CN117970152A - State evaluation method and device of power battery, computer equipment and storage medium - Google Patents
State evaluation method and device of power battery, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117970152A CN117970152A CN202410010769.5A CN202410010769A CN117970152A CN 117970152 A CN117970152 A CN 117970152A CN 202410010769 A CN202410010769 A CN 202410010769A CN 117970152 A CN117970152 A CN 117970152A
- Authority
- CN
- China
- Prior art keywords
- evaluation
- individual
- vehicle
- evaluation parameter
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 334
- 230000014759 maintenance of location Effects 0.000 claims abstract description 127
- 238000000034 method Methods 0.000 claims abstract description 69
- 230000032683 aging Effects 0.000 claims abstract description 52
- 238000004590 computer program Methods 0.000 claims abstract description 22
- 125000004122 cyclic group Chemical group 0.000 claims description 4
- 238000001514 detection method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000010354 integration Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 239000000306 component Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000012935 Averaging Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012854 evaluation process Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
Abstract
The present application relates to a state evaluation method, apparatus, computer device, storage medium and computer program product of a power battery. The method comprises the following steps: determining individual actual values of a plurality of evaluation parameters based on the operating data of the vehicle to be evaluated; determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range; for any evaluation parameter, determining an individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter; and based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result. By adopting the method, the accuracy of state evaluation of the power battery can be improved.
Description
Technical Field
The present application relates to the field of power battery technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for evaluating a state of a power battery.
Background
With the rapid development of the new energy automobile industry, the series of commercial activities such as new energy automobile maintenance, automobile insurance sales, second-hand car trade, power battery cascade utilization and the like are rapidly developed, and the traffic volume is rapidly increased. The power battery is a core component of the new energy automobile, accounts for about 40% of the cost of the new energy automobile, and the residual life directly determines the safety and the residual value of the new energy automobile and is also a core parameter basis necessary for developing the service. Therefore, the evaluation technology of the state of the power battery is inevitably a core technology for supporting the development of the after-market of the new energy automobile.
However, since the chemical reaction inside the lithium battery is complex and the factors affecting the life are many, the battery capacity fade process is subject to the co-coupling effect of multiple factors, resulting in a relatively large technical difficulty for accurate assessment of the state of the power battery.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a state evaluation method, apparatus, computer device, computer-readable storage medium, and computer program product of a power battery that can improve accuracy of state evaluation for the power battery.
In one aspect, the present application provides a method for evaluating a state of a power battery, including:
Determining individual actual values of a plurality of evaluation parameters based on the operating data of the vehicle to be evaluated;
Determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
For any evaluation parameter, determining an individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter;
And based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result.
In one embodiment, the determining the individual retention rate of the targeted evaluation parameter based on the individual actual value and the individual nominal value of the targeted evaluation parameter includes:
a ratio of the individual actual value of the targeted evaluation parameter to the individual nominal value is determined and is taken as the individual retention rate of the targeted evaluation parameter.
In one embodiment, the evaluating the aging degree of the power battery of the vehicle to be evaluated based on the individual retention rate of each evaluation parameter to obtain an evaluation result includes:
determining initial values of all the evaluation parameters, and drawing a first radar chart based on the initial values;
Drawing a second radar map based on the individual retention of each of the evaluation parameters;
and determining the ratio of the area of the second radar chart to the area of the first radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
In one embodiment, the method further comprises: determining a vehicle group to which the vehicle to be evaluated belongs;
determining a population actual value of each evaluation parameter based on the operating data of the vehicle population;
determining a group nominal value of each evaluation parameter based on the operation data of the vehicle group in the target mileage range;
for any evaluation parameter, determining the population retention of the targeted evaluation parameter based on the population actual value and the population nominal value of the targeted evaluation parameter.
In one embodiment, the evaluating the aging degree of the power battery of the vehicle to be evaluated based on the individual retention rate of each evaluation parameter to obtain an evaluation result includes:
Drawing a second radar map based on the individual retention of each of the evaluation parameters;
Drawing a third radar map based on the population retention of each of the evaluation parameters;
and determining the ratio of the area of the second radar chart to the area of the third radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
In one embodiment, the method further comprises: acquiring a plurality of resource exchange values of the vehicle to be evaluated;
and determining the predicted resource exchange value of the vehicle to be evaluated according to the individual retention rate, the group retention rate and the average value of the resource exchange values.
In one embodiment, the operational data includes at least one of:
Running mileage, charging time, cyclic charge and discharge times, total current, total voltage, battery temperature, battery cell voltage and motor peak temperature in the driving process in the charging process, and battery peak temperature in the charging process;
the evaluation parameters include at least one of:
Segment capacity, charge per hundred kilometers, self-discharge rate, voltage uniformity value, motor temperature peak, battery temperature peak.
On the other hand, the application also provides a state evaluation device of the power battery, which comprises:
a first determination module for determining individual actual values of a plurality of evaluation parameters based on the operation data of the vehicle to be evaluated;
The second determining module is used for determining the individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
a third determining module, configured to determine, for any one of the evaluation parameters, an individual retention rate of the evaluation parameter based on the individual actual value and the individual nominal value of the evaluation parameter;
And the evaluation module is used for evaluating the aging degree of the power battery of the vehicle to be evaluated based on the individual retention rate of each evaluation parameter to obtain an evaluation result.
