Vehicle residual value evaluation method and system for intelligent electric vehicle

Abstract

The invention discloses a vehicle residual value evaluation method and system for an intelligent electric vehicle, and relates to the technical field of vehicle residual value evaluation, and the method comprises the steps: building a multi-level vehicle state evaluation model based on an obtained vehicle evaluation index; establishing an evaluation factor set and an evaluation state set according to the index quantity of the index layer; classifying the vehicle evaluation indexes; on the basis of the evaluation factor set and the evaluation state set, index state membership degree vectors, corresponding to all states, of all indexes in the classified indexes are calculated; determining the influence weight of each index of the index layer on each index of the corresponding sub-target layer; establishing an index state matrix based on the index state membership degree vector; based on the influence weight and the index state matrix, calculating to obtain a membership degree vector of the sub-target layer and a state membership degree vector of the evaluation target layer; and a final vehicle state evaluation result, a corresponding health attenuation coefficient and final vehicle cost are obtained through calculation. According to the invention, residual value evaluation of the intelligent electric vehicle can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of automobile residual value assessment, and in particular to a vehicle residual value assessment method and system for an intelligent electric vehicle. Background Art

[0002] In recent years, the new energy vehicle industry has achieved remarkable development worldwide. With the continuous advancement of technology, the sales and ownership of electric vehicles continue to increase. With the development of intelligent technology, electric vehicles equipped with assisted driving and intelligent driving functions are becoming more and more popular. Consumers are increasingly accepting of smart electric vehicles, but at the same time they are also concerned about their residual value. Since the technology and market demand are relatively new, its residual value assessment system has not yet been perfected, causing consumers to have concerns when purchasing. Due to the significant differences between smart electric vehicles and traditional fuel vehicles in terms of power systems, energy use, technical characteristics, etc., the traditional vehicle residual value assessment method cannot be fully applied to the parameter assessment of smart electric vehicles. Summary of the invention

[0003] The purpose of this application is to provide a vehicle residual value assessment method and system for smart electric vehicles, which can support the residual value assessment of smart electric vehicles, provide objective and fair assessment results for used car transactions, improve the transparency of used car transactions, increase consumer confidence in new energy vehicles, and further promote the prosperity of the new energy vehicle market.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a vehicle residual value assessment method for an intelligent electric vehicle, comprising the following steps.

[0006] Obtain vehicle evaluation indicators; the vehicle evaluation indicators include: three-electric power system indicators, intelligent driving system indicators and other system indicators; the three-electric power system indicators include: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, motor and electronic control component historical failure rate, battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operating maximum temperature rise rate, motor controller maximum temperature rise rate and high-voltage component maximum temperature rise rate; the intelligent driving system indicators include: historical failure rate, hardware aging degree and function availability; the other system indicators include: vehicle handling, driving stability, cabin wear degree and body appearance wear degree.

[0007] Based on the vehicle evaluation index, a multi-level vehicle status evaluation model is established; the multi-level vehicle status evaluation model includes: an evaluation target layer, a sub-target layer and an index layer.

[0008] Based on the multi-level vehicle status evaluation model, an evaluation factor set and an evaluation status set are established according to the indicator quantities of the indicator layer.

[0009] The vehicle evaluation indicators are classified to obtain classified indicators; the classified indicators include: numerical indicators and descriptive indicators.

[0010] Based on the evaluation factor set and the evaluation state set, the indicator state membership vector of each indicator in the classified indicators corresponding to each state is calculated respectively.

[0011] Determine the influence weight of each indicator in the indicator layer on each indicator in the corresponding sub-target layer.

[0012] Based on the indicator state membership vector, an indicator state matrix is ​​established.

[0013] Based on the influence weights and the indicator state matrix, the membership vector of the sub-target layer and the state membership vector of the evaluation target layer are calculated.

[0014] Calculating the final state evaluation result of the vehicle according to the state membership vector of the evaluation target layer; According to the final state evaluation result of the vehicle, the corresponding health attenuation coefficient is calculated.

[0015] Based on the health decay coefficient, a final vehicle cost is calculated.

[0016] In a second aspect, the present application provides a vehicle residual value assessment system for an intelligent electric vehicle, which is used to implement the vehicle residual value assessment method for an intelligent electric vehicle described above, and the vehicle residual value assessment system for an intelligent electric vehicle includes the following contents.

[0017] A vehicle evaluation index acquisition unit is used to acquire vehicle evaluation indicators; the vehicle evaluation indicators include: three-electric power system indicators, intelligent driving system indicators and other system indicators; the three-electric power system indicators include: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, motor and electronic control component historical failure rate, battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operation maximum temperature rise rate, motor controller maximum temperature rise rate and high-voltage component maximum temperature rise rate; the intelligent driving system indicators include: historical failure rate, hardware aging degree and function availability; the other system indicators include: vehicle handling, driving stability, cabin wear degree and body appearance wear degree.

[0018] The multi-level vehicle state evaluation model establishing unit is used to establish a multi-level vehicle state evaluation model based on the vehicle evaluation index; the multi-level vehicle state evaluation model includes: an evaluation target layer, a sub-target layer and an index layer.

[0019] The evaluation factor set and evaluation state set establishing unit is used to establish the evaluation factor set and the evaluation state set according to the index quantity of the index layer based on the multi-level vehicle state evaluation model.

[0020] The classification unit is used to classify the vehicle evaluation indicators to obtain classified indicators; the classified indicators include: numerical indicators and descriptive indicators.

[0021] The first membership vector calculation unit is used to calculate the indicator state membership vector of each indicator in the classified indicators corresponding to each state based on the evaluation factor set and the evaluation state set.

