A new energy vehicle energy chain diagnosis method and system based on cloud big data

Abstract

The application discloses a new energy vehicle energy chain diagnosis method and system based on cloud big data. A plurality of use scenarios are determined according to a vehicle driving environment and a vehicle condition; energy consumption data of key components in each use scenario are screened out from the cloud big data; an energy consumption distribution probability density feature set of each key component in each use scenario is obtained according to the energy consumption data of the key components; an energy consumption distribution feature value of each key component of the vehicle is obtained according to energy consumption data of each key component during driving of the vehicle to be diagnosed, a use scenario and an energy consumption distribution probability density feature set corresponding to the use scenario; and it is considered that energy consumption of a key component is abnormal when the energy consumption distribution feature value of the key component is lower than a set threshold. According to the application, vehicles with abnormal energy consumption can be investigated and handled in a vehicle off-line detection stage, so that the flow of fault vehicles into a terminal is reduced; and faults and their positions can be quickly and accurately determined in a user use stage, so that enterprises are assisted in monitoring faults.

Description

Technical Field

[0001] This invention belongs to the field of big data analysis technology for new energy vehicles, specifically relating to a new energy vehicle energy chain diagnosis method and system based on cloud-based big data. Background Technology

[0002] In the diagnostic analysis of automotive electronic control units, there is rarely a direct diagnostic analysis of vehicle energy consumption and range performance. Often, these issues can only be addressed after a fault occurs. In addition, when new cars are being inspected before leaving the production line, abnormal energy consumption phenomena are often not detected and warnings are not issued in time. This prevents companies from analyzing and improving the situation in advance and reducing the likelihood of defective vehicles reaching end users.

[0003] As automobiles become increasingly electrified and intelligent, and cloud data processing technology becomes more and more widespread, new methods have emerged for diagnosing the energy consumption performance of new energy vehicles. At the same time, vehicles now offer a wider variety of accessories and driving functions, but whether users are using these functions correctly and appropriately in actual use is not always the case.

[0004] Current OBD diagnostic technology for vehicles primarily stores preset fault information in the vehicle's memory. A specific procedure then reads the fault codes from the memory and displays them to repair personnel, facilitating quick and accurate identification of the nature and location of the fault. However, it rarely diagnoses abnormal energy consumption; it lacks diagnostic capabilities for energy consumption anomalies caused by non-fault-related improper use, and it cannot provide users with reasonable suggestions or corrective measures for such issues. Therefore, it cannot assist manufacturers in monitoring faults and improving vehicle quality. Summary of the Invention

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

[0006] One of the objectives of this invention is a cloud-based big data-driven energy chain diagnostic method for new energy vehicles, comprising:

[0007] Multiple usage scenarios are determined based on the vehicle's driving environment and condition;

[0008] Energy consumption data of key components in each usage scenario are extracted from big data in the cloud;

[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 within this energy consumption distribution interval; the energy consumption distribution interval is multiple 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 status. 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 status 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 status, 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 probability density of energy consumption distribution for each key component in each interval is statistically analyzed. These intervals and the probability density of energy consumption distribution constitute the energy consumption probability density feature set of that key component. For example, the probability density represents the proportion of vehicles. In cloud-based big data, the energy consumption probability density feature set of the electric drive assembly's drive energy consumption 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%} represents that the probability density of the electric drive assembly's energy consumption distribution in the range of (160Wh / km,170Wh / km) in the cloud big data is 15%, that is, the proportion of vehicles is 15%.

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

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

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

[0021] Furthermore, when the energy consumption of critical components is abnormal, the system also includes vehicle operation diagnosis. Methods for vehicle operation diagnosis include:

[0022] When the energy consumption of the electric drive system in a key component is abnormal, the vehicle speed and / or the duration of the window being open are obtained. If the vehicle speed is greater than the set speed and / or the duration of the window being open is greater than the set duration, it is considered that the high speed or the high-speed window opening is causing abnormal energy consumption.

[0023] When the energy consumption of the air conditioning compressor or PTC in a key component is abnormal, the duration of the vehicle's windows being open and the degree of window opening are obtained. If the duration of the window being open is greater than a set duration and the degree of window opening is greater than a set ratio, it is considered that the window opening caused the compressor or PTC energy consumption to be abnormal.

