CN112485695A - Detection method and device for power battery - Google Patents
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- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
Abstract
The invention discloses a detection method and a detection device for a power battery. Wherein, the method comprises the following steps: acquiring a target capacity attenuation degree of the power battery, wherein the target capacity attenuation degree is used for representing the capacity variation of the power battery; the method comprises the steps of obtaining consistency scores of the power batteries, wherein the consistency scores are used for representing whether the capacity of each single battery in the power batteries is consistent or not; and determining the state of the power battery based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery. The invention solves the technical problem that the performance of the power battery can not be evaluated in the prior art.
Description
Technical Field
The invention relates to the field of power batteries, in particular to a detection method and a detection device for a power battery.
Background
The standard Of SOH (State Of Health) is defined as follows: the ratio of the capacity discharged from a slow charge state to a cut-off voltage at a certain rate to the nominal capacity corresponding to the rate under the standard condition. Therefore, SOH at different magnifications tends to be different values. If the batteries in various use scenes need to be compared by the same standard, the current SOH of all the batteries needs to be converted into the same low-rate discharge rate by a certain method, for example, the full charge of 0.1C is taken as a measurement standard.
However, such conditions are generally applicable only to laboratory equipment, and are relatively rare in large-scale on-board applications. The common SOH estimation method generally extracts some features in a charge-discharge curve, realizes a mapping relation between the features and the SOH, and establishes a data-driven model. And then the SOH can be mapped only by extracting the characteristics from the real vehicle data. The best method is to carry out a full-life-cycle aging test on the battery of the vehicle in a laboratory, and a data drive model is established in advance. However, the accelerated aging test requires several months or even years to decay the battery to a certain extent, and therefore most manufacturers do not perform the related test for cost saving. In addition, along with the increasing of models and battery cell types flowing into the market, it is more difficult to establish models for various battery cells.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a detection method and a detection device for a power battery, which are used for at least solving the technical problem that the performance of the power battery cannot be evaluated in the prior art.
According to an aspect of an embodiment of the present invention, there is provided a method for detecting a state of a power battery, the method including: acquiring a target capacity attenuation degree of the power battery, wherein the target capacity attenuation degree is used for representing the capacity variation of the power battery; the method comprises the steps of obtaining consistency scores of the power batteries, wherein the consistency scores are used for representing whether the capacity of each single battery in the power batteries is consistent or not; and determining the state of the power battery based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery.
Optionally, the state of the power battery is determined based on the capacity fade degree and the consistency score value, and the method further comprises: carrying out proportional operation on the target capacity attenuation degree and the consistency score value based on a preset proportional coefficient to obtain a state score of the power battery; the state of the power battery is determined based on the state score.
Optionally, a target capacity fade of the power battery is obtained, and the method further includes: acquiring charging data of a power battery; determining a first capacity attenuation degree and a second capacity attenuation degree of the power battery based on the charging data, wherein the first capacity attenuation degree is determined based on the capacity of the power battery, and the second capacity attenuation degree is determined based on the voltage of each single battery in the power battery; and determining the difference value between the first capacity attenuation degree and the second capacity attenuation degree as a target capacity attenuation degree.
Optionally, the method further comprises determining a first degree of capacity fade of the power battery based on the charging data, and the method further comprises: determining a first estimate of power battery capacity at a first time based on the charging data; determining an estimated value error of the power battery capacity at a second time based on the charging data, wherein the second time is after the first time; processing the error of the estimated value of the capacity of the power battery by using a Kalman filter to obtain a corrected value of the capacity of the power battery at a second moment; and determining a first capacity attenuation degree of the power battery based on the first estimated value and the corrected value.
Optionally, based on the charging data, an error of the estimated value of the power battery capacity at the second time is determined, and the method further comprises: determining a second estimated value of the capacity of the power battery based on the charging data, wherein the second estimated value is determined based on the charging amount of the power battery between the first time and the second time; determining a third estimated value of the capacity of the power battery based on the charging data, wherein the third estimated value is determined based on the open circuit voltage of the power battery at the second moment; based on the second estimate and the third estimate, an estimate error of the power battery capacity is determined.
