Power battery residual value evaluation method and apparatus, vehicle, medium, and program

Abstract

The present application relates to the technical field of vehicles, and in particular to a power battery residual value evaluation method and apparatus, a vehicle, a medium, and a program. The method comprises: acquiring current operation data of a power battery and a reference characteristic value of a power battery parameter; on the basis of the current operation data, predicting a first characteristic value of the power battery parameter; on the basis of a second characteristic value of the power battery parameter predicted by the cloud and the first characteristic value, calculating a first score of the power battery with respect to the health state, wherein the cloud predicts the second characteristic value on the basis of the current operation data; on the basis of the reference characteristic value of the power battery parameter, calculating a second score of the power battery with respect to the consistency difference; and on the basis of the first score and the second score, generating a residual value evaluation result of the power battery. Therefore, the problems in the related art that the estimation of the health state of power batteries is inaccurate, and the full life cycle of batteries cannot be managed and controlled are solved.

Description

Methods, devices, vehicles, media and procedures for assessing the residual value of power batteries

[0001] Cross-reference to related applications

[0002] This application is based on and claims priority to Chinese Patent Application No. 202411223699.8, filed on September 2, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of vehicle technology, and in particular to a method, apparatus, vehicle, medium and procedure for evaluating the residual value of a power battery. Background Technology

[0004] In recent years, the new energy vehicle market has developed rapidly. With a large number of electric vehicles entering the market, many vehicle power batteries have gradually aged. However, the research and development of technologies related to power battery life degradation are still immature. Battery life degradation estimation has become a hot research topic in recent years. Through extensive engineering practice, it has been found that implementing estimation strategies in the battery management system is the main method used by major OEMs and battery manufacturers to predict life degradation. This method has a certain life prediction effect, but due to limitations such as the long battery aging test period, the overly simple estimation strategy, and the limited processing power of the battery management system, it is difficult to update the battery model and related parameters in the vehicle controller in a large proportion of cases. Therefore, this method is not effective in engineering applications.

[0005] Currently, most related technologies use algorithms based on charging data or historical data to estimate the health status of power batteries. However, this can easily lead to inaccurate estimation results and make it impossible to manage the entire life cycle of the battery. Summary of the Invention

[0006] This application provides a method, apparatus, vehicle, medium, and procedure for assessing the residual value of a power battery, in order to solve the problems in the related art, such as inaccurate estimation of the health status of power batteries and the inability to control the entire life cycle of the battery.

[0007] The first aspect of this application provides a method for assessing the residual value of a power battery, comprising the following steps: acquiring current operating data of the power battery and reference characteristic values ​​of power battery parameters; predicting a first characteristic value of the power battery parameters based on the current operating data; calculating a first score of the power battery's health status based on a second characteristic value of the power battery parameters predicted by the cloud and the first characteristic value, wherein the cloud predicts the second characteristic value based on the current operating data; calculating a second score of the power battery's consistency difference based on the reference characteristic values ​​of the power battery parameters; and generating a residual value assessment result of the power battery based on the first score and the second score.

[0008] Optionally, in one embodiment of this application, the step of calculating the first score of the power battery's health status based on the second characteristic value of the cloud-predicted power battery parameters and the first characteristic value includes: identifying the internal resistance retention rate and capacity retention rate of the first characteristic value and the second characteristic value respectively; calculating the final internal resistance retention rate based on the respective internal resistance retention rate and weight of the first characteristic value and the second characteristic value respectively; calculating the final capacity retention rate based on the respective capacity retention rate and weight of the first characteristic value and the second characteristic value respectively; and calculating the first score of the power battery's health status based on the final internal resistance retention rate, the final capacity retention rate, and the respective weights of the final internal resistance retention rate and the final capacity retention rate.

[0009] Optionally, in one embodiment of this application, before calculating the first score of the power battery's health status based on the second characteristic value of the power battery parameters predicted by the cloud and the first characteristic value, the method further includes: obtaining the difference between the current parameters of the power battery and the previous parameters; querying a preset table based on the difference to determine the degree of degradation of the current parameters; determining the priority ranking of the power battery parameters based on the degree of degradation of each power battery parameter; and determining the respective weight of the parameters in each characteristic value of the power battery based on the priority ranking.

[0010] Optionally, in one embodiment of this application, after calculating the first score of the power battery's health status based on the second characteristic value of the predicted power battery parameters from the cloud and the first characteristic value, the method further includes: updating the internal resistance retention rate and the capacity retention rate from the cloud based on the final internal resistance retention rate and the final capacity retention rate, wherein the cloud updates the second characteristic value based on the updated final internal resistance retention rate and the final capacity retention rate; and updates the first score of the power battery's health status based on the first characteristic value and the updated second characteristic value.

