New energy automobile energy chain diagnosis method and system based on cloud big data

Abstract

The invention discloses a new energy automobile energy chain diagnosis method and system based on cloud big data. Determining a plurality of use scenes according to the vehicle driving environment and the vehicle condition; screening out energy consumption data of key components in each use scene from the cloud big data; obtaining an energy consumption distribution probability density feature set of each key component in each use scene according to the energy consumption data of the key component; obtaining an energy consumption distribution characteristic value of each key part of the vehicle according to the energy consumption data of each key part of the vehicle to be subjected to energy chain diagnosis, the use scene and the energy consumption distribution probability density characteristic set corresponding to the use scene during the driving period; and when the energy consumption distribution characteristic value of a certain key component is lower than a set threshold value, considering that the energy consumption of the key component is abnormal. According to the invention, the vehicle with abnormal energy consumption can be checked at the vehicle offline detection stage, so that fault vehicles are prevented from flowing into the terminal; faults and positions of the faults can be quickly and accurately determined in a user use stage, and enterprises are assisted to monitor the faults.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data analysis of new energy vehicles, and specifically relates to a new energy vehicle energy chain diagnosis method and system based on cloud big data. Background Art

[0002] In the diagnostic analysis of automotive electronic control units, there are few direct diagnostic analyses of vehicle energy consumption and endurance performance, and faults are often only handled after they occur. In addition, when new vehicles are tested off the line, it is often impossible to detect abnormal energy consumption and issue early warnings, resulting in the company's inability to analyze and improve in advance, and reduce the chance of vehicles with quality defects being transferred to end users.

[0003] Today's cars are becoming more and more electrified and intelligent, and cloud data processing technology is becoming more and more popular, which has also brought new methods for diagnosing the energy consumption performance of new energy vehicles. At the same time, the various accessories and driving functions of vehicles are becoming more and more abundant, but it is not always clear whether users have used these functions correctly and appropriately in actual use.

[0004] The current OBD diagnostic technology of vehicles mainly stores preset fault information in the vehicle's memory, reads the fault code from the memory through a certain program, and prompts the maintenance personnel to quickly and accurately determine the nature and location of the fault. However, there is little diagnosis of abnormal energy chain consumption; there is a lack of diagnosis for abnormal energy consumption caused by improper use of non-fault-related problems, and it is impossible to provide users with reasonable suggestions and corrections for such problems; therefore, it is unable to assist enterprises in monitoring faults and improving vehicle quality. Summary of the invention

[0005] In order to quickly and accurately determine the nature and location of the fault, and to assist enterprises in monitoring faults and improving the factory quality of vehicles, the present invention proposes a new energy vehicle energy chain diagnosis method and system based on cloud big data.

[0006] A new energy vehicle energy chain diagnosis method based on cloud big data to achieve one of the purposes of the present invention includes:

[0007] Determine multiple usage scenarios based on the vehicle's driving environment and condition;

[0008] Filter out the energy consumption data of key components in each usage scenario from the cloud big data;

[0009] Obtain the energy consumption distribution probability density feature set of each key component in each usage scenario based on the energy consumption data of the key components; the energy consumption distribution probability density feature set includes the energy consumption distribution interval of each key component and the energy consumption distribution probability density of each energy consumption distribution interval; the energy consumption distribution probability density is the proportion of the number of vehicles in the cloud data whose energy consumption of the key components of the vehicle is in this energy consumption distribution interval; the energy consumption distribution interval is a plurality of intervals divided according to the energy consumption values of the key components of the vehicle; for example, it is divided into (0, 150], (150, 160], (160, 170], ……, (280, +∞) according to the energy consumption distribution characteristics of the electric drive assembly drive energy consumption.

[0010] Obtain the energy consumption distribution characteristic value of each key component of the vehicle based on the energy consumption data, usage scenario, and the corresponding energy consumption distribution probability density feature set of each key component during the driving of the vehicle to be diagnosed for the energy chain.

[0011] When the energy consumption distribution characteristic value of the key component of the vehicle is lower than the set threshold, it is considered that the energy consumption of the key component of the vehicle is abnormal; the set threshold of each key component is a calibration value or set according to experience.

[0012] The usage scenario is determined according to the working condition, temperature, and vehicle state. The working conditions include: urban working condition, suburban working condition, highway working condition, which are distinguished by vehicle speed. For example, the speed of the urban working condition is less than V1; the vehicle speed of the suburban working condition is (V1, V2), and the vehicle speed of the highway working condition is greater than or equal to V2, where V1 < V2 < V3; the temperature includes extremely cold, low temperature, high temperature, and normal temperature. The extremely cold is less than T1; the low temperature is [T1, T2), the normal temperature is [T2, T3); the high temperature is greater than or equal to T3, where T1 < T2 < T3 < T4. The vehicle state includes: before leaving the factory, within the warranty period, and outside the warranty period. Before leaving the factory means that the vehicle mileage is less than or equal to the set mileage; within the warranty period and outside the warranty period are determined according to time. If the factory age is less than or equal to the set age, it is within the warranty period, otherwise it is outside the warranty period.

[0013] According to the above working conditions, temperature, and vehicle state, multiple vehicle usage scenario combinations can be obtained. For example, usage scenario one: urban working condition, normal temperature, within the warranty period; usage scenario two: highway working condition, normal temperature, within the warranty period, which will not be elaborated here.

[0014] The method for obtaining the energy consumption distribution probability density feature set includes:

[0015] In the cloud big data, obtain the energy consumption data of each key component of the vehicle according to each usage scenario of the vehicle. The key components are the components that consume electric energy and affect the endurance in the electric vehicle energy flow, including: electric drive assembly, electric compressor, PTC, DCDC, OBC, power battery.

[0016] The energy consumption data of each key component is divided into intervals; the energy consumption distribution probability density of each key component in each interval is counted, and the interval and energy consumption distribution probability density constitute the energy consumption distribution probability density feature set of the key component; for example, the probability density is the proportion of the number of vehicles, and the energy consumption distribution probability density feature set of the electric drive assembly driving energy consumption in the cloud big data is:

[0017] {{[0,150],8.9%},{(150,160],13.4%},{(160,170],15%},{(170,180],36.1%},{(180,190],17.3%},{(190,200],6.3%},{(200,210],2.1%},{(210,220],0.6%},{ (220,230],0.1%},{(230,240],0.1%},{(240,250],0.1%}}; the unit is wh / km, where {(160,170],15%} means that in the cloud big data, the probability density of the electric drive assembly driving energy consumption distributed in the interval of (160wh / km,170wh / km] is 15%, that is, the number of vehicles accounts for 15%.