Optionally, the second capacity fade degree of the power battery is determined based on the charging data, and the method further comprises: acquiring the voltage of a plurality of single batteries in the power battery; determining the highest voltage and the lowest voltage in the voltages of the plurality of single batteries; based on the highest voltage and the lowest voltage, a second degree of capacity fade is determined.
Optionally, the acquiring of the charging data of the power battery comprises: acquiring identification information of a power battery; and acquiring the charging data of the power battery corresponding to the identification information.
Optionally, after acquiring the charging data of the power battery, the method further comprises: preprocessing the charging data, wherein the preprocessing comprises at least one of the following steps: removing abnormal values and filling empty values; a first degree of capacity fade and a second degree of capacity fade are determined based on the pre-processed charge data.
Optionally, a consistency score value of the power battery is obtained, and the method further comprises: acquiring performance parameters of each single battery in the power battery; acquiring the temperature of each single battery in the power battery; the consistency score value is determined based on the performance parameters of each of the unit cells and the temperature of each of the unit cells.
According to another aspect of the embodiments of the present invention, there is also provided a device for detecting the state of a power battery, the method including: the first obtaining module is used for obtaining a target capacity attenuation degree of the power battery, wherein the target capacity attenuation degree is used for representing the capacity variation of the power battery;
the second acquisition module is used for acquiring consistency score values corresponding to the power batteries, wherein the consistency score values are used for representing whether the capacities of the single batteries in the power batteries are consistent or not;
and the determining module is used for determining the state of the power battery based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, which includes a stored program, wherein when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the above-mentioned power battery detection method.
According to another aspect of the embodiments of the present invention, there is also provided a processor, where the processor is configured to execute a program, where the program executes the method for detecting a power battery described above.
In the embodiment of the invention, a method for detecting the state of a power battery is provided, which can be used for firstly obtaining the target capacity attenuation degree of the power battery, wherein the target capacity attenuation degree is used for representing the capacity variation of the power battery; then, obtaining the consistency score value of the power battery, wherein the consistency score value is used for representing whether the capacity of each single battery in the power battery is consistent or not; the state of the power battery can be determined based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery, the technical effect of accurately determining the aging degree of the power battery is achieved, the technical problem that the performance of the power battery cannot be evaluated in the prior art is solved, and meanwhile, the method for estimating the health state of the battery based on the big data lays a foundation for continuous application of the big data.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flow chart of a method for testing a power battery according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an alternative battery health assessment module according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an alternative battery health calculation model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an alternative exemplary power battery charging profile according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an alternative differential pressure capacity modified charging curve for a power cell in accordance with an embodiment of the present invention;
FIG. 6 is a flow chart of an alternative power cell detection method according to an embodiment of the present invention;
FIG. 7 is a schematic illustration of an alternative overall distribution of brand vehicle battery health in accordance with an embodiment of the present invention;
FIG. 8 is a schematic diagram of a battery health profile of an alternative single vehicle battery in accordance with an embodiment of the present invention;
FIG. 9 is a graphical illustration of an alternative vehicle battery health mean value versus time in accordance with an embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating an alternative zonal average of battery health comparison in accordance with an embodiment of the present invention;
fig. 11 is a schematic diagram of a detection device for a power battery according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for detecting a power battery, where the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and where a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a flow chart of a power battery detection method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
and step S102, acquiring the target capacity attenuation degree of the power battery.
The target capacity attenuation degree is used for representing the capacity variation of the power battery.
The power battery in the above steps may be a lead-acid power battery, a nickel-hydrogen power battery, or a lithium-ion power battery, and the kind of the power battery is not limited herein.
In the above steps, the target capacity fading degree may be one of important indexes for representing the health degree of the power battery. The accurate acquisition of the target capacity attenuation degree is an important basis for accurately measuring the health degree and the service life of the power battery. The capacity of the power battery is gradually reduced along with the service life of the power battery.
And step S104, acquiring the consistency score value of the power battery.
Wherein, the consistency score value is used for representing whether the capacity of each single battery in the power battery is consistent or not.