[0024] When the DCDC energy consumption in a key component is abnormal, the headlight on duration is obtained. If the light sensor determines it is daytime and the headlight on duration exceeds the set duration, it is considered that the headlights were mistakenly turned on, causing the DCDC energy consumption to be abnormal.

[0025] When the electric drive system in a critical component experiences abnormal energy recovery, the vehicle's regenerative braking status is obtained. If the vehicle's regenerative braking is off, it is assumed that the off regenerative braking results in low electric drive energy recovery, thus causing abnormal energy recovery in the electric drive system.

[0026] Furthermore, it also includes the following judgment when the energy consumption of the electric drive system in a critical component is abnormal:

[0027] The vehicle's acceleration index is calculated based on the vehicle's acceleration and travel distance over multiple set sampling periods;

[0028] If the vehicle speed is less than the set speed and the acceleration index is greater than the first calibration value, or if the vehicle speed is greater than the set speed and the acceleration index is greater than the second calibration value, then it is considered that aggressive driving has caused abnormal energy consumption of the electric drive system.

[0029] Furthermore, it also includes correcting the vehicle's acceleration and travel distance within a set number of sampling periods, the correction method comprising:

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

[0031] Furthermore, when the energy consumption of a critical component is abnormal, it also includes initiating a critical component performance diagnostic.

[0032] One of the diagnostic methods includes:

[0033] Calculate the sum of the target power consumption for each load under the key component 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, the load energy consumption of the key component is considered abnormal; the key components include: OBC and DC-DC.

[0035] The second diagnostic method includes:

[0036] When the energy consumption at the high-voltage end of the DC-DC converter in a critical component is abnormal:

[0037] The average output power and operating efficiency of the DC-DC converter are calculated based on the input and output voltage and current information. The target efficiency of the DC-DC converter is calculated based on its maximum load power and average output power. When the difference between the target efficiency and the operating efficiency exceeds the calibrated value, the DC-DC converter is considered to have abnormal efficiency performance, leading to abnormal energy consumption at the high-voltage side. The calculation method for the target efficiency of the DC-DC converter includes:

[0038]

[0039] in:

[0040] η t_DCDC For the target efficiency of DC-DC; P out_DCDC P represents the average power output of the DC-DC converter. max_DCDC is the maximum load power of the DC-DC converter; a0, a1, a2, a3, and a4 are constant parameters obtained using the least squares method based on DC-DC efficiency test data.

[0041] When the compressor in a critical component experiences abnormal energy consumption:

[0042] If the compressor discharge pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or if the compressor discharge 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 air conditioner compressor discharge pressure leads to abnormal compressor energy consumption.

[0043] When the energy recovery consumption of the electric drive system in a critical component is abnormal:

[0044] When the SOC is less than the set value and the braking system has not disabled regenerative braking:

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

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

[0047] When the electric drive system in a critical component experiences abnormal power consumption:

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

[0049] Based on the calibrated table of motor torque and speed relationship, 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 and the actual efficiency of the motor is greater than the rated value, it is determined that the abnormal motor efficiency performance leads to abnormal drive energy consumption of the electric drive system.

[0051] A new energy vehicle energy chain diagnostic system based on cloud-based big data, which achieves the second objective of this invention, includes:

[0052] Vehicle usage scenario analysis module: used to determine various usage scenarios based on the vehicle's driving environment and vehicle condition;

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

[0054] Based on the energy consumption data of the key components, an energy consumption distribution probability density feature set for each key component under each usage scenario is obtained; 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.

[0055] Energy consumption anomaly judgment module: used to judge whether the vehicle energy consumption is abnormal. The judgment method includes: obtaining the energy consumption distribution feature value of each key component of the vehicle based on the energy consumption data of each key component during the driving period of the vehicle to be diagnosed, the usage scenario, and the energy consumption distribution probability density feature set corresponding to the usage scenario; when the energy consumption distribution feature value of the key component of the vehicle is lower than a set threshold, the energy consumption of the key component of the vehicle is considered to be abnormal.