[0018] The calculation method of the energy consumption distribution characteristic value of the key components includes:

[0019] Obtain energy consumption data of a key component of a vehicle in a certain usage scenario; obtain a probability density feature set of energy consumption distribution of the key component in the usage scenario from cloud big data;

[0020] Determine the interval where the energy consumption distribution probability density feature set corresponding to the energy consumption data of the key component is located; calculate the sum of the energy consumption distribution probability densities corresponding to all other distribution intervals whose lower limit value in the corresponding energy consumption distribution probability density feature set is greater than the interval where the energy consumption distribution is located, which is the energy consumption distribution characteristic value of the key component.

[0021] Furthermore, when the energy consumption of key components is abnormal, the vehicle operation is diagnosed, and the vehicle operation diagnosis method includes:

[0022] When the energy consumption of the electric drive system in the key components is abnormal, the vehicle speed and / or window opening time are obtained. When the vehicle speed is greater than the set speed and / or the window opening time is greater than the set time, it is considered that high speed or high-speed window opening causes abnormal energy consumption;

[0023] When the energy consumption of the air-conditioning compressor or the PTC in the key components is abnormal, the window opening time and window opening degree of the vehicle are obtained. When the window opening time is greater than the set time and the window opening degree is greater than the set ratio value, it is considered that the window opening causes the compressor or PTC energy consumption to be abnormal;

[0024] When the DCDC energy consumption in the key components is abnormal, the vehicle's headlight on time is obtained. When the light sensor determines that it is daytime and the vehicle's headlight on time is greater than the set time, it is considered that the headlight is mistakenly turned on, resulting in abnormal DCDC energy consumption;

[0025] When the electric drive system in the key components recovers energy abnormally, the vehicle's brake energy recovery status is obtained. If the vehicle's brake energy recovery is in the off state, it is considered that the brake energy recovery is turned off, resulting in low electric drive recovery energy, thereby causing the electric drive system to recover energy abnormally.

[0026] Furthermore, when the energy consumption of the electric drive system in the key component is abnormal, the following judgment is also included:

[0027] Calculating the acceleration index of the vehicle according to the acceleration and travel distance of the vehicle within a set plurality of sampling periods;

[0028] When the vehicle speed is less than the set speed and the acceleration index is greater than the first calibration value, or the vehicle speed is greater than the set speed and the acceleration index is greater than the second calibration value; it is considered that the intense driving causes abnormal energy consumption of the electric drive system.

[0029] Furthermore, the method further includes correcting the acceleration and travel distance of the vehicle within a plurality of set sampling periods, wherein the correction method includes:

[0030] When the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and driving distance of the sampling point are discarded and do not participate in the calculation of the acceleration index.

[0031] Furthermore, when the energy consumption of a key component is abnormal, it also includes starting a key component performance diagnosis;

[0032] One of the diagnostic methods includes:

[0033] Calculate the sum of the target power consumption of each load under the key components to obtain the total target power consumption value;

[0034] When the difference between the output power of the key component and the total target power consumption value is greater than the calibration value, it is considered that the load energy consumption of the key component is abnormal; the key components include: OBC, DCDC.

[0035] The second diagnostic method includes:

[0036] When the energy consumption of the DCDC high voltage end in key components is abnormal:

[0037] The average power output by the DCDC and the working efficiency of the DCDC are calculated according to the voltage and current information of the input and output of the DCDC; the target efficiency of the DCDC is calculated according to the maximum load power of the DCDC and the average power output by the DCDC; when the difference between the target efficiency of the DCDC and the working efficiency of the DCDC is greater than the calibration value, it is considered that the abnormality of the DCDC efficiency performance leads to abnormal energy consumption at the high-voltage end of the DCDC; wherein the calculation method of the target efficiency of the DCDC includes:

[0038]

[0039] in:

[0040] η t_DCDC is the target efficiency of DCDC; P out_DCDC is the average power of DCDC output; P max_DCDC is the maximum load power of DCDC; a0, a1, a2, a3, a4 are constant term parameters obtained by the least square method based on DCDC efficiency test data.

[0041] When the compressor energy consumption in key components is abnormal:

[0042] If the compressor exhaust pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or the compressor exhaust pressure is greater than the second calibration value after the second set time after the air conditioner is turned on, it is considered that the abnormal exhaust pressure of the air conditioner compressor leads to abnormal compressor energy consumption;

[0043] When the electric drive system in key components recovers energy abnormally:

[0044] When the SOC is less than the set value and the braking system does not disable braking energy recovery:

[0045] If the permissible negative power of the motor is less than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible input power of the battery, it is determined that the permissible input power of the battery is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0046] If the permissible negative power of the motor is greater than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible negative power of the motor, it is determined that the permissible braking power of the motor is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0047] When the electric drive system in key components consumes abnormal energy:

[0048] Calculate the efficiency of the motor based on the input current and voltage of the motor controller, and the torque and speed at the motor output;

[0049] According to the calibrated motor torque and speed relationship table, the target efficiency of the motor is calculated by interpolating the torque and speed at the motor output end;

[0050] When the difference between the target efficiency of the motor and the efficiency of the motor is greater than the calibration value, it is determined that the motor efficiency performance is abnormal, resulting in abnormal driving energy consumption of the electric drive system.

[0051] A new energy vehicle energy chain diagnostic system based on cloud big data to achieve the second purpose of the present invention includes:

[0052] Vehicle usage scenario analysis module: used to determine multiple usage scenarios based on the vehicle driving environment and vehicle conditions;

[0053] Energy consumption distribution probability density feature set acquisition module: used to filter out energy consumption data of key components in each usage scenario from cloud big data;

[0054] According to the energy consumption data of the key components, an energy consumption distribution probability density feature set of each key component in each usage scenario is obtained; the energy consumption distribution probability density feature set includes an energy consumption distribution interval of each key component and an energy consumption distribution probability density of each energy consumption distribution interval;

[0055] Energy consumption abnormality judgment module: used to judge whether the vehicle's energy consumption is abnormal. The judgment method includes: obtaining the energy consumption distribution characteristic value of each key component of the vehicle according to the energy consumption data of each key component during driving of the vehicle to be diagnosed by the energy chain, the usage scenario and the energy consumption distribution probability density feature set corresponding to the usage scenario; when the energy consumption distribution characteristic value of the key component of the vehicle is lower than the set threshold, it is considered that the energy consumption of the key component of the vehicle is abnormal.