In the above step, the battery consistency scoring may refer to that, in the use process of the power battery, due to individual differences of each single battery composing the power battery, the charging and discharging processes of each single battery are not completely synchronous, and further, due to the fact that the production processes of each single battery are not completely consistent, after the same device is in the same use state, the capacities of each single battery are not consistent. The consistency score is an indicator of the magnitude of the difference that characterizes the inconsistency.
And step S106, determining the state of the power battery based on the target capacity attenuation degree and the consistency score value.
Wherein, the state of the power battery is used for representing the aging degree of the power battery.
The state of the power battery in the above steps can refer to the battery capacity health degree, that is, the aging degree of the power battery is represented, the battery capacity health degree is a value obtained by performing proportional operation on the target capacity attenuation degree and the consistency score value, the value reflects the health state of the battery, the battery capacity health degree can intuitively judge the capacity attenuation amount and the consistency good or bad degree of the battery, and an intuitive and accurate judgment standard is provided for a user.
In an alternative embodiment, the battery health evaluation module 26 shown in fig. 2 may determine the health of the power battery based on the capacity attenuation degree output by the capacity attenuation estimation module 22 and the battery consistency output by the battery consistency evaluation module 24, where the battery health reflects the current health state of the power battery and the residual value of the power battery after decommissioning, and provides data support for the application environment of the power battery for gradient utilization, so as to finally recover the raw material of the battery, and finally build a theoretical basis for the management of the full life cycle of the battery.
In the embodiment of the invention, a method for detecting the state of a power battery is provided, which can be used for firstly obtaining the target capacity attenuation degree of the power battery, wherein the target capacity attenuation degree is used for representing the capacity variation of the power battery; then, obtaining the consistency score value of the power battery, wherein the consistency score value is used for representing whether the capacity of each single battery in the power battery is consistent or not; the state of the power battery can be determined based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery, the technical effect of accurately determining the aging degree of the power battery is achieved, the technical problem that the performance of the power battery cannot be evaluated in the prior art is solved, and meanwhile, the method for estimating the health state of the battery based on the big data lays a foundation for continuous application of the big data.
Optionally, the state of the power battery is determined based on the capacity fade degree and the consistency score value, and the method further comprises: carrying out proportional operation on the target capacity attenuation degree and the consistency score value based on a preset proportional coefficient to obtain a state score of the power battery; the state of the power battery is determined based on the state score.
The calculation model of the battery health degree in the above steps is shown in fig. 3, and the calculation formula of the battery health degree is as follows:
n*SOH_final+m*Consistency_score＝Score_final，
in the formula, 2 coefficients such as n and m represent capacity attenuation ratio and battery Consistency ratio respectively, SOH _ final represents capacity attenuation degree, Consistency _ Score represents battery Consistency Score, and Score _ final represents battery health degree. The coefficients can determine the proportion specifically suitable for the data source according to different data conditions of the cloud platform. In the embodiment of the present invention, through data analysis and a large amount of actual data verification, it can be determined that the scaling factors are n-0.3 and m-0.7, respectively.
In an optional embodiment, the vehicle networking platform can realize the storage of all static information of the vehicle and the real-time monitoring and storage of vehicle dynamic information, wherein the static information of the vehicle mainly comprises vehicle basic information, vehicle-mounted terminal information, battery information and motor information; the dynamic information of the vehicle comprises a vehicle running state, a battery state, a motor state, alarm information and the like; the battery image analysis needs to extract static information and dynamic data of the vehicle from the internet of vehicles platform. And finally obtaining the health state of the battery through battery capacity attenuation estimation and battery consistency analysis of the data of the Internet of vehicles platform.
Optionally, a target capacity fade of the power battery is obtained, and the method further includes: acquiring charging data of a power battery; determining a first capacity attenuation degree and a second capacity attenuation degree of the power battery based on the charging data, wherein the first capacity attenuation degree is determined based on the capacity of the power battery, and the second capacity attenuation degree is determined based on the voltage of each single battery in the power battery; and determining the difference value between the first capacity attenuation degree and the second capacity attenuation degree as a target capacity attenuation degree.