In another aspect, the present application also provides a computer device, including a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
Determining individual actual values of a plurality of evaluation parameters based on the operating data of the vehicle to be evaluated;
Determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
For any evaluation parameter, determining an individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter;
And based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result.
In another aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
Determining individual actual values of a plurality of evaluation parameters based on the operating data of the vehicle to be evaluated;
Determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
For any evaluation parameter, determining an individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter;
And based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result.
In another aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
Determining individual actual values of a plurality of evaluation parameters based on the operating data of the vehicle to be evaluated;
Determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
For any evaluation parameter, determining an individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter;
And based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result.
According to the state evaluation method, the state evaluation device, the computer equipment, the storage medium and the computer program product of the power battery, firstly, the individual actual values of a plurality of evaluation parameters are determined based on the running data of the vehicle to be evaluated, and thus, the comprehensiveness of the evaluation parameters can be improved by combining various evaluation parameters to complete subsequent state evaluation; then, based on the operation data of the vehicle to be evaluated in the target mileage range, determining the individual nominal value of each evaluation parameter; in this way, the equality of determining the individual nominal values can be improved; then, for any evaluation parameter, determining the individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter; and finally, based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result. Therefore, under the condition of comprehensively combining individual retention rates of a plurality of evaluation parameters, the state evaluation of the power battery is carried out, and the accuracy of the state evaluation of the power battery can be effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a flow chart of a state estimation of a power cell in one embodiment;
FIG. 2 is a radar pictorial representation of state assessment based on individual retention in one embodiment;
FIG. 3 is a radar pictorial view of another embodiment for state assessment based on population retention;
FIG. 4 is a block diagram showing a state evaluation device of a power battery in one embodiment;
Fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In an exemplary embodiment, as shown in fig. 1, a method for evaluating the state of a power battery is provided, and the method is applied to a computer device (the computer device may be a terminal or a server) for explanation, and includes the following steps 102 to 108. Wherein:
Step 102, determining individual actual values of a plurality of evaluation parameters based on the operation data of the vehicle to be evaluated.
In actual practice, the computer device may determine a plurality of evaluation parameters for evaluating the degree of aging of the power battery of the vehicle from the operation data of the vehicle to be evaluated. After determining the plurality of evaluation parameters, the degree of aging of the power battery of the vehicle to be evaluated may be determined from the evaluation parameters. The operating data of the vehicle includes actual values of a plurality of operating parameters, and the actual value of each of the estimated parameters may be determined based on the value of at least one of the operating parameters. For the actual value of the evaluation parameter, it may be referred to as an individual actual value of the evaluation parameter with respect to a single vehicle, and as a population actual value of the evaluation parameter with respect to a population of vehicles.
The operation data is described with respect to operation data including at least one of: running mileage, charging time, cyclic charge and discharge times, total current, total voltage, battery temperature, battery cell voltage and motor peak temperature in the driving process in the charging process, and battery peak temperature in the charging process.
Accordingly, the evaluation parameter comprises at least one of: segment capacity, charge per hundred kilometers, self-discharge rate, voltage uniformity value, motor temperature peak, battery temperature peak.
Wherein for the segment capacity in the evaluation parameter, the computer device may determine the charge capacity for the power battery as the segment capacity in the evaluation parameter based on the total current and the charge time during charging in the operation data, in case the battery state of charge SOC of the power battery of the vehicle to be evaluated is in a specified increase segment. For example, the charge capacity in a 5% SOC segment range is calculated by an ampere-hour integration method, and the 5% SOC segment range refers to a segment range in which the variation value of SOC during charging is 5%, such as 85% -90%.
For each hundred kilometer charge in the evaluation parameter, a charge capacity of the vehicle to be evaluated for one hundred kilometer mileage can be determined as each hundred kilometer charge in the evaluation parameter based on the total current and the charge time in the charging process. And calculating the charge quantity of the automobile running for 100 kilometers by using an ampere-hour integration method based on the total current and time in the charging process of the new energy automobile to be evaluated.
For the self-discharge rate in the evaluation parameter, a time-average voltage difference before and after flameout and standing of the vehicle to be evaluated can be determined based on the total voltage and the charging time in the charging process as the self-discharge rate in the evaluation parameter.
And aiming at the voltage consistency value in the evaluation parameter, if the SOC of the power battery of the vehicle to be evaluated reaches a specified threshold value in the charging process, determining the highest voltage value and the lowest voltage value in all battery cells in a battery pack of the vehicle to be evaluated, and taking the difference value between the highest voltage value and the lowest voltage value as the voltage consistency value in the evaluation parameter. For example, based on the new energy automobile to be evaluated, when the battery SOC reaches 90% in the charging process, the difference between the highest value and the lowest value of all battery cell voltages in the battery pack is extracted.
Acquiring a motor temperature peak value of a vehicle to be evaluated in the last driving process, and taking the motor temperature peak value as a motor temperature peak value in an evaluation parameter; and acquiring the temperature peak value of the battery pack of the vehicle to be evaluated in the last charging process, and taking the temperature peak value as the battery temperature peak value in the evaluation parameters.
Step 104, determining individual nominal values of all evaluation parameters based on the operation data of the vehicle to be evaluated in the target mileage range.