[0022] The influence weight determination unit is used to determine the influence weight of each indicator of the indicator layer on each indicator of the corresponding sub-target layer.

[0023] The indicator state matrix establishing unit is used to establish the indicator state matrix based on the indicator state membership vector.

[0024] The second membership vector calculation unit is used to calculate the membership vector of the sub-target layer and the state membership vector of the evaluation target layer based on the influence weight and the indicator state matrix.

[0025] The vehicle final state evaluation result calculation unit is used to calculate the vehicle final state evaluation result according to the state membership vector of the evaluation target layer.

[0026] The health attenuation coefficient calculation unit is used to calculate the corresponding health attenuation coefficient according to the final state evaluation result of the vehicle.

[0027] The final vehicle cost calculation unit is used to calculate the final vehicle cost based on the health decay coefficient.

[0028] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle residual value assessment method for the smart electric vehicle described above.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle residual value assessment method for the smart electric vehicle described above.

[0030] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the vehicle residual value assessment method for the smart electric vehicle described above.

[0031] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0032] The present application provides a method and system for evaluating the residual value of a smart electric vehicle, the method comprising: obtaining vehicle evaluation indicators; the vehicle evaluation indicators include: three-electric power system indicators, intelligent driving system indicators and other system indicators; through the analysis of the vehicle's historical operating data and offline evaluation data, the impact of the vehicle's historical usage on the vehicle's status and the current vehicle's actual status can be fully taken into consideration, which can greatly improve the accuracy of the vehicle's residual value evaluation results. Based on the vehicle evaluation indicators, a multi-level vehicle status evaluation model is established; the multi-level vehicle status evaluation model includes: an evaluation target layer, a sub-target layer and an indicator layer; by setting a hierarchical indicator framework, the subjective and objective evaluation factors that affect the vehicle's residual value can be effectively integrated to achieve an effective evaluation of the vehicle's residual value. Based on the multi-level vehicle status evaluation model, an evaluation factor set and an evaluation status set are established according to the indicator quantity of the indicator layer; the vehicle evaluation indicators are classified to obtain the classified indicators; the classified indicators include: numerical indicators and descriptive indicators; based on the evaluation factor set and the evaluation status set, the indicator state membership vector of each indicator in the classified indicators corresponding to each state is calculated respectively; the influence weight of each indicator of the indicator layer on each indicator of the corresponding sub-target layer is determined; based on the indicator state membership vector, an indicator state matrix is ​​established; based on the influence weight and the indicator state matrix, the membership vector of the sub-target layer and the state membership vector of the evaluation target layer are calculated; according to the final state evaluation result of the vehicle, the corresponding health attenuation coefficient is calculated; based on the health attenuation coefficient, the final vehicle cost is calculated. Based on the fuzzy evaluation principle and according to the definition of the hierarchical indicators, the indicator state membership function is defined respectively and the indicator state vector is determined, and the weight vector of each layer indicator is determined by using different weight determination methods, and finally the hierarchical comprehensive evaluation of the vehicle health status is completed, and the vehicle health attenuation coefficient is determined according to the evaluation result; the current vehicle residual value is evaluated based on the vehicle initial cost and the vehicle health attenuation system. This application can support the residual value assessment of smart electric vehicles, provide objective and fair assessment results for used car transactions, improve the transparency of used car transactions, increase consumer confidence in new energy vehicles, and further promote the prosperity of the new energy vehicle market. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A schematic flow chart of a method for evaluating residual value of a smart electric vehicle provided in accordance with one embodiment of the present application.

[0035] Figure 2 A schematic diagram of a multi-level vehicle status assessment model provided in one embodiment of the present application.

[0036] Figure 3 A schematic diagram of the functional modules of a vehicle residual value assessment system for an intelligent electric vehicle provided in one embodiment of the present application.

[0037] Figure 4 A schematic diagram of another functional module of a vehicle residual value assessment system for a smart electric vehicle provided in one embodiment of the present application.

[0038] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0041] In an exemplary embodiment, Figure 1 As shown, a vehicle residual value assessment method for an intelligent electric vehicle is provided, and the method includes the following steps S1 to S11.

[0042] S1: Obtain vehicle evaluation indicators; the vehicle evaluation indicators include: three-electric power system indicators, intelligent driving system indicators and other system indicators; the three-electric power system indicators include: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, motor and electronic control component historical failure rate, battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operating maximum temperature rise rate, motor controller maximum temperature rise rate and high-voltage component maximum temperature rise rate; the intelligent driving system indicators include: historical failure rate, hardware aging degree and function availability; the other system indicators include: vehicle handling, driving stability, cabin wear degree and body appearance wear degree.

[0043] S2: Based on the vehicle evaluation metrics, establish a multi-level vehicle status evaluation model; the multi-level vehicle status evaluation model includes: an evaluation objective layer, a sub-objective layer, and an index layer.

[0044] S3: Based on the multi-level vehicle status evaluation model, establish an evaluation factor set and an evaluation status set according to the metrics in the index layer.

[0045] S4: Classify the vehicle evaluation metrics to obtain the classified metrics; the classified metrics include: numerical metrics and descriptive metrics.

[0046] S5: Based on the evaluation factor set and the evaluation status set, calculate the index status membership degree vectors corresponding to each index in the classified metrics for each status.

[0047] S6: Determine the influence weights of each index in the index layer on each index in the corresponding sub-objective layer.

[0048] S7: Based on the index status membership degree vectors, establish an index status matrix.

[0049] S8: Based on the influence weights and the index status matrix, calculate the membership degree vector of the sub-objective layer and the status membership degree vector of the evaluation objective layer.