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

[0057] When the energy consumption of the electric drive system in a key component is abnormal, the vehicle speed and / or the duration of the window being open are obtained. If the vehicle speed is greater than the set speed and / or the duration of the window being open is greater than the set duration, it is considered that the high speed or the high-speed window opening is causing abnormal energy consumption.

[0058] When the energy consumption of the air conditioning compressor or PTC in a key component is abnormal, the duration of the vehicle's windows being open and the degree of window opening are obtained. If the duration of the window being open is greater than a set duration and the degree of window opening is greater than a set ratio, it is considered that the window opening caused the compressor or PTC energy consumption to be abnormal.

[0059] When the DCDC energy consumption in a key component is abnormal, the headlight on duration is obtained. If the light sensor determines it is daytime and the headlight on duration exceeds the set duration, it is considered that the headlights were mistakenly turned on, causing the DCDC energy consumption to be abnormal.

[0060] When the electric drive system in a critical component experiences abnormal energy recovery, the vehicle's regenerative braking status is obtained. If the vehicle's regenerative braking is off, it is assumed that the off regenerative braking results in low electric drive energy recovery, thus causing abnormal energy recovery in the electric drive system.

[0061] Furthermore, it also includes a second vehicle operation judgment module, used to judge vehicle operation when the power consumption of the electric drive system in key components is abnormal. The judgment method includes:

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

[0063] Furthermore, it also includes a correction module: used to correct the vehicle's acceleration and travel distance within a set number of sampling periods, the correction method including:

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

[0065] Furthermore, it also includes a first critical component performance diagnostic module, used to diagnose the performance of critical components when energy consumption is abnormal, in order to locate the critical components causing the abnormal vehicle energy consumption. The diagnostic methods include:

[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, the load power consumption of the key component is considered to be abnormal; the key components include: DC-DC converter and OBC.

[0067] Furthermore, it also includes a second key component performance diagnostic module, used to diagnose the performance of key components when energy consumption is abnormal, in order to locate the key components causing abnormal vehicle energy consumption. The diagnostic methods include:

[0068] When the energy consumption at the high-voltage end of the DC-DC converter in a critical component is abnormal:

[0069] The average output power and operating efficiency of the DC-DC are calculated based on the input and output voltage and current information. The target efficiency of the DC-DC is calculated based on the maximum load power and the average output power. When the difference between the target efficiency and the operating efficiency of the DC-DC is greater than the calibrated value, it is considered that the DC-DC efficiency performance is abnormal, resulting in abnormal energy consumption at the high-voltage end of the DC-DC.

[0070] When the compressor in a critical component experiences abnormal energy consumption:

[0071] If the compressor discharge pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or if the compressor discharge 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 air conditioner compressor discharge pressure leads to abnormal compressor energy consumption.

[0072] When the energy recovery consumption of the electric drive system in a critical component is abnormal:

[0073] When the SOC is less than the set value and the braking system has not disabled regenerative braking:

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

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

[0076] When the electric drive system in a critical component experiences abnormal power consumption:

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

[0078] Based on the calibrated table of motor torque and speed relationship, 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 and the actual efficiency of the motor is greater than the rated value, it is determined that the abnormal motor efficiency performance leads to abnormal drive energy consumption of the electric drive system.

[0080] The beneficial effects of this invention include:

[0081] During the vehicle off-line inspection phase: For vehicles with abnormal energy consumption, conduct investigations and handle them to reduce the chance of faulty vehicles reaching the terminal.

[0082] During the user usage phase: For vehicles with abnormal energy consumption due to improper use, the energy consumption anomaly analysis described in this invention can be used to formulate improvement suggestions for users and push them to users, thereby reducing energy consumption, increasing range, reducing user complaints, and improving user satisfaction.

[0083] During the user usage phase: For vehicles with abnormal energy consumption due to component malfunctions, guide enterprises to promptly troubleshoot and implement improvement plans for the malfunctioning components to ensure user performance and reduce negative public opinion. Attached Figure Description

[0084] Figure 1 This is a flowchart illustrating the method described in this invention;

[0085] Figure 2 This is a block diagram of the system described in this invention. Detailed Implementation

[0086] The following detailed embodiments are provided to explain the technical solutions of the claims of this invention, so that those skilled in the art can understand the claims. The scope of protection of this invention is not limited to the following specific embodiments. Any modifications made by those skilled in the art that incorporate the technical solutions of the claims but differ from the following detailed embodiments are also within the scope of protection of this invention.