[0056] Furthermore, it also includes: a first vehicle operation judgment module, which is used to analyze the cause of abnormal energy consumption of vehicle operation when the energy consumption of key components is abnormal, and the analysis method includes:

[0057] When the energy consumption of the electric drive system in the key components is abnormal, the vehicle speed and / or window opening time are obtained. When the vehicle speed is greater than the set speed and / or the window opening time is greater than the set time, it is considered that high speed or high-speed window opening causes abnormal energy consumption;

[0058] When the energy consumption of the air-conditioning compressor or the PTC in the key components is abnormal, the window opening time and window opening degree of the vehicle are obtained. When the window opening time is greater than the set time and the window opening degree is greater than the set ratio value, it is considered that the window opening causes the compressor or PTC energy consumption to be abnormal;

[0059] When the DCDC energy consumption in the key components is abnormal, the vehicle's headlight on time is obtained. When the light sensor determines that it is daytime and the vehicle's headlight on time is greater than the set time, it is considered that the headlight is mistakenly turned on, resulting in abnormal DCDC energy consumption;

[0060] When the electric drive system in the key components recovers energy abnormally, the vehicle's brake energy recovery status is obtained. If the vehicle's brake energy recovery is in the off state, it is considered that the brake energy recovery is turned off, resulting in low electric drive recovery energy, thereby causing the electric drive system to recover energy abnormally.

[0061] Furthermore, a second vehicle operation judgment module is included, which is used to judge the vehicle operation when the energy consumption of the electric drive system in the key component is abnormal, and the judgment method includes:

[0062] The vehicle's acceleration index is calculated based on the vehicle's acceleration and driving distance within a set number of sampling periods; when the vehicle's speed is less than the set speed and the acceleration index is greater than a first calibration value, or the vehicle's speed is greater than the set speed and the acceleration index is greater than a second calibration value; it is considered that intense driving has caused abnormal energy consumption of the electric drive system.

[0063] Furthermore, a correction module is also included: used to correct the acceleration and travel distance of the vehicle within a set plurality of sampling periods, and the correction method includes:

[0064] When the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and driving distance of the sampling point are discarded and do not participate in the calculation of the acceleration index.

[0065] Furthermore, it also includes a first key component performance diagnosis module, which is used to diagnose the key component performance when the energy consumption is abnormal, so as to locate the key component that causes the abnormal energy consumption of the vehicle. The diagnosis method includes:

[0066] The sum of the target power consumption of each load under the key component is calculated to obtain the total target power consumption value; when the difference between the output power of the key component and the total target power consumption value is greater than the calibration value, it is considered that the load energy consumption of the key component is abnormal; the key components include: DCDC, OBC.

[0067] Furthermore, a second key component performance diagnosis module is included, which is used to diagnose the key component performance when the energy consumption is abnormal, so as to locate the key component that causes the abnormal energy consumption of the vehicle. The diagnosis method includes:

[0068] When the energy consumption of the DCDC high voltage end in key components is abnormal:

[0069] The average power output by the DCDC and the working efficiency of the DCDC are calculated according to the voltage and current information of the input and output of the DCDC; the target efficiency of the DCDC is calculated according to the maximum load power of the DCDC and the average power output by the DCDC; when the difference between the target efficiency of the DCDC and the working efficiency of the DCDC is greater than the calibration value, it is considered that the abnormality of the DCDC efficiency performance leads to the abnormal energy consumption at the high-voltage end of the DCDC;

[0070] When the compressor energy consumption in key components is abnormal:

[0071] If the compressor exhaust pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or the compressor exhaust pressure is greater than the second calibration value after the second set time after the air conditioner is turned on, it is considered that the abnormal exhaust pressure of the air conditioner compressor leads to abnormal compressor energy consumption;

[0072] When the electric drive system in key components recovers energy abnormally:

[0073] When the SOC is less than the set value and the braking system does not disable braking energy recovery:

[0074] If the permissible negative power of the motor is less than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible input power of the battery, it is determined that the permissible input power of the battery is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0075] If the permissible negative power of the motor is greater than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible negative power of the motor, it is determined that the permissible braking power of the motor is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0076] When the electric drive system in key components consumes abnormal energy:

[0077] Calculate the efficiency of the motor based on the input current and voltage of the motor controller, and the torque and speed at the motor output;

[0078] According to the calibrated motor torque and speed relationship table, the target efficiency of the motor is calculated by interpolating the torque and speed at the motor output end;

[0079] When the difference between the target efficiency of the motor and the efficiency of the motor is greater than the calibration value, it is determined that the motor efficiency performance is abnormal, resulting in abnormal driving energy consumption of the electric drive system.

[0080] The beneficial effects of the present invention include:

[0081] During the vehicle offline inspection phase: conduct inspections and treatments for vehicles with abnormal energy consumption to reduce the chance of faulty vehicles entering the terminal;

[0082] During the user use phase: for vehicles with abnormal energy consumption caused by improper use, based on the abnormal energy consumption analysis described in the present invention, improvement suggestions can be formulated for users and pushed to users, so as to reduce energy consumption and improve battery life, reduce user complaints and improve user satisfaction;

[0083] During the user use stage: For vehicles with abnormal energy consumption due to abnormal components, guide companies to promptly conduct troubleshooting and improvement plans for the abnormal components to ensure user performance and reduce the occurrence of negative public opinion. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a schematic diagram of the process of the method of the present invention;

[0085] Figure 2 It is a module block diagram of the system described in the present invention. DETAILED DESCRIPTION

[0086] The following specific implementations are used to explain the technical solutions of the claims of the present invention so that those skilled in the art can understand the claims. The protection scope of the present invention is not limited to the following specific implementation structures. The technical solutions of the claims of the present invention made by those skilled in the art but different from the following specific implementations are also within the protection scope of the present invention.

[0087] A new energy vehicle energy chain diagnosis method based on cloud big data, such as Figure 1 As shown, the following steps are included:

[0088] S1. Determine the key components of power consumption and battery life

[0089] According to the energy flow of electric vehicles, the key components that determine power consumption and endurance are mainly electric drive assembly, electric compressor, PTC, DCDC, OBC, power battery, etc.

[0090] S2. Divide vehicle usage scenarios

[0091] According to the working conditions, temperature and vehicle status, the vehicle usage scenarios are divided into the following Table 1

[0092] Table 1 Vehicle usage scenarios

[0093]

[0094] S3. Combined vehicle usage scenarios

[0095] According to the usage scenarios in Table 1, the following 36 key scenario combinations are analyzed.

[0096] Table 2 Vehicle usage scenario combinations

[0097]

[0098] S4. Cloud Data Integration

[0099] In the cloud big data, for various usage scenario combinations in Table 2, the energy consumption data of the vehicle in the cloud big data is screened out, and the energy consumption distribution probability density characteristic data of the characteristic performance parameter distribution of the key components described in step S1 is extracted and integrated therefrom;

[0100] The characteristic performance parameters of the key components in this embodiment refer to the energy consumption (wh / km) of the electric drive assembly during the driving phase, the energy recovered by the electric drive assembly (wh / km), the energy consumption of the electric compressor (wh / km), the energy consumption of the PTC (wh / km), the energy consumption of the DCDC (wh / km), the OBC charging efficiency, and the available electric energy SOE (kWh) when the power battery is fully charged. The following Table 3 shows the probability density of the energy consumption distribution of the electric drive assembly during the warranty period, at normal temperature, and in urban conditions.