The charging data in the above steps may be obtained by reading a charging/discharging record on the device, and extracting the charging duration, the voltage value when the battery is fully charged, the discharging duration, the discharging power, and the time from discharging to the cut-off voltage and the nominal battery capacity data from the charging/discharging record, so as to calculate the first and second capacity fading degrees of the battery, respectively.
The first capacity attenuation degree can be obtained by a basic capacity calculation module, wherein the basic capacity calculation comprises a rated capacity calculation and a current capacity calculation.
In an alternative embodiment, the power battery data of each electric vehicle during charging is selected from the platform data, and the graph 4 shows that the δ Ah1 and the δ Ah2 are calculated by integrating the total current with time. Wherein: delta SOC1 refers to SOC segments taken between 40% and 60%; δ Ah1 is the amount of charge in the block section. Delta SOC2 refers to the SOC slice taken between 20% and 80%, and delta Ah2 is the amount of charge in that slice.
The rated capacity can be calculated by the ratio of the charging SOC to the charging capacity in the delta SOC1 section in the charging process, and the SOC is not generally corrected in the algorithm because the voltage rise in the section is gentle.
Crati is δ Ah1/δ Soc1, where Crati is the rated capacity value.
The current capacity can be calculated by data with the SoH charging interval being more than 20% -80% in the charging process, and the SOC is generally corrected in the algorithm due to the fact that the voltage and the SOC linearity are high at the two ends, so that the SOC accuracy is relatively high at the two ends.
Ccurr ═ δ Ah2/δ Soc2, where Crurr is the current capacity value.
Due to the fact that error factors such as measurement errors, SOC calculation errors, platform data discreteness and temperature differences exist objectively, a calculation result based on charging data of a certain 1 time has certain errors, n charging curves are selected to calculate rated capacity and current capacity and take the average value of the rated capacity and the current capacity, and n is generally 10-20.
According to the definition of SOH:
wherein, the SOHcal is the SOH value output by the basic capacity calculation module.
Optionally, the method further comprises determining a first degree of capacity fade of the power battery based on the charging data, and the method further comprises: determining a first estimate of power battery capacity at a first time based on the charging data; determining an estimated value error of the power battery capacity at a second time based on the charging data, wherein the second time is after the first time; processing the error of the estimated value of the capacity of the power battery by using a Kalman filter to obtain a corrected value of the capacity of the power battery at a second moment; and determining a first capacity attenuation degree of the power battery based on the first estimated value and the corrected value.
The first time in the above steps may be the last time, the second time may be the current time, and the first estimated value of the first time may be an initial value of the battery capacity of a certain battery; the estimate error may be an error between a capacity estimate obtained from the current signal and a capacity estimate obtained from the voltage signal.
The Kalman filtering in the above steps may be an algorithm that performs optimal estimation on the system state by inputting and outputting observation data through a system using a linear system state equation. The optimal estimation can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system.
In an alternative embodiment, a first estimation value of the power battery capacity at the previous time may be estimated based on the charging data, and the estimation values of the power battery capacity at the current time are respectively estimated through the current signal and the voltage signal, so as to determine an estimation value error of the power battery capacity at the current time, and then the estimation value error is processed by using a kalman filter processor, so as to obtain a correction value of the power battery capacity, so as to determine the first capacity attenuation degree based on the capacity correction value and the first estimation value.
Optionally, based on the charging data, an error of the estimated value of the power battery capacity at the second time is determined, and the method further comprises: determining a second estimated value of the capacity of the power battery based on the charging data, wherein the second estimated value is determined based on the charging amount of the power battery between the first time and the second time; determining a third estimated value of the capacity of the power battery based on the charging data, wherein the third estimated value is determined based on the open circuit voltage of the power battery at the second moment; based on the second estimate and the third estimate, an estimate error of the power battery capacity is determined.
In an alternative embodiment, the current and SOC change of the current charging cycle may be determined based on the charging amount of the current charging cycle, so that a second estimated value of the power battery capacity at the current time may be estimated, a third estimated value of the power battery capacity at the current time may be estimated based on the open-circuit voltage and the SOC, and then a covariance between the second estimated value and the third estimated value may be calculated, so that an estimated value error may be obtained.
