New energy vehicle user experience evaluation method and device based on Internet of Vehicles data

Abstract

The invention relates to the technical field of Internet of Vehicles big data analysis, and provides a new energy vehicle user experience evaluation method and device based on Internet of Vehicles data, and the method comprises the steps: determining the full-power nominal endurance and the full-power actual endurance of a target vehicle based on the Internet of Vehicles data, and determining the endurance index score of the target vehicle; based on the Internet of Vehicles data, determining a target battery recession value of the target vehicle, and determining a battery recession index score of the target vehicle; based on the Internet of Vehicles data, determining target hundred-kilometer fast charging time consumption of the target vehicle, and determining a charging time index score of the target vehicle; and generating a user experience score of the target vehicle according to a weighted result of the endurance index score, the battery recession index score and the charging time index score. According to the technical scheme provided by one or more embodiments, the vehicle performance and the user experience can be objectively and comprehensively evaluated in multiple dimensions in a long time span.

Description

Technical Field

[0001] The present disclosure relates to the technical field of big data analysis of the Internet of Vehicles, and particularly to a method and device for evaluating the user experience of new energy vehicles based on Internet of Vehicles data. Background Art

[0002] With the continuous progress of technology, the driving range of new energy vehicles has increased and the charging efficiency has been greatly improved. At the same time, the intelligent experience of new energy vehicles is also constantly upgraded. Therefore, the acceptance of new energy vehicles by users is increasing, and the national ownership of new energy vehicles is also increasing.

[0003] However, in the prior art, for the user experience of new energy vehicles, a single-dimensional and short-time test and evaluation method is mostly used. A single-dimensional and short-time evaluation method is adopted to evaluate the vehicle performance, so as to indicate the user's driving experience. Such an incomplete evaluation method often leads to unfair evaluation results. Summary of the Invention

[0004] In view of this, one or more embodiments of the present disclosure provide a method and device for evaluating the user experience of new energy vehicles based on Internet of Vehicles data, which can objectively and comprehensively evaluate the vehicle performance and user experience in multiple dimensions over a long time span.

[0005] On the one hand, the present disclosure provides a method for evaluating the user experience of new energy vehicles based on Internet of Vehicles data, where the Internet of Vehicles data includes vehicle driving data and vehicle charging data, and the method includes: based on the Internet of Vehicles data, determining the full-charge nominal driving range and the full-charge actual driving range of the target vehicle, and determining the driving range ability index score of the target vehicle according to the full-charge nominal driving range and the full-charge actual driving range; based on the Internet of Vehicles data, determining the target battery degradation value of the target vehicle, and determining the battery degradation index score of the target vehicle according to the target battery degradation value; based on the Internet of Vehicles data, determining the target fast charging time per 100 kilometers of the target vehicle, and determining the charging time index score of the target vehicle based on the target fast charging time per 100 kilometers; generating the user experience score of the target vehicle according to the weighted results of the driving range ability index score, the battery degradation index score, and the charging time index score.

[0006] On the other hand, the present disclosure also provides a new energy vehicle user experience evaluation device based on vehicle networking data, where the vehicle networking data includes vehicle driving data and vehicle charging data. The device includes: a cruising range ability scoring unit, configured to determine the nominal full-charge cruising range and the actual full-charge cruising range of a target vehicle based on the vehicle networking data, and determine the cruising range ability index score of the target vehicle according to the nominal full-charge cruising range and the actual full-charge cruising range; a battery degradation scoring unit, configured to determine the target battery degradation value of the target vehicle based on the vehicle networking data, and determine the battery degradation index score of the target vehicle according to the target battery degradation value; a charging ability scoring unit, configured to determine the target fast charging time per 100 kilometers of the target vehicle based on the vehicle networking data, and determine the charging time index score of the target vehicle based on the target fast charging time per 100 kilometers; a user experience scoring unit, configured to generate the user experience score of the target vehicle according to the weighted results of the cruising range ability index score, the battery degradation index score, and the charging time index score.

[0007] On the other hand, the present disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program, and when the computer program is executed by the processor, it implements the above-mentioned new energy vehicle user experience evaluation method based on vehicle networking data.

[0008] On the other hand, the present disclosure also provides a computer-readable storage medium, which is used to store a computer program, and when the computer program is executed by a processor, it implements the above-mentioned new energy vehicle user experience evaluation method based on vehicle networking data.

[0009] The technical solutions provided by one or more embodiments of the present disclosure, based on the acquisition of historical vehicle driving and historical vehicle charging data, can, through big data analysis means, deduce the actual drivable mileage (i.e., the actual full-charge cruising range) of the vehicle under real working conditions, the healthy degradation degree of the vehicle battery after long-distance driving (i.e., the target battery degradation value), and the rapid energy replenishment rate (i.e., the target fast charging time per 100 kilometers). Then, after further calculating these three types of data, the scores of the vehicle in the three dimensions of the cruising range ability index, the battery degradation index, and the charging time index can be determined, so as to objectively and accurately evaluate the performance of all aspects of the vehicle. Finally, by performing weighted processing on the scores of the three dimensions, the user experience score of the vehicle can be determined, ensuring the objectivity, accuracy, and comprehensiveness of the user experience score.