[0101] Table 3 Probability density characteristics of energy consumption distribution during warranty period, normal temperature, and urban conditions - electric drive assembly

[0102]

[0103] S5. Update of probability density characteristic data of energy consumption distribution of key components under each scenario combination

[0104] The various energy consumption distribution probability density feature data extracted in S4 are stored in the specified module;

[0105] S6. Vehicle energy consumption diagnosis 1 - Electric drive system drive

[0106] When a vehicle completes a driving section, the energy consumption distribution probability density feature set S1 of the corresponding key components under the same travel scenario is retrieved from the data stored in S5 according to the travel scenario of the vehicle in this section; at the same time, the energy consumption result y1 of the electric drive system driven by this vehicle in this section is obtained. * , based on the energy consumption distribution range in S1, determine y1 * The interval

[0107] The energy consumption distribution characteristic value P1 is calculated according to the following formula:

[0108]

[0109] in, In the energy consumption distribution probability density feature set S1 of the key component, the lower limit of the energy consumption distribution interval is greater than The sum of the energy consumption distribution probability densities of all k intervals;

[0110] Here are some examples:

[0111] When a vehicle completes a driving period, the travel scenario of the vehicle in this period is within the warranty period, at normal temperature, and in urban conditions as shown in Table 3; the energy consumption Y1 of the electric drive assembly driving stage is 205wh / km; at this time, it falls in the interval of 200<Y1≤210, then the probability P1 is the sum of the energy consumption distribution probability densities corresponding to the following intervals of energy consumption Y1>210: 210<Y1≤220, 220<Y1≤230, 230<Y1≤240, 240<Y1≤250 (the energy consumption distribution probability density of all intervals of Y1>250 is 0, so it is not listed again), that is: 0.5%+0.1%+0.1%+0.1%=0.8%;

[0112] when (Where, When the threshold is set as a calibration or based on experience, the abnormal energy consumption cause analysis of the key component is initiated.

[0113] The same method is used to diagnose the energy consumption of electric drive system recovery, electric compressor, PTC, DCDC, OBC, and power battery, and the energy consumption diagnosis of other key components is obtained: electric drive system recovery, electric compressor energy consumption, PTC energy consumption, DCDC energy consumption, and OBC efficiency.

[0114] The abnormal energy consumption data caused by the vehicle fault diagnosed in this step is eliminated from the cloud big data; finally, based on the cloud energy flow big data, the energy consumption distribution probability density characteristic data under each scenario combination is iteratively updated at specified time intervals.

[0115] S7. Vehicle operation diagnosis.

[0116] When S6 diagnoses abnormal energy consumption, it starts the "Vehicle Operation" diagnosis in this step to determine whether the vehicle has improper settings and driving operations. The diagnosis is as follows:

[0117] (1) High-speed window opening diagnosis

[0118] When the diagnosis result of S6 is "high energy consumption of electric drive system", if the average speed of the vehicle during this driving is ≥60kph, and the window opening time of the vehicle is >5min, it is judged as "high-speed window opening leads to abnormal energy consumption";

[0119] (2) Window opening diagnosis when air conditioning is on

[0120] When the diagnosis result of S6 is "high energy consumption of air conditioner compressor" or "high energy consumption of PTC", if the vehicle window is opened for more than 10 minutes and the window opening degree is more than 20%, it is judged as "opening the window causes high energy consumption of compressor or PTC"

[0121] (3) Headlight error diagnosis: When the diagnosis result of S6 is "high DCDC load", if the light sensor determines that it is daytime and the headlight is on for more than 5 minutes, it is determined that "headlight error causes high DCDC power consumption".

[0122] (4) Intense driving diagnosis

[0123] When the diagnosis result of S6 is "high energy consumption of electric drive system", the acceleration index β is calculated according to the following formula (1): 加 , when the vehicle speed is less than 65km / h, if β 加 >Calibration value β city ; When the vehicle speed is greater than 65 kph, if β 加 > Calibration value β high ; it will be judged as “intense driving leads to high energy consumption”.

[0124]

[0125] Where: i refers to the sampling point; a i Refers to the acceleration of the vehicle at sampling point i, in m / s 2 ;d i Refers to the driving distance of the vehicle at sampling point i, in km.

[0126] In order to exclude the data of the stable driving stage from the acceleration index β 加 The impact of a i and d i Make corrections, the correction method is as follows:

[0127] when a i <0.2m / s 2 When i =0;d i =0.

[0128] (5) Determination of braking energy recovery setting

[0129] When the diagnostic result of S6 is "low energy recovery of the electric drive system", if the vehicle's braking energy recovery is in the off state, it is determined as "low energy recovery of the electric drive due to the braking energy recovery being off".

[0130] S8. Key Component Performance Analysis

[0131] If the S6 diagnosis confirms that the vehicle is operating normally, it initiates a diagnostic analysis of energy consumption of key components.

[0132] (1) DCDC efficiency diagnosis

[0133] When the diagnosis result of S6 is "high consumption at the high-voltage end of DCDC", the efficiency diagnosis of DCDC is started.

[0134] First, according to the voltage and current information of the DCDC input and output, calculate the average power P of the DCDC output out_DCDC 、DCDC working efficiency η DCDC , calculate the target efficiency η of DCDC according to formula (2) t_DCDC The efficiency difference of DCDC is η diff =η t_DCDC -η DCDC , when η diff Greater than the calibration value η DCDC0 When , it is determined that the DCDC efficiency performance is abnormal.

[0135]

[0136] Where: P max_DCDC is the maximum load power of DCDC; a0, a1, a2, a3, a4 are constant term parameters obtained by the least square method based on DCDC efficiency test data.

[0137] (2) DCDC load power diagnosis

[0138] The module is embedded with the power consumption list of low-voltage electrical appliances shown in Table 4 below, and the low-voltage target power consumption P of the vehicle is calculated according to formula (3): t_low , low voltage power consumption difference P diff_low =P out_DCDC -P t_low , when P diff_low Greater than the calibration value P low_0 When , it is determined that the energy consumption of the low-voltage accessories is abnormal.

[0139] Table 4 Power consumption list of low voltage accessories

[0140] Serial number appendix Target power consumption (W) 1 Cooling Fan - Low Speed 150 2 Cooling Fan - High Speed 300 3 Daytime running lights 40 4 Wiper 210 5 Position lights 50 6 Headlights - Low beam 35 7 Headlights-High beam 55 8 …… ……

[0141]

[0142] Where t0 refers to the sampling start time, unit is s; t end refers to the sampling end time, unit is s; j refers to the attachment number; P j Refers to the target power of the accessory, n represents the number of accessories, unit is W; S j Refers to the working status of the accessory. When the accessory is not working, it takes 0, and when it is working, it takes the duty cycle or 1.