Optionally, the second capacity fade degree of the power battery is determined based on the charging data, and the method further comprises: acquiring the voltage of a plurality of single batteries in the power battery; determining the highest voltage and the lowest voltage in the voltages of the plurality of single batteries; based on the highest voltage and the lowest voltage, a second degree of capacity fade is determined.
The second capacity attenuation degree in the above steps can be obtained by the differential pressure capacity correction module, the battery system is composed of hundreds of strings of batteries, and the performance difference of the battery monomers tends to be gradually enlarged due to the individual difference of the batteries and the influence of temperature in the use process, so that the battery system is limited by the battery monomer with the worst performance at the two ends of the charging and discharging curve, and the available capacity is attenuated. The cell performance difference is mainly represented in the measurable data by the highest cell voltage and the lowest cell voltage. When the voltage difference is enlarged, the influence on the current capacity of the battery system is also increased. Therefore, the capacity change caused by the pressure difference must be calculated and corrected.
In an optional embodiment, the SOC corresponding to the lowest cell voltage in the charging process is taken as a threshold, in general, the voltage threshold of the ternary battery is taken as 3.9V, and the voltage threshold of the lithium iron phosphate battery is taken as 3.4V. Then searching the SOC corresponding to the highest cell voltage in the charging process as the threshold value, calculating the SOC difference value corresponding to the two points, and outputting
The algorithm obtains the capacity fade value caused by the maximum pressure difference of the monomer, as shown in fig. 5.
QCvol＝Crati[SoC(min，vol)-SoC(max，vol)]，
Wherein, Ucell is the cell voltage; the SOC (max, vol) is the SOC when the monomer corresponding to the highest voltage of the monomer reaches a charge cut-off threshold value; the SOC (min, vol) is the SOC when the monomer corresponding to the lowest voltage of the monomer reaches a charge cut-off threshold value; the SOHvol is an SOH value output by the differential pressure capacity correction module; the curve A is a battery monomer charging curve corresponding to the monomer highest voltage Umax, and the curve B is a battery monomer charging curve corresponding to the monomer lowest voltage Umin.
Optionally, the acquiring of the charging data of the power battery comprises: acquiring identification information of a power battery; and acquiring the charging data of the power battery corresponding to the identification information.
The function of acquiring the identification information of the power battery in the above steps may be to determine the corresponding power battery, that is, after the performance of the power battery is evaluated, the information of performance degradation of each power battery may be accurately displayed. The capacity attenuation curve of a single battery system can be obtained, meanwhile, other data types in the platform big data, such as region, vehicle type, battery type and other information are called, and multi-dimensional battery health state comparison results can be output, so that big data application service is provided.
Optionally, after acquiring the charging data of the power battery, the method further comprises: preprocessing the charging data, wherein the preprocessing comprises at least one of the following steps: removing abnormal values and filling empty values; a first degree of capacity fade and a second degree of capacity fade are determined based on the pre-processed charge data.
In the above steps, the abnormal value elimination method includes a Lauda criterion method, a ShowWiler criterion method, a Dixon criterion method, a Romanofsky criterion method, and a Grabas criterion method. The processing methods of vacancy value filling include a deletion method, special value filling, average value filling, hot card filling, K-means filling, integration method using all possible value filling and combination, regression method, expectation value maximization method, multiple filling and C4.5 method. The purpose of the preprocessing is to obtain more accurate data.
Optionally, a consistency score value of the power battery is obtained, and the method further comprises: acquiring performance parameters of each single battery in the power battery; acquiring the temperature of each single battery in the power battery; the consistency score value is determined based on the performance parameters of each of the unit cells and the temperature of each of the unit cells.
In the above steps, the main influence factors of the battery monomer and the battery capacity attenuation can be temperature, charge and discharge depth, charge and discharge multiplying power and calendar life. In addition, the change of the internal resistance of the battery also has a corresponding relation with the change of the capacity, but the power health state is influenced to a greater extent. For a single power battery system, due to the integration of a large number of battery cells, the inconsistency of the cells and the inconsistency of the ambient temperature increase along with the service time, which leads to the increase of the inconsistency of the cell performance, and the attenuation of the battery system is also remarkably accelerated.