[0050] S9: According to the status membership degree vector of the evaluation objective layer, calculate the final vehicle status evaluation result.

[0051] S10: According to the final vehicle status evaluation result, calculate the corresponding health decay coefficient.

[0052] S11: Based on the health decay coefficient, calculate the final vehicle cost.

[0053] Implement the above steps S1 to S11. According to the vehicle configuration, divide the vehicle system into three parts, namely the three-electric power system, the intelligent driving system, and the remaining other systems, and determine the proportion of each system in the vehicle's total cost. According to the vehicle system division, combined with vehicle-related data, including vehicle historical operation monitoring data, vehicle offline evaluation and detection data, etc., establish a multi-level vehicle status evaluation model from subjective and objective perspectives. Based on the fuzzy evaluation principle, according to the definition of hierarchical indicators, define the membership function of the indicator status and determine the indicator status vector respectively. Use different weight determination methods to determine the weight vector of each layer of indicators, and finally complete the hierarchical comprehensive evaluation of the vehicle health status, and determine the vehicle health attenuation coefficient according to the evaluation result. Use the cost reset method to complete the evaluation of the current vehicle's residual value based on the vehicle's initial cost and the vehicle health attenuation system. It can support the evaluation of the residual value of intelligent electric vehicles, provide objective and fair evaluation results for used car transactions, improve the transparency of used car transactions, enhance consumers' confidence in new energy vehicles, and further promote the prosperity of the new energy vehicle market.

[0054] As an alternative implementation, in step S1, it specifically includes the following content.

[0055] S101: Obtain the vehicle VIN information and vehicle model information; the vehicle model information includes vehicle configuration and new car price.

[0056] S102: Obtain vehicle-related data according to the vehicle VIN information; the vehicle-related data includes: vehicle historical usage basic data and inspection and evaluation basic data; the vehicle historical usage basic data includes: vehicle historical operation monitoring data, faults, repair records, and component replacement situations; the inspection and evaluation basic data includes: battery evaluation data, motor and electronic control evaluation data, intelligent driving system evaluation data, and vehicle test drive evaluation result data.

[0057] S103: Based on the vehicle model information and the vehicle-related data, perform evaluation index extraction and calculation to obtain vehicle evaluation indexes.

[0058] Specifically, obtain the vehicle VIN information and vehicle model information. The model information includes vehicle configuration, new car price, etc. According to the model configuration, determine the proportion of the cost of each main system component, and divide the whole vehicle into three parts: the three-electric power system, the intelligent driving system, and other systems. Other systems may include cockpit, braking, steering and other systems.

[0059] The vehicle-related data is obtained based on the vehicle VIN. The related data may include basic data on the vehicle's historical use and basic data on inspection and evaluation. Basic data on the vehicle's historical use includes all related data information such as the vehicle's historical operation monitoring data, faults, maintenance records, and parts replacement, in order to determine the performance and health of each part; basic inspection and evaluation data refers to relevant data obtained through offline inspection and evaluation of vehicles to be traded, including professional battery evaluation data, motor and electronic control evaluation data, intelligent driving system evaluation data, vehicle test drive evaluation and evaluation result data, etc. Test drives are mainly conducted by professionals or vehicle transaction parties, and the overall performance and driving experience of the vehicle are evaluated.

[0060] According to the actual situation of the data that can be obtained from the vehicle to be evaluated, combined with the following indicator item description, the evaluation indicators of the vehicle to be evaluated are extracted and calculated. The evaluation indicators can be divided into the following three categories according to the vehicle composition.

[0061] The three electric power system indicators are determined based on the vehicle's historical operating data: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, and motor and electronic control component historical failure rate; the following indicators are determined based on vehicle evaluation data: battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operating maximum temperature rise rate, motor controller maximum temperature rise rate, and high-voltage component maximum temperature rise rate.

[0062] Intelligent driving system indicators: Determine the historical failure rate and hardware aging degree of the intelligent driving system based on historical data; determine the functional availability of the intelligent driving system based on vehicle test drive evaluation data.

[0063] Other system indicators: Determine the vehicle's handling and driving stability based on the vehicle test ride evaluation, determine the degree of wear and tear on the vehicle cabin and the exterior of the vehicle body, etc.

[0064] The specific definitions and calculation methods of each indicator are shown below.

[0065] a. The battery historical failure rate characterizes the reliability of the battery system during its historical use. It is expressed by the number of failure alarms per unit mileage. The calculation formula is as follows.

[0066] ; in, Indicates the battery failure rate indicator; Indicates the number of battery alarms in the battery operation data before this evaluation; Indicates the current vehicle's cumulative mileage in km.

[0067] b. Battery historical usage intensity, which reflects the usage of the battery system under extreme conditions, is represented by the proportion of the battery operation time in the historical operation time under conditions such as high-rate charge and discharge current (greater than 2C) and low-temperature environment usage (the maximum battery temperature < -5°C). The specific calculation formula is as follows.

[0068] ; Among them, represents the battery usage intensity index, that is, the proportion of the extreme-condition operation duration of the battery; represents the extreme-condition operation duration of the battery; represents the total operation duration of the power battery of the current vehicle.

[0069] c. Remaining battery cycle life refers to the remaining number of charge and discharge cycles before the battery reaches the retirement condition, which is represented by the following formula.

[0070] ; Among them, represents the remaining battery cycle life index; represents the total available cycle times of the battery, which can be determined according to the battery performance of the vehicle; represents the number of cycles that the power battery of the current vehicle has gone through, which can be determined through battery operation data.