[0011] Optionally, in one embodiment of this application, the step of calculating the second score of the power battery regarding consistency differences based on the reference characteristic values ​​of the power battery parameters includes: identifying the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity in the reference characteristic values; calculating the difference value based on the reference charge, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity, and their respective actual values; and calculating the second score of the power battery regarding consistency differences based on the difference values ​​and weights of the reference charge, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity.

[0012] Optionally, in one embodiment of this application, generating the residual value assessment result of the power battery based on the first score and the second score includes: obtaining the respective weights of the first score and the second score; calculating a total score based on the first score, the second score, and the first score and the second score; and generating the residual value assessment result of the power battery based on the total score.

[0013] A second aspect of this application provides a power battery residual value assessment device, comprising: a first acquisition module for acquiring current operating data of the power battery and reference characteristic values ​​of power battery parameters; a prediction module for predicting a first characteristic value of the power battery parameters based on the current operating data; a first calculation module for calculating a first score of the power battery's health status based on a second characteristic value of the power battery parameters predicted by a cloud and the first characteristic value, wherein the cloud predicts the second characteristic value based on the current operating data; a second calculation module for calculating a second score of the power battery's consistency difference based on the reference characteristic values ​​of the power battery parameters; and a diagnosis module for generating a residual value assessment result of the power battery based on the first score and the second score.

[0014] Optionally, in one embodiment of this application, the first calculation module is further configured to: identify the internal resistance retention rate and capacity retention rate of the first feature value and the second feature value respectively; calculate the final internal resistance retention rate according to the respective weights of the internal resistance retention rate and internal resistance retention rate of the first feature value and the second feature value respectively; calculate the final capacity retention rate according to the respective weights of the capacity retention rate and capacity retention rate of the first feature value and the second feature value respectively; and calculate a first score of the power battery regarding its health status according to the final internal resistance retention rate, the final capacity retention rate, and the respective weights of the final internal resistance retention rate and the final capacity retention rate.

[0015] Optionally, in one embodiment of this application, it further includes: a second acquisition module, used to acquire the degradation level of the power battery; and to determine the respective weights of the internal resistance retention rate and the capacity retention rate according to the degradation level.

[0016] Optionally, in one embodiment of this application, it further includes: an update module, configured to update the internal resistance retention rate and capacity retention rate of the cloud according to the final internal resistance retention rate and the final capacity retention rate, the cloud updates the second feature value according to the final internal resistance retention rate and the final capacity retention rate, and updates the first score of the power battery regarding its health status and the residual value assessment result of the power battery according to the first feature value and the updated second feature value, so as to complete the assessment of the entire life cycle by cyclically updating the residual value scoring system.

[0017] Optionally, in one embodiment of this application, the second calculation module is further configured to: identify the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity among the reference feature values; calculate the difference value based on the reference charge, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity, and their respective actual values; and calculate a second score for the consistency difference of the power battery based on the difference values ​​and weights of the reference charge, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity.

[0018] Optionally, in one embodiment of this application, the diagnostic module is further configured to: obtain the respective weights of the first score and the second score; calculate a total score based on the first score, the second score, and the first score and the second score; and generate a residual value assessment result of the power battery based on the total score.

[0019] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the power battery residual value assessment method as described in the above embodiments.

[0020] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to perform the power battery residual value assessment method as described in the above embodiments.

[0021] A fifth aspect of this application provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed, they implement the power battery residual value assessment method as described in the above embodiments.

[0022] Therefore, this application has at least the following beneficial effects:

[0023] Because vehicle-side computing is limited by real-time requirements and computing resources, selecting operational data at fixed intervals during the data acquisition phase can easily lead to missing or abnormal key data, thus affecting the accuracy of the calculation results. Conversely, limited computing power during the calculation phase results in untimely data processing, further impacting the accuracy of the calculation results. Therefore, this application's embodiment predicts the characteristic values ​​of power battery parameters on both the vehicle and cloud sides based on the current operational data of the power battery. It utilizes cloud-collected vehicle operational data to fill in missing values, thereby compensating for blank values ​​on the vehicle side and improving the accuracy of the calculation results. Furthermore, cloud computing resources are superior, allowing for continuous prediction of power battery parameter characteristic values. Therefore, by combining cloud-based and vehicle-side prediction results, the accuracy of the first health score of the power battery is improved, thereby enhancing the accuracy of the power battery's residual value assessment and enabling full lifecycle management and maintenance of the battery.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 is a flowchart of a method for evaluating the residual value of a power battery according to an embodiment of this application;