[0143] (3) OBC efficiency diagnosis

[0144] When the diagnostic result of S6 is "OBC input end consumption is high", and the SOC is less than 95%, and in the period after charging for 5 minutes, the OBC efficiency diagnosis is performed. The efficiency diagnosis method of OBC is the same as the efficiency diagnosis of DCDC, and its low-voltage accessories are shown in Table 4 above. The technical effect of this step is: when the SOC is high or for a period of time at the beginning of charging, the charging power will generally be lower than the target charging power, and the efficiency of OBC will be lower at this time, so the restrictions on SOC and charging time are added to reduce the impact of this charging condition on the OBC charging rate.

[0145] (4) Electric compressor power consumption diagnosis

[0146] When the diagnosis result is "compressor consumption is high", the compression power consumption diagnosis is started. Within the first 30 minutes of turning on the air conditioner, the compressor exhaust pressure PAC> the calibration value Pmax1; or, after the air conditioner is turned on for 30 minutes, the compressor exhaust pressure PAC> the calibration value Pmax2; it is determined that the air conditioner compressor exhaust pressure is abnormal.

[0147] (5) Electric drive system recovery diagnosis

[0148] When the S6 diagnostic result is "Abnormally low electric drive system recovery", the following electric drive system recovery diagnostic analysis is initiated:

[0149] If SOC is less than 95% and the braking system does not disable the braking energy recovery, when the permissible negative power P of the motor -MCU_a (driving is positive, braking is negative) less than the allowable input power P of the battery -HVB_In (input is negative, output is positive), and the wheel side requests braking power P B_Re (Braking is negative, driving is positive) less than the allowable input power P of the battery -HVB_In It is determined that the allowable input power of the battery is insufficient, resulting in low recovery. The allowable negative power of the motor refers to the minimum power that the motor is allowed to recover during braking energy recovery; when the motor drives the vehicle forward, the power of the motor is defined as a positive value, and when the motor performs braking energy recovery, the power of the motor is defined as a negative value. The allowable input power of the battery refers to the minimum charging power that the battery can accept; the power discharged by the battery is defined as a positive value, and the power charged to the battery is defined as a negative value. The wheel-side requested braking power refers to the wheel-side requested power when the vehicle brakes; when the vehicle is driven, the wheel-side requested power is defined as a positive value, and when the vehicle is braked, the wheel-side requested power is defined as a negative value.

[0150] If SOC is less than 95% and the braking system does not disable the braking energy recovery, when the permissible negative power P of the motor -MCU_a Greater than the allowable input power P of the battery -HVB_In , and the wheel side requires braking power P B_Re Less than the permissible negative power P of the motor -MCU_aWhen , it is determined that the motor's allowable braking power is insufficient, resulting in low recovery.

[0151] (6) Analysis of driving efficiency of electric drive system

[0152] When the diagnosis result is "abnormal driving energy consumption of the electric drive system", according to the input current I in_mcu , voltage U in_muc , Torque T at the motor output out_em , speed N out_em , use formula (4) to calculate the motor efficiency η em ; and according to the motor efficiency table in the implant module (see Table 5), use the torque T at the motor output end out_em , speed N out_em Interpolation calculation of the motor's target efficiency η em_T ; Efficiency difference of motor assembly η em_diff =η em_T -η em , η em_diff Greater than the calibration value η em0 When the motor efficiency performance is abnormal.

[0153]

[0154] Table 5 Motor assembly efficiency

[0155]

[0156] S9, New car off-line testing

[0157] During the off-line inspection of new vehicles, when the diagnosis result is abnormal energy consumption, the abnormal vehicle can be intercepted in time and the problem can be investigated to prevent the abnormal vehicle from entering the warehouse and eventually flowing into the terminal market.

[0158] S10, End-user Usage Improvement

[0159] When the diagnosis result is abnormal energy consumption, if the diagnosis module confirms that the user is using the vehicle improperly, suggestions for improvement can be pushed to the user, such as driving mode settings, suggestions for using high-power accessories, driving habit improvements, insufficient tire pressure, maintenance suggestions for aging parts, etc.

[0160] S11. Early warning and improvement of negative public opinion at the terminal

[0161] When the diagnosis result is abnormal energy consumption, the company will promptly arrange fault analysis and formulate a response plan for the vehicle fault point; during vehicle maintenance, the company will formulate and recommend maintenance plans to users based on the diagnostic information stored in the cloud to ensure that the vehicle is in good performance condition.

[0162] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0163] The embodiment of the present invention also provides a new energy vehicle energy chain diagnosis method based on cloud big data, including:

[0164] Determine multiple usage scenarios based on the vehicle's driving environment and condition;

[0165] Filter out the energy consumption data of key components in each usage scenario from the cloud big data;

[0166] According to the energy consumption data of the key components, an energy consumption distribution probability density feature set of each key component in each usage scenario is obtained; the energy consumption distribution probability density feature set includes an energy consumption distribution interval of each key component and an energy consumption distribution probability density of each energy consumption distribution interval;

[0167] Obtaining energy consumption distribution characteristic values ​​of each key component of the vehicle according to energy consumption data of each key component during driving of the vehicle to be diagnosed with an energy chain, a usage scenario, and an energy consumption distribution probability density characteristic set corresponding to the usage scenario;

[0168] When the energy consumption distribution characteristic value of the key component of the vehicle is lower than a set threshold, it is considered that the energy consumption of the key component of the vehicle is abnormal.

[0169] In some embodiments, when the energy consumption of key components is abnormal, the vehicle operation is further diagnosed, and the vehicle operation diagnosis method includes:

[0170] When the energy consumption of the electric drive system in the key components is abnormal, the vehicle speed and / or window opening time are obtained. When the vehicle speed is greater than the set speed and / or the window opening time is greater than the set time, it is considered that high speed or high-speed window opening causes abnormal energy consumption;

[0171] When the energy consumption of the air-conditioning compressor or the PTC in the key components is abnormal, the window opening time and window opening degree of the vehicle are obtained. When the window opening time is greater than the set time and the window opening degree is greater than the set ratio value, it is considered that the window opening causes the compressor or PTC energy consumption to be abnormal;

[0172] When the DCDC energy consumption in the key components is abnormal, the vehicle's headlight on time is obtained. When the light sensor determines that it is daytime and the vehicle's headlight on time is greater than the set time, it is considered that the headlight is mistakenly turned on, resulting in abnormal DCDC energy consumption;

[0173] When the electric drive system in the key component recovers energy abnormally, the vehicle's braking energy recovery status is obtained. If the vehicle's braking energy recovery is in the off state, it is considered that the braking energy recovery is turned off, causing the electric drive system to recover energy abnormally.

[0174] In some embodiments, when the energy consumption of the electric drive system in the key component is abnormal, the following judgment is also included:

[0175] Calculating the acceleration index of the vehicle according to the acceleration and travel distance of the vehicle within a set plurality of sampling periods;

[0176] When the vehicle speed is less than the set speed and the acceleration index is greater than the first calibration value, or the vehicle speed is greater than the set speed and the acceleration index is greater than the second calibration value; it is considered that the intense driving causes abnormal energy consumption of the electric drive system.