[0071] d. Historical failure rate of the motor and electronic control components, which characterizes the usage reliability of the motor and electronic control components, can be represented by the number of fault alarms per unit mileage. The calculation formula is as follows.

[0072] ; Among them, represents the failure rate index of the motor and electronic control components; represents the number of battery alarm times in the battery operation data before this evaluation; represents the cumulative mileage that the current vehicle has traveled, with the unit of km.

[0073] e. Battery capacity retention rate, that is, the ratio of the current available capacity of the battery to the initial available capacity, is calculated by the following formula.

[0074] ; Among them, represents the current available capacity of the battery; represents the initial available capacity of the battery.

[0075] f. Battery consistency status, which characterizes the performance consistency among the series-connected battery cells inside the battery system, determines the battery consistency status evaluation score according to the evaluation data using the defined analysis method. The scoring range is 0 - 100.

[0076] g. Battery safety status, which characterizes the safety of the battery system during use. The battery safety status evaluation score is also calculated based on the evaluation data according to the defined analysis method. The score range is 0-100.

[0077] h. The motor energy conversion rate can reflect the degree of motor aging. The current motor output useful energy and motor input total energy are calculated based on the evaluation data and then determined by dividing the two. The calculation formula is shown below.

[0078] ; in, Indicates the energy conversion efficiency of the motor; Indicates the useful energy output by the motor, which can be calculated based on the motor speed and power; It represents the total energy input to the motor, which can be calculated based on the motor input voltage and current.

[0079] i. The maximum temperature rise rate of the motor during operation, the maximum temperature rise rate of the motor controller, and the maximum temperature rise rate of the high-voltage components can reflect the performance degradation of components such as motors and electronic controls, and can be calculated using the following formula.

[0080] ; in, represents the rate of temperature rise, Indicates the current maximum temperature of the motor, motor controller or high-voltage component during the evaluation operation; Indicates the maximum temperature of a motor, motor controller or high-voltage component under the same operating conditions at the beginning of use.

[0081] j. The historical failure rate of the intelligent driving system characterizes the reliability of the intelligent driving system. It can be expressed by the number of failure alarms per unit mileage. The calculation formula is shown below.

[0082] ; in, Indicates the battery failure rate indicator; Indicates the number of battery alarms in the battery operation data before this evaluation; Indicates the current vehicle's cumulative mileage in km.

[0083] k. The degree of hardware aging of the intelligent driving system characterizes the theoretical degree of decline in system performance with increasing usage time. It can be calculated by dividing the vehicle's usage time by the rated life span. The calculation formula is shown below.

[0084] ; in, Indicates the degree of system aging; Indicates the total time since the system was put into use; Indicates the rated service life of the system.

[0085] l. Intelligent driving system function availability indicates the maintenance status of the vehicle's intelligent driving related functions. This indicator is evaluated by evaluators who participate in the vehicle test drive of the transaction. The evaluators assign weights to the current status of the function availability based on pre-set status levels (including 5 levels: "excellent", "good", "average", "poor" and "extremely poor"). For example, if the evaluator believes that the status may be between excellent and good, the corresponding weight allocation vector is [0.5, 0.5, 0, 0, 0]; or if the evaluator believes that the status is between excellent and good and is more inclined to good, the vector can be determined to be [0.3, 0.7, 0, 0, 0].

[0086] m. Vehicle handling refers to the vehicle's ability to respond to the driver's control during the driving process. This indicator is also quantitatively evaluated by the test drive evaluators involved in the transaction.

[0087] n. Driving stability refers to the ability of a vehicle to maintain normal driving status and direction under the influence of external factors during driving. The indicator is quantitatively evaluated by the test drive evaluators involved in the transaction.

[0088] o. The degree of wear and tear of the cabin and the exterior of the vehicle refers to the wear and tear of the vehicle's interior and the damage to the exterior of the vehicle. The evaluators who participate in the vehicle test drive of the transaction can conduct a quantitative assessment of the status of this indicator.

[0089] As an optional implementation, Figure 2 As shown, in step S2, based on the vehicle evaluation index, a multi-level vehicle status evaluation model is established, which is divided into three levels, namely, the evaluation target layer reflecting the vehicle health status, the sub-target layer of each part divided by system components, and the specific indicator layer. According to the definition and classification of the indicator items, the vehicle health status is fuzzily evaluated.

[0090] (1) Establish a set of evaluation factors and a set of evaluation status.

[0091] As an optional implementation, in step S3, the following contents are specifically included.

[0092] According to the hierarchical model of state evaluation indicators, the evaluation factor set is established through the indicator layer indicators. , Indicates the first component of a vehicle Influencing factors, taking the three-electric power system as an example, , to They respectively represent the battery's historical failure rate, battery's historical usage intensity, battery's remaining cycle life, motor and electronic control component's historical failure rate, current battery capacity retention rate, current battery consistency status, current battery safety status, current motor energy conversion rate, motor's maximum operating temperature rise rate, motor controller's maximum temperature rise rate and high-voltage component's maximum temperature rise rate.

[0093] Create a review status collection ,in, Representative In this embodiment, the vehicle health status and the status of each evaluation index are divided into five levels: "excellent", "good", "average", "poor", and "very poor", that is, , respectively , , , , .

[0094] As an optional implementation, in step S4, according to the definition of the evaluation indicators, the indicators are divided into numerical indicators and descriptive indicators. The numerical indicators include the historical failure rate of the battery, the historical usage intensity of the battery, the remaining cycle life of the battery, the historical failure rate of the motor and electronic control components, the current battery capacity retention rate, the current battery consistency status, the current battery safety status, the current motor energy conversion rate, the maximum temperature rise rate of the motor operation, the maximum temperature rise rate of the motor controller, the maximum temperature rise rate of the high-voltage components, the historical failure rate of the intelligent driving system, and the aging degree of the intelligent driving system hardware; the descriptive indicators include the functional availability of the intelligent driving system, the vehicle's controllability, driving stability, and the aesthetics and wear degree of the cabin and body.