[0027] Figure 2 is a schematic diagram of the vehicle-side battery consistency difference and residual value assessment system provided according to the embodiments of this application;

[0028] Figure 3 shows the system framework of the battery life estimation method based on vehicle-cloud integration provided according to the embodiments of this application;

[0029] Figure 4 is a detailed flowchart of cloud-based SOH estimation provided according to an embodiment of this application;

[0030] Figure 5 is a flowchart of cloud-based SOR estimation provided according to an embodiment of this application;

[0031] Figure 6 is a block diagram of a power battery residual value assessment device according to an embodiment of this application;

[0032] Figure 7 is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0033] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0034] The following description, with reference to the accompanying drawings, outlines a method, apparatus, vehicle, medium, and procedure for assessing the residual value of a power battery according to embodiments of this application. Addressing the issues mentioned in the background section regarding inaccurate estimation of the health status of power batteries and the inability to manage the entire lifecycle of the battery, this application provides a method for assessing the residual value of a power battery. In this method, a first characteristic value and a second characteristic value of the power battery parameters are predicted on both the vehicle and cloud sides based on the current operating data of the power battery. A first score regarding the health status of the power battery is calculated based on the first and second characteristic values. A second score regarding consistency differences is calculated based on reference characteristic values ​​of the power battery parameters. The residual value assessment result of the power battery is generated based on the first and second scores, thereby improving the accuracy of the residual value assessment and enabling its use for full lifecycle management and maintenance. This solves the problems of inaccurate estimation of the health status of power batteries and the inability to manage the entire lifecycle of the battery in related technologies.

[0035] Specifically, Figure 1 is a flowchart illustrating a method for evaluating the residual value of a power battery provided in an embodiment of this application.

[0036] As shown in Figure 1, the residual value assessment method for power batteries includes the following steps:

[0037] In step S101, the current operating data of the power battery and the reference characteristic values ​​of the power battery parameters are obtained.

[0038] It is understood that the embodiments of this application can obtain the current operating data of the power battery and the reference characteristic values ​​of the power battery parameters, so as to predict the first characteristic value and the second characteristic value of the power battery parameters based on the current operating data.

[0039] In step S102, the first characteristic value of the power battery parameters is predicted based on the current operating data.

[0040] Among them, the first feature value is the SOH value and SOR value of the power battery predicted based on the vehicle-side algorithm.

[0041] It is understood that the embodiments of this application can predict the first characteristic value of the power battery parameters based on the current operating data, so as to calculate the first score of the power battery's health status based on the second characteristic value and the first characteristic value of the power battery parameters.

[0042] It should be noted that the SOH value and SOR value correspond to the capacity retention rate and internal resistance retention rate of the power battery parameters, respectively. As shown in Figure 2, the first characteristic value is predicted based on the vehicle-side related algorithm. The specific process is as follows: First, the current state of charge of the power battery is calculated using the ampere-hour integral algorithm, and then the standard capacity retention rate is obtained by correcting the temperature data. Then, Kalman filtering and Arrhenius nonlinear least squares fitting are used to eliminate errors and improve the accuracy of capacity retention rate estimation to obtain the health status of the power battery calculated by the vehicle side, so as to determine the first characteristic value of the power battery parameters.

[0043] Specifically, the ampere-hour integral method estimates the amount of electricity generated during the charging and discharging process by accumulation. The calculation formula is as follows:

[0044] Where CN represents the rated capacity retention rate, I(t) represents the battery current at time t, and SOC0 represents the initial value of charging and discharging. Therefore, the ampere-hour integration method is used to estimate the capacity retention rate, and 25℃ is set as the standard temperature for temperature correction. Based on this, the standard capacity retention rate is obtained by correcting the temperature data. The calculation formula is as follows: SOH′=SOH*(1-α*(T-25℃) / 10).

[0045] In step S103, a first score of the power battery’s health status is calculated based on the second characteristic value and the first characteristic value of the power battery parameters predicted by the cloud, wherein the cloud predicts the second characteristic value based on the current operating data.

[0046] The second characteristic value is the SOH and SOR values ​​of the power battery predicted based on cloud-based algorithms.

[0047] It is understood that in this application embodiment, the first score of the power battery’s health status is calculated based on the second characteristic value and the first characteristic value of the power battery parameters predicted by the cloud. The cloud predicts the second characteristic value based on the current operating data so as to generate the residual value assessment result of the power battery based on the first score and the second score.