[0010] The technical solutions provided by one or more embodiments of the present disclosure can evaluate the performance changes of a vehicle over a long period of time from multiple dimensions such as the vehicle's true endurance, battery degradation degree, and battery fast charging rate. Furthermore, the user experience of the vehicle can be objectively and comprehensively evaluated, which is beneficial for users to clearly understand the vehicle's performance and for automobile manufacturers to optimize and improve the driving reliability of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The features and advantages of the embodiments of the present disclosure will be more clearly understood by referring to the accompanying drawings. The drawings are schematic and should not be construed as imposing any limitation on the present disclosure. In the drawings:

[0012] Figure 1 FIG. shows a schematic diagram of the steps of a new energy vehicle user experience evaluation method based on vehicle networking data in one embodiment of the present disclosure;

[0013] Figure 2 FIG. shows a schematic diagram of the process for calculating the endurance capacity index score in one embodiment of the present disclosure;

[0014] Figure 3 FIG. shows a schematic diagram of the process for calculating the battery degradation index score in one embodiment of the present disclosure;

[0015] Figure 4 FIG. shows a schematic diagram of the process for calculating the charging time index score in one embodiment of the present disclosure;

[0016] Figure 5 FIG. shows a schematic diagram of the functional modules of a new energy vehicle user experience evaluation device based on vehicle networking data in one embodiment of the present disclosure;

[0017] Figure 6 FIG. shows a schematic diagram of the structure of an electronic device in one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0019] Please refer to Figure 1 , a new energy vehicle user experience evaluation method based on vehicle networking data provided by one embodiment of the present disclosure. The vehicle networking data may include vehicle driving data and vehicle charging data, and the method may include the following multiple steps.

[0020] S1: Based on the vehicle networking data, determine the full-charge nominal cruising range and the full-charge actual cruising range of the target vehicle, and determine the scoring of the cruising ability index of the target vehicle according to the full-charge nominal cruising range and the full-charge actual cruising range.

[0021] In this embodiment, the vehicle driving data may include but are not limited to driving mileage, driving speed, driving time, driving trajectory, and driving behavior (such as acceleration, deceleration, braking, steering, etc.). The vehicle charging data may include but are not limited to charging time, state of charge change of the battery, and charging conditions (such as current, voltage, temperature, etc.). By using the vehicle networking data including vehicle driving data and vehicle charging data, big data analysis can be performed to deduce various vehicle performance state information. For example, the actual drivable mileage of the vehicle under real working conditions (i.e., the full-charge actual cruising range), the degree of health degradation of the vehicle battery after long-distance driving (i.e., the target battery degradation value), and the rapid energy replenishment rate of the vehicle battery (i.e., the target time for fast charging per 100 kilometers).

[0022] In some actual application scenarios, the vehicle networking data may include one or more vehicle attribute data such as vehicle model, vehicle identification number, license plate number, vehicle identification number (VIN), vehicle color, and body size, which are used to identify and distinguish vehicles.

[0023] In some actual application scenarios, the vehicle networking data may include one or more environmental perception data such as body image data, lidar point cloud data, spatio-temporal positioning information, traffic sign recognition results, road conditions, and traffic conditions, so as to classify and evaluate the user experience of the vehicle driving environment. For example, the user experience scores of vehicles driving on urban roads for a long time and vehicles driving on rural roads for a long time can be discussed separately.

[0024] In this embodiment, the full-charge nominal cruising range refers to the theoretical maximum driving mileage measured according to specific test standards (such as CLTC, NEDC, WLTP, etc.) when the vehicle is in a full-charge state. The full-charge nominal cruising range is reference data obtained by vehicle manufacturers based on standardized test conditions and is used to describe the cruising ability of the vehicle under ideal conditions. The full-charge actual cruising range refers to the actual mileage that the vehicle can travel according to actual driving conditions (including road conditions, driving habits, environmental temperature, vehicle load, etc.) when the vehicle is in a full-charge state. The full-charge actual cruising range reflects the actual cruising performance of the vehicle in daily use scenarios and is usually lower than the nominal cruising range. The full-charge nominal cruising range and the full-charge actual cruising range can intuitively reflect the user's expectations and actual experience of the vehicle's cruising range, and can guide users to reasonably arrange the travel distance according to the actual cruising range. The scoring of the cruising ability index determined according to the full-charge nominal cruising range and the full-charge actual cruising range can objectively and accurately evaluate the cruising performance of the vehicle, which helps vehicle manufacturers to do a good job in vehicle energy management and cruising range calibration, so that the nominal mileage of the vehicle is closer to the user's real experience and reduces the inconvenience caused to users by misjudgment of the remaining mileage.

[0025] In some embodiments, based on vehicle networking data, the starting state of charge, the ending state of charge, the starting mileage, and the ending mileage of multiple charging segments of a target vehicle can be determined. Based on the starting state of charge, the ending state of charge, the starting mileage, and the ending mileage, the actual full-charge driving range of the target vehicle can be determined. After calculating the ratio of the actual full-charge driving range to the nominal full-charge driving range of the target vehicle, based on this mileage ratio, the scoring of the driving range ability index of the target vehicle can be determined.

[0026] Specifically, based on the change information of the state of charge (SOC) before and after charging and the change information of the driving mileage of multiple charging segments, the actual full-charge driving range of the vehicle can be determined through a preset formula or algorithm.

[0027] It should be noted that the vehicle networking data of "new vehicles" can be selected to determine the actual full-charge driving range and calculate the scoring of the driving range ability index. A "new vehicle" can be a vehicle with an accumulated driving mileage less than a preset value (such as 5000 km). The difference between the actual full-charge driving range and the nominal full-charge driving range of "new vehicles" is more concentrated on the difference between the actual driving conditions and the ideal conditions, making the scoring of the driving range ability index more instructive.