[0095] (2) Establish membership function.

[0096] As an optional implementation, in step S5, the following contents are specifically included.

[0097] S501: Establishing the indicator state membership function using the fuzzy distribution method.

[0098] S502: Obtaining the indicator state membership vector of each numerical indicator according to the indicator state membership function.

[0099] S503: Determine the indicator state membership vector of each descriptive indicator according to the evaluation results of the professional evaluators or the vehicle test driving personnel.

[0100] Specifically, the fuzzy distribution method is used to establish the indicator state membership function for the numerical indicators, and then the indicator state membership vector is calculated based on the membership function. The semi-ascending trapezoid, semi-descending trapezoid and trapezoidal distribution functions are used to define the membership functions of each numerical indicator, and the definitions of each indicator state membership function are as follows.

[0101] When the battery historical failure rate is excellent, the indicator state membership function is as follows.

[0102] ; When the battery historical failure rate is good, the indicator state membership function is as follows.

[0103] ; When the battery historical failure rate is normal, the indicator state membership function is as follows.

[0104] ; When the battery historical failure rate is poor, the indicator state membership function is as follows.

[0105] ; When the battery historical failure rate is extremely poor, the indicator state membership function is as follows.

[0106] ; When the battery history usage intensity is excellent, the indicator state membership function is as follows.

[0107] ; When the battery history usage intensity is good, the indicator state membership function is as follows.

[0108] ; When the battery history usage intensity is normal, the indicator state membership function is as follows.

[0109] ; When the battery history usage intensity is poor, the indicator state membership function is as follows.

[0110] ; When the battery history usage intensity is extremely poor, the indicator state membership function is as follows.

[0111] ; When the remaining cycle life of the battery is excellent, the indicator state membership function is as follows.

[0112] ; When the remaining cycle life of the battery is good, the indicator state membership function is as follows.

[0113] ; When the remaining cycle life of the battery is average, the indicator state membership function is as follows.

[0114] ; When the remaining cycle life of the battery is poor, the indicator state membership function is as follows.

[0115] ; When the remaining cycle life of the battery is extremely poor, the indicator state membership function is as follows.

[0116] ; When the historical failure rate of motors and electronic control components is excellent, the indicator state membership function is as follows.

[0117] ; When the historical failure rate of motors and electronic control components is good, the indicator state membership function is as follows.

[0118] ; When the historical failure rate of motors and electronic control components is normal, the indicator state membership function is as follows.

[0119] ; When the historical failure rate of motors and electronic control components is poor, the indicator state membership function is as follows.

[0120] ; When the historical failure rate of motors and electronic control components is extremely poor, the indicator state membership function is as follows.

[0121] ; When the battery capacity retention rate is excellent, the indicator state membership function is as follows.

[0122] ; When the battery capacity retention rate is good, the indicator state membership function is as follows.

[0123] ; When the battery capacity retention rate is normal, the indicator state membership function is as follows.

[0124] ; When the battery capacity retention rate is poor, the indicator state membership function is as follows.

[0125] ; When the battery capacity retention rate is extremely poor, the indicator state membership function is as follows.

[0126] ; When the battery consistency status and the battery safety status are excellent, the indicator status membership function is as follows.

[0127] ; When the battery consistency status and the battery safety status are good, the indicator status membership function is as follows.

[0128] ; When the battery consistency status and the battery safety status are general, the indicator status membership function is as follows.

[0129] ; When the battery consistency status and the battery safety status are poor, the indicator status membership function is as follows.

[0130] ; When the battery consistency status and the battery safety status are extremely poor, the indicator status membership function is as follows.

[0131] ; When the motor energy conversion rate is excellent, the indicator state membership function is as follows.

[0132] ; When the motor energy conversion rate is good, the indicator state membership function is as follows.

[0133] ; When the motor energy conversion rate is normal, the indicator state membership function is as follows.

[0134] ; When the motor energy conversion rate is poor, the indicator state membership function is as follows.

[0135] ; When the motor energy conversion rate is extremely poor, the indicator state membership function is as follows.

[0136] ; When the maximum temperature rise rate of motor operation, the maximum temperature rise rate of motor controller and the maximum temperature rise rate of high-voltage components are excellent, the indicator state membership function is as follows.

[0137] ; When the maximum temperature rise rate of the motor operation, the maximum temperature rise rate of the motor controller and the maximum temperature rise rate of the high-voltage components are good, the indicator state membership function is as follows.

[0138] ; When the maximum temperature rise rate of the motor operation, the maximum temperature rise rate of the motor controller and the maximum temperature rise rate of the high-voltage component are normal, the indicator state membership function is as follows.

[0139] ; When the maximum temperature rise rate of the motor operation, the maximum temperature rise rate of the motor controller and the maximum temperature rise rate of the high-voltage components are poor, the indicator state membership function is as follows.

[0140] ; When the maximum temperature rise rate of the motor operation, the maximum temperature rise rate of the motor controller and the maximum temperature rise rate of the high-voltage component are extremely poor, the indicator state membership function is as follows.

[0141] ; When the historical failure rate of the intelligent driving system is excellent, the indicator state membership function is as follows.

[0142] ; When the historical failure rate of the intelligent driving system is good, the indicator state membership function is as follows.