[0087] A cloud-based big data-driven method for diagnosing the energy chain of new energy vehicles, such as... Figure 1 As shown, it includes the following steps:

[0088] S1. Key components that determine power consumption and battery life.

[0089] Based on the energy flow of electric vehicles, the key components determining energy consumption and driving range are mainly the electric drive assembly, electric compressor, PTC, DC-DC converter, OBC, and power battery.

[0090] S2. Classify vehicle usage scenarios

[0091] Based on operating conditions, temperature, and vehicle status, vehicle usage scenarios are categorized as shown in Table 1 below.

[0092] Table 1 Vehicle Usage Scenarios

[0093]

[0094] S3, Combined vehicle usage scenario

[0095] Based on the usage scenarios categorized in Table 1, the following 36 key scenario combinations were identified.

[0096] Table 2 Vehicle Usage Scenarios Combinations

[0097]

[0098] S4, cloud data integration

[0099] In the cloud big data, for the various usage scenario combinations in Table 2, the energy consumption data of vehicles in the cloud big data is filtered out, and the energy consumption distribution probability density feature data of the characteristic performance parameter distribution of the key components mentioned in step S1 is extracted and integrated.

[0100] In this embodiment, the key component's characteristic performance parameters 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 (Power Transmission Control) system (Wh / km), the energy consumption of the DC-DC converter (DC-DC converter) (Wh / km), the OBC (On-Board Charge) efficiency, and the available energy (SOE) of the power battery when fully charged (kWh). Table 3 below illustrates the probability density distribution of energy consumption during the driving and recovery phases of the electric drive assembly under normal temperature and urban operating conditions within the warranty period.

[0101] Table 3. Probability density characteristics of energy consumption distribution under normal temperature and urban operating conditions during the warranty period—Electric drive assembly

[0102]

[0103] S5. Update of probability density feature data of energy consumption distribution of key components under various scenario combinations.

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

[0105] S6, Vehicle Energy Consumption Diagnosis 1 – Electric Drive System

[0106] After a vehicle completes a driving segment, the energy consumption distribution probability density feature set S1 of the corresponding key components under similar driving scenarios is retrieved from the data stored in S5 based on the vehicle's travel scenario for that segment; simultaneously, the energy consumption result y1 of the electric drive system during that segment of the journey is also retrieved. * Based on the energy consumption distribution interval in S1, determine y1. * The interval

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

[0108]

[0109] in, This indicates that 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 probability densities of energy consumption distribution across all k intervals;

[0110] For example:

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

[0112] when (in the formula, When a threshold is set for calibration or based on experience, initiate an analysis of the cause of abnormal energy consumption in the critical component.

[0113] Using the same method, energy consumption diagnoses were performed on the electric drive system recycling, electric compressor, PTC, DC-DC, OBC, and power battery to obtain energy consumption diagnoses for other key components: electric drive system recycling, electric compressor energy consumption, PTC energy consumption, DC-DC energy consumption, and OBC efficiency.

[0114] Abnormal energy consumption data caused by vehicle malfunctions diagnosed in this step are removed from the cloud big data. Finally, based on the cloud energy flow big data, the energy consumption distribution probability density feature data under each scenario combination are iteratively updated at specified time intervals.

[0115] S7, Vehicle Operation Diagnosis.

[0116] Once S6 detects abnormal energy consumption, it initiates the "Vehicle Operation" diagnostic in this step to determine if there are any improper vehicle settings or driving operations. The diagnosis is as follows:

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

[0118] When the S6's diagnosis is "high energy consumption of electric drive system", if the average speed of the vehicle during this drive is ≥60kph and the duration of the window being open is >5min, it is determined to be "abnormal energy consumption caused by high-speed window opening".

[0119] (2) Diagnosis with windows open while air conditioning is on

[0120] When the S6 diagnostic result is "high air conditioning compressor energy consumption" or "high PTC energy consumption", if the vehicle's windows are open for more than 10 minutes or the window opening degree is more than 20%, it is determined that "opening windows causes high compressor or PTC energy consumption".

[0121] (3) Headlight malfunction 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 malfunction caused high DCDC power consumption".