[0028] In a practical application example, a calculation formula for the scoring of the driving range ability index is as follows:

[0029]

[0030] Among them, val is the ratio of the actual driving mileage of the vehicle per 100% SOC to the nominal driving mileage of the vehicle per 100% SOC. The initial calculation result range of val is 0 - 120. If the initial calculation result of val is greater than 100, then 100 is taken. The actual calculation result range of val is 0 - 100. The actual calculation result of val is the scoring of the driving range ability index. The closer this value is to 100, the higher the matching degree between the actual driving range ability and the nominal driving range ability of the vehicle.

[0031] Define the sum of all small driving segments between two adjacent charging segments as a driving segment, and n is the total number of driving segments. D i start is the starting mileage of the next charging segment of the vehicle, in km; D i-1 end is the ending mileage of the previous charging segment of the vehicle, in km; SOC i-1 end is the ending SOC of the previous charging segment of the vehicle; SOC i startThe starting SOC for the next charging segment of the vehicle. D 标称 The nominal full charge range of the vehicle, in km.

[0032] In a practical application example, refer to Figure 2 , a calculation process for the scoring of the range ability index. First, vehicle number screening and mileage screening are performed to screen out the vehicle networking data of a certain number of new vehicles. Secondly, since weather conditions such as low temperature (e.g., below 10 degrees Celsius) and high temperature (e.g., above 30 degrees Celsius) have a greater impact on the range of new energy vehicles, the vehicle networking data under normal temperature (e.g., between 10 degrees Celsius and 30 degrees Celsius) can be selected to calculate the scoring of the range ability index of the vehicle.

[0033] S2: Based on the vehicle networking data, determine the target battery degradation value of the target vehicle, and based on the target battery degradation value, determine the battery degradation index score of the target vehicle.

[0034] In this embodiment, the battery degradation value can reflect the degradation of the battery health. The battery degradation index score determined according to the battery degradation value can objectively and accurately evaluate the battery performance of the vehicle, which is conducive to the user's reasonable expectations for the vehicle service life and vehicle residual value. The battery degradation index score can not only prompt the user to use the vehicle scientifically and regularly detect the battery performance of the vehicle, but also guide vehicle manufacturers and power battery manufacturers to do a good job in the health management of the entire life cycle of the vehicle battery and reduce the mileage degradation of the vehicle.

[0035] In some embodiments, based on the vehicle networking data, the standard value of the equivalent full charge capacity before the target vehicle reaches the first mileage can be calculated. Based on the vehicle networking data, the measured value of the equivalent full charge capacity after the target vehicle reaches the second mileage can also be calculated. According to the standard value of the equivalent full charge capacity and the measured value of the equivalent full charge capacity, the target battery health state of the target vehicle can be determined. According to the target battery health state, the target battery degradation value of the target vehicle can be determined.

[0036] Specifically, the first mileage can be set to a relatively short mileage (e.g., 5000 km). At this time, the battery health of the vehicle is minimally depleted, and the battery equivalent full charge capacity calculated therefrom can be used as a standard value. The second mileage can be set to a relatively long mileage (e.g., 80,000 km). At this time, the battery health of the vehicle is significantly depleted, and the battery equivalent full charge capacity calculated therefrom can be used as an evaluation value. By utilizing the difference between the equivalent full charge capacity standard value and the equivalent full charge capacity evaluation value, the target battery health state of the target vehicle can be evaluated. Through the target battery degradation value, the target battery health state can be intuitively reflected. As the battery usage time or vehicle mileage increases, the target battery degradation value will gradually increase. A low target battery degradation value means that the battery can provide performance close to its original capacity, and the user's driving experience is better.

[0037] In a practical application example, using the vehicle networking data before the cumulative driving mileage of the target vehicle is less than 5000 km, the equivalent full charge capacity standard value can be calculated, and its formula is as follows:

[0038]

[0039] Wherein, Q new is the equivalent full charge capacity standard value, I is the charging current, t is the charging time, SOC end is the SOC at the end of the charging segment, and SOC start is the SOC at the start of the charging segment.

[0040] Using the vehicle networking data after the cumulative driving mileage of the same target vehicle is greater than 80,000 km, the equivalent full charge capacity evaluation value can be calculated, and its formula is as follows:

[0041]

[0042] Wherein, Q mil8 is the equivalent full charge capacity standard value, I is the charging current, t is the charging time, SOC end is the SOC at the end of the charging segment, and SOC start is the SOC at the start of the charging segment.

[0043] On this basis, through the formula the target battery health state SOH mil8 of the target vehicle can be determined. Then, using the formula Dec mil8 = 1 - SOH mil8 , the target battery degradation value Dec mil8 of the target vehicle can be obtained.

[0044] In some embodiments, determining the battery degradation index score of the target vehicle according to the target battery degradation value includes: obtaining the sample battery degradation values of a number of sample vehicles; arranging the sample battery degradation values in descending order according to the numerical size; determining the first quartile of the degradation value, the second quartile of the degradation value, and the third quartile of the degradation value according to the sorted sample battery degradation values; determining the interquartile range of the degradation value based on the first quartile of the degradation value and the third quartile of the degradation value; determining the upper limit of the degradation value based on the first quartile of the degradation value and the interquartile range of the degradation value; determining the lower limit of the degradation value based on the third quartile of the degradation value and the interquartile range of the degradation value; if the target battery degradation value is equal to or less than the lower limit of the degradation value, the battery degradation index score is 100 points; if the target battery degradation value is equal to or greater than the upper limit of the degradation value, the battery degradation index score is 60 points; if the target battery degradation value is equal to the second quartile of the degradation value, the battery degradation index score is 80 points; if the target battery degradation value is between the upper limit and the lower limit of the degradation value and not equal to the second quartile of the degradation value, calculate the battery degradation index score by linear interpolation.