In actual implementation, the mean value of each evaluation parameter in the target mileage range can be determined by the running data of the vehicle in the target mileage range relative to the single vehicle to be evaluated, and the mean value is taken as the personal nominal value of the evaluation parameter for convenience of description. For example, taking the range of the target mileage of 0-500 km as an example, the actual value of each evaluation parameter under the conditions that the vehicle to be evaluated runs 100 km, 200 km, 300 km, 400 km and 500 km can be respectively determined, for each evaluation parameter, the 5 actual values are averaged to be the individual nominal value of the evaluation parameter,
Step 106, for any evaluation parameter, determining the individual retention rate of the evaluation parameter based on the individual actual value and the individual nominal value of the evaluation parameter.
In actual implementation, the retention rate refers to the retention ratio of the actual value of the evaluation parameter relative to the nominal value of the evaluation parameter. For example, the capacity retention rate generally refers to the ratio of the capacity that the battery can retain to the initial capacity after a certain period of application. The higher the capacity retention, the better the stability of the battery. Therefore, in order to accurately evaluate the degree of aging of the power battery of the vehicle to be evaluated, the individual retention rate of each evaluation parameter is first determined separately for the vehicle to be evaluated. The individual retention rate for each evaluation parameter may be determined by: a ratio of the individual actual value of the targeted evaluation parameter to the individual nominal value is determined and the ratio is taken as the individual retention rate of the targeted evaluation parameter.
And step 108, based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result.
In actual implementation, the computer device evaluates the aging degree of the power battery of the evaluation vehicle through the current value (i.e., the actual value) of the individual retention rate of each evaluation parameter and the initial value of the individual retention rate of each evaluation parameter, so as to obtain an evaluation result. In view of the large number of evaluation parameters, the computer device may draw a radar chart composed of individual retention rates of all the evaluation parameters, and reduce the operation for the degree of aging of the power battery to an operation of determining the area of the radar chart. A radar chart is a visual chart, also known as a spider-web, star-shaped, or polar plot. It takes a central point as a starting point, and a plurality of rays extend outwards from the central point, wherein each ray represents the retention rate of an evaluation parameter. The point or line segment on each ray represents the value of the retention rate of the evaluation parameter in different dimensions, so that the consumption of computing resources can be reduced, the evaluation accuracy can be ensured, and the aging degree of the power battery can be intuitively displayed.
In the state evaluation method of the power battery, the individual actual values of the plurality of evaluation parameters are determined based on the running data of the vehicle to be evaluated, so that the comprehensiveness of the evaluation parameters can be improved by comprehensively combining various evaluation parameters to complete the subsequent state evaluation; determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range; in this way, the balance of determining the individual nominal value can be improved, and for any evaluation parameter, the individual retention rate of the evaluation parameter is determined based on the individual actual value and the individual nominal value of the evaluation parameter; and based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result. Therefore, under the condition of comprehensively combining individual retention rates of a plurality of evaluation parameters, the state evaluation of the power battery can be performed, and the accuracy of the state evaluation of the power battery is effectively improved.
Describing the evaluation manner, in an exemplary embodiment, based on the individual retention rate of each evaluation parameter, the aging degree of the power battery of the vehicle to be evaluated is evaluated, to obtain an evaluation result, including: determining initial values of all evaluation parameters, and drawing a first radar chart based on all the initial values; drawing a second radar map based on individual retention of each evaluation parameter; and determining the ratio of the area of the second radar chart to the area of the first radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
In actual implementation, for each evaluation parameter, an initial value of individual retention rate of each evaluation parameter is set, typically to 100%, at the time of shipment of each power battery. The computer equipment draws a first radar chart according to the initial value of the individual retention rate of each evaluation parameter, draws a second radar chart according to the current value (actual value) of the individual retention rate of each evaluation parameter, and determines the area of the first radar chart and the area of the second radar chart according to a preset polygonal area calculation mode. And directly taking the ratio of the area of the first radar chart to the area of the second radar chart as an evaluation result obtained by evaluating the aging degree of the power battery.
Illustratively, as shown in fig. 2, a first radar chart determined from an initial value of the individual retention rate of each evaluation parameter is shown in fig. 2, and a second radar chart determined from a current value of the individual retention rate of each evaluation parameter is shown in fig. 2 b. And determining that the area of the first radar chart is S1, and the area of the second radar chart is S2, and grading the aging degree of the power battery to be evaluated to be S1/S2.
In this embodiment, the determination manner of determining the aging degree of the power battery to be evaluated is converted into the determination manner of determining the area of the different radar patterns, so that the computational complexity of evaluating the aging degree of the power battery can be reduced. Meanwhile, the aging degree of the power battery can be visually displayed through the radar chart.
When the aging degree of the power battery is evaluated, the initial value of the individual retention rate of the evaluation parameter is evaluated, and the population retention rate of the evaluation parameter may be evaluated to improve the comprehensiveness of the evaluation result. Describing population retention for the evaluation parameters, in one exemplary embodiment, determining a vehicle population to which the vehicle under evaluation belongs; determining a population actual value of each evaluation parameter based on the operation data of the vehicle population; determining a group nominal value of each evaluation parameter based on the running data of the vehicle group in the target mileage range; for any evaluation parameter, determining the population retention of the targeted evaluation parameter based on the population actual value and the population nominal value of the targeted evaluation parameter.