[0122] (4) Aggressive driving diagnostics

[0123] When the diagnostic result of S6 is "high drive energy consumption of electric drive system", the acceleration index β is calculated according to the following formula (1). 加 When the vehicle speed is <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 If so, it is determined that "aggressive driving leads to high energy consumption".

[0124]

[0125] In the formula: i refers to the sampling point; a i The acceleration of the vehicle at sampling point i is expressed in m / s². 2 ;d i This refers to the distance the vehicle traveled at sampling point i, measured in km.

[0126] To exclude data from the stable driving phase from influencing the acceleration index β 加 The impact also needs to be considered for a i and d i The correction method is as follows:

[0127] when a i <0.2m / s 2 At that time, a i =0; d i =0.

[0128] (5) Braking energy recovery setting determination

[0129] If the S6 diagnostic result is "low energy recovery of the electric drive system", and the vehicle's brake energy recovery is turned off, then it is determined that "low energy recovery of the electric drive is caused by brake energy recovery being turned off".

[0130] S8, Performance Analysis of Key Components

[0131] If the S6 diagnostic confirms that the vehicle is operating normally, then the energy consumption diagnostic analysis of key components will be initiated.

[0132] (1) DCDC efficiency diagnosis

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

[0134] First, based on the input and output voltage and current information of the DC-DC converter, calculate the average output power P of the DC-DC converter. out_DCDC The efficiency η of DC-DC DCDC The target efficiency η of DC-DC is calculated according to formula (2). t_DCDC The efficiency difference η between DC-DC converters diff =η t_DCDC -η DCDC When η diff Greater than the calibration value η DCDC0 When this occurs, it is determined that the DC-DC efficiency performance is abnormal.

[0135]

[0136] In the formula: P max_DCDC is the maximum load power of the DC-DC converter; a0, a1, a2, a3, and a4 are constant parameters obtained using the least squares method based on DC-DC efficiency test data.

[0137] (2) DC-DC load power diagnosis

[0138] The power consumption list of low-voltage electrical appliances in Table 4 is embedded in the module, and the target low-voltage 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 standard amount P low_0 At that time, it was determined that the low-pressure accessory energy consumption was abnormal.

[0139] Table 4 Low-voltage accessory power consumption list

[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 windshield wipers 210 5 Position lights 50 6 Headlights - Low Beam 35 7 Headlights - High Beams 55 8 …… ……

[0141]

[0142] In the formula, t0 refers to the sampling start time, in seconds; t end The sampling end time is indicated in seconds; j refers to the attachment number; P... j The target power of the attachments is indicated by 'n', where 'n' represents the number of attachments, measured in watts (W) or s. j This indicates the working status of the attachment. When the attachment is not working, the value is 0; when it is working, the value is the duty cycle or 1.

[0143] (3) OBC efficiency diagnosis

[0144] When the S6 diagnostic result is "High OBC input consumption," and the SOC is less than 95%, and this occurs after 5 minutes of charging, then OBC efficiency is diagnosed. The OBC efficiency diagnosis method is the same as that for DC-DC converters, and its low-voltage accessories are shown in Table 4 above. The technical effect of this step is that when the SOC is high or during the initial period of charging, the charging power is generally lower than the target charging power. At this time, the OBC efficiency will be lower. Therefore, by increasing the constraints on SOC and charging time, the impact of this charging condition on the OBC charging rate is reduced.

[0145] (4) Electric compressor power consumption diagnosis

[0146] When the diagnosis result is "high compressor consumption", the compressor power consumption diagnosis is initiated. If the compressor discharge pressure PAC is greater than the calibrated value Pmax1 within the first 30 minutes of turning on the air conditioner; or if the compressor discharge pressure PAC is greater than the calibrated value Pmax2 after the air conditioner has been turned on for 30 minutes, it is determined that the air conditioner compressor discharge pressure is abnormal.