In an alternative embodiment, for a large number of power batteries, the attenuation of the new energy automobile in the actual operation process is influenced by various internal and external variables, and the attenuation characteristics can be obviously different even if the battery systems are of the same brand and the same batch.
A preferred embodiment of the present invention will be described in detail with reference to fig. 6. As shown in fig. 6, the method may include the steps of:
step S601, providing an initial value of battery capacity;
step S602, obtaining a capacity estimation value at the moment K;
step S603, obtaining current and SOC from the charging data;
step S604, obtaining a capacity estimation value at the moment K +1 through the step S602 and the step S603;
step S605, obtaining a capacity estimation value error based on the capacity at the time K;
step S606, obtaining the voltage at the moment K + 1;
step S607, obtaining the capacity estimation value at the moment K + 1;
step S608, obtaining a capacity estimation value error based on the voltage at the time K + 1;
step S609, obtaining a Kalman gain through a capacity estimation value error obtained based on the capacity at the moment K and a capacity estimation value error obtained based on the voltage at the moment K + 1;
step S610, correcting deviation;
step S611, obtaining a capacity correction value at the time K + 1;
and step S612, filtering the output capacity value.
In the above steps, firstly, estimating the current capacity based on the previous capacity and the current and SOC change of the current charging cycle, wherein the previous capacity is the estimated value of the capacity at the first time, and the current capacity is the estimated value of the capacity at the second time; then estimating the current battery capacity based on the open-circuit voltage and the SOC, starting to calculate the covariance of two capacity estimation values under the optimal working condition of correcting the capacity to 25 ℃, and filtering based on a fuzzy Kalman model to obtain a corrected capacity calculation value; carrying out cyclic calculation in the next charging process; and finally outputting the historical change trend of the capacity.
In an alternative embodiment, fig. 7 shows the SOH distribution of all vehicles (268) of a certain brand of electric vehicle, which acquires data herein, and it can be seen that the health degree of the batteries of the brand of vehicle is mostly concentrated in 96-100, and the overall health condition is good. In practical application, by performing big data analysis on SOHs of a plurality of brands of vehicles, manufacturers can be instructed to improve and users can be instructed to buy electric vehicles.
In an optional embodiment, fig. 8 shows the SOH change trend of a certain vehicle in shandong province from 2016 to 2017 in 3, and SOH on-line monitoring is performed on the electric vehicle of each user through a large data platform, so that guidance can be provided for the health state management of the battery of the single vehicle, the abnormal change of the SOH can be found in time, fault early warning is made, the driving safety is ensured, and the economic loss of the user is reduced.
In an alternative embodiment, FIG. 9 shows the SOH mean trend of all vehicles in each area of a brand. By utilizing the accumulated big data and the proposed estimation method, the SOH overall change trend of all running vehicles in the market can be analyzed, so that a manufacturer can make policy control in the aspect of after-sale maintenance, and the SOH overall change trend can be used as an information source for reversely guiding production and manufacturing.
In an alternative embodiment, fig. 10 shows the variation trend of the mean SOH value of the vehicle in 4 provinces, and it can be seen that the SOH overall level in zhejiang province is lower than that in other provinces, while the SOH condition in north Hu province is the most favorable. Therefore, the influence factors can be further analyzed aiming at the regions with poor SOH conditions, such as road conditions, driving habits of drivers, climate, battery manufacturing and selling links and the like, the method is beneficial to appointing a proper battery management scheme aiming at the regional characteristics, and provides guidance such as selling, after-sale service and the like for manufacturers.
In an alternative embodiment, further, the SOH averages for all the vehicles of brand a and brand B, both 96.94 for brand a and 97.07 for brand B, were obtained by estimation analysis, showing that brand B has a better battery state of health. Through the actual analysis and comparison of the SOH of the sold vehicles on the market, the SOH analysis and comparison method is beneficial to stimulating the competitive improvement of various manufacturers, promotes the benign development of new energy vehicles and power battery industries, and can also provide certain guidance for the vehicle purchase and maintenance of users.