In actual implementation, firstly, a vehicle group to which the vehicle to be evaluated belongs is determined, and the delivery configuration of the power batteries of the vehicles in the vehicle group is consistent, so that each vehicle in the vehicle group can be the vehicle with the same vehicle type, the same operating property and the same operating environment in order to ensure the evaluation precision. For each evaluation parameter, determining the actual value of the current evaluation parameter of N vehicles, and taking the average value of the actual values of the N current evaluation parameters as the actual value of the group of the evaluation parameters. Within the target mileage range, for each vehicle in the vehicle group, determining the average value of each vehicle for any evaluation parameter as the group nominal value of the evaluation parameter. The ratio of the actual value of the population of each evaluation parameter to the nominal value of the population is taken as the population retention rate of the evaluation parameter.
Illustratively, the vehicle population includes 100 vehicles, the current values of 100 evaluation parameters a are determined for the evaluation parameters a, and the population actual values of the evaluation parameters a are obtained by averaging the 100 current values. And (3) counting actual values of the evaluation parameters A in the vehicle group within the range of 0-500 km, and assuming that 1000 actual values of the evaluation parameters A are obtained, and averaging the 1000 actual values of the evaluation parameters A to serve as a group nominal value of the evaluation parameters A. Finally, the ratio of the actual value of the population of the evaluation parameter A to the nominal value of the population of the evaluation parameter A is taken as the population retention rate of the evaluation parameter A.
In this embodiment, the population retention rate of the evaluation parameter is determined by the relevant value of the evaluation parameter in the vehicle population, providing the calculation accuracy of the population retention rate.
In one exemplary embodiment, a method of evaluating the degree of aging of a power battery of a vehicle to be evaluated is described with respect to an individual retention rate based on an evaluation parameter and a population retention rate of the evaluation parameter. Specific: drawing a second radar map based on individual retention of each evaluation parameter; drawing a third radar chart based on population retention of each evaluation parameter; and determining the ratio of the area of the second radar chart to the area of the third radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
In actual implementation, the computer equipment draws a second radar chart according to the actual value of the individual retention rate of each evaluation parameter, draws a third radar chart according to the current value (actual value) of the group retention rate of each evaluation parameter, and determines the area of the second radar chart and the area of the third radar chart according to a preset polygonal area calculation mode. And directly taking the ratio of the area of the second radar chart to the area of the third radar chart as an evaluation result obtained by evaluating the aging degree of the power battery.
Illustratively, as shown in fig. 3, a in fig. 3 is a second radar chart determined from actual values of individual retention rates of respective evaluation parameters, and b in fig. 3 is a third radar chart determined from actual values of population retention rates of respective evaluation parameters. And determining that the area of the second radar chart is S2, and the area of the third radar chart is S3, and grading the aging degree of the power battery to be evaluated to be S2/S3.
In the embodiment, the population retention rate of the evaluation parameters and the individual retention rate of the evaluation parameters are combined to evaluate the aging degree of the power battery, so that the comprehensiveness of the evaluation operation can be improved, and the accuracy of the evaluation result can be provided.
In an exemplary embodiment, after determining the state of the power battery of the vehicle to be evaluated, the predicted resource exchange value of the vehicle to be evaluated may also be determined based on the current state of the power battery, which specifically includes: acquiring a plurality of resource exchange values of a vehicle to be evaluated; and determining the predicted resource exchange value of the vehicle to be evaluated according to the individual retention rate, the group retention rate and the average value of the plurality of resource exchange values.
In actual implementation, the prediction of the resource exchange value for the vehicle to be evaluated may be performed based on the result of the evaluation of the degree of aging of the power battery. The specific prediction mode may be to obtain a resource exchange mean value corresponding to a plurality of resource exchange values of the vehicle to be evaluated; then, as described above, the score of the aging degree of the vehicle to be evaluated is determined according to the individual retention rate and the group retention rate, and the product of the resource exchange mean and the score is used as the predicted resource exchange value of the vehicle to be evaluated.
In this embodiment, the resource exchange value of the vehicle to be evaluated is determined through the evaluation result, so that the accuracy of predicting the resource exchange value can be improved, and the application scenario of the scoring result can be expanded.
And cooperatively evaluating the aging state of the power battery based on the individual retention rate of the evaluation parameter and the group retention rate of the evaluation parameter, so as to keep the evaluation comprehensive.
In order to explain the state evaluation method of the power battery in detail, an embodiment is described below, in this embodiment, based on online evaluation of individual operation parameters and online evaluation of group performance parameters, the aging degree of the power battery is cooperatively evaluated, and the method is a new energy automobile performance double-layer online evaluation method based on big data.
First, description is made regarding an on-line evaluation based on individual operation parameters, wherein the on-line evaluation based on individual operation parameters refers to an evaluation method of determining an evaluation parameter required for evaluating the degree of aging of a vehicle using operation data of a single vehicle, and then predicting the degree of aging of the vehicle using a retention rate of the evaluation parameter. For ease of illustration, the retention of the evaluation parameter relative to the individual vehicle may be referred to as the individual retention in the foregoing.
Specific implementations of online assessment based on individual operating parameters are described. Based on the collected large data of the operation of the new energy automobile, the operation mileage, time and cycle charge and discharge times of the new energy automobile to be evaluated are obtained, and the operation data corresponding to the operation parameters such as total current, total voltage, battery temperature, battery cell voltage, motor peak temperature, battery peak temperature and the like in the running process are obtained.