[0048] It should be noted that the first score can be determined by the first characteristic value predicted by the vehicle-side algorithm and the second characteristic value predicted by the cloud algorithm for the two basic indicators of the power battery parameters: capacity retention rate and internal resistance retention rate.

[0049] Specifically, as shown in Figure 3, the process of predicting the second characteristic value of power battery parameters in the cloud is as follows:

[0050] For estimating the capacity retention rate in power battery parameters, the battery pack capacity retention rate is first estimated using ampere-hour integration, then the error is eliminated using an intelligent filtering algorithm, and the battery pack capacity retention rate is corrected according to temperature. Finally, the Arrhenius model is used to fit all SOH data to predict the capacity retention rate of the power battery.

[0051] To estimate the internal resistance retention rate of the power battery, the voltage difference at the moment when the high current switches to low current at the SOC in the middle of the charging stage is divided by the current difference to estimate the internal resistance retention rate of the battery pack.

[0052] The capacity retention rate estimation is shown in Figure 4, and the specific process is as follows:

[0053] (1) Obtain the required charging segment data after cleaning and filtering the vehicle-side BMS source data;

[0054] (2) Obtain the initial capacity retention rate Capini of the battery and filter the charging data segment with SOC range minSOC<25% to calculate the current capacity retention rate;

[0055] (3) After filtering the data used to calculate the current capacity retention rate (Cap), when calculating the current capacity retention rate, the i-th (i≥1) qualified charging data segment is obtained, and the current capacity retention rate calculated for this charging data segment is denoted as Cap(i). The i-th current capacity retention rate is calculated for the i-th qualified charging data segment and denoted as Capk(i). The capacity retention rate for this part is obtained by integrating the current and time corresponding to the minSOC to the maxSOC and denoted as Ahsum. The formula for calculating Ahsum is as follows:

[0056] In the above formula, t1 represents the charging start time of the charging segment, t2 represents the charging end time of the charging segment, and I(t) represents the current value at time t in the charging segment.

[0057] The current capacity retention rate Capk(i) is calculated using the following formula: Capk(ii) = Ahsum / (maxSOC - minSOC)

[0058] The current capacity retention rate Cap(ii) is calculated using the following formula:

[0059] (4) The calculated current capacity retention rate is filtered and the ratio of it to the initial capacity retention rate is obtained as SOH, which can characterize the degree of battery life degradation; SOH(ii)=Cap(ii) / Capini

[0060] (5) Battery temperature is the biggest factor affecting battery life degradation. The temperature is uniformly corrected to 25℃ to obtain a new SOH, denoted as NewSOH. The formula is: NewSOH=SOH×(1-0.02×(T-25) / 10)

[0061] (6) Based on the estimation of real-time SOH using historical charging data segments of vehicles, future vehicle battery life can also be predicted. The method used is to fit the SOH data of all vehicles using the Arrhenius model, where the Arrhenius model formula is:

[0062] This application uses the following simplified formula: y = a·e bx

[0063] In the above formula, a and b are fixed values, x is the cumulative mileage (km), and y is the fitted curve (SOH).

[0064] The charging process of battery packs is mostly multi-stage constant current charging. The internal resistance at the current switching point can basically represent the average level of the battery's internal resistance. Therefore, the internal resistance of the battery pack is estimated by dividing the voltage difference at the moment when the large current switches to a small current at the SOC point in the middle of the charging stage by the current difference. The calculated internal resistance is the internal resistance value between 10s and 30s. Therefore, the specific process for estimating the internal resistance of the power battery is shown in Figure 5, as follows:

[0065] (1) Collect historical battery data using the BMS platform;

[0066] (2) The obtained BMS returned data is filtered and cleaned. A large amount of useless data is removed by Python algorithm to obtain the first generation of data such as voltage, current and SOC required for battery life assessment.

[0067] (3) Filter out the time periods when the battery is in a multi-stage constant current charging state;

[0068] (4) Extract the voltage and current values ​​for the two stages before and after the charging transition:

[0069] When extracting effective internal resistance to calculate micro-segments, the standard is as follows: for two current values ​​before and after a current jump in the same charging data segment, we have:

[0070] The kth charging current value I K :I K >75A;

[0071] The (k+1)th charging current value I k+1 30A k+1 <60A;

[0072] ​The current switching point is generally in the middle of the charging phase, during the SOC period. The internal resistance at this point can basically represent the average level of the battery's internal resistance.

