery to be tested at the kth detection moment according to the first working condition data;
the first predicted value is calculated from the first cycle life and the first calendar life.
3. The battery testing method according to claim 2, wherein the obtaining the first cycle life of the battery to be tested at the kth test time according to the first operating condition data includes:
Calculating the cycle times of the battery to be tested at the kth detection moment according to the first working condition data;
and acquiring the first cycle life according to the cycle times.
4. The battery detection method according to claim 2, wherein the first operating condition data corresponding to the kth detection time includes a state of charge, a charge-discharge current, and a sampling time at the kth detection time;
the first battery health state data comprises a first observed value, and the first observed value represents an actual calculated value of the battery health state of the battery to be tested;
the step of determining the first battery health status data of the battery to be tested at the kth detection time according to the first working condition data comprises the following steps:
acquiring rated capacity of the battery to be detected and the state of charge of the (k+1) th detection moment;
calculating the battery capacity of the battery to be detected at the (k+1) th detection moment according to the charge state at the (k+1) th detection moment, the charge state at the (k) th detection moment, the charge-discharge current and the sampling time;
and calculating the first observation value according to the battery capacity of the battery to be detected at the (k+1) th detection moment and the rated capacity.
5. The battery detection method according to claim 4, wherein the second battery state of health data includes a second predicted value for predicting a battery state of health of the battery to be detected;
the step of predicting and calculating the second battery health status data of the battery to be detected at the kth+1th detection time according to the first battery health status data corresponding to the kth detection time comprises the following steps:
calculating a first difference between the first predicted value and the first observed value;
calculating a first gain of the first difference according to the first working condition data;
and calculating the second predicted value corresponding to the (k+1) th detection moment according to the first predicted value, the first difference value and the first gain.
6. The battery detection method according to claim 5, wherein the method further comprises:
acquiring a reference value of the state of health of the battery;
calculating a second difference between the second predicted value and the reference value;
and determining the health degree of the battery to be tested at the (k+1) th detection moment according to the preset numerical range to which the second difference value belongs.
7. The battery detection method according to claim 6, wherein the obtaining the reference value of the state of health of the battery includes:
Acquiring a sample set, wherein the sample set comprises data of respective battery health states of a plurality of sample batteries, and the working condition data of the sample batteries are matched with the working condition data of the battery to be detected at the (k+1) th detection moment;
counting the normal distribution of the data in the sample set;
an intermediate value of the normal distribution is determined as the reference value.
8. A battery detection device, characterized by comprising:
the device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring first working condition data of a battery to be detected at a kth detection moment, k is a positive integer, and the first working condition data represents an actual measurement value of a parameter affecting the battery health state of the battery to be detected;
the calculating unit is used for determining first battery health state data of the battery to be tested at the kth detection moment according to the first working condition data;
and the detection unit is used for predicting the second battery health state data of the battery to be detected at the (k+1) th detection moment according to the first battery health state data.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.
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