[0147] (5) Electric drive system recovery diagnosis

[0148] When the diagnostic result of S6 is "Electric drive system recovery is abnormally low", the following analysis of the electric drive system recovery diagnosis will be initiated:

[0149] If SOC < 95% and regenerative braking is not disabled in the braking system, when the allowable negative power P of the motor... -MCU_a (Drive is positive, braking is negative) less than the battery's allowable input power P -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 battery's allowable input power P -HVB_In When the regenerative braking is insufficient, it is determined that the battery's allowable input power is insufficient, resulting in low regeneration. The allowable negative power of the motor is the minimum power that the motor is allowed to regenerate during braking energy recovery; the motor power is defined as a positive value when the motor is driving the vehicle forward, and a negative value when the motor is performing braking energy recovery. The allowable input power of the battery refers to the minimum charging power that the battery can accept; the power of the battery discharging externally is defined as a positive value, and the power of charging the battery is defined as a negative value. The wheel-side requested braking power refers to the power requested by the wheels when the vehicle brakes; the wheel-side requested power is defined as a positive value when the vehicle is driving, and a negative value when the vehicle is braking.

[0150] If SOC < 95% and regenerative braking is not disabled in the braking system, when the allowable negative power P of the motor... -MCU_a Greater than the battery's allowable input power P -HVB_In And the wheel-side braking power P is requested. B_Re Less than the allowable negative power P of the motor -MCU_aAt that time, it was determined that the allowable braking power of the motor was insufficient, resulting in low recovery.

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

[0152] When the diagnostic result is "abnormal drive energy consumption of the electric drive system", the input current I of the motor controller is used as a reference. in_mcu Voltage U in_muc The torque T at the motor output end out_em Rotational speed N out_em The efficiency η of the motor is calculated using formula (4). em And based on the motor efficiency table in the implanted module (see Table 5), the torque T at the motor output is used. out_em Rotational speed N out_em Interpolation calculation of the target efficiency η of the motor em_T The efficiency difference η of the motor assembly em_diff =η em_T -η em η em_diff Greater than the calibration value η em0 When this occurs, it is determined that the motor efficiency performance is abnormal.

[0153]

[0154] Table 5. Motor Assembly Efficiency

[0155]

[0156] S9, New Vehicle Off-Line Inspection

[0157] During the new vehicle off-line inspection process, if the diagnosis result indicates abnormal energy consumption, the abnormal vehicle can be intercepted in a timely manner, and the problem can be investigated to prevent the abnormal vehicle from entering the warehouse and ultimately flowing into the end market.

[0158] S10, Improved end-user experience

[0159] When the diagnostic result indicates abnormal energy consumption, if the diagnostic module confirms that it is due to improper user operation, it can push suggestions for improvement to the user, such as driving mode settings, suggestions for using high-power accessories, suggestions for improving driving habits, low tire pressure, and maintenance suggestions for aging parts, etc.

[0160] S11. Terminal Negative Public Opinion Warning and Improvement

[0161] When the diagnosis result is abnormal energy consumption, the company promptly arranges fault analysis and formulates response plans for the vehicle's fault points; during vehicle maintenance, based on the diagnostic information stored in the cloud, maintenance plans are formulated and recommended to users to ensure that the vehicle is in good performance condition.

[0162] It should be understood that the sequence number of each step in the above embodiments does not imply 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 embodiments of the present invention.

[0163] This invention also provides a new energy vehicle energy chain diagnostic method based on cloud-based big data, including:

[0164] Multiple usage scenarios are determined based on the vehicle's driving environment and condition;

[0165] Energy consumption data of key components in each usage scenario are extracted from big data in the cloud;

[0166] Based on the energy consumption data of the key components, an energy consumption distribution probability density feature set for each key component under each usage scenario is obtained; 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.

[0167] The energy consumption distribution feature value of each key component of the vehicle is obtained based on the energy consumption data of each key component during the driving period of the vehicle to be diagnosed, the usage scenario, and the energy consumption distribution probability density feature set corresponding to the usage scenario.

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

[0169] In some embodiments, when the energy consumption of a critical component is abnormal, the method further includes diagnosing vehicle operation, wherein the vehicle operation diagnosis method includes:

[0170] When the energy consumption of the electric drive system in a key component is abnormal, the vehicle speed and / or the duration of the window being open are obtained. If the vehicle speed is greater than the set speed and / or the duration of the window being open is greater than the set duration, it is considered that the high speed or the high-speed window opening is causing abnormal energy consumption.