[0045] In a practical application example, after arranging the sample battery degradation values of a number of sample vehicles in descending order, the quartiles of the degradation value can be calculated. The first quartile of the degradation value (Q1) represents that 25% of all sample battery degradation values are lower than this value; the second quartile of the degradation value (Q2) represents the value that divides the sample battery degradation values into two equal upper and lower parts; the third quartile of the degradation value (Q3) represents that 25% of all sample battery degradation values are higher than this value.

[0046] Subtracting the first quartile of the degradation value from the third quartile of the degradation value can determine the interquartile range (IQR) of the degradation value, which is used to include 50% of the data in the middle of the sample degradation values. Based on the first quartile of the degradation value and the interquartile range of the degradation value, the upper limit of the degradation value can be determined. For example, the interquartile range of the degradation value and the first quartile of the degradation value can be scaled proportionally and then summed. Preferably, the calculation formula for the upper limit of the degradation value is: Q3 + 1.5 * IQR. Based on the third quartile of the degradation value and the interquartile range of the degradation value, the lower limit of the degradation value can be determined. For example, the interquartile range of the degradation value and the third quartile of the degradation value can be scaled proportionally and then the difference is calculated. Preferably, the calculation formula for the lower limit of the degradation value is: Q1 - 1.5 * IQR. In the process of calculating the upper limit and the lower limit of the degradation value, preferably 1.5 times of IQR can cover the fluctuation range of most normal data and can also exclude outliers.

[0047] In this application example, the rule for calculating the battery degradation index score according to linear interpolation can be as follows: If the second quartile of the degradation value ≤ the target battery degradation value ≤ the upper limit of the degradation value, then the battery degradation index score = (target battery degradation value - second quartile of the degradation value) × (upper limit score of the degradation value - second quartile score of the degradation value) / (upper limit of the degradation value - second quartile of the degradation value) + second quartile score of the degradation value; If the lower limit of the degradation value ≤ the target battery degradation value ≤ the second quartile of the degradation value, then the battery degradation index score = (target battery degradation value - second quartile of the degradation value) × (lower limit score of the degradation value - second quartile score of the degradation value) / (lower limit of the degradation value - second quartile of the degradation value) + second quartile score of the degradation value.

[0048] For example, for the battery degradation values of 80,000 kilometers of multiple sample vehicles of the same vehicle model, the lower quartile is 7%, the median (second quartile) is 8%, and the upper quartile is 9%. The lower limit of the degradation value is 4%, and the upper limit of the degradation value is 12%. If the target battery degradation value of the target vehicle is 3%, then the battery degradation index score = 100; If the target battery degradation value of the target vehicle is 5%, then the battery degradation index score = (5% - 8%) × (100 - 80) / (4% - 8%) + 80 = 95; If the target battery degradation value of the target vehicle is 8%, then the battery degradation index score = 80; If the target battery degradation value of the target vehicle is 10%, then the battery degradation index score = (10% - 8%) × (60 - 80) / (12% - 8%) + 80 = 70; If the target battery degradation value of the target vehicle is 15%, then the battery degradation index score = 60.

[0049] In a practical application example, please refer to Figure 3 , for the calculation process of the endurance capacity index score, it is first necessary to filter out the vehicle networking data before the first mileage of the target vehicle and the vehicle networking data after the second mileage of the target vehicle. Subsequently, the battery degradation value and the battery degradation index score of the vehicle can be calculated.

[0050] S3: Based on the vehicle networking data, determine the target 100-kilometer fast charging time of the target vehicle, and based on the target 100-kilometer fast charging time, determine the charging time index score of the target vehicle.

[0051] In this embodiment, the target 100-kilometer fast charging time can reflect the fast charging efficiency of the vehicle and mainly affect the user's experience of the charging waiting time during fast charging. The charging time index score can be used to guide vehicle manufacturers and charging facility construction enterprises to grasp the overall situation of the industry's fast charging time, urge enterprises to accelerate the breakthrough of fast charging technology on the premise of ensuring safety, shorten the fast charging time, and improve the user's charging experience.

[0052] In some embodiments, based on vehicle networking data, the durations, mileage change data, state of charge change data, and power consumption data of multiple fast charging segments of a target vehicle can be determined. According to the duration, the mileage change data, the state of charge change data, and the power consumption data, the fast charging time required for the target vehicle to travel 100 kilometers can be determined. Among them, a charging segment with a charging power within a preset power range and a state of charge within a preset numerical range can be defined as a fast charging segment. For example, a charging segment with an SOC range between 10% and 80% and a charging power of 11 - 120 kw is defined as a fast charging segment.

[0053] In a practical application example, a calculation formula for the fast charging time required for a vehicle to travel 100 kilometers is as follows:

[0054]

[0055] Among them, tpk is the fast charging time required for the vehicle to travel 100 kilometers; n is the number of valid fast charging segments within the statistical period; t i is the duration of the i-th fast charging segment within the evaluation period; D i is the driving mileage from the i-th fast charging segment to the next charging segment; ΔSOC i充电 is SOC i-1 end - SOC i start , representing the difference between the COC at the end of the (i - 1)-th fast charging segment and the SOC at the start of the i-th fast charging segment; ΔSOC i行驶 is D i start - D i-1 end , representing the total SOC consumed by all small driving segments between two adjacent charging segments.