[0177] In some embodiments, the method further includes correcting the acceleration and travel distance of the vehicle within a plurality of set sampling periods, wherein the correction method includes:

[0178] When the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and driving distance of the sampling point are discarded and do not participate in the calculation of the acceleration index.

[0179] In some embodiments, when the energy consumption of a key component is abnormal, the key component performance diagnosis is also started, and the diagnosis method includes:

[0180] Calculate the sum of the target power consumption of each load under the key components to obtain the total target power consumption value;

[0181] When the difference between the output power of the key component and the total target power consumption value is greater than a calibrated value, it is considered that the load energy consumption of the key component is abnormal.

[0182] In some embodiments, when the energy consumption of a key component is abnormal, the key component performance diagnosis is also started, and the diagnosis method includes:

[0183] When the energy consumption of the DCDC high voltage end in key components is abnormal:

[0184] The average power output by the DCDC and the working efficiency of the DCDC are calculated according to the voltage and current information of the input and output of the DCDC; the target efficiency of the DCDC is calculated according to the maximum load power of the DCDC and the average power output by the DCDC; when the difference between the target efficiency of the DCDC and the working efficiency of the DCDC is greater than the calibration value, it is considered that the abnormality of the DCDC efficiency performance leads to the abnormal energy consumption at the high-voltage end of the DCDC;

[0185] When the compressor energy consumption in key components is abnormal:

[0186] If the compressor exhaust pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or the compressor exhaust pressure is greater than the second calibration value after the second set time after the air conditioner is turned on, it is considered that the abnormal exhaust pressure of the air conditioner compressor leads to abnormal compressor energy consumption;

[0187] When the electric drive system in key components recovers energy abnormally:

[0188] When the SOC is less than the set value and the braking system does not disable braking energy recovery:

[0189] If the permissible negative power of the motor is less than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible input power of the battery, it is determined that the permissible input power of the battery is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0190] If the permissible negative power of the motor is greater than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible negative power of the motor, it is determined that the permissible braking power of the motor is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0191] When the electric drive system in key components consumes abnormal energy:

[0192] Calculate the efficiency of the motor based on the input current and voltage of the motor controller, and the torque and speed at the motor output;

[0193] According to the calibrated motor torque and speed relationship table, the target efficiency of the motor is calculated by interpolating the torque and speed at the motor output end;

[0194] When the difference between the target efficiency of the motor and the efficiency of the motor is greater than the calibration value, it is determined that the motor efficiency performance is abnormal, resulting in abnormal driving energy consumption of the electric drive system.

[0195] The embodiment of the present invention also provides a new energy vehicle energy chain diagnosis system based on cloud big data, including:

[0196] Vehicle usage scenario analysis module: used to determine multiple usage scenarios based on the vehicle driving environment and vehicle conditions;

[0197] Energy consumption distribution probability density feature set acquisition module: used to filter out energy consumption data of key components in each usage scenario from cloud big data;

[0198] According to the energy consumption data of the key components, an energy consumption distribution probability density feature set of each key component in each usage scenario is obtained; the energy consumption distribution probability density feature set includes an energy consumption distribution interval of each key component and an energy consumption distribution probability density of each energy consumption distribution interval;

[0199] Energy consumption abnormality judgment module: used to judge whether the vehicle's energy consumption is abnormal. The judgment method includes: obtaining the energy consumption distribution characteristic value of each key component of the vehicle according to the energy consumption data of each key component during driving of the vehicle to be diagnosed by the energy chain, the usage scenario and the energy consumption distribution probability density feature set corresponding to the usage scenario; when the energy consumption distribution characteristic value of the key component of the vehicle is lower than the set threshold, it is considered that the energy consumption of the key component of the vehicle is abnormal.

[0200] In some embodiments, the first vehicle operation judgment module is further included, which is used to analyze the cause of abnormal energy consumption of the vehicle operation when the energy consumption of the key components is abnormal. The analysis method includes:

[0201] When the energy consumption of the electric drive system in the key components is abnormal, the vehicle speed and / or window opening time are obtained. When the vehicle speed is greater than the set speed and / or the window opening time is greater than the set time, it is considered that high speed or high-speed window opening causes abnormal energy consumption;

[0202] When the energy consumption of the air-conditioning compressor or the PTC in the key components is abnormal, the window opening time and window opening degree of the vehicle are obtained. When the window opening time is greater than the set time and the window opening degree is greater than the set ratio value, it is considered that the window opening causes the compressor or PTC energy consumption to be abnormal;

[0203] When the DCDC energy consumption in the key components is abnormal, the vehicle's headlight on time is obtained. When the light sensor determines that it is daytime and the vehicle's headlight on time is greater than the set time, it is considered that the headlight is mistakenly turned on, resulting in abnormal DCDC energy consumption;

[0204] When the electric drive system in the key component recovers energy abnormally, the vehicle's braking energy recovery status is obtained. If the vehicle's braking energy recovery is in the off state, it is considered that the braking energy recovery is turned off, causing the electric drive system to recover energy abnormally.

[0205] In some embodiments, a second vehicle operation judgment module is further included, which is used to judge the vehicle operation when the energy consumption of the electric drive system in the key component is abnormal. The judgment method includes:

[0206] The vehicle's acceleration index is calculated based on the vehicle's acceleration and driving distance within a set number of sampling periods; when the vehicle's speed is less than the set speed and the acceleration index is greater than a first calibration value, or the vehicle's speed is greater than the set speed and the acceleration index is greater than a second calibration value; it is considered that intense driving has caused abnormal energy consumption of the electric drive system.

[0207] In some embodiments, a correction module is further included: used to correct the acceleration and travel distance of the vehicle within a set plurality of sampling periods, and the correction method includes:

[0208] When the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and driving distance of the sampling point are discarded and do not participate in the calculation of the acceleration index.

[0209] In some embodiments, a first key component performance diagnosis module is further included, which is used to diagnose the performance of key components when energy consumption is abnormal, so as to locate the key components that cause abnormal energy consumption of the vehicle. The diagnosis method includes:

[0210] The sum of the target power consumption of each load under the key component is calculated to obtain a total target power consumption value; when the difference between the output power of the key component and the total target power consumption value is greater than a calibration value, it is considered that the load energy consumption of the key component is abnormal.