Example 2
According to the embodiment of the present invention, a device for detecting a power battery is further provided, where the device may perform the method for detecting a power battery in the foregoing embodiment, and a specific implementation manner and a preferred application scenario are the same as those in the foregoing embodiment, and are not described herein again.
Fig. 11 is a schematic diagram of a detection device for a power battery according to an embodiment of the present invention, as shown in fig. 11, the device includes:
a first obtaining module 1102, configured to obtain a target capacity attenuation degree of the power battery, where the target capacity attenuation degree is used to represent a capacity variation of the power battery;
a second obtaining module 1104, configured to obtain a consistency score value corresponding to the power battery, where the consistency score value is used to represent whether capacities of each unit battery in the power battery are consistent;
a determining module 1106, configured to determine a state of the power battery based on the target capacity fade degree and the consistency score value, where the state of the power battery is used to characterize an aging degree of the power battery.
Optionally, the determining module includes: the operation unit is used for carrying out proportional operation on the target capacity attenuation degree and the consistency score value based on a preset proportional coefficient to obtain a state score of the power battery; and the first determination unit is used for determining the state of the power battery based on the state score.
Optionally, the first obtaining module includes: the first acquisition unit is used for acquiring charging data of the power battery; a second determination unit, configured to determine a first capacity attenuation degree and a second capacity attenuation degree of the power battery based on the charging data, wherein the first capacity attenuation degree is determined based on the capacity of the power battery, and the second capacity attenuation degree is determined based on the voltage of each unit battery in the power battery; and the third determining unit is used for determining the difference value between the first capacity attenuation degree and the second capacity attenuation degree as the target capacity attenuation degree.
Optionally, the second determination unit includes: a first determining subunit configured to determine, based on the charging data, a first estimated value of a power battery capacity at a first time; a second determining subunit configured to determine, based on the charging data, an error of an estimated value of the power battery capacity at a second time, the second time being subsequent to the first time; the first obtaining subunit is used for processing the error of the estimated value of the capacity of the power battery by using a Kalman filter to obtain a corrected value of the capacity of the power battery at a second moment; and the third determining subunit is used for determining the first capacity attenuation degree of the power battery based on the first estimated value and the corrected value.
Optionally, the second determining subunit is configured to determine, based on the charging data, a second estimated value of the capacity of the power battery, wherein the second estimated value is determined based on a charging amount of the power battery between the first time and the second time; the second determining subunit is further configured to determine, based on the charging data, a third estimated value of the capacity of the power battery, wherein the third estimated value is determined based on an open-circuit voltage of the power battery at a second time; the second determining subunit is further configured to determine an error in the estimated value of the power battery capacity based on the second estimated value and the third estimated value.
Optionally, the second determining unit further includes: the second acquiring subunit is used for acquiring the voltages of a plurality of single batteries in the power battery; a third determining subunit for determining the highest voltage and the lowest voltage among the voltages of the plurality of unit cells; and a fourth determining subunit, configured to determine the second capacity fade degree based on the highest voltage and the lowest voltage.
Optionally, the first obtaining unit is used for obtaining identification information of the power battery; the first acquisition unit is also used for acquiring the charging data of the power battery corresponding to the identification information.
Optionally, the apparatus further comprises: the processing module is used for preprocessing the charging data, wherein the preprocessing comprises at least one of the following steps: removing abnormal values and filling empty values; the determination module is further configured to determine a first degree of capacity fade and a second degree of capacity fade based on the pre-processed charging data.
Optionally, the second obtaining module includes: the second acquisition unit is used for acquiring the performance parameters of each single battery in the power battery; the second acquiring unit is also used for acquiring the temperature of each single battery in the power battery; and a third determination unit for determining a consistency score value based on the performance parameter of each unit cell and the temperature of each unit cell.
Example 3
According to an embodiment of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for detecting the state of the power battery in embodiment 1.