The manner in which the evaluation parameters are determined from the individual operating parameters is as follows:
Determining the zone capacity in the evaluation parameters according to the total current and time in the charging process in the operation data: and calculating the charging capacity in the 5% SOC section range by using an ampere-hour integration method through the total current and time in the charging process of the new energy automobile to be evaluated as the section capacity in the evaluation parameter. The 5% SOC segment may be understood as a 5% increase change value of the SOC value, and the corresponding segment may be a segment where any SOC value with a 5% interval between segments is located, for example, the SOC value increases from 85% to 90%.
Determining a hundred kilometer charge in the evaluation parameter according to the total current and time in the charging process in the operation data: based on the total current and time in the charging process of the new energy automobile to be evaluated, the ampere-hour integration method is utilized to calculate the charging quantity of the automobile running for 100 kilometers as an evaluation parameter, namely the hundred kilometers charging quantity.
And calculating the time average voltage difference before and after the vehicle is flameout and is stationary (the system is closed and is not charged) according to the total voltage and time of the battery of the new energy automobile to be evaluated, and taking the time average voltage difference as an evaluation parameter, namely the self-discharge rate.
And extracting the difference between the highest value and the lowest value of all the battery cell voltages in the battery pack as evaluation parameter-voltage consistency when the SOC of the battery reaches 90% in the charging process according to the new energy automobile to be evaluated.
And extracting a motor temperature peak value of the new energy automobile in the last trip process as an evaluation parameter, namely the motor temperature peak value.
And extracting the temperature peak value of the battery pack of the new energy automobile in the last charging process as an evaluation parameter, namely the battery temperature peak value.
By combining the operation data of the vehicle to be evaluated, the actual values of all the evaluation parameters are obtained, and then the average value of all the evaluation parameters of the vehicle is used as the nominal value of all the evaluation parameters of the vehicle within the range of 0-500 km of operation of the vehicle to be evaluated in a determination mode of all the evaluation parameters. For example, the values of the above evaluation parameters of 100 km, 200 km, 300 km, 400 km and 500 km of the vehicle are respectively determined, the obtained 5 values are averaged for any one evaluation parameter, and the obtained average value is taken as the nominal value of the evaluation parameter. For each evaluation parameter, the retention of that evaluation parameter is determined from the actual value of the evaluation parameter and the corresponding nominal value, (retention of the individual vehicle relative to the evaluation parameter is referred to as personal retention). The individual retention of this evaluation parameter is as follows: taking the current section capacity/nominal section capacity=capacity retention rate of the vehicle to be evaluated, and taking the ratio of the nominal value of the parameters such as hundred kilometers charge quantity, self-discharge rate, voltage consistency, motor temperature peak value, battery temperature peak value and the like of the vehicle to be evaluated to the current value of each parameter as: hundred kilometer charge retention, self-discharge retention, voltage uniformity retention, motor temperature peak retention, battery temperature peak retention.
In practical applications, the state of the power battery may be estimated based on the individual retention rate of the estimated parameter, where the state may indicate the aging degree of the power battery, and the specific determination manner is: when the new energy automobile is evaluated, the initial value of the individual retention rate of each evaluation parameter is utilized to draw a radar chart shown in fig. 2a, the initial value of the individual retention rate is usually 100%, the shadow area S1 in the radar chart shown in fig. 2 (a) is determined, the actual value of the individual retention rate of each evaluation parameter is utilized to draw a radar chart shown in fig. 2 b, and the shadow areas S2 and S2/S1 in the radar chart shown in fig. 2 (b) are calculated to be the aging scores of the automobile to be evaluated.
Further, in order to be able to promote the overall evaluation of the power battery state of the individual vehicle under evaluation, the state of the power battery of the vehicle under evaluation may be comprehensively evaluated by determining the actual value of the population retention rate of each evaluation parameter with respect to the vehicle population, and combining the foregoing actual value of the individual retention rate of each evaluation parameter with respect to the individual vehicle under evaluation.
The description is directed to an online evaluation based on a population performance parameter, which in fact evaluates the parameter relative to a population of vehicles.
The method comprises the steps of dividing groups by taking vehicle types, vehicle attributes and cities as boundaries, and acquiring all vehicles which belong to the same vehicle type and the same attribute as the vehicle to be evaluated and run in the same city range from the operation data of the new energy vehicles collected under the standard of national standard 32960.
Based on the determination mode of the actual value of the evaluation parameter relative to the individual, the section capacity, hundred kilometer charge amount, self-discharge rate, voltage consistency, motor temperature peak value, battery temperature peak value and the like of the new energy automobile group in different running mileage sections are respectively calculated by taking 10000km as a gradient. The average value of each evaluation parameter is used as a group parameter (namely, the evaluation parameter is relative to the vehicle group) under different operation mileage segments; taking the ratio of the nominal value of parameters such as the current section capacity/nominal section capacity=capacity retention rate of the vehicle group, the hundred kilometers charge amount of the vehicle to be evaluated, the self-discharge rate, the voltage consistency, the motor temperature peak value, the battery temperature peak value and the like to the current value of each parameter as respectively: a hundred kilometer charge retention rate with respect to a vehicle group (i.e., a group retention rate of hundred kilometers charge in the foregoing), a self-discharge retention rate with respect to a vehicle group (i.e., a group retention rate of self-discharge in the foregoing), a voltage consistency retention rate with respect to a vehicle group, a motor temperature peak retention rate with respect to a vehicle group, a battery temperature peak retention rate with respect to a vehicle group.