[0073] (5) Find the micro-segment of voltage and current jump that meets the charging current condition;

[0074] For the k-th and (k+1)-th current values ​​in the same charging segment, the voltage value corresponding to the k-th current value is denoted as U. A The current value is denoted as I. A The voltage value corresponding to the (k+1)th current value is denoted as U. B The current value is denoted as I. B ;

[0075] (6) Calculate the ratio of the voltage and current difference between the two stages to obtain the estimated resistance value;

[0076] When estimating internal resistance, the ratio of the voltage difference to the current difference between the two stages is used to calculate the magnitude of the internal resistance. For U A I A U B I B Perform the following calculations:

[0077] The internal resistance value r calculated by the above formula is the predicted internal resistance value of the battery at the moment of current jump, and SOR = current predicted internal resistance value / rated internal resistance value.

[0078] In one embodiment of this application, before calculating the first score of the power battery's health status based on the second and first feature values ​​of the power battery parameters predicted by the cloud, the method further includes: obtaining the difference between the current parameters of the power battery and the previous parameters; querying a preset table based on the difference to determine the degree of degradation of the current parameters; determining the priority ranking of the power battery parameters based on the degree of degradation of each power battery parameter; and determining the respective weight of each parameter in each feature value of the power battery based on the priority ranking.

[0079] The preset table can be labeled without specific limitations.

[0080] It is understood that, in the embodiments of this application, the respective weights of internal resistance retention rate and capacity retention rate can be determined according to priority order, so as to facilitate the subsequent calculation of the first score of the power battery’s health status based on the respective weights of internal resistance retention rate and capacity retention rate.

[0081] It should be noted that the degree of degradation and priority of each parameter of the power battery affect their respective weights; among them, the parameters of the power battery can include: internal resistance, capacity, charge, voltage, temperature, etc.

[0082] Specifically, the difference between the current parameter obtained in this calculation and the historical reference parameter obtained in the previous calculation is calculated, and the degree of degradation of the current parameter is determined based on the difference of each parameter; the degree of degradation of each parameter is compared to determine the priority of each parameter of the power battery, and the priority and degree of degradation of each parameter of the power battery are input into the hierarchical structure model constructed by the AHP method to obtain the weight of the corresponding parameter.

[0083] This application utilizes the AHP (Analytic Hierarchy Process) method to determine the weights of various parameters of the power battery. The hierarchical model constructed using the AHP method takes the residual value state of the power battery as the top-level objective and the various parameters of the power battery as the criteria for the middle layer. Then, based on expert judgment or data analysis, the relative importance of the power battery parameters among themselves and with respect to the power battery is determined. The relative importance is then input into the mathematical model and the weight of each parameter is output. The weight indicates the importance of each parameter to the assessment of the residual value state of the power battery.

[0084] In one embodiment of this application, a first score for the health status of the power battery is calculated based on a second characteristic value and a first characteristic value of the power battery parameters predicted by the cloud, including: identifying the internal resistance retention rate and capacity retention rate of the first characteristic value and the second characteristic value respectively; calculating the final internal resistance retention rate based on the respective internal resistance retention rate and weight of the first characteristic value and the second characteristic value respectively; calculating the final capacity retention rate based on the respective capacity retention rate and weight of the first characteristic value and the second characteristic value respectively; and calculating the first score for the health status of the power battery based on the final internal resistance retention rate, the final capacity retention rate, and the respective weights of the final internal resistance retention rate and the final capacity retention rate.

[0085] It is understood that, in the embodiments of this application, the final internal resistance retention rate can be calculated based on the respective internal resistance retention rate and weight of the first and second characteristic values, the final capacity retention rate can be calculated based on the respective capacity retention rate and weight of the first and second characteristic values, and the first score of the power battery regarding its health status can be calculated based on the final internal resistance retention rate, the final capacity retention rate, and the respective weights of the final internal resistance retention rate and the final capacity retention rate, thereby improving the accuracy of the prediction of the health status of the power battery.

[0086] It should be noted that, assuming the weights of internal resistance retention rate are C or D, and the weights of capacity retention rate are A and B, the final capacity retention rate is SOH1×A+SOH2×B=SOH3, the final internal resistance retention rate is SOR1×C+SOR2×D=SOR3, and the weights of final internal resistance retention rate and final capacity retention rate are F, then the formula for calculating the first health score of the power battery based on the final internal resistance retention rate, the final capacity retention rate, and the weights of the final internal resistance retention rate and the final capacity retention rate is: SOH3×E+SOR3×F=X.

[0087] In one embodiment of this application, after calculating the first score of the power battery's health status based on the second characteristic value and the first characteristic value of the power battery parameters predicted by the cloud, the method further includes: updating the internal resistance retention rate and the capacity retention rate in the cloud based on the final internal resistance retention rate and the final capacity retention rate, wherein the cloud updates the second characteristic value based on the updated final internal resistance retention rate and the final capacity retention rate; and updating the first score of the power battery's health status based on the first characteristic value and the updated second characteristic value.

[0088] It is understood that the embodiments of this application can update the internal resistance retention rate and capacity retention rate in the cloud according to the final internal resistance retention rate and the final capacity retention rate, thereby enabling iterative updates to the health status of the entire power battery and realizing control over the health status of the battery throughout its entire life cycle.

[0089] For example, since the first score is determined based on the calculation of SOH3 and SOR3 in the two formulas SOH1×A+SOH2×B=SOH3 and SOR1×C+SOR2×D=SOR3, it is necessary to use SOH3 and SOR3 to replace SOH1 and SOR1.

[0090] Specifically, the update mainly includes the following three points:

[0091] 1. In addition to updating the cloud-based internal resistance retention rate and capacity retention rate by receiving the final weighted values ​​from the vehicle terminal;

[0092] 2. After the cloud-weighted update, the capacity retention rate and internal resistance retention rate are also distributed to the vehicle. The vehicle performs real-time updates of the final capacity retention rate and internal resistance retention rate in the residual value scoring system, as well as the final residual value total score.

[0093] 3. After the vehicle-side updates the weighted total score in real time, it will simultaneously calculate the latest capacity retention rate and internal resistance retention rate after considering consistency and upload them to the cloud. The cloud will then perform blank filling and update the capacity retention rate and internal resistance retention rate according to point 1, as well as the cloud-based weighted score.

[0094] In step S104, a second score for consistency differences of the power battery is calculated based on the reference characteristic values ​​of the power battery parameters.

[0095] It is understood that, in the embodiments of this application, a second score for the consistency difference of the power battery can be calculated based on the reference characteristic values ​​of the power battery parameters, so as to generate the residual value assessment result of the power battery based on the first score and the second score.

[0096] It should be noted that the consistency difference in this application refers to the difference in the power battery caused by the difference in each individual cell. The reference characteristic values ​​of the power battery parameters include: charge, voltage, internal resistance, temperature, and capacity. Among them, charge, internal resistance, and capacity can be calculated, while voltage and temperature can be obtained directly. Furthermore, the internal resistance and capacity of the reference characteristic values ​​of the power battery parameters are calculated differently from the above-mentioned calculation methods for SOH and SOR regarding the state of health. This application calculates the difference value based on the reference characteristic values ​​and the corresponding actual characteristic values.

[0097] In one embodiment of this application, calculating a second score for consistency differences of the power battery based on reference characteristic values ​​of power battery parameters includes: identifying reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity among the reference characteristic values; calculating difference values ​​based on the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity, and their respective actual values; and calculating a second score for consistency differences of the power battery based on the difference values ​​and weights of the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity.

[0098] It is understood that the embodiments of this application can identify reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity in the reference characteristic values; calculate the difference value based on the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity and their respective actual values; and calculate a second score for the consistency difference of the power battery based on the difference value and weight of the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity, thereby improving the accuracy of the subsequent residual value assessment of the power battery.

[0099] It should be noted that after calculating the consistency differences of the five characteristic parameters of battery consistency voltage, temperature, internal resistance, capacity and capacity, the unweighted scores of each indicator are calculated, and the weighted scores of each indicator are calculated by AHP weighting method; the second score of this application can be: capacity difference value × 37% + voltage difference value × 23% + internal resistance difference value × 18% + temperature difference value × 12% + capacity difference value × 10%.

[0100] In step S105, the residual value assessment result of the power battery is generated based on the first score and the second score.

[0101] It is understood that the embodiments of this application can generate residual value assessment results of power batteries based on the first score and the second score, thereby accurately generating residual value assessment results of power batteries, and can also be used for battery life cycle management and maintenance, with strong applicability.

[0102] It should be noted that residual value assessment refers to the assessment of the remaining value of a power battery at its current lifespan, which can help to better plan the battery's lifespan and reduce operating costs.

[0103] In one embodiment of this application, generating a residual value assessment result of the power battery based on a first score and a second score includes: obtaining the respective weights of the first score and the second score; calculating a total score based on the first score, the second score, and the respective weights of the first score and the second score; and generating a residual value assessment result of the power battery based on the total score.

[0104] It is understood that the embodiments of this application can: calculate a total score based on a first score, a second score, and the respective weights of the first score and the second score; and generate a residual value assessment result for the power battery based on the total score, so as to improve the accuracy of the residual value assessment and thus accurately understand the remaining value of the power battery.

[0105] For example, the total score = (SOH3 × 60% + SOR3 × 40%) × 50% + (difference in power × 37% + difference in voltage × 23% + difference in internal resistance × 18% + difference in temperature × 12% + difference in capacity × 10%) × 50%.

[0106] According to the residual value assessment method for power batteries proposed in this application, the first characteristic value and the second characteristic value of the power battery parameters are predicted on the vehicle and the cloud respectively based on the current operating data of the power battery. A first score of the power battery’s health status is calculated based on the first characteristic value and the second characteristic value. A second score of the power battery’s consistency difference is calculated based on the reference characteristic value of the power battery parameters. The residual value assessment result of the power battery is generated based on the first score and the second score, thereby improving the accuracy of the residual value assessment of the power battery and also being used for battery life cycle management and maintenance.

[0107] Next, the residual value assessment device for power batteries proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0108] Figure 6 is a block diagram of a power battery residual value evaluation device according to an embodiment of this application.

[0109] As shown in Figure 6, the power battery residual value assessment device 10 includes: a first acquisition module 100, a prediction module 200, a first calculation module 300, a second calculation module 400, and a diagnostic module 500.

[0110] The module 100 is used to acquire the current operating data of the power battery and the reference characteristic values ​​of the power battery parameters; the prediction module 200 is used to predict the first characteristic value of the power battery parameters based on the current operating data; the first calculation module 300 is used to calculate the first score of the power battery's health status based on the second characteristic value of the power battery parameters predicted by the cloud and the first characteristic value, wherein the cloud predicts the second characteristic value based on the current operating data; the second calculation module 400 is used to calculate the second score of the power battery regarding consistency differences based on the reference characteristic values ​​of the power battery parameters; and the diagnosis module 500 is used to generate the residual value assessment result of the power battery based on the first score and the second score.

[0111] In one embodiment of this application, the first calculation module 300 is further configured to: identify the internal resistance retention rate and capacity retention rate of the first feature value and the second feature value respectively; calculate the final internal resistance retention rate according to the respective weights of the internal resistance retention rate and the internal resistance retention rate of the first feature value and the second feature value respectively; calculate the final capacity retention rate according to the respective weights of the capacity retention rate and the capacity retention rate of the first feature value and the second feature value respectively; and calculate a first score of the power battery's health status according to the final internal resistance retention rate, the final capacity retention rate, and the respective weights of the final internal resistance retention rate and the final capacity retention rate.

[0112] In one embodiment of this application, it further includes: a second acquisition module, used to acquire the degradation level of the power battery; and to determine the respective weights of the internal resistance retention rate and the capacity retention rate according to the degradation level.

[0113] In one embodiment of this application, it further includes: an update module, configured to update the internal resistance retention rate and capacity retention rate in the cloud according to the final internal resistance retention rate and the final capacity retention rate, wherein the cloud updates the second characteristic value according to the updated final internal resistance retention rate and the final capacity retention rate; and updates the first score of the power battery regarding its health status according to the first characteristic value and the updated second characteristic value.

[0114] In one embodiment of this application, the second calculation module 400 is further configured to: identify the reference charge, reference voltage, reference internal resistance, reference temperature and reference capacity in the reference feature values; calculate the difference value based on the reference charge, reference voltage, reference internal resistance, reference temperature and reference capacity and their respective actual values; and calculate a second score of the power battery regarding consistency difference based on the difference value and weight of the reference charge, reference voltage, reference internal resistance, reference temperature and reference capacity.

[0115] In one embodiment of this application, the diagnostic module 500 is further configured to: obtain the respective weights of the first score and the second score; calculate a total score based on the first score, the second score, and the respective weights of the first score and the second score; and generate a residual value assessment result for the power battery based on the total score.

[0116] It should be noted that the foregoing explanation of the embodiment of the power battery residual value assessment method also applies to the power battery residual value assessment device of this embodiment, and will not be repeated here.

[0117] According to the power battery residual value assessment device proposed in the embodiments of this application, the device predicts the first characteristic value and the second characteristic value of the power battery parameters on the vehicle and the cloud respectively based on the current operating data of the power battery, calculates the first score of the power battery regarding its health status based on the first characteristic value and the second characteristic value, calculates the second score of the power battery regarding its consistency difference based on the reference characteristic value of the power battery parameters, and generates the residual value assessment result of the power battery based on the first score and the second score. This can improve the accuracy of the residual value assessment of the power battery and can also be used for battery life cycle management and maintenance.

[0118] Figure 7 is a structural schematic diagram of a vehicle provided in an embodiment of this application. The vehicle may include:

[0119] The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0120] When the processor 702 executes the program, it implements the power battery residual value evaluation method provided in the above embodiments.

[0121] Furthermore, the vehicle also includes:

[0122] Communication interface 703 is used for communication between memory 701 and processor 702.

[0123] The memory 701 is used to store computer programs that can run on the processor 702.

[0124] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0125] If the memory 701, processor 702, and communication interface 703 are implemented independently, they can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in Figure 7, but this does not imply that there is only one bus or one type of bus.

[0126] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0127] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0128] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the above-described method for evaluating the residual value of a power battery.

[0129] This application also provides a computer program product, including a computer program or instructions, characterized in that, when the computer program or instructions are executed, they implement the above-mentioned method for evaluating the residual value of power batteries.

[0130] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0131] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0132] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0133] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0134] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for evaluating the residual value of a power battery, characterized in that, Includes the following steps: Obtain the current operating data of the power battery and the reference characteristic values ​​of the power battery parameters; Predict the first characteristic value of the power battery parameters based on the current operating data; A first score for the health status of the power battery is calculated based on the second characteristic value of the power battery parameters predicted by the cloud and the first characteristic value, wherein the cloud predicts the second characteristic value based on the current operating data; A second score regarding consistency differences of the power battery is calculated based on the reference characteristic values ​​of the power battery parameters; The residual value assessment result of the power battery is generated based on the first score and the second score.

2. The method for evaluating the residual value of a power battery according to claim 1, characterized in that, The calculation of the first score of the power battery's health status based on the second characteristic value of the cloud-predicted power battery parameters and the first characteristic value includes: Identify the internal resistance retention rate and capacity retention rate of the first feature value and the second feature value, respectively; The final internal resistance retention rate is calculated based on the respective internal resistance retention rates and weights of the first and second eigenvalues, and the final capacity retention rate is calculated based on the respective capacity retention rates and weights of the first and second eigenvalues. The first score of the power battery's health status is calculated based on the final internal resistance retention rate, the final capacity retention rate, and the respective weights of the final internal resistance retention rate and the final capacity retention rate.

3. The method for evaluating the residual value of a power battery according to claim 2, characterized in that, Before calculating the first health score of the power battery based on the second characteristic value of the predicted power battery parameters from the cloud and the first characteristic value, the process further includes: Obtain the difference between the current parameters of the power battery and the previous parameters; The degree of attenuation of the current parameter is determined by querying a preset table based on the difference. The priority order of the power battery parameters is determined based on the degree of degradation of each power battery parameter. The weights of the parameters in each characteristic value of the power battery are determined according to the priority ranking.

4. The method for evaluating the residual value of a power battery according to claim 3, characterized in that, After calculating the first health score of the power battery based on the second characteristic value of the predicted power battery parameters from the cloud and the first characteristic value, the process further includes: The cloud updates the internal resistance retention rate and the capacity retention rate based on the final internal resistance retention rate and the final capacity retention rate, wherein the cloud updates the second feature value based on the updated final internal resistance retention rate and the final capacity retention rate; The first score of the power battery regarding its health status is updated based on the first characteristic value and the updated second characteristic value.

5. The method for evaluating the residual value of a power battery according to claim 1, characterized in that, The calculation of the second score regarding consistency differences of the power battery based on the reference characteristic values ​​of the power battery parameters includes: Identify the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity among the reference characteristic values; The difference value is calculated based on the reference charge, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity, as well as their respective actual values; A second score for consistency differences of the power battery is calculated based on the differences and weights of the reference charge, reference voltage, reference internal resistance, reference temperature, and reference capacity.

6. The method for evaluating the residual value of a power battery according to claim 1, characterized in that, The process of generating the residual value assessment result of the power battery based on the first score and the second score includes: Obtain the respective weights of the first score and the second score; The total score is calculated based on the first score, the second score, and the first score and the second score. The residual value assessment result of the power battery is generated based on the total score.

7. A device for evaluating the residual value of a power battery, characterized in that, include: The first acquisition module is used to acquire the current operating data of the power battery and the reference characteristic values ​​of the power battery parameters; The prediction module is used to predict the first characteristic value of the power battery parameters based on the current operating data; The first calculation module is used to calculate a first score of the power battery’s health status based on the second characteristic value of the power battery parameters predicted by the cloud and the first characteristic value, wherein the cloud predicts the second characteristic value based on the current operating data; The second calculation module is used to calculate a second score of the power battery regarding consistency differences based on the reference characteristic values ​​of the power battery parameters; The diagnostic module is used to generate a residual value assessment result for the power battery based on the first score and the second score.

8. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the power battery residual value assessment method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they are used to implement the power battery residual value assessment method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the power battery residual value assessment method as described in any one of claims 1-6.

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