[0143]

[0144] When the historical failure rate of the intelligent driving system is average, the indicator state membership function is as follows.

[0145] ; When the historical failure rate of the intelligent driving system is poor, the indicator state membership function is as follows.

[0146] ; When the historical failure rate of the intelligent driving system is extremely poor, the indicator state membership function is as follows.

[0147] ; When the hardware aging degree of the intelligent driving system is excellent, the indicator state membership function is as follows.

[0148] ; When the hardware aging degree of the intelligent driving system is good, the indicator state membership function is as follows.

[0149] ; When the hardware aging degree of the intelligent driving system is normal, the indicator state membership function is as follows.

[0150] ; When the hardware aging degree of the intelligent driving system is poor, the indicator state membership function is as follows.

[0151] ; When the hardware aging degree of the intelligent driving system is extremely poor, the indicator state membership function is as follows.

[0152] ; For descriptive indicators, the indicator state membership vector is directly determined based on the evaluation results of professional evaluators or vehicle test drivers. If multiple people are involved, the opinions of each person can be combined to determine the average of these membership vectors, determine the membership of the characteristic quantity indicator corresponding to each state and form a membership vector. The expression is as follows.

[0153] ; in, Indicates the status level of an indicator The membership degree of Indicates Personnel involved in the test drive evaluation, is the total number of assessors.

[0154] (3) Determine the weight coefficient.

[0155] As an optional implementation, in step S6, since the degree of influence of each evaluation index on the vehicle state evaluation result is different, the influence weight of each index on the calculation of the evaluation result is determined according to the following method.

[0156] According to the description in step S1, the vehicle is divided into three parts: the three-electric power system, the intelligent driving system and other systems. According to the vehicle configuration and the price information of the components, the proportion of the three-electric power system and the intelligent driving system in the total cost of the vehicle can be determined. According to the proportion of the cost of each part, the weight of the sub-goal layer when evaluating the vehicle status is determined. For example, the cost of the three-electric power system accounts for 40% of the total vehicle cost, the intelligent driving system accounts for 10%, and the remaining systems account for 50%. The weight vectors of the above three sub-goal layers in the vehicle status evaluation are: .

[0157] The weight of the indicator layer included in each sub-goal layer on the evaluation of the sub-goal layer is determined by the relative influence or degree of influence between the indicators determined by expert experience through methods such as hierarchical analysis and decision-making and laboratory method (DEMATEL). The weight of each indicator on the corresponding sub-goal layer indicator can be expressed as: ; in, Indicates The weight of each indicator on the upper-level sub-goal; is the number of indicators included in each sub-goal in the sub-goal layer. For example, the three-electric power system has 11 indicators, and its indicator weight vector includes 11 items, that is, .

[0158] (4) Conduct hierarchical and multi-level evaluation.

[0159] In step S7, the indicator status matrix of each part is established according to the membership function of each evaluation indicator and the evaluation results of the professionals on the descriptive indicators.

[0160] ; in, Indicates sub-goals, The value is 1, 2, 3; Indicates The number of indicators included in each sub-goal, Indicates the degree of membership of the indicator corresponding to the state.

[0161] Since the residual value of the vehicle is greatly affected by the shortcomings in the evaluation process, when evaluating the vehicle status, the fuzzy synthesis calculation method is used in this embodiment to determine the index status membership, that is, the index status is first multiplied by its weight to reflect the weight of different characteristic quantities in the unit evaluation; then the maximum value is taken to highlight the index factors that play a major role in the vehicle status, so that the comprehensive evaluation result obtained is more reliable.

[0162] In step S8, the calculation formula of the membership vector of the sub-target layer is as follows.

[0163] ; ; ; in, is the membership vector of the three-electric power system indicators; is the membership vector of the intelligent driving system indicator; is the membership vector of other system indicators; is the influence weight of each indicator on the three-electric power system indicators; is the weight of each indicator’s impact on the intelligent driving system indicator; is the weight of each indicator’s impact on other system indicators; is the state matrix of the three-electric power system indicators; It is the state matrix of the intelligent driving system indicators; is the status matrix of other system indicators; Represents the fuzzy relation composition operation.

[0164] The calculation formula of the state membership vector of the evaluation target layer is as follows.

[0165] ; ; in, is the state membership vector for evaluating the target layer; is the weight vector of the sub-target layer; It is a matrix composed of the membership vectors of the three electric power system indicators, the membership vectors of the intelligent driving system indicators, and the membership vectors of other system indicators.

[0166] As an optional implementation, in step S9, after the state membership vector of the vehicle evaluation target layer is determined, the final evaluation result of the vehicle state is calculated using the weighted average method. According to the vehicle state level classification, the corresponding vehicle attenuation value is assigned to each state level, such as directly taking {100%, 80%, 60%, 30%, 0%} to correspond to the five levels of "excellent", "good", "average", "poor", and "extremely poor".

[0167] As an optional implementation, in step S10, the health attenuation coefficient corresponding to the final state evaluation result of the vehicle is calculated using the following formula.

[0168] ; in, is the health attenuation coefficient; is the attenuation value corresponding to different status levels; To evaluate the elements in the state membership vector of the target layer; is the number of elements.

[0169] As an optional implementation, in step S11, the final vehicle cost is calculated based on the cost reset method and the health decay coefficient.

[0170] ; in, is the final vehicle cost; The minimum cost you would pay to purchase a new vehicle identical to the vehicle being assessed; is the health attenuation coefficient.