[0211] In some embodiments, a second key component performance diagnosis module is further included, which is used to diagnose the key component performance when the energy consumption is abnormal, so as to locate the key component that causes the abnormal energy consumption of the vehicle. The diagnosis method includes:

[0212] When the energy consumption of the DCDC high voltage end in key components is abnormal:

[0213] The average power output by the DCDC and the working efficiency of the DCDC are calculated according to the voltage and current information of the input and output of the DCDC; the target efficiency of the DCDC is calculated according to the maximum load power of the DCDC and the average power output by the DCDC; when the difference between the target efficiency of the DCDC and the working efficiency of the DCDC is greater than the calibration value, it is considered that the abnormality of the DCDC efficiency performance leads to the abnormal energy consumption at the high-voltage end of the DCDC;

[0214] When the compressor energy consumption in key components is abnormal:

[0215] If the compressor exhaust pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or the compressor exhaust pressure is greater than the second calibration value after the second set time after the air conditioner is turned on, it is considered that the abnormal exhaust pressure of the air conditioner compressor leads to abnormal compressor energy consumption;

[0216] When the electric drive system in key components recovers energy abnormally:

[0217] When the SOC is less than the set value and the braking system does not disable braking energy recovery:

[0218] If the permissible negative power of the motor is less than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible input power of the battery, it is determined that the permissible input power of the battery is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0219] If the permissible negative power of the motor is greater than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible negative power of the motor, it is determined that the permissible braking power of the motor is insufficient, resulting in abnormal energy recovery consumption of the electric drive system;

[0220] When the electric drive system in key components consumes abnormal energy:

[0221] Calculate the efficiency of the motor based on the input current and voltage of the motor controller, and the torque and speed at the motor output;

[0222] According to the calibrated motor torque and speed relationship table, the target efficiency of the motor is calculated by interpolating the torque and speed at the motor output end;

[0223] When the difference between the target efficiency of the motor and the efficiency of the motor is greater than the calibration value, it is determined that the motor efficiency performance is abnormal, resulting in abnormal driving energy consumption of the electric drive system.

[0224] An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the steps of the method described in the present invention are implemented, which will not be repeated here.

[0225] The computer-readable storage medium may be the data transmission device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (smartmedia card, SMC), a secure digital (secure digital, SD) card, a flash card (flash card), etc., provided on the computer device.

[0226] Furthermore, the computer-readable storage medium may include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data to be output or has been output.

[0227] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0228] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0229] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0230] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0231] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A new energy vehicle energy chain diagnosis method based on cloud big data, characterized in that: include: Determine multiple usage scenarios based on the vehicle's driving environment and condition; Filter out the energy consumption data of key components in each usage scenario from the cloud big data; According to the energy consumption data of the key components, an energy consumption distribution probability density feature set of each key component in each usage scenario is obtained; the energy consumption distribution probability density feature set includes an energy consumption distribution interval of each key component and an energy consumption distribution probability density of each energy consumption distribution interval; Obtaining energy consumption distribution characteristic values ​​of each key component of the vehicle according to energy consumption data of each key component during driving of the vehicle to be diagnosed with an energy chain, a usage scenario, and an energy consumption distribution probability density characteristic set corresponding to the usage scenario; When the energy consumption distribution characteristic value of the key component of the vehicle is lower than a set threshold, it is considered that the energy consumption of the key component of the vehicle is abnormal.

2. The new energy vehicle energy chain diagnosis method based on cloud big data as claimed in claim 1 is characterized in that: Also includes: When the energy consumption of key components is abnormal, the vehicle operation is also diagnosed. The diagnostic methods include: When the energy consumption of the electric drive system in the key components is abnormal, the vehicle speed and / or window opening time are obtained. When the vehicle speed is greater than the set speed and / or the window opening time is greater than the set time, it is considered that high speed or high-speed window opening causes abnormal energy consumption; When the energy consumption of the air-conditioning compressor or the PTC in the key components is abnormal, the window opening time and window opening degree of the vehicle are obtained. When the window opening time is greater than the set time and the window opening degree is greater than the set ratio value, it is considered that the window opening causes the compressor or PTC energy consumption to be abnormal; When the DCDC energy consumption in the key components is abnormal, the vehicle's headlight on time is obtained. When the light sensor determines that it is daytime and the vehicle's headlight on time is greater than the set time, it is considered that the headlight is mistakenly turned on, resulting in abnormal DCDC energy consumption; When the electric drive system in the key component recovers energy abnormally, the vehicle's braking energy recovery status is obtained. If the vehicle's braking energy recovery is in the off state, it is considered that the braking energy recovery is turned off, causing the electric drive system to recover energy abnormally.

3. The new energy vehicle energy chain diagnosis method based on cloud big data as claimed in claim 1 or 2, characterized in that: Also includes: When the energy consumption of the electric drive system in key components is abnormal, the following judgments are also included: Calculating the acceleration index of the vehicle according to the acceleration and travel distance of the vehicle within a set plurality of sampling periods; When the vehicle speed is less than the set speed and the acceleration index is greater than the first calibration value, or the vehicle speed is greater than the set speed and the acceleration index is greater than the second calibration value; it is considered that the intense driving causes abnormal energy consumption of the electric drive system.

4. The new energy vehicle energy chain diagnosis method based on cloud big data as claimed in claim 3 is characterized in that: The method also includes correcting the acceleration and travel distance of the vehicle within a set plurality of sampling periods, wherein the correction method includes: When the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and driving distance of the sampling point are discarded and do not participate in the calculation of the acceleration index.

5. The new energy vehicle energy chain diagnosis method based on cloud big data as claimed in claim 1, characterized in that: When the energy consumption of key components is abnormal, key component performance diagnosis is also started. The diagnostic methods include: Calculate the sum of the target power consumption of each load under the key components to obtain the total target power consumption value; When the difference between the output power of the key component and the total target power consumption value is greater than a calibrated value, it is considered that the load energy consumption of the key component is abnormal.

6. The new energy vehicle energy chain diagnosis method based on cloud big data as claimed in claim 1 or 5, characterized in that: When the energy consumption of key components is abnormal, key component performance diagnosis is also started. The diagnostic methods include: When the energy consumption of the DCDC high voltage end in key components is abnormal: The average power output by the DCDC and the working efficiency of the DCDC are calculated according to the voltage and current information of the input and output of the DCDC; the target efficiency of the DCDC is calculated according to the maximum load power of the DCDC and the average power output by the DCDC; when the difference between the target efficiency of the DCDC and the working efficiency of the DCDC is greater than the calibration value, it is considered that the abnormality of the DCDC efficiency performance leads to the abnormal energy consumption at the high-voltage end of the DCDC; When the compressor energy consumption in key components is abnormal: If the compressor exhaust pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or the compressor exhaust pressure is greater than the second calibration value after the second set time after the air conditioner is turned on, it is considered that the abnormal exhaust pressure of the air conditioner compressor leads to abnormal compressor energy consumption; When the electric drive system in key components recovers energy abnormally: When the SOC is less than the set value and the braking system does not disable braking energy recovery: If the permissible negative power of the motor is less than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible input power of the battery, it is determined that the permissible input power of the battery is insufficient, resulting in abnormal energy recovery consumption of the electric drive system; If the permissible negative power of the motor is greater than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible negative power of the motor, it is determined that the permissible braking power of the motor is insufficient, resulting in abnormal energy recovery consumption of the electric drive system; When the electric drive system in key components consumes abnormal energy: Calculate the efficiency of the motor based on the input current and voltage of the motor controller, and the torque and speed at the motor output; According to the calibrated motor torque and speed relationship table, the target efficiency of the motor is calculated by interpolating the torque and speed at the motor output end; When the difference between the target efficiency of the motor and the efficiency of the motor is greater than the calibration value, it is determined that the motor efficiency performance is abnormal, resulting in abnormal driving energy consumption of the electric drive system.