When the vehicle is evaluated, the individual retention rate of 8 evaluation parameters of the vehicle to be evaluated and the group retention rate of 8 evaluation parameters under the corresponding mileage are taken to respectively determine the corresponding ratio, and the corresponding score is calculated. If the ratio is >1, the change parameter is marked as excellent, and a value greater than 0.6 and less than 1 is marked as good, and a value less than 0.6 is marked as bad.
The method is used for calculating the average value of all the evaluation parameters of the vehicle group within the range of 0-500 km of operation history of the vehicle group, and the average value is used as the nominal value (nominal group nominal value) of all the evaluation parameters of the vehicle group. The mean value of the 8 evaluation parameters of the vehicle population at different mileage intervals is compared with the population nominal value, and the corresponding area S3 is calculated in a radar map, as shown in fig. 3 (b).
In addition, S2/s3=b is calculated as a group evaluation parameter, when the new energy automobile is evaluated, the average price value C of the current automobile is obtained through a network, and the product of the average price value C and the group evaluation setting B is used as the current price.
By applying the embodiment of the application, the accurate online evaluation of the performance of the power battery in the vehicle-mounted environment can be realized; the problem of power battery detects with high costs is solved, improves the convenience and the reliability of on-line measuring. The method has the following beneficial effects:
1) Realizing the effective on-line evaluation of active and retired new energy automobiles
In the embodiment, the vehicle state is completely estimated based on the new energy automobile big data meeting the current new energy automobile acquisition standard, and no entity test equipment is needed. Therefore, the evaluation process can be completely carried out on line, is suitable for evaluating the residual values of active and retired new energy automobiles, and completely meets the development requirements of the after-market of the new energy automobiles.
2) The detection process is convenient and reliable
When the vehicle state evaluation is carried out by utilizing the embodiment, the evaluation inquiry operation can be completed only by utilizing an on-line system to input key information such as vehicle vin codes, and the like, so that the vehicle state evaluation system is convenient and quick; meanwhile, all algorithms in the evaluation process are based on real data of vehicle operation, an epitaxial estimation process does not exist, and the evaluation method is high in reliability and high in interpretation.
3) Low detection cost
The vehicle state is completely estimated based on the new energy automobile big data under the current new energy automobile acquisition standard, no physical test equipment is needed, a traditional charge and discharge process is not needed in the detection and estimation process, and the detection and estimation process has low cost due to almost no equipment aging maintenance cost, site cost, electricity cost and other detection and estimation costs.
4) Can realize double evaluation of vehicle performance and maintenance rate
According to the embodiment, the aging conditions of key parts such as a vehicle motor and a battery can be calculated based on the current running parameters of the vehicle, and the aging conditions of the vehicle performance can be accurately evaluated; meanwhile, the current residual value of the vehicle can be reasonably evaluated by combining with the second-hand car quotation of the market, the floor type vehicle has strong floor type property and wide market prospect.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a state evaluation device of the power battery for realizing the state evaluation method of the power battery. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the state evaluation device for one or more power batteries provided below may refer to the limitation of the state evaluation method for a power battery hereinabove, and will not be repeated herein.
In an exemplary embodiment, as shown in fig. 4, there is provided a state evaluation device of a power battery, including: a first determination module 410, a second determination module 420, a third determination module 430, and an evaluation module 440, wherein:
a first determination module 410 is configured to determine individual actual values of a plurality of evaluation parameters based on operational data of the vehicle under evaluation.
The second determining module 420 is configured to determine an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated within the target mileage range.
The third determining module 430 is configured to determine, for any evaluation parameter, an individual retention rate of the evaluation parameter based on the individual actual value and the individual nominal value of the evaluation parameter.
And the evaluation module 440 is used for evaluating the aging degree of the power battery of the vehicle to be evaluated based on the individual retention rate of each evaluation parameter to obtain an evaluation result.
In one embodiment, the third determining module is further configured to determine a ratio of the individual actual value of the targeted evaluation parameter to the individual nominal value, and take the ratio as the individual retention rate of the targeted evaluation parameter.
In one embodiment, the evaluation module is further configured to determine initial values of each evaluation parameter, and draw a first radar chart based on each initial value; drawing a second radar map based on individual retention of each evaluation parameter; and determining the ratio of the area of the second radar chart to the area of the first radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
In one embodiment, the third determining module is further configured to determine a vehicle group to which the vehicle to be evaluated belongs; determining a population actual value of each evaluation parameter based on the operation data of the vehicle population; determining a group nominal value of each evaluation parameter based on the running data of the vehicle group in the target mileage range; for any evaluation parameter, determining the population retention of the targeted evaluation parameter based on the population actual value and the population nominal value of the targeted evaluation parameter.
In one embodiment, the third determining module is further configured to map a second radar map based on the individual retention rates of the respective evaluation parameters; drawing a third radar chart based on population retention of each evaluation parameter; and determining the ratio of the area of the second radar chart to the area of the third radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
In one embodiment, the assessment module is further configured to obtain a plurality of resource exchange values for the vehicle under assessment; and determining the predicted resource exchange value of the vehicle to be evaluated according to the individual retention rate, the group retention rate and the average value of the plurality of resource exchange values.