[0171] To sum up, by setting up a hierarchical indicator framework, the subjective and objective evaluation factors that affect the vehicle residual value can be effectively integrated to achieve an effective assessment of the vehicle residual value; by analyzing the vehicle's historical operation data and offline evaluation data, the impact of the vehicle's historical usage on the vehicle status and the current actual status of the vehicle can be fully taken into consideration, which can greatly improve the accuracy of the vehicle residual value assessment results.

[0172] Based on the same inventive concept, the embodiment of the present application also provides a vehicle residual value assessment system for an intelligent electric vehicle for implementing the vehicle residual value assessment method for an intelligent electric vehicle involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more vehicle residual value assessment systems for intelligent electric vehicles provided below can refer to the limitations of the vehicle residual value assessment method for intelligent electric vehicles above, and will not be repeated here.

[0173] In an exemplary embodiment, Figure 3 As shown, a vehicle residual value evaluation system for an intelligent electric vehicle is provided, and the vehicle residual value evaluation system for an intelligent electric vehicle includes the following contents.

[0174] The vehicle evaluation index acquisition unit M1 is used to obtain vehicle evaluation indicators; the vehicle evaluation indicators include: three electric power system indicators, intelligent driving system indicators and other system indicators; the three electric power system indicators include: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, motor and electronic control component historical failure rate, battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operation maximum temperature rise rate, motor controller maximum temperature rise rate and high-voltage component maximum temperature rise rate; the intelligent driving system indicators include: historical failure rate, hardware aging degree and function availability; the other system indicators include: vehicle handling, driving stability, cabin wear degree and body appearance wear degree.

[0175] The multi-level vehicle state evaluation model establishing unit M2 is used to establish a multi-level vehicle state evaluation model based on the vehicle evaluation index; the multi-level vehicle state evaluation model includes: an evaluation target layer, a sub-target layer and an index layer.

[0176] The evaluation factor set and evaluation state set establishing unit M3 is used to establish an evaluation factor set and an evaluation state set according to the index quantity of the index layer based on the multi-level vehicle state evaluation model.

[0177] The classification unit M4 is used to classify the vehicle evaluation indicators to obtain classified indicators; the classified indicators include: numerical indicators and descriptive indicators.

[0178] The first membership vector calculation unit M5 is used to calculate the indicator state membership vector of each indicator in the classified indicators corresponding to each state based on the evaluation factor set and the evaluation state set.

[0179] The influence weight determination unit M6 is used to determine the influence weight of each indicator of the indicator layer on each indicator of the corresponding sub-target layer.

[0180] The indicator state matrix establishing unit M7 is used to establish an indicator state matrix based on the indicator state membership vector.

[0181] The second membership vector calculation unit M8 is used to calculate the membership vector of the sub-target layer and the state membership vector of the evaluation target layer based on the influence weight and the indicator state matrix.

[0182] The vehicle final state evaluation result calculation unit M9 is used to calculate the vehicle final state evaluation result according to the state membership vector of the evaluation target layer.

[0183] The health attenuation coefficient calculation unit M10 is used to calculate the corresponding health attenuation coefficient according to the final state evaluation result of the vehicle.

[0184] The final vehicle cost calculation unit M11 is used to calculate the final vehicle cost based on the health attenuation coefficient.

[0185] As an optional implementation, Figure 4 As shown, according to the above evaluation method, a corresponding intelligent electric residual value evaluation system can be developed. The system consists of a user interaction module, a data screening and acquisition module, an indicator extraction module, and a residual value calculation module.

[0186] User interaction module: responsible for interacting with users, entering vehicle information and feeding back residual value assessment results; Data screening and acquisition module: responsible for directly retrieving the data required for residual value assessment from the database or interacting with other systems based on vehicle information; Index extraction module: responsible for retrieving raw data and calculating vehicle residual value evaluation indicators based on index calculation logic; Residual value calculation module: responsible for calculating the residual value of the vehicle, determining the vehicle status based on the residual value assessment indicators, and completing the vehicle residual value analysis and calculation.

[0187] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store vehicle evaluation indicators. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a vehicle residual value evaluation method for an intelligent electric vehicle is implemented.

[0188] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0189] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above-mentioned method embodiments are implemented when the processor executes the computer program.

[0190] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0191] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned method embodiments are implemented.

[0192] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0193] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating the residual value of an intelligent electric vehicle, characterized in that: The vehicle residual value assessment method of the smart electric vehicle comprises: Obtain vehicle evaluation indicators; the vehicle evaluation indicators include: three-electric power system indicators, intelligent driving system indicators and other system indicators; the three-electric power system indicators include: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, motor and electronic control component historical failure rate, battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operation maximum temperature rise rate, motor controller maximum temperature rise rate and high-voltage component maximum temperature rise rate; the intelligent driving system indicators include: historical failure rate, hardware aging degree and function availability; the other system indicators include: vehicle controllability, driving stability, cabin wear degree and body appearance wear degree; Based on the vehicle evaluation index, a multi-level vehicle status evaluation model is established; the multi-level vehicle status evaluation model includes: an evaluation target layer, a sub-target layer and an indicator layer; Based on the multi-level vehicle status evaluation model, an evaluation factor set and an evaluation status set are established according to the indicator quantities of the indicator layer; Classifying the vehicle evaluation indicators to obtain classified indicators; the classified indicators include: numerical indicators and descriptive indicators; Based on the evaluation factor set and the evaluation state set, respectively calculating the indicator state membership vector of each indicator in the classified indicators corresponding to each state; Determine the influence weight of each indicator of the indicator layer on each indicator of the corresponding sub-target layer; Based on the indicator state membership vector, establishing an indicator state matrix; Based on the influence weight and the indicator state matrix, a membership vector of the sub-target layer and a state membership vector of the evaluation target layer are calculated; Calculating the final state evaluation result of the vehicle according to the state membership vector of the evaluation target layer; According to the final state evaluation result of the vehicle, a corresponding health attenuation coefficient is calculated; Based on the health decay coefficient, a final vehicle cost is calculated.