7. A new energy vehicle energy chain diagnostic system based on cloud big data, characterized in that: include: Vehicle usage scenario analysis module: used to determine various vehicle usage scenarios based on the vehicle's driving environment and condition; Energy consumption distribution probability density feature set acquisition module: used to filter out energy consumption data of key vehicle components in each vehicle usage scenario from cloud big data, and obtain energy consumption distribution probability density feature set of each key component in each usage scenario according to the energy consumption data of the key components; the energy consumption distribution probability density feature set includes energy consumption distribution interval of each key component and energy consumption distribution probability density of each energy consumption distribution interval; Energy consumption abnormality judgment module: used to judge whether the vehicle's energy consumption is abnormal, the judgment method includes: obtaining energy consumption data during vehicle driving; obtaining the energy consumption distribution characteristic value of the vehicle according to the vehicle usage scenario and the energy consumption distribution probability density feature set corresponding to the usage scenario; when the energy consumption distribution characteristic value of the key component of the vehicle is lower than the set threshold, it is considered that the energy consumption of the key component of the vehicle is abnormal.

8. The new energy vehicle energy chain diagnostic system based on cloud big data as claimed in claim 7, characterized in that: Also includes: The first vehicle operation judgment module is used to analyze the cause of abnormal energy consumption of vehicle operation when the energy consumption of key components is abnormal. The analysis method includes: When the energy consumption of the electric drive system in the key components is abnormal, the vehicle speed and / or window opening time are obtained. When the vehicle speed is greater than the set speed and / or the window opening time is greater than the set time, it is considered that high speed or high-speed window opening causes abnormal energy consumption; When the energy consumption of the air-conditioning compressor or the PTC in the key components is abnormal, the window opening time and window opening degree of the vehicle are obtained. When the window opening time is greater than the set time and the window opening degree is greater than the set ratio value, it is considered that the window opening causes the compressor or PTC energy consumption to be abnormal; When the DCDC energy consumption in the key components is abnormal, the vehicle's headlight on time is obtained. When the light sensor determines that it is daytime and the vehicle's headlight on time is greater than the set time, it is considered that the headlight is mistakenly turned on, resulting in abnormal DCDC energy consumption; When the electric drive system in the key component recovers energy abnormally, the vehicle's braking energy recovery status is obtained. If the vehicle's braking energy recovery is in the off state, it is considered that the braking energy recovery is turned off, causing the electric drive system to recover energy abnormally.

9. The new energy vehicle energy chain diagnostic system based on cloud big data as claimed in claim 7, characterized in that: The system further includes a second vehicle operation judgment module, which is used to judge the vehicle operation when the energy consumption of the electric drive system in the key component is abnormal. The judgment method includes: The vehicle's acceleration index is calculated based on the vehicle's acceleration and driving distance within a set number of sampling periods; when the vehicle's speed is less than the set speed and the acceleration index is greater than a first calibration value, or the vehicle's speed is greater than the set speed and the acceleration index is greater than a second calibration value; it is considered that intense driving has caused abnormal energy consumption of the electric drive system.

10. The new energy vehicle energy chain diagnostic system based on cloud big data as claimed in claim 9, characterized in that: It also includes a correction module: used to correct the acceleration and travel distance of the vehicle within a set plurality of sampling periods, and the correction method includes: When the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and driving distance of the sampling point are discarded and do not participate in the calculation of the acceleration index.

11. The new energy vehicle energy chain diagnosis method based on cloud big data as claimed in claim 7, characterized in that: It also includes a first key component performance diagnosis module, which is used to diagnose the performance of key components when energy consumption is abnormal, so as to locate the key components that cause abnormal vehicle energy consumption. The diagnosis method includes: The sum of the target power consumption of each load under the key component is calculated to obtain a total target power consumption value; when the difference between the output power of the key component and the total target power consumption value is greater than a calibration value, it is considered that the load energy consumption of the key component is abnormal.

12. The new energy vehicle energy chain diagnosis method based on cloud big data according to claim 7 or 11, characterized in that: It also includes a second key component performance diagnosis module, which is used to diagnose the key component performance when the energy consumption is abnormal, so as to locate the key component that causes the abnormal energy consumption of the vehicle. The diagnosis method includes: When the energy consumption of the DCDC high voltage end in key components is abnormal: The average power output by the DCDC and the working efficiency of the DCDC are calculated according to the voltage and current information of the input and output of the DCDC; the target efficiency of the DCDC is calculated according to the maximum load power of the DCDC and the average power output by the DCDC; when the difference between the target efficiency of the DCDC and the working efficiency of the DCDC is greater than the calibration value, it is considered that the abnormality of the DCDC efficiency performance leads to the abnormal energy consumption at the high-voltage end of the DCDC; When the compressor energy consumption in key components is abnormal: If the compressor exhaust pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or the compressor exhaust pressure is greater than the second calibration value after the second set time after the air conditioner is turned on, it is considered that the abnormal exhaust pressure of the air conditioner compressor leads to abnormal compressor energy consumption; When the electric drive system in key components recovers energy abnormally: When the SOC is less than the set value and the braking system does not disable braking energy recovery: If the permissible negative power of the motor is less than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible input power of the battery, it is determined that the permissible input power of the battery is insufficient, resulting in abnormal energy recovery consumption of the electric drive system; If the permissible negative power of the motor is greater than the permissible input power of the battery, and the wheel-side requested braking power is less than the permissible negative power of the motor, it is determined that the permissible braking power of the motor is insufficient, resulting in abnormal energy recovery consumption of the electric drive system; When the electric drive system in key components consumes abnormal energy: Calculate the efficiency of the motor based on the input current and voltage of the motor controller, and the torque and speed at the motor output; According to the calibrated motor torque and speed relationship table, the target efficiency of the motor is calculated by interpolating the torque and speed at the motor output end; When the difference between the target efficiency of the motor and the efficiency of the motor is greater than the calibration value, it is determined that the motor efficiency performance is abnormal, resulting in abnormal driving energy consumption of the electric drive system.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the new energy vehicle energy chain diagnosis method based on cloud big data as described in any one of claims 1, 2, 4, and 5 are implemented.

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