Example 4
According to an embodiment of the present invention, there is also provided a processor, configured to execute a program, where the program executes the method for detecting the power battery status in embodiment 1.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (12)
1. A method for detecting the state of a power battery is characterized by comprising the following steps:
acquiring a target capacity attenuation degree of a power battery, wherein the target capacity attenuation degree is used for representing the capacity variation of the power battery;
acquiring consistency score values of the power batteries, wherein the consistency score values are used for representing whether the capacities of the single batteries in the power batteries are consistent or not;
and determining the state of the power battery based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery.
2. The method of claim 1, wherein determining the state of the power cell based on the capacity fade and the consistency score value comprises:
performing proportional operation on the target capacity attenuation degree and the consistency score value based on a preset proportional coefficient to obtain a state score of the power battery;
determining a state of the power battery based on the state score.
3. The method of claim 1, wherein obtaining a target capacity fade for the power cell comprises:
acquiring charging data of the power battery;
determining a first capacity attenuation degree and a second capacity attenuation degree of the power battery based on the charging data, wherein the first capacity attenuation degree is determined based on the capacity of the power battery, and the second capacity attenuation degree is determined based on the voltage of each single battery in the power battery;
and determining the difference value of the first capacity attenuation degree and the second capacity attenuation degree as the target capacity attenuation degree.
4. The method of claim 3, wherein determining a first degree of capacity fade for the power cell based on the charging data comprises:
determining a first estimate of the power battery capacity at a first time based on the charging data;
determining an error in the estimated value of the power battery capacity at a second time based on the charging data, wherein the second time is subsequent to the first time;
processing the estimated value error of the power battery capacity by using a Kalman filter to obtain a corrected value of the power battery capacity at the second moment;
and determining a first capacity attenuation degree of the power battery based on the first estimated value and the corrected value.
5. The method of claim 4, wherein determining an error in the estimate of the power battery capacity at the second time based on the charging data comprises:
determining a second estimate of the power battery capacity based on the charging data, wherein the second estimate is determined based on a charge of the power battery between the first time and the second time;
determining a third estimated value of the capacity of the power battery based on the charging data, wherein the third estimated value is determined based on the open circuit voltage of the power battery at the second time;
determining an estimated value error of the power battery capacity based on the second estimated value and the third estimated value.
6. The method of claim 3, wherein determining a second degree of capacity fade for the power cell based on the charging data comprises:
acquiring the voltage of a plurality of single batteries in the power battery;
determining the highest voltage and the lowest voltage in the voltages of the plurality of single batteries;
determining the second capacity fade rate based on the highest voltage and the lowest voltage.
7. The method of claim 3, wherein obtaining charging data for the power battery comprises:
acquiring identification information of the power battery;
and acquiring the charging data of the power battery corresponding to the identification information.
8. The method of claim 3, wherein after obtaining the charging data for the power cell, the method further comprises:
preprocessing the charging data, wherein the preprocessing comprises at least one of: removing abnormal values and filling empty values;
determining the first and second degrees of capacity fade based on the pre-processed charging data.
9. The method according to claim 1, wherein obtaining the consistency score value of the power cell comprises:
acquiring performance parameters of each single battery in the power battery;
acquiring the temperature of each single battery in the power battery;
determining the consistency score value based on the performance parameter of each single battery and the temperature of each single battery.
10. A power cell state detection device, comprising:
the device comprises a first obtaining module, a second obtaining module and a control module, wherein the first obtaining module is used for obtaining a target capacity attenuation degree of a power battery, and the target capacity attenuation degree is used for representing the capacity variation of the power battery;
the second acquisition module is used for acquiring consistency score values corresponding to the power batteries, wherein the consistency score values are used for representing whether the capacities of the single batteries in the power batteries are consistent or not;
and the determining module is used for determining the state of the power battery based on the target capacity attenuation degree and the consistency score value, wherein the state of the power battery is used for representing the aging degree of the power battery.
11. A computer-readable storage medium, comprising a stored program, wherein when the program runs, the computer-readable storage medium controls a device to execute the method for detecting the state of a power battery according to any one of claims 1 to 9.
12. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the method for detecting the status of a power battery according to any one of claims 1 to 9 when running.
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