2. The method for evaluating the residual value of an intelligent electric vehicle according to claim 1, characterized in that: Get vehicle evaluation indicators, including: Obtain vehicle VIN information and vehicle model information; the vehicle model information includes vehicle configuration and new car price; Acquire vehicle-related data according to the vehicle VIN information; the vehicle-related data includes: basic data on vehicle historical use and basic data on inspection and evaluation; the basic data on vehicle historical use includes: historical vehicle operation monitoring data, faults, maintenance records and parts replacement; the basic inspection and evaluation data includes: battery evaluation data, motor and electronic control evaluation data, intelligent driving system evaluation data and vehicle test drive evaluation result data; Based on the vehicle model information and the vehicle-related data, evaluation index extraction and calculation are performed to obtain vehicle evaluation index.

3. The method for evaluating the residual value of an intelligent electric vehicle according to claim 1, characterized in that: Based on the evaluation factor set and the evaluation state set, respectively calculating the indicator state membership vector of each indicator in the classified indicators corresponding to each state, specifically including: The fuzzy distribution method is used to establish the indicator state membership function; Calculate the indicator state membership vector of each numerical indicator according to the indicator state membership function; According to the evaluation results of professional evaluators or vehicle test drivers, the indicator state membership vector of each descriptive indicator is determined.

4. The method for evaluating the residual value of an intelligent electric vehicle according to claim 1, characterized in that: The calculation formula of the membership vector of the sub-target layer and the state membership vector of the evaluation target layer is: ； ； ； ； ； in, is the membership vector of the three-electric power system indicators; is the membership vector of the intelligent driving system indicator; is the membership vector of other system indicators; is the influence weight of each indicator on the three-electric power system indicators; is the weight of each indicator’s impact on the intelligent driving system indicator; is the weight of each indicator’s impact on other system indicators; is the state matrix of the three-electric power system indicators; It is the state matrix of the intelligent driving system indicators; is the status matrix of other system indicators; is the state membership vector for evaluating the target layer; is the weight vector of the sub-target layer; It is a matrix composed of the membership vectors of the three electric power system indicators, the membership vectors of the intelligent driving system indicators, and the membership vectors of other system indicators.

5. The method for evaluating the residual value of an intelligent electric vehicle according to claim 1, characterized in that: The calculation formula of the health attenuation coefficient is: ； in, is the health attenuation coefficient; is the attenuation value corresponding to different status levels; To evaluate the elements in the state membership vector of the target layer; is the number of elements.

6. The method for evaluating the residual value of an intelligent electric vehicle according to claim 1, characterized in that: The calculation formula of the final vehicle cost is: ； in, is the final vehicle cost; The minimum cost you would pay to purchase a new vehicle identical to the vehicle being assessed; is the health attenuation coefficient.

7. A vehicle residual value assessment system for an intelligent electric vehicle, used to implement the vehicle residual value assessment method for an intelligent electric vehicle according to any one of claims 1 to 6, characterized in that: The vehicle residual value evaluation system of the smart electric vehicle comprises: A vehicle evaluation index acquisition unit, used to acquire vehicle evaluation indexes; the vehicle evaluation indexes include: three-electric power system indexes, intelligent driving system indexes and other system indexes; the three-electric power system indexes include: battery historical failure rate, battery historical usage intensity, battery remaining cycle life, motor and electronic control component historical failure rate, battery capacity retention rate, battery consistency status, battery safety status, motor energy conversion rate, motor operation maximum temperature rise rate, motor controller maximum temperature rise rate and high-voltage component maximum temperature rise rate; the intelligent driving system indexes include: historical failure rate, hardware aging degree and function availability; the other system indexes include: vehicle controllability, driving stability, cabin wear degree and body appearance wear degree; A multi-level vehicle state evaluation model establishment unit is used to establish a multi-level vehicle state evaluation model based on the vehicle evaluation index; the multi-level vehicle state evaluation model includes: an evaluation target layer, a sub-target layer and an index layer; An evaluation factor set and evaluation state set establishing unit, used to establish an evaluation factor set and an evaluation state set according to the index quantity of the index layer based on the multi-level vehicle state evaluation model; A classification unit, used to classify the vehicle evaluation index to obtain classified indexes; the classified indexes include: numerical indexes and descriptive indexes; A first membership vector calculation unit, configured to calculate, based on the evaluation factor set and the evaluation state set, the indicator state membership vector of each indicator in the classified indicators corresponding to each state; An influence weight determination unit, used to determine the influence weight of each indicator of the indicator layer on each indicator of the corresponding sub-target layer; An indicator state matrix establishing unit, used to establish an indicator state matrix based on the indicator state membership vector; A second membership vector calculation unit, configured to calculate the membership vector of the sub-target layer and the state membership vector of the evaluation target layer based on the influence weight and the indicator state matrix; A vehicle final state evaluation result calculation unit, used to calculate the vehicle final state evaluation result according to the state membership vector of the evaluation target layer; A health attenuation coefficient calculation unit, used to calculate the corresponding health attenuation coefficient according to the final state evaluation result of the vehicle; The final vehicle cost calculation unit is used to calculate the final vehicle cost based on the health decay coefficient.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle residual value assessment method for the smart electric vehicle described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle residual value assessment method for the smart electric vehicle described in any one of claims 1-6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle residual value assessment method for the smart electric vehicle described in any one of claims 1-6 is implemented.

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