In one embodiment, the operational data includes at least one of: running mileage, charging time, cyclic charge and discharge times, total current, total voltage, battery temperature, battery cell voltage and motor peak temperature in the driving process in the charging process, and battery peak temperature in the charging process.
In one embodiment, the evaluation parameter comprises at least one of: segment capacity, charge per hundred kilometers, self-discharge rate, voltage uniformity value, motor temperature peak, battery temperature peak.
The respective modules in the state evaluation device of the power battery described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and an internal structure diagram thereof may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of state estimation of a power cell.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method of evaluating a state of a power cell, the method comprising:
Determining individual actual values of a plurality of evaluation parameters based on the operating data of the vehicle to be evaluated;
Determining an individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
For any evaluation parameter, determining an individual retention rate of the aimed evaluation parameter based on the individual actual value and the individual nominal value of the aimed evaluation parameter;
And based on the individual retention rate of each evaluation parameter, evaluating the aging degree of the power battery of the vehicle to be evaluated to obtain an evaluation result.
2. The method of claim 1, wherein the determining the individual retention of the targeted evaluation parameter based on the individual actual value and the individual nominal value of the targeted evaluation parameter comprises:
a ratio of the individual actual value of the targeted evaluation parameter to the individual nominal value is determined and is taken as the individual retention rate of the targeted evaluation parameter.
3. The method according to claim 1, wherein the evaluating the degree of aging of the power battery of the vehicle under evaluation based on the individual retention rate of each evaluation parameter to obtain an evaluation result includes:
determining initial values of all the evaluation parameters, and drawing a first radar chart based on the initial values;
Drawing a second radar map based on the individual retention of each of the evaluation parameters;
and determining the ratio of the area of the second radar chart to the area of the first radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
4. The method according to claim 1, wherein the method further comprises:
Determining a vehicle group to which the vehicle to be evaluated belongs;
determining a population actual value of each evaluation parameter based on the operating data of the vehicle population;
determining a group nominal value of each evaluation parameter based on the operation data of the vehicle group in the target mileage range;
for any evaluation parameter, determining the population retention of the targeted evaluation parameter based on the population actual value and the population nominal value of the targeted evaluation parameter.
5. The method according to claim 4, wherein the evaluating the degree of aging of the power battery of the vehicle under evaluation based on the individual retention rate of each evaluation parameter to obtain the evaluation result includes:
Drawing a second radar map based on the individual retention of each of the evaluation parameters;
Drawing a third radar map based on the population retention of each of the evaluation parameters;
and determining the ratio of the area of the second radar chart to the area of the third radar chart, and taking the ratio as an evaluation result obtained by evaluating the aging degree of the power battery.
6. The method according to claim 4, wherein the method further comprises:
Acquiring a plurality of resource exchange values of the vehicle to be evaluated;
and determining the predicted resource exchange value of the vehicle to be evaluated according to the individual retention rate, the group retention rate and the average value of the resource exchange values.
7. The method of any one of claims 1 to 6, wherein the operational data comprises at least one of:
Running mileage, charging time, cyclic charge and discharge times, total current, total voltage, battery temperature, battery cell voltage and motor peak temperature in the driving process in the charging process, and battery peak temperature in the charging process;
the evaluation parameters include at least one of:
Segment capacity, charge per hundred kilometers, self-discharge rate, voltage uniformity value, motor temperature peak, battery temperature peak.
8. A state evaluation device of a power battery, characterized by comprising:
a first determination module for determining individual actual values of a plurality of evaluation parameters based on the operation data of the vehicle to be evaluated;
The second determining module is used for determining the individual nominal value of each evaluation parameter based on the operation data of the vehicle to be evaluated in the target mileage range;
a third determining module, configured to determine, for any one of the evaluation parameters, an individual retention rate of the evaluation parameter based on the individual actual value and the individual nominal value of the evaluation parameter;
And the evaluation module is used for evaluating the aging degree of the power battery of the vehicle to be evaluated based on the individual retention rate of each evaluation parameter to obtain an evaluation result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410010769.5A CN117970152A (en) | 2024-01-04 | 2024-01-04 | State evaluation method and device of power battery, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410010769.5A CN117970152A (en) | 2024-01-04 | 2024-01-04 | State evaluation method and device of power battery, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117970152A true CN117970152A (en) | 2024-05-03 |
Family