[0171] When the energy consumption of the air conditioning compressor or PTC in a key component is abnormal, the duration of the vehicle's windows being open and the degree of window opening are obtained. If the duration of the window being open is greater than a set duration and the degree of window opening is greater than a set ratio, it is considered that the window opening caused the compressor or PTC energy consumption to be abnormal.

[0172] When the DCDC energy consumption in a key component is abnormal, the headlight on duration is obtained. If the light sensor determines it is daytime and the headlight on duration exceeds the set duration, it is considered that the headlights were mistakenly turned on, causing the DCDC energy consumption to be abnormal.

[0173] When the electric drive system in a critical component experiences abnormal energy recovery, the vehicle's regenerative braking status is obtained. If the vehicle's regenerative braking is off, it is assumed that the off regenerative braking caused the abnormal energy recovery in the electric drive system.

[0174] In some embodiments, the method further includes: when the power consumption of the electric drive system in a critical component is abnormal, the method further includes the following determination:

[0175] The vehicle's acceleration index is calculated based on the vehicle's acceleration and travel distance over multiple set sampling periods;

[0176] If the vehicle speed is less than the set speed and the acceleration index is greater than the first calibration value, or if the vehicle speed is greater than the set speed and the acceleration index is greater than the second calibration value, then it is considered that aggressive driving has caused abnormal energy consumption of the electric drive system.

[0177] In some embodiments, the method further includes correcting the vehicle's acceleration and travel distance over a set number of sampling periods, the correction method comprising:

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

[0179] In some embodiments, when the energy consumption of a critical component is abnormal, the method further includes initiating a critical component performance diagnostic, the diagnostic method of which includes:

[0180] Calculate the sum of the target power consumption for each load under the key component 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 is greater than the calibration value, the load energy consumption of the key component is considered abnormal.

[0182] In some embodiments, when the energy consumption of a critical component is abnormal, the method further includes initiating a critical component performance diagnostic, the diagnostic method of which includes:

[0183] When the energy consumption at the high-voltage end of the DC-DC converter in a critical component is abnormal:

[0184] The average output power and operating efficiency of the DC-DC are calculated based on the input and output voltage and current information. The target efficiency of the DC-DC is calculated based on the maximum load power and the average output power. When the difference between the target efficiency and the operating efficiency of the DC-DC is greater than the calibrated value, it is considered that the DC-DC efficiency performance is abnormal, resulting in abnormal energy consumption at the high-voltage end of the DC-DC.

[0185] When the compressor in a critical component experiences abnormal energy consumption:

[0186] If the compressor discharge pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or if the compressor discharge 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 air conditioner compressor discharge pressure leads to abnormal compressor energy consumption.

[0187] When the energy recovery consumption of the electric drive system in a critical component is abnormal:

[0188] When the SOC is less than the set value and the braking system has not disabled regenerative braking:

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

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

[0191] When the electric drive system in a critical component experiences abnormal power consumption:

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

[0193] Based on the calibrated table of motor torque and speed relationship, 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 and the actual efficiency of the motor is greater than the rated value, it is determined that the abnormal motor efficiency performance leads to abnormal drive energy consumption of the electric drive system.

[0195] This invention also provides a new energy vehicle energy chain diagnostic system based on cloud-based big data, comprising:

[0196] Vehicle usage scenario analysis module: used to determine various usage scenarios based on the vehicle's driving environment and vehicle condition;

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

[0198] Based on the energy consumption data of the key components, an energy consumption distribution probability density feature set for each key component under each usage scenario is obtained; 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.

[0199] Energy consumption anomaly judgment module: used to judge whether the vehicle energy consumption is abnormal. The judgment method includes: obtaining the energy consumption distribution feature value of each key component of the vehicle based on the energy consumption data of each key component during the driving period of the vehicle to be diagnosed, the usage scenario, and the energy consumption distribution probability density feature set corresponding to the usage scenario; when the energy consumption distribution feature value of the key component of the vehicle is lower than a set threshold, the energy consumption of the key component of the vehicle is considered to be abnormal.

[0200] In some embodiments, the system further includes: a first vehicle operation judgment module, used to analyze the cause of abnormal energy consumption in vehicle operation when the energy consumption of a critical component is abnormal, the analysis method including:

[0201] When the energy consumption of the electric drive system in a key component is abnormal, the vehicle speed and / or the duration of the window being open are obtained. If the vehicle speed is greater than the set speed and / or the duration of the window being open is greater than the set duration, it is considered that the high speed or the high-speed window opening is causing abnormal energy consumption.

[0202] When the energy consumption of the air conditioning compressor or PTC in a key component is abnormal, the duration of the vehicle's windows being open and the degree of window opening are obtained. If the duration of the window being open is greater than a set duration and the degree of window opening is greater than a set ratio, it is considered that the window opening caused the compressor or PTC energy consumption to be abnormal.

[0203] When the DCDC energy consumption in a key component is abnormal, the headlight on duration is obtained. If the light sensor determines it is daytime and the headlight on duration exceeds the set duration, it is considered that the headlights were mistakenly turned on, causing the DCDC energy consumption to be abnormal.

[0204] When the electric drive system in a critical component experiences abnormal energy recovery, the vehicle's regenerative braking status is obtained. If the vehicle's regenerative braking is off, it is assumed that the off regenerative braking caused the abnormal energy recovery in the electric drive system.

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

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

[0207] In some embodiments, a correction module is further included: for correcting the vehicle's acceleration and travel distance within a set number of sampling periods, the correction method comprising:

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

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

[0210] 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, the load energy consumption of the key component is considered abnormal.

[0211] In some embodiments, a second critical component performance diagnosis module is also included, used to diagnose the performance of critical components when energy consumption is abnormal, so as to locate the critical components causing the abnormal vehicle energy consumption. The diagnosis method includes:

[0212] When the energy consumption at the high-voltage end of the DC-DC converter in a critical component is abnormal:

[0213] The average output power and operating efficiency of the DC-DC are calculated based on the input and output voltage and current information. The target efficiency of the DC-DC is calculated based on the maximum load power and the average output power. When the difference between the target efficiency and the operating efficiency of the DC-DC is greater than the calibrated value, it is considered that the DC-DC efficiency performance is abnormal, resulting in abnormal energy consumption at the high-voltage end of the DC-DC.

[0214] When the compressor in a critical component experiences abnormal energy consumption:

[0215] If the compressor discharge pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or if the compressor discharge 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 air conditioner compressor discharge pressure leads to abnormal compressor energy consumption.

[0216] When the energy recovery consumption of the electric drive system in a critical component is abnormal:

[0217] When the SOC is less than the set value and the braking system has not disabled regenerative braking:

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

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

[0220] When the electric drive system in a critical component experiences abnormal power consumption:

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

[0222] Based on the calibrated table of motor torque and speed relationship, 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 and the actual efficiency of the motor is greater than the rated value, it is determined that the abnormal motor efficiency performance leads to abnormal drive energy consumption of the electric drive system.

[0224] This invention also provides a computer-readable storage medium storing a computer program, which includes program instructions that, when executed by a processor, implement the various steps of the method described in this invention, which will not be elaborated further here.

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

[0226] Furthermore, the computer-readable storage medium may include both internal storage units and external storage devices of the computer 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 that is to be output or has already been output.

[0227] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0229] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0230] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0231] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A new energy vehicle energy chain diagnostic method based on cloud-based big data, characterized in that, include: Multiple usage scenarios are determined based on the vehicle's driving environment and condition; Energy consumption data of key components in each usage scenario are extracted from big data in the cloud; Based on the energy consumption data of the key components, an energy consumption distribution probability density feature set for each key component under each usage scenario is obtained; 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 feature value of each key component of the vehicle is obtained based on the energy consumption data of each key component during the driving period of the vehicle to be diagnosed, 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 a key component of the vehicle is lower than a set threshold, the energy consumption of that key component of the vehicle is considered abnormal. Methods for obtaining the probability density feature set of energy consumption distribution include: Based on the usage scenarios of each vehicle, energy consumption data of each key component of the vehicle is obtained from the big data in the cloud. The key components are the components that consume electric energy and have range in the energy flow of electric vehicles, including: electric drive assembly, electric compressor, PTC, DC-DC, OBC, and power battery. 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 calculated, and the intervals and the energy consumption distribution probability density constitute the energy consumption distribution probability density feature set of the