[0056] In some embodiments, determining the charging time index score of the target vehicle based on the target 100-kilometer fast charging time consumption includes: obtaining the sample 100-kilometer fast charging time consumption of a number of sample vehicles; arranging the sample 100-kilometer fast charging time consumption in descending order according to the numerical value; determining the first quartile of the fast charging time consumption, the second quartile of the fast charging time consumption, and the third quartile of the fast charging time consumption according to the sorted sample 100-kilometer fast charging time consumption; determining the interquartile range of the fast charging time consumption based on the first quartile of the fast charging time consumption and the third quartile of the fast charging time consumption; determining the upper limit of the fast charging time consumption based on the first quartile of the fast charging time consumption and the interquartile range of the fast charging time consumption; determining the lower limit of the fast charging time consumption based on the third quartile of the fast charging time consumption and the interquartile range of the fast charging time consumption; if the target 100-kilometer fast charging time consumption is equal to or less than the lower limit of the fast charging time consumption, the charging time index score is 100 points; if the target 100-kilometer fast charging time consumption is equal to or greater than the upper limit of the fast charging time consumption, the charging time index score is 60 points; if the target 100-kilometer fast charging time consumption is equal to the second quartile of the fast charging time consumption, the charging time index score is 80 points; if the target 100-kilometer fast charging time consumption is between the upper limit and the lower limit of the fast charging time consumption and is not equal to the second quartile of the fast charging time consumption, calculate the charging time index score according to linear interpolation.

[0057] In a practical application example, taking the sample 100-kilometer fast charging time consumption of multiple sample vehicles in the same-level models and arranging them in descending order according to the numerical value, the upper quartile Q 75 of the fast charging time consumption, the lower quartile Q 25 and the median Q 50 can be calculated. Subsequently, the interquartile range of the fast charging time consumption IQR = Q 75 - Q 25 can be calculated, the upper limit of the fast charging time consumption = Q 75 + 1.5 * IQR, and the lower limit of the fast charging time consumption = Q 25 - 1.5 * IQR. If the target 100-kilometer fast charging time consumption of the target vehicle falls below or at the lower limit of the fast charging time consumption, the corresponding charging time index score is 100 points; if the target 100-kilometer fast charging time consumption of the target vehicle falls at the median of the fast charging time consumption, the corresponding charging time index score is 80 points; if the target 100-kilometer fast charging time consumption of the target vehicle falls above or at the upper limit of the fast charging time consumption, the corresponding charging time index score is 60 points.

[0058] In other cases, the charging time index score can be calculated by linear interpolation. For example, for the median fast charging time ≤ the target fast charging time per 100 kilometers ≤ the upper limit of the fast charging time, the charging time index score = (the target fast charging time per 100 kilometers - the median fast charging time) × (60 - the median fast charging time score) / (the upper limit of the fast charging time - the median fast charging time) + the median fast charging time score; for the lower limit of the fast charging time ≤ the target fast charging time per 100 kilometers ≤ the median fast charging time, the charging time index score = (the target fast charging time per 100 kilometers - the median fast charging time) × (100 - the median fast charging time score) / (the lower limit of the fast charging time - the median fast charging time) + the median fast charging time score.

[0059] In a practical application example, please refer to Figure 4 , a calculation process of the charging time index score. First, the required vehicle networking data needs to be screened out. Secondly, since weather conditions such as low temperature (e.g., below 10 degrees Celsius) and high temperature (e.g., above 30 degrees Celsius) have a great impact on the charging efficiency of new energy vehicles, the vehicle networking data under normal temperature (e.g., between 10 degrees Celsius and 30 degrees Celsius) can be selected to calculate the charging time index score per 100 kilometers of the vehicle.

[0060] S4: Generate the user experience score of the target vehicle according to the weighted results of the endurance ability index score, the battery degradation index score, and the charging time index score.

[0061] In this embodiment, by performing weighted processing on the scores of the three dimensions, the user experience score of the vehicle can be determined, ensuring the objectivity, accuracy, and comprehensiveness of the user experience score.

[0062] In some embodiments, according to the endurance ability index score, a first entropy value can be determined, and the first entropy value characterizes the data dispersion degree of the endurance ability index score. According to the battery degradation index score, a second entropy value can be determined, and the second entropy value characterizes the data dispersion degree of the battery degradation index score. According to the charging time index score, a third entropy value can be determined, and the third entropy value characterizes the data dispersion degree of the charging time index score. Based on the first entropy value, the second entropy value, and the third entropy value, a first weight of the endurance ability index score, a second weight of the battery degradation index score, and a third weight of the charging time index score can be determined. Using the first weight, the second weight, and the third weight, weighted calculation is performed on the endurance ability index score, the battery degradation index score, and the charging time index score to generate the user experience score.

[0063] In a practical application example, a calculation formula for the user experience score is as follows:

[0064] Score 体验指数 = w a × Score 续航能力 + w b × Score 电池衰退

[0065] + w c × Score 百公里充电时间

[0066] Among them, Score 体验指数 is the total score of user experience; w a is the weight of the score of the battery life index; Score 续航能力 is the score of the battery life index; w b is the weight of the score of the battery degradation index; Score 电池衰退 is the score of the battery degradation index; w c is the weight of the score of the charging time index; Score 百公里充电时间 is the score of the charging time index.