ID=90858878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410010769.5A Pending CN117970152A (en) | 2024-01-04 | 2024-01-04 | State evaluation method and device of power battery, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117970152A (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012174102A (en) * | 2011-02-23 | 2012-09-10 | Fujitsu Ten Ltd | Vehicle evaluation device, vehicle evaluation system, vehicle evaluation method and vehicle evaluation program |
| CN114295998A (en) * | 2021-12-28 | 2022-04-08 | 东软睿驰汽车技术（沈阳）有限公司 | Method, device and equipment for predicting service life of power battery and storage medium |
| CN114487852A (en) * | 2021-12-24 | 2022-05-13 | 深圳供电局有限公司 | Power battery complementary energy detection method and device, computer equipment and storage medium |
| CN114707908A (en) * | 2022-05-18 | 2022-07-05 | 北京钛镁新技术有限公司 | Power battery rating determination method and device and electronic equipment |
| CN114755596A (en) * | 2021-12-15 | 2022-07-15 | 广州汽车集团股份有限公司 | Battery aging prediction method and automobile |
| CN115392134A (en) * | 2022-09-23 | 2022-11-25 | 四川新能源汽车创新中心有限公司 | Vehicle power battery comprehensive performance evaluation method and related device |
| CN115598531A (en) * | 2022-10-31 | 2023-01-13 | 浙江凌骁能源科技有限公司（Cn） | Method for estimating state of health of vehicle battery and training method of estimation model thereof |
| CN116027202A (en) * | 2023-01-13 | 2023-04-28 | 武汉蔚能电池资产有限公司 | Battery health evaluation method, device, computer equipment and storage medium |
| CN116540098A (en) * | 2023-04-11 | 2023-08-04 | 浙江凌骁能源科技有限公司 | Battery residual value evaluation method, device, equipment and readable storage medium |
| CN117129897A (en) * | 2023-08-28 | 2023-11-28 | 上海玫克生储能科技有限公司 | Method, system, storage medium and terminal for scoring health states in battery module/battery cluster |
-
2024
- 2024-01-04 CN CN202410010769.5A patent/CN117970152A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2012174102A (en) * | 2011-02-23 | 2012-09-10 | Fujitsu Ten Ltd | Vehicle evaluation device, vehicle evaluation system, vehicle evaluation method and vehicle evaluation program |
| CN114755596A (en) * | 2021-12-15 | 2022-07-15 | 广州汽车集团股份有限公司 | Battery aging prediction method and automobile |
| CN114487852A (en) * | 2021-12-24 | 2022-05-13 | 深圳供电局有限公司 | Power battery complementary energy detection method and device, computer equipment and storage medium |
| CN114295998A (en) * | 2021-12-28 | 2022-04-08 | 东软睿驰汽车技术（沈阳）有限公司 | Method, device and equipment for predicting service life of power battery and storage medium |
| CN114707908A (en) * | 2022-05-18 | 2022-07-05 | 北京钛镁新技术有限公司 | Power battery rating determination method and device and electronic equipment |
| CN115392134A (en) * | 2022-09-23 | 2022-11-25 | 四川新能源汽车创新中心有限公司 | Vehicle power battery comprehensive performance evaluation method and related device |
| CN115598531A (en) * | 2022-10-31 | 2023-01-13 | 浙江凌骁能源科技有限公司（Cn） | Method for estimating state of health of vehicle battery and training method of estimation model thereof |
| CN116027202A (en) * | 2023-01-13 | 2023-04-28 | 武汉蔚能电池资产有限公司 | Battery health evaluation method, device, computer equipment and storage medium |
| CN116540098A (en) * | 2023-04-11 | 2023-08-04 | 浙江凌骁能源科技有限公司 | Battery residual value evaluation method, device, equipment and readable storage medium |
| CN117129897A (en) * | 2023-08-28 | 2023-11-28 | 上海玫克生储能科技有限公司 | Method, system, storage medium and terminal for scoring health states in battery module/battery cluster |
Non-Patent Citations (2)
| Title |
|---|
| 吴礼军 等: "汽车整车性能主观评价", 30 September 2016, 北京理工大学出版社, pages: 272 * |
| 蒲松林 等: "基于因子回归分析和分层雷达图法的电动汽车运行状态评估", 电力自动化设备, vol. 35, no. 06, 30 June 2015 (2015-06-30), pages 44 - 51 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Song et al. | Intelligent state of health estimation for lithium-ion battery pack based on big data analysis | |
| Hong et al. | Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered | |
| Tian et al. | Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles | |
| Wang et al. | Probability based remaining capacity estimation using data-driven and neural network model | |
| US10393819B2 (en) | Method and apparatus for estimating state of battery | |
| Liu et al. | State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm | |
| CN113064089B (en) | Internal resistance detection method, device, medium and system of power battery | |
| CN112213643B (en) | Method, system and equipment for predicting initial capacity and state of health of battery | |
| CN107741568B (en) | Lithium battery SOC estimation method based on state transition optimization RBF neural network | |
| CN112818602B (en) | Battery digital twin control method and device based on big data analysis | |
| CN115808922B (en) | Commercial electric vehicle energy consumption prediction method and device and computer equipment | |
| CN115805810B (en) | Battery fault prediction method, apparatus, device, storage medium, and program product | |
| Zhang et al. | State of health estimation for lithium-ion batteries on few-shot learning | |
| CN115754726A (en) | Battery life prediction and maintenance method, electronic equipment and storage medium | |
| CN116482540A (en) | Analysis and prediction method, device and system for battery voltage inconsistency | |
| CN114970734A (en) | Abnormal battery determination method, abnormal battery determination device, computer equipment and storage medium | |
| CN114646888A (en) | Assessment method and system for capacity attenuation of power battery | |
| Li et al. | The state of charge estimation of lithium-ion battery based on battery capacity | |
| Wang et al. | A fast estimation method for state-of-health of retired batteries based on health features | |
| Schoch | Battery life optimal operation of electric vehicles | |
| CN117169743A (en) | Battery health state estimation method and device based on partial data and model fusion | |
| CN117970152A (en) | State evaluation method and device of power battery, computer equipment and storage medium | |
| CN112363058B (en) | Lithium ion battery safety degree estimation method and device based on impedance spectrum and Markov characteristic | |
| Zhang et al. | A novel method of battery pack energy health estimation based on visual feature learning | |
| CN115808636A (en) | Battery capacity loss test method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |