Method and device for evaluating residual value of power battery, vehicle, medium and program

Abstract

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

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a power battery residual value evaluation method and device, vehicle, medium and program. BACKGROUND

[0002] In recent years, the new energy vehicle market has developed rapidly. With a large number of electric vehicles entering the market for some time, many vehicle power batteries have gradually aged, but the related technical research and development of power battery life attenuation have not yet matured. Battery life attenuation estimation has become a hot issue in recent years. Through a large number of engineering practices, it is found that the estimation strategy in the battery management system is the main way for major manufacturers and battery manufacturers to predict life attenuation. This method has a certain life prediction effect, but is limited by the long time period of battery aging test, the simple estimation strategy, the limited processing capacity of the battery management system, and other reasons. A large proportion of battery models and related parameters in the vehicle controller cannot be updated, but this method has poor effect in engineering application.

[0003] In related technologies, the health state of the power battery is currently estimated by using algorithms such as charging data or historical data, but this can easily lead to inaccurate estimation results and cannot control the entire life cycle of the battery life. SUMMARY

[0004] The present application provides a power battery residual value evaluation method, device, vehicle, medium and program to solve the problems of inaccurate estimation of the health state of the power battery and inability to control the entire life cycle of the battery life in related technologies.

[0005] The first aspect embodiment of the present application provides a power battery residual value evaluation method, comprising the following steps: obtaining current running data of a power battery and reference characteristic values of power battery parameters; predicting a first characteristic value of the power battery parameter according to the current running data; calculating a first score of the power battery on the health state according to a second characteristic value of the power battery parameter predicted by the cloud and the first characteristic value, wherein the cloud predicts the second characteristic value based on the current running data; calculating a second score of the power battery on consistency difference according to the reference characteristic values of the power battery parameters; and generating a residual value evaluation result of the power battery according to the first score and the second score.

[0006] Optionally, in an embodiment of the present application, the calculating the first score of the power battery on the health state according to the second characteristic value of the cloud-predicted power battery parameter and the first characteristic value comprises: identifying the internal resistance retention rate and the capacity retention rate of the first characteristic value and the second characteristic value respectively; calculating a final internal resistance retention rate according to the internal resistance retention rate of the first characteristic value and the second characteristic value and the weight of the internal resistance retention rate, and calculating a final capacity retention rate according to the capacity retention rate of the first characteristic value and the second characteristic value and the weight of the capacity retention rate; and calculating the first score of the power battery on the health state according to the final internal resistance retention rate, the final capacity retention rate, and the weight of the final internal resistance retention rate and the final capacity retention rate.

[0007] Optionally, in an embodiment of the present application, before the calculating the first score of the power battery on the health state according to the second characteristic value of the cloud-predicted power battery parameter and the first characteristic value, the method further comprises: obtaining the difference between the current parameter of the power battery and the last parameter; determining the attenuation degree of the current parameter according to the difference by querying a preset table; determining the priority order of the power battery parameters according to the attenuation degree of each power battery parameter; and determining the weight of each parameter in the characteristic value of the power battery according to the priority order.

[0008] Optionally, in an embodiment of the present application, after the calculating the first score of the power battery on the health state according to the second characteristic value of the cloud-predicted power battery parameter and the first characteristic value, the method further comprises: updating the internal resistance retention rate and the capacity retention rate of 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 updating the first score of the power battery on the health state according to the first characteristic value and the updated second characteristic value.

[0009] Optionally, in an embodiment of the present application, the calculating the second score of the power battery on the consistency difference according to the reference characteristic value of the power battery parameter comprises: identifying the reference capacity, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity in the reference characteristic value; calculating a difference value according to the reference capacity, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity and the actual value corresponding to each; and calculating the second score of the power battery on the consistency difference according to the difference value and the weight of the reference capacity, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity.

[0010] Optionally, in an embodiment of the present application, the generating the residual value evaluation result of the power battery according to the first score and the second score comprises: obtaining respective weights of the first score and the second score; calculating a total score according to the first score, the second score, and the first score and the second score; and generating the residual value evaluation result of the power battery according to the total score.

[0011] The second aspect embodiment of the present application provides a power battery residual value evaluation device, comprising: a first obtaining module configured to obtain current running data of a power battery and reference characteristic values of power battery parameters; a prediction module configured to predict a first characteristic value of the power battery parameters according to the current running data; a first calculating module configured to calculate a first score of the power battery on a health state according to 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 running data; a second calculating module configured to calculate a second score of the power battery on a consistency difference according to the reference characteristic values of the power battery parameters; and a diagnosis module configured to generate a residual value evaluation result of the power battery according to the first score and the second score.

[0012] Optionally, in an embodiment of the present application, the first calculating module is further configured to: identify respective internal resistance retention rates and capacity retention rates of the first characteristic value and the second characteristic value; calculate a final internal resistance retention rate according to the respective internal resistance retention rates of the first characteristic value and the second characteristic value and respective weights of the internal resistance retention rates, and calculate a final capacity retention rate according to the respective capacity retention rates of the first characteristic value and the second characteristic value and respective weights of the capacity retention rates; and calculate the first score of the power battery on the health state according to the final internal resistance retention rate, the final capacity retention rate, and respective weights of the final internal resistance retention rate and the final capacity retention rate.

[0013] Optionally, in an embodiment of the present application, the device further comprises a second obtaining module configured to obtain a degradation level of the power battery; and configured to determine the respective weights of the internal resistance retention rates and the respective weights of the capacity retention rates according to the degradation level.

[0014] Optionally, in an embodiment of the present application, the device further comprises an updating module configured to update the internal resistance retention rate and the capacity retention rate of the cloud according to the final internal resistance retention rate and the final capacity retention rate, and configured to update the second characteristic value of the cloud according to the final internal resistance retention rate and the final capacity retention rate, and update the first score of the power battery on the health state and the residual value evaluation result of the power battery according to the first characteristic value and the updated second characteristic value, so as to cyclically update the residual value score system to complete the evaluation of the whole life cycle.

[0015] Optionally, in an embodiment of the present application, the second calculation module is further configured to: identify reference electric quantity, reference voltage, reference internal resistance, reference temperature and reference capacity in the reference characteristic value; calculate a difference value according to the reference electric quantity, the reference voltage, the reference internal resistance, the reference temperature and the reference capacity and respective actual values; and calculate a second score of the power battery on consistency difference according to the respective difference values and weights of the reference electric quantity, the reference voltage, the reference internal resistance, the reference temperature and the reference capacity.

[0016] Optionally, in an embodiment of the present application, the diagnosis module is further configured to: obtain respective weights of the first score and the second score; calculate a total score according to the first score, the second score and the first score and the second score; and generate a residual value evaluation result of the power battery according to the total score.

[0017] The third aspect embodiment of the present application provides a vehicle, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to perform the power battery residual value evaluation method as described in the above embodiments.

[0018] The fourth aspect embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, and the program is executed by a processor to perform the power battery residual value evaluation method as described in the above embodiments.

[0019] The fifth aspect embodiment of the present application provides a computer program product, comprising a computer program or instructions, and when the computer program or instructions are executed, the power battery residual value evaluation method as described in the above embodiments is implemented.

[0020] Therefore, the present application has at least the following beneficial effects:

[0021] Since the vehicle-side calculation is limited by real-time requirements and computing resources, it is easy to cause missing or abnormal key data when selecting running data at a certain step interval in the data acquisition stage, thereby affecting the accuracy of the calculation result. In the calculation stage, due to limited computing power, data processing is not timely, thereby affecting the accuracy of the calculation result. Therefore, in the embodiments of the present application, the characteristic values of the power battery parameters are predicted at the vehicle side and the cloud side respectively according to the current running data of the power battery, the missing values are filled by using the running data of the vehicle collected by the cloud side to make up for the blank values at the vehicle side, so as to improve the accuracy of the calculation result, and the cloud computing resources are more optimal, which can continuously predict the characteristic values of the power battery parameters. Therefore, by combining the prediction results of the cloud side and the vehicle side, the accuracy of the first score of the power battery on the health state is improved, thereby improving the accuracy of the residual value evaluation of the power battery, and the whole life cycle management and maintenance of the battery can be realized.

[0022] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and / or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0023] The above and / or additional aspects and advantages of the application will become apparent and be more readily understood through reference to the following description, taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 A flow chart of a method for evaluating the residual value of a power battery according to an embodiment of the application;

[0025] Figure 2 A schematic diagram of a battery consistency difference and residual value evaluation system at the vehicle end according to an embodiment of the application;

[0026] Figure 3 A system framework of a battery life estimation method based on vehicle-cloud integration according to an embodiment of the application;

[0027] Figure 4 A specific flow chart of cloud SOH estimation according to an embodiment of the application;

[0028] Figure 5 A flow chart of cloud SOR estimation according to an embodiment of the application;

[0029] Figure 6 A block diagram of a device for evaluating the residual value of a power battery according to an embodiment of the application;

[0030] Figure 7 A structural schematic diagram of a vehicle according to an embodiment of the application. DETAILED DESCRIPTION

[0031] Embodiments of the application are described in detail below with reference to the attached drawing figures, wherein the same or like reference numerals are used throughout the drawing figures to refer to the same or like elements or elements having the same or similar functionality. The embodiments described below are exemplary and are intended to be illustrative of the application rather than limiting.

[0032] A power battery residual value evaluation method, device, vehicle, medium and program are described below with reference to the accompanying drawings. In view of the inaccurate estimation of the health state of the power battery in the related art mentioned in the background, and the inability to control the whole life cycle of the battery, the present application provides a power battery residual value evaluation method. In the method, the first characteristic value and the second characteristic value of the power battery parameters are predicted at the vehicle end and the cloud end respectively according to the current running data of the power battery, and the first score of the power battery on the health state is calculated according to the first characteristic value and the second characteristic value. The second score of the power battery on the consistency difference is calculated based on the reference characteristic value of the power battery parameters, and the residual value evaluation result of the power battery is generated according to the first score and the second score, so as to improve the accuracy of the residual value evaluation of the power battery, and also to be used for the whole life cycle control and maintenance of the battery. Thus, the problems of inaccurate estimation of the health state of the power battery in the related art, and inability to control the whole life cycle of the battery are solved.

[0033] Specifically, Figure 1 A flowchart of a power battery residual value evaluation method provided by an embodiment of the present application is shown in the figure.

[0034] As Figure 1 shown, the power battery residual value evaluation method includes the following steps:

[0035] In step S101, the current running data of the power battery and the reference characteristic value of the power battery parameters are obtained.

[0036] It can be understood that the current running data of the power battery and the reference characteristic value of the power battery parameters can be obtained by the present application, so as to predict the first characteristic value and the second characteristic value of the power battery parameters based on the current running data subsequently.

[0037] In step S102, the first characteristic value of the power battery parameters is predicted according to the current running data.

[0038] Among them, the first characteristic value is the SOH value and the SOR value of the power battery predicted based on the vehicle end algorithm.

[0039] It can be understood that the first characteristic value of the power battery parameters can be predicted according to the current running data by the present application, so as to calculate the first score of the power battery on the health state based on the second characteristic value and the first characteristic value of the power battery parameters subsequently.

[0040] It should be noted that the SOH value and the SOR value correspond to the capacity retention rate of the power battery parameters and the internal resistance retention rate of the power battery parameters respectively; as Figure 2As shown, the first characteristic value is predicted based on the vehicle-end related algorithm, and the specific process is as follows: first, the state of charge of the current power battery is calculated by using the ampere-hour integration algorithm, and the standard capacity retention rate is obtained based on the corrected temperature data; the Kalman filter and the Arrhenius nonlinear least squares method are used to fit and eliminate errors to improve the accuracy of the capacity retention rate estimation to obtain the health state of the power battery calculated by the vehicle end to determine the first characteristic value of the power battery parameter.

[0041] Specifically, the ampere-hour integration method estimates the electric quantity in the charging and discharging process by accumulation, and the calculation formula is as follows:

[0042]

[0043] wherein CN represents the rated capacity retention rate, I(t) represents the current of the battery at time t, and SOC0 represents the starting 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, and the standard capacity retention rate is obtained based on the corrected temperature data, and the calculation formula is as follows:

[0044] SOH' = SOH * (1 - a * (T - 25℃) / 10),

[0045] In step S103, a first score of the power battery with respect to the health state is calculated according to the second characteristic value and the first characteristic value of the power battery parameter predicted by the cloud end, wherein the second characteristic value is predicted by the cloud end based on the current running data.

[0046] wherein the second characteristic value is the SOH value and the SOR value of the power battery predicted based on the cloud-end algorithm.

[0047] It can be understood that the first score of the power battery with respect to the health state is calculated according to the second characteristic value and the first characteristic value of the power battery parameter predicted by the cloud end, wherein the second characteristic value is predicted by the cloud end based on the current running data, so as to generate the residual value evaluation result of the power battery according to the first score and the second score subsequently.

[0048] It should be noted that the first score can be determined by the first characteristic value predicted by the vehicle-end algorithm and the second characteristic value predicted by the cloud-end algorithm from the two basic indexes of the capacity retention rate and the internal resistance retention rate in the power battery parameter.

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

[0050] For the estimation of the capacity retention rate in the power battery parameters, the capacity retention rate of the battery pack is estimated by using the ampere-hour integral, the error is eliminated by using the intelligent filtering algorithm, and the capacity retention rate of the battery pack is corrected according to the temperature, and all SOH data are fitted by using the Arrhenius model, so as to predict the capacity retention rate of the power battery.

[0051] For the estimation of the internal resistance retention rate in the power battery parameters, the voltage difference at the moment when the large current is switched to the small current at the middle SOC in the charging section is divided by the current difference to estimate the internal resistance retention rate of the battery pack.

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

[0053] (1) The required charging section data of the BMS source data at the vehicle end after cleaning and screening is obtained;

[0054] (2) The initial capacity retention rate Capini of the factory battery is obtained, and the charging data section with SOC interval minSOC<25% is screened for calculating the current capacity retention rate;

[0055] (3) After the condition judgment of the data screened for calculating the current capacity retention rate (Cap), the i-th (i≥1) charging data section meeting the requirements is obtained, and the current capacity retention rate calculated in the charging data section is recorded as Cap(i). The i-th current capacity retention rate is calculated in the i-th charging data section meeting the requirements, and is recorded as Capk(i). The current and time corresponding to minSOC to maxSOC are integrated to obtain the capacity retention rate of this part, which is recorded as Ahsum. The formula for calculating Ahsum is as follows:

[0056]

[0057] In the above formula, t1 represents the charging start time of the charging section, t2 represents the charging end time of the charging section, and I(t) represents the current value at t in the charging section.

[0058] The i-th current capacity retention rate Capk(i) is calculated, and the formula is as follows:

[0059] Capk(ii)=Ahsum / (maxSOC-minSOC)

[0060] The current capacity retention rate Cap(ii) is calculated, and the formula is as follows:

[0061]

[0062] (4) The current capacity retention rate calculated is filtered and the ratio to the initial capacity retention rate is obtained, which is SOH, and can represent the degree of battery life attenuation.

[0063] SOH(ii) = Cap(ii) / Capini

[0064] (5) Battery temperature as the biggest factor affecting battery life attenuation, the temperature is uniformly corrected to 25℃, get the new SOH, recorded as NewSOH, its formula is:

[0065] NewSOH = SOH x (1-0.02 x (T-25) / 10)

[0066] (6) On the basis of estimating real-time SOH with vehicle historical charging data segment, the future vehicle battery life can also be predicted. The method adopted is to fit all vehicle SOH data with Arrhenius model, wherein the Arrhenius model formula is:

[0067]

[0068] The application adopts the following formula for simplification:

[0069] y = a e bx

[0070] In the above formula, a and b are fixed values, x is the cumulative mileage (km), and y is the fitted curve (SOH).

[0071] The charging process of the battery pack is mostly multi-stage constant current charging, and the internal resistance at the current switching point can basically represent the average level of the battery internal resistance. Therefore, the voltage difference at the time when the large current switching to small current at the middle SOC in the charging segment is divided by the current difference to estimate the internal resistance of the battery pack. The calculated internal resistance is the 10s-30s internal resistance value; therefore, the specific process of estimating the internal resistance of the power battery is as shown in Figure 5 , and is as follows:

[0072] (1) Use BMS platform to collect historical data of the battery;

[0073] (2) Screen and clean the obtained BMS return data, use Python algorithm to eliminate a large amount of useless data, and obtain the first generation data of voltage, current and SOC required for battery life evaluation;

[0074] (3) Screen out the time period when the battery is in multi-stage constant current charging state;

[0075] (4) Extract the voltage and current values of the two stages before and after the charging jump:

[0076] When extracting the effective internal resistance calculation microsegment, the standard is that for the two current values before and after the current jump in the same charging data segment:

[0077] The kth charging current value I K: I K >75A;

[0078] k+1th charging current value I k+1 : 30A k+1 <60A;

[0079] At the current switching point, which is generally in the middle of the charging period, the internal resistance can basically represent the average level of the battery internal resistance.

[0080] (5) find the micro section of voltage and current jump that meets the charging current condition;

[0081] For the kth and k+1th current values in the same charging period, the voltage value corresponding to the kth current value is denoted as U A , and the current value is denoted as I A ; the voltage value corresponding to the k+1th current value is denoted as U B , and the current value is denoted as I B ;

[0082] (6) Calculate the resistance estimation value by the ratio of the voltage and current difference of the two stages;

[0083] When estimating the internal resistance, the ratio of the voltage difference and the current difference of the two stages is used to calculate the internal resistance. For U A , I A , U B , I B , the following calculation is performed:

[0084]

[0085] The internal resistance value r calculated by the above formula is the predicted internal resistance value of the battery at the current jump time, and SOR = current internal resistance prediction value / rated internal resistance value.

[0086] In an embodiment of the present application, before calculating the first score of the power battery on the health state according to the second characteristic value and the first characteristic value of the predicted power battery parameter from the cloud, the difference between the current parameter of the power battery and the last parameter is obtained; the decay degree of the current parameter is determined by querying a preset table according to the difference; the priority order of the power battery parameters is determined according to the decay degree of each power battery parameter; and the respective weights of the parameters in the characteristic values of the power battery are determined according to the priority order.

[0087] The preset table can be calibrated and is not specifically limited.

[0088] It can be understood that the priority order can be used to determine the respective weights of the internal resistance retention rate and the capacity retention rate, so as to calculate the first score of the power battery on the health state according to the respective weights of the internal resistance retention rate and the capacity retention rate subsequently.

[0089] It should be noted that the attenuation degree and priority of each parameter of the power battery affect the respective weight; wherein, the parameters of the power battery can include: internal resistance, capacity, power, voltage, temperature, etc.

[0090] Specifically, a difference value is calculated for the current parameter obtained this time and the historical reference parameter obtained last time, and the attenuation degree of each parameter is calculated according to the difference value of each parameter; the priority of each parameter of the power battery is determined by comparing the attenuation degree of each parameter, and the priority and attenuation degree 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.

[0091] The application determines the weight of each parameter of the power battery by using the AHP (Analytic Hierarchy Process) method, wherein, the hierarchical structure model constructed by the AHP method takes the residual value state of the power battery as the top target, takes each parameter of the power battery as the criterion of the middle layer, then determines the relative importance between the parameters of the power battery and the parameters relative to the power battery according to expert judgment or data analysis, and inputs the relative importance degree into the mathematical model to output the weight of each parameter, wherein, the weight indicates the importance degree of each parameter for the residual value state evaluation of the power battery.

[0092] In an embodiment of the application, the first score of the power battery on the health state is calculated according to the second characteristic value and the first characteristic value of the power battery parameter predicted by the cloud, comprising: identifying the internal resistance retention rate and the capacity retention rate of the first characteristic value and the second characteristic value respectively; calculating the final internal resistance retention rate according to the internal resistance retention rate of the first characteristic value and the second characteristic value and the weight of the internal resistance retention rate, and calculating the final capacity retention rate according to the capacity retention rate of the first characteristic value and the second characteristic value and the weight of the capacity retention rate; calculating the first score of the power battery on the health state according to the final internal resistance retention rate, the final capacity retention rate, and the weight of the final internal resistance retention rate and the final capacity retention rate.

[0093] It can be understood that the embodiment of the application can calculate the final internal resistance retention rate according to the internal resistance retention rate of the first characteristic value and the second characteristic value and the weight of the internal resistance retention rate, calculate the final capacity retention rate according to the capacity retention rate of the first characteristic value and the second characteristic value and the weight of the capacity retention rate, and calculate the first score of the power battery on the health state according to the final internal resistance retention rate, the final capacity retention rate, and the weight of the final internal resistance retention rate and the final capacity retention rate, thereby improving the accuracy of the health state prediction of the power battery.

[0094] It should be noted that, assuming the internal resistance retention rate is respectively weighted as C or D, the capacity retention rate is respectively weighted as A and B, the final capacity retention rate SOH1xA+SOH2xB=SOH3, the final internal resistance retention rate is SOR1xC+SOR2xD=SOR3, the final internal resistance retention rate is E, and the respective weight of the final capacity retention rate is F, then the formula for calculating the first score of the power battery on the health state according to the final internal resistance retention rate, the final capacity retention rate, and the respective weight of the final internal resistance retention rate and the final capacity retention rate is: SOH3xE+SOR3xF=X.

[0095] In an embodiment of the present application, after calculating the first score of the power battery on the health state according to the second characteristic value and the first characteristic value of the cloud-end predicted power battery parameter, it further includes: updating the internal resistance retention rate and the capacity retention rate of the cloud-end according to the final internal resistance retention rate and the final capacity retention rate, wherein the cloud-end updates the second characteristic value according to the updated final internal resistance retention rate and the final capacity retention rate; updating the first score of the power battery on the health state according to the first characteristic value and the updated second characteristic value.

[0096] It can be understood that the embodiments of the present application can update the internal resistance retention rate and the capacity retention rate of the cloud-end according to the final internal resistance retention rate and the final capacity retention rate, so as to be able to iteratively update the health state of the entire power battery, and realize the management and control of the health state of the whole life cycle of the battery.

[0097] For example, since the first score is determined based on the calculation of SOH3 and SOR3 in the two formulas SOH1xA+SOH2xB=SOH3; SOR1xC+SOR2xD=SOR3, SOH3 and SOR3 need to be used to replace SOH1 and SOR1.

[0098] Specifically, the update mainly includes the following three points:

[0099] 1. The cloud-end accepts the final weighted capacity retention rate and internal resistance retention rate of the vehicle-end to update the weighted score of the cloud-end internal resistance retention rate and capacity retention rate;

[0100] 2. The cloud-end weighted updated capacity retention rate and internal resistance retention rate are also downloaded to the vehicle-end, the vehicle-end performs real-time update of the final capacity retention rate and internal resistance retention rate of the residual value scoring system, and embodies the final residual value total score;

[0101] 3. After the vehicle-end final real-time update of the weighted total score, the latest capacity retention rate and internal resistance retention rate are calculated and uploaded to the cloud-end, and the cloud-end performs blank filling and future prediction of the capacity retention rate and internal resistance retention rate again according to the first point, and updates the cloud-end weighted score.

[0102] In step S104, the second score of the power battery on the consistency difference is calculated according to the reference characteristic value of the power battery parameter.

[0103] It can be understood that the second score of the power battery on the consistency difference can be calculated according to the reference characteristic value of the power battery parameter, so as to generate the residual value evaluation result of the power battery according to the first score and the second score subsequently.

[0104] It should be noted that the consistency difference of the present application refers to the difference of each single battery of the power battery causing the difference of the power battery, and the reference characteristic value of the power battery parameter includes: power, voltage, internal resistance, temperature and capacity; wherein the power, internal resistance and capacity can be calculated, and the voltage and temperature can be directly obtained; and the internal resistance and capacity of the reference characteristic value of the power battery parameter are different from the calculation method of the SOH and SOR above; the present application is based on the difference value calculated according to the reference characteristic value and the corresponding actual characteristic value.

[0105] In an embodiment of the present application, the second score of the power battery on the consistency difference is calculated according to the reference characteristic value of the power battery parameter, including: identifying the reference power, reference voltage, reference internal resistance, reference temperature and reference capacity in the reference characteristic value; calculating the difference value according to the reference power, reference voltage, reference internal resistance, reference temperature and reference capacity and the respective actual values; calculating the second score of the power battery on the consistency difference according to the difference value and the weight of the reference power, reference voltage, reference internal resistance, reference temperature and reference capacity respectively.

[0106] It can be understood that the reference power, reference voltage, reference internal resistance, reference temperature and reference capacity in the reference characteristic value can be identified, the difference value can be calculated according to the reference power, reference voltage, reference internal resistance, reference temperature and reference capacity and the respective actual values, and the second score of the power battery on the consistency difference can be calculated according to the difference value and the weight of the reference power, reference voltage, reference internal resistance, reference temperature and reference capacity respectively, so as to improve the accuracy of the residual value evaluation of the power battery subsequently.

[0107] It should be noted that after calculating the consistency difference of the five characteristic parameter indexes of the battery consistency voltage, temperature, internal resistance, capacity and power, the weighted score of each index is calculated by the weighting method of AHP; the second score of the present application can be: the difference value of the power × 37% + the difference value of the voltage × 23% + the difference value of the internal resistance × 18% + the difference value of the temperature × 12% + the difference value of the capacity × 10%.

[0108] In step S105, the residual value evaluation result of the power battery is generated according to the first score and the second score.

[0109] It can be understood that the embodiments of the present application can generate the residual value evaluation result of the power battery according to the first score and the second score, so as to accurately generate the residual value evaluation result of the power battery, and also can be used for battery whole life cycle management and maintenance, and has strong applicability.

[0110] It should be noted that the residual value evaluation refers to the evaluation of the remaining value of the current life of the power battery, which can better plan the use cycle of the battery and reduce the operation cost.

[0111] In an embodiment of the present application, the residual value evaluation result of the power battery is generated according to the first score and the second score, comprising: obtaining respective weights of the first score and the second score; calculating a total score according to the first score, the second score, and the respective weights of the first score and the second score; and generating the residual value evaluation result of the power battery according to the total score.

[0112] It can be understood that the embodiments of the present application can calculate the total score according to the first score, the second score, and the respective weights of the first score and the second score, and generate the residual value evaluation result of the power battery according to the total score, so as to improve the accuracy of the residual value evaluation, and thus accurately understand the remaining value of the power battery.

[0113] For example, the total score = (SOH3x60%+SOR3x40%)x50%+(difference value of the electric quantityx37%+difference value of the voltage x23%+difference value of the internal resistance x18%+difference value of the temperature x12%+difference value of the capacity x10%)x50%.

[0114] According to the power battery residual value evaluation method proposed in the embodiments of the present application, the first characteristic value and the second characteristic value of the power battery parameters are predicted at the vehicle end and the cloud end respectively according to the current running data of the power battery, the first score of the power battery about the health state is calculated according to the first characteristic value and the second characteristic value, the second score of the power battery about the consistency difference is calculated based on the reference characteristic value of the power battery parameters, and the residual value evaluation result of the power battery is generated according to the first score and the second score, so as to improve the accuracy of the power battery residual value evaluation, and also can be used for battery whole life cycle management and maintenance.

[0115] Secondly, the power battery residual value evaluation device according to the embodiments of the present application is described with reference to the accompanying drawings.

[0116] Figure 6 is a block schematic diagram of the power battery residual value evaluation device in the embodiments of the present application.

[0117] As shown in Figure 6 , the power battery residual value evaluation device 10 comprises a first acquisition module 100, a prediction module 200, a first calculation module 300, a second calculation module 400, and a diagnosis module 500.

[0118] The acquisition module 100 is configured to acquire current operation data of the power battery and reference characteristic values of the power battery parameters; the prediction module 200 is configured to predict first characteristic values of the power battery parameters according to the current operation data; the first calculation module 300 is configured to calculate a first score of the power battery on a health state according to second characteristic values of the power battery parameters predicted by a cloud and the first characteristic values, wherein the cloud predicts the second characteristic values based on the current operation data; the second calculation module 400 is configured to calculate a second score of the power battery on a consistency difference according to the reference characteristic values of the power battery parameters; and the diagnosis module 500 is configured to generate a residual value evaluation result of the power battery according to the first score and the second score.

[0119] In an embodiment of the present application, the first calculation module 300 is further configured to: identify the respective internal resistance retention rates and capacity retention rates of the first characteristic values and the second characteristic values; calculate a final internal resistance retention rate according to the respective internal resistance retention rates of the first characteristic values and the second characteristic values and respective weights of the internal resistance retention rates, and calculate a final capacity retention rate according to the respective capacity retention rates of the first characteristic values and the second characteristic values and respective weights of the capacity retention rates; and calculate the first score of the power battery on the health state according to the final internal resistance retention rate, the final capacity retention rate, and respective weights of the final internal resistance retention rate and the final capacity retention rate.

[0120] In an embodiment of the present application, the second acquisition module is further configured to acquire a degradation level of the power battery; and the respective weights of the internal resistance retention rates and the capacity retention rates are determined according to the degradation level.

[0121] In an embodiment of the present application, the update module is further configured to update the internal resistance retention rate and the capacity retention rate of the cloud according to the final internal resistance retention rate and the final capacity retention rate, wherein the cloud updates the second characteristic values according to the updated final internal resistance retention rate and the final capacity retention rate, and updates the first score of the power battery on the health state according to the first characteristic values and the updated second characteristic values.

[0122] In an embodiment of the present application, the second calculation module 400 is further configured to: identify reference electric quantity, reference voltage, reference internal resistance, reference temperature, and reference capacity in the reference characteristic values; calculate difference values according to the reference electric quantity, the reference voltage, the reference internal resistance, the reference temperature, the reference capacity, and respective actual values corresponding thereto; and calculate the second score of the power battery on the consistency difference according to the respective difference values and weights of the reference electric quantity, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity.

[0123] In an embodiment of the present application, the diagnosis module 500 is further configured to: acquire respective weights of the first score and the second score; calculate a total score according to the first score, the second score, and the respective weights of the first score and the second score; and generate the residual value evaluation result of the power battery according to the total score.

[0124] It should be noted that the foregoing explanation of the power battery residual value evaluation method embodiment is also applicable to the power battery residual value evaluation device of this embodiment, which will not be described here.

[0125] The power battery residual value evaluation device provided by the embodiment of the present application predicts the first characteristic value and the second characteristic value of the power battery parameter at the vehicle end and the cloud end respectively according to the current running data of the power battery, calculates the first score of the power battery about the health state according to the first characteristic value and the second characteristic value, calculates the second score of the power battery about the consistency difference based on the reference characteristic value of the power battery parameter, and generates the residual value evaluation result of the power battery according to the first score and the second score, so as to improve the accuracy of the residual value evaluation of the power battery and also be used for the whole life cycle management and maintenance of the battery.

[0126] Figure 7 The vehicle provided by the embodiment of the present application is shown in the structural schematic diagram. The vehicle can include:

[0127] The memory 701, the processor 702 and the computer program stored in the memory 701 and executable on the processor 702.

[0128] The processor 702 implements the power battery residual value evaluation method provided in the above embodiments when executing the program.

[0129] Further, the vehicle further includes:

[0130] The communication interface 703 is used for communication between the memory 701 and the processor 702.

[0131] The memory 701 is used to store the computer program executable on the processor 702.

[0132] The memory 701 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0133] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected with each other through a bus and complete communication between 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. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 7 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0134] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can complete communication between each other through an internal interface.

[0135] The processor 702 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0136] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program or instructions, and the computer program or instructions are executed by a processor to implement the power battery residual value evaluation method.

[0137] The embodiments of the present application also provide a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed to implement the power battery residual value evaluation method.

[0138] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the description of the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0139] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0140] Any process or method descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions (or steps) in the process, and that the various embodiments of the application can include additional or fewer steps or processes in alternative implementations, as will be appreciated by those skilled in the art. The various embodiments of the application can be implemented in hardware, software, firmware, or a combination thereof, as desired.

[0141] It should be understood that parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. As in another embodiment, if implemented in hardware, any one or more of the following technologies known in the art can be used: discrete logic circuit with logic gates for implementing logic functions on data signals, application specific integrated circuit with appropriate combination logic gates, programmable gate array (PGA), field programmable gate array (FPGA), etc.

[0142] Those skilled in the art of the present technology can understand that all or part of the steps carried out by the above-described embodiment method can be completed by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium. The program, when executed, includes one or a combination of steps of the method embodiment.

Claims

1. A method for evaluating residual value of a power battery, characterized in that: The following steps are involved: Obtaining the current operating data of the power battery and reference characteristic values ​​of the power battery parameters; Predicting a first characteristic value of the power battery parameter according to the current operating data; Get the difference between the current parameters of the power battery and the previous parameters; Querying a preset table according to the difference value to determine the attenuation degree of the current parameter; Determine the priority ranking of power battery parameters according to the attenuation degree of each power battery parameter; Determining respective weights of parameters in each characteristic value of the power battery according to the priority sorting; Calculating a first score of the power battery regarding the health state based on a second eigenvalue of a power battery parameter predicted by the cloud and the first eigenvalue, including: identifying an internal resistance retention rate and a capacity retention rate of each of the first eigenvalue and the second eigenvalue; calculating a final internal resistance retention rate based on the internal resistance retention rates and the weights of the internal resistance retention rates of each of the first eigenvalue and the second eigenvalue, and calculating a final capacity retention rate based on the capacity retention rates and the weights of the capacity retention rates of each of the first eigenvalue and the second eigenvalue; and calculating the first score of the power battery regarding the health state 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, wherein the cloud predicts the second eigenvalue based on the current operating data; Updating the internal resistance retention rate and the capacity retention rate of 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; Updating a first score of the power battery regarding the health state according to the first characteristic value and the updated second characteristic value; calculating a second score of the power battery regarding the consistency difference according to the reference characteristic value of the power battery parameter; A residual value assessment result of the power battery is generated according to the first score and the second score.

2. The power battery residual value evaluation method according to claim 1, characterized in that: The calculating a second score of the power battery regarding consistency difference based on the reference characteristic value of the power battery parameter includes: Identify the reference electrical quantity, reference voltage, reference internal resistance, reference temperature and reference capacity in the reference characteristic values; Calculating a difference value according to the reference power, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity and their corresponding actual values; A second score of the power battery regarding consistency difference is calculated based on respective difference values ​​and weights of the reference power quantity, the reference voltage, the reference internal resistance, the reference temperature, and the reference capacity.

3. The power battery residual value evaluation method according to claim 1, characterized in that: Generating a residual value evaluation result of the power battery according to the first score and the second score includes: Obtaining 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; A residual value assessment result of the power battery is generated according to the total score.

4. A power battery residual value evaluation device, characterized in that: The method for evaluating the residual value of a power battery according to any one of claims 1 to 3 comprises: A first acquisition module is used to acquire current operating data of the power battery and reference characteristic values ​​of power battery parameters; a prediction module, configured to predict a first characteristic value of the power battery parameter based on the current operating data; a first calculation module, configured to calculate a first score of the health state of the power battery based on a second eigenvalue of a power battery parameter predicted by the cloud and the first eigenvalue, wherein the cloud predicts the second eigenvalue based on the current operating data; a second calculation module, configured to calculate a second score of the power battery regarding consistency difference based on the reference characteristic value of the power battery parameter; A diagnosis module is used to generate a residual value assessment result of the power battery according to the first score and the second score.

5. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power battery residual value assessment method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, it is used to implement the power battery residual value assessment method according to any one of claims 1 to 3.

7. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the power battery residual value assessment method according to any one of claims 1 to 3 is implemented.

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