[0067] In a practical application example, the entropy weight method can be used to calculate the weights of each evaluation index. The entropy value of the entropy weight method can judge the dispersion degree of each index. The specific calculation method is as follows:

[0068] (1) For n samples and m indexes, then x ij is the value of the j-th index of the i-th sample (i = 1, 2..., n; j = 1, 2,..., m);

[0069] (2) Index normalization processing

[0070]

[0071] (3) Calculate the proportion of the i-th sample value under the j-th index in this index:

[0072]

[0073] (4) Calculate the entropy value of the j-th index:

[0074]

[0075] Among them, K = 1 / ln(n) > 0, satisfying e j ≥ 0;

[0076] (5) Calculate the information entropy redundancy (difference):

[0077] d j = 1 - e j

[0078] (6) Calculate the weights of each index:

[0079]

[0080] According to the above steps (1) to (6), the weight w 权重a , w 权重b , w 权重c values can be calculated. Subsequently, substituting the weight values into the preset formula, the total user experience score can be calculated: Total user experience score = weight a × battery life score + weight b × battery degradation score + weight c × charging time per 100 kilometers score.

[0081] Based on the vehicle historical driving and vehicle historical charging data, the technical solution provided by one or more embodiments of the present disclosure can, through big data analysis means, deduce the actual drivable mileage (i.e., actual full-charge driving range) of the vehicle under real working conditions, the degree of healthy degradation of the vehicle battery after long-distance driving (i.e., the target battery degradation value), and the rapid energy replenishment rate (i.e., the target fast charging time per 100 kilometers). Then, after further calculating these three types of data, the scores of the vehicle in the three dimensions of battery life index, battery degradation index, and charging time index can be determined, so as to objectively and accurately evaluate all aspects of the vehicle's performance. Finally, by performing weighted processing on the scores of the three dimensions, the user experience score of the vehicle can be determined, ensuring objectivity, accuracy, and comprehensiveness of the user experience score.

[0082] The technical solution provided by one or more embodiments of the present disclosure can evaluate the performance changes of the vehicle in multiple dimensions such as the vehicle's actual driving range, battery degradation degree, and battery fast charging rate for a long time, and then can objectively and comprehensively evaluate the user experience of the vehicle, which is not only beneficial for users to clearly understand the performance of the vehicle, but also beneficial for automobile manufacturers to optimize and improve the driving reliability of the vehicle.

[0083] Please refer to Figure 5 , the present disclosure also provides a new energy vehicle user experience evaluation device based on vehicle networking data. The vehicle networking data includes vehicle driving data and vehicle charging data. The device includes:

[0084] A battery life scoring unit 100, configured to determine the full-charge nominal driving range and the full-charge actual driving range of the target vehicle based on the vehicle networking data, and determine the battery life index score of the target vehicle according to the full-charge nominal driving range and the full-charge actual driving range;

[0085] A battery degradation scoring unit 200, configured to determine the target battery degradation value of the target vehicle based on the vehicle networking data, and determine the battery degradation index score of the target vehicle according to the target battery degradation value;

[0086] A charging ability scoring unit 300 is configured to determine the target fast charging time per 100 kilometers of the target vehicle based on the vehicle networking data, and determine the charging time index score of the target vehicle based on the target fast charging time per 100 kilometers.

[0087] A user experience scoring unit 400 is configured to generate the user experience score of the target vehicle according to the weighted result of the endurance ability index score, the battery degradation index score, and the charging time index score.

[0088] In one embodiment, the endurance ability scoring unit 100 is specifically configured to determine the charging start state of charge, the charging end state of charge, the charging start mileage, and the charging end mileage of multiple charging segments of the target vehicle based on the vehicle networking data; determine the actual full charge endurance according to the charging start state of charge, the charging end state of charge, the charging start mileage, and the charging end mileage; calculate the mileage ratio of the actual full charge endurance to the nominal full charge endurance; and determine the endurance ability index score according to the mileage ratio.

[0089] In one embodiment, the battery degradation scoring unit 200 includes a battery degradation subunit 201. The battery degradation subunit 201 is configured to calculate the standard value of the equivalent full charge capacity of the target vehicle before reaching the first mileage based on the vehicle networking data; calculate the measured value of the equivalent full charge capacity of the target vehicle after reaching the second mileage based on the vehicle networking data; determine the target battery health state of the target vehicle according to the standard value of the equivalent full charge capacity and the measured value of the equivalent full charge capacity; and determine the target battery degradation value according to the target battery health state.

[0090] In one embodiment, the battery degradation scoring unit 200 includes a battery scoring subunit 202. The battery scoring subunit 202 is configured to obtain sample battery degradation values of a number of sample vehicles; sort the sample battery degradation values in descending order of numerical magnitude; determine a first quartile of the degradation value, a second quartile of the degradation value, and a third quartile of the degradation value according to the sorted sample battery degradation values; determine an interquartile range of the degradation value based on the first quartile of the degradation value and the third quartile of the degradation value; determine an upper limit of the degradation value based on the first quartile of the degradation value and the interquartile range of the degradation value; determine a lower limit of the degradation value based on the third quartile of the degradation value and the interquartile range of the degradation value; if the target battery degradation value is equal to or less than the lower limit of the degradation value, the battery degradation index score is 100 points; if the target battery degradation value is equal to or greater than the upper limit of the degradation value, the battery degradation index score is 60 points; if the target battery degradation value is equal to the second quartile of the degradation value, the battery degradation index score is 80 points; if the target battery degradation value is between the upper limit and the lower limit of the degradation value and is not equal to the second quartile of the degradation value, calculate the battery degradation index score by linear interpolation.

[0091] In one embodiment, the charging capacity scoring unit 300 includes a charging time subunit 301. The charging time subunit 301 is configured to determine, based on the vehicle networking data, the duration, mileage change data, state of charge change data, and power consumption data of multiple fast charging segments of the target vehicle, where the fast charging segment is a charging segment with a charging power in a preset power range and a state of charge in a preset numerical range; and determine the target fast charging time per 100 kilometers according to the duration, the mileage change data, the state of charge change data, and the power consumption data.

[0092] In one embodiment, the charging ability scoring unit 300 includes a charging score sub-unit 302. The charging score sub-unit 302 is configured to obtain the sample fast charging time for every 100 kilometers of a number of sample vehicles; sort the sample fast charging times for every 100 kilometers in descending order of numerical value; determine the first quartile of the fast charging time, the second quartile of the fast charging time, and the third quartile of the fast charging time according to the sorted sample fast charging times for every 100 kilometers; determine the interquartile range of the fast charging time based on the first quartile of the fast charging time and the third quartile of the fast charging time; determine the upper limit of the fast charging time based on the first quartile of the fast charging time and the interquartile range of the fast charging time; determine the lower limit of the fast charging time based on the third quartile of the fast charging time and the interquartile range of the fast charging time; if the target fast charging time for every 100 kilometers is equal to or less than the lower limit of the fast charging time, the charging time index score is 100 points; if the target fast charging time for every 100 kilometers is equal to or greater than the upper limit of the fast charging time, the charging time index score is 60 points; if the target fast charging time for every 100 kilometers is equal to the second quartile of the fast charging time, the charging time index score is 80 points; if the target fast charging time for every 100 kilometers is between the upper limit and the lower limit of the fast charging time and not equal to the second quartile of the fast charging time, calculate the charging time index score according to linear interpolation.

[0093] In one embodiment, the user experience scoring unit 400 is specifically configured to determine a first entropy value according to the endurance ability index score, where the first entropy value represents the data dispersion degree of the endurance ability index score; determine a second entropy value according to the battery degradation index score, where the second entropy value represents the data dispersion degree of the battery degradation index score; determine a third entropy value according to the charging time index score, where the third entropy value represents the data dispersion degree of the charging time index score; determine a first weight of the endurance ability index score, a second weight of the battery degradation index score, and a third weight of the charging time index score based on the first entropy value, the second entropy value, and the third entropy value; and perform weighted calculation on the endurance ability index score, the battery degradation index score, and the charging time index score by using the first weight, the second weight, and the third weight to generate the user experience score.

[0094] Each unit illustrated in the above embodiments may be specifically implemented by a computer chip or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0095] For the convenience of description, when describing the above device, it is divided into various units according to functions and described separately. Of course, when implementing this application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0096] Please refer to Figure 6 , the present disclosure also provides an electronic device, which includes a memory and a processor. The memory is used to store a computer program. When the computer program is executed by the processor, the above-mentioned method for evaluating the user experience of new energy vehicles based on vehicle networking data is implemented.

[0097] The present disclosure also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed by a processor, the above-mentioned method for evaluating the user experience of new energy vehicles based on vehicle networking data is implemented.

[0098] Among them, the processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or a combination of the above types of chips.

[0099] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of the present disclosure. By running the non-transitory software programs, instructions, and modules stored in the memory, the processor can execute various functional applications and data processing of the processor, that is, implement the methods in the above method embodiments.

[0100] The memory can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor, etc. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof.

[0101] Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0102] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and storage medium, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0103] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

[0104] Although the embodiments of the present disclosure are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure. Such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for evaluating the user experience of new energy vehicles based on vehicle networking data, characterized in that, The vehicle networking data includes vehicle driving data and vehicle charging data, and the method includes: Based on the vehicle networking data, determining the full-charge nominal cruising range and the full-charge actual cruising range of the target vehicle, and determining the cruising ability index score of the target vehicle according to the full-charge nominal cruising range and the full-charge actual cruising range; Based on the vehicle networking data, determining the target battery degradation value of the target vehicle, and determining the battery degradation index score of the target vehicle according to the target battery degradation value; Based on the vehicle networking data, determining the target fast charging time per 100 kilometers of the target vehicle, and determining the charging time index score of the target vehicle based on the target fast charging time per 100 kilometers; Generating a user experience score of the target vehicle according to the weighted result of the cruising ability index score, the battery degradation index score, and the charging time index score.

2. The method according to claim 1, wherein The step of based on the vehicle networking data, determining the full-charge nominal cruising range and the full-charge actual cruising range of the target vehicle, and determining the cruising ability index score of the target vehicle according to the full-charge nominal cruising range and the full-charge actual cruising range, includes: Based on the vehicle networking data, determining the charging start state of charge, the charging end state of charge, the charging start mileage, and the charging end mileage of multiple charging segments of the target vehicle; Determining the full-charge actual cruising range according to the charging start state of charge, the charging end state of charge, the charging start mileage, and the charging end mileage; Calculating the mileage ratio of the full-charge actual cruising range to the full-charge nominal cruising range; Determining the cruising ability index score according to the mileage ratio.

3. The method according to claim 1, wherein The step of based on the vehicle networking data, determining the target battery degradation value of the target vehicle, includes: Based on the vehicle networking data, calculating the standard value of the equivalent full charge capacity before the target vehicle reaches the first mileage; Based on the vehicle networking data, calculating the measured value of the equivalent full charge capacity after the target vehicle reaches the second mileage; Determining the target battery health state of the target vehicle according to the standard value of the equivalent full charge capacity and the measured value of the equivalent full charge capacity; Determining the target battery degradation value according to the target battery health state.

4. The method according to claim 1 or 3, characterized in that, The step of determining the battery degradation index score of the target vehicle according to the target battery degradation value, includes: Obtaining the sample battery degradation values of a number of sample vehicles; Arranging the sample battery degradation values in descending order of numerical magnitude; According to the sorted sample battery degradation values, determining the first quartile of the degradation value, the second quartile of the degradation value, and the third quartile of the degradation value; Based on the first quartile of the degradation value and the third quartile of the degradation value, determining the interquartile range of the degradation value; Based on the first quartile of the degradation value and the interquartile range of the degradation value, determining the upper limit of the degradation value; Based on the third quartile of the degradation value and the interquartile range of the degradation value, determining the lower limit of the degradation value; If the target battery degradation value is equal to or less than the lower limit of the degradation value, the battery degradation index score is 100 points; If the target battery degradation value is equal to or greater than the upper limit of the degradation value, the battery degradation index score is 60 points; If the target battery degradation value is equal to the second quartile of the degradation value, the battery degradation index score is 80 points; If the target battery degradation value is between the upper limit and the lower limit of the degradation value and is not equal to the second quartile of the degradation value, calculate the battery degradation index score according to linear interpolation.

5. The method according to claim 1, wherein The determining of the target vehicle's target fast charging time per 100 kilometers based on the vehicle networking data includes: Based on the vehicle networking data, determine the duration, mileage change data, state of charge change data, and power consumption data of multiple fast charging segments of the target vehicle, where the fast charging segment is a charging segment with a charging power in a preset power range and a state of charge in a preset value range; Determine the target fast charging time per 100 kilometers according to the duration, the mileage change data, the state of charge change data, and the power consumption data.

6. The method according to claim 1 or 5, characterized in that The determining of the charging time index score of the target vehicle based on the target fast charging time per 100 kilometers includes: Obtain the sample fast charging time per 100 kilometers of several sample vehicles; Arrange the sample fast charging time per 100 kilometers in descending order according to the numerical value; According to the sorted sample fast charging time per 100 kilometers, determine the first quartile, the second quartile, and the third quartile of the fast charging time; Based on the first quartile of the fast charging time and the third quartile of the fast charging time, determine the interquartile range of the fast charging time; Based on the first quartile of the fast charging time and the interquartile range of the fast charging time, determine the upper limit of the fast charging time; Based on the third quartile of the fast charging time and the interquartile range of the fast charging time, determine the lower limit of the fast charging time; If the target fast charging time per 100 kilometers is equal to or less than the lower limit of the fast charging time, the charging time index score is 100 points; If the target fast charging time per 100 kilometers is equal to or greater than the upper limit of the fast charging time, the charging time index score is 60 points; If the target fast charging time per 100 kilometers is equal to the second quartile of the fast charging time, the charging time index score is 80 points; If the target fast charging time per 100 kilometers is between the upper limit and the lower limit of the fast charging time and is not equal to the second quartile of the fast charging time, calculate the charging time index score according to linear interpolation.

7. The method according to claim 1, wherein The generating of the user experience score of the target vehicle according to the weighted results of the endurance capacity index score, the battery degradation index score, and the charging time index score includes: According to the endurance capacity index score, determine the first entropy value, where the first entropy value represents the degree of dispersion of the endurance capacity index score; According to the battery degradation index score, determine the second entropy value, where the second entropy value represents the degree of dispersion of the battery degradation index score; According to the charging time index score, determine the third entropy value, where the third entropy value represents the degree of dispersion of the charging time index score; Based on the first entropy value, the second entropy value, and the third entropy value, determine the first weight of the endurance capacity index score, the second weight of the battery degradation index score, and the third weight of the charging time index score; Using the first weight, the second weight, and the third weight, a weighted calculation is performed on the endurance ability index score, the battery degradation index score, and the charging time index score to generate the user experience score.

8. An evaluation device for the user experience of new energy vehicles based on vehicle networking data, characterized in that, The vehicle networking data includes vehicle driving data and vehicle charging data, and the device includes: An endurance ability scoring unit, configured to determine the full-charge nominal endurance and the full-charge actual endurance of the target vehicle based on the vehicle networking data, and determine the endurance ability index score of the target vehicle according to the full-charge nominal endurance and the full-charge actual endurance; A battery degradation scoring unit, configured to determine the target battery degradation value of the target vehicle based on the vehicle networking data, and determine the battery degradation index score of the target vehicle according to the target battery degradation value; A charging ability scoring unit, configured to determine the target time taken for fast charging per 100 kilometers of the target vehicle based on the vehicle networking data, and determine the charging time index score of the target vehicle based on the target time taken for fast charging per 100 kilometers; A user experience scoring unit, configured to generate the user experience score of the target vehicle according to the weighted results of the endurance ability index score, the battery degradation index score, and the charging time index score.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory is used to store a computer program. When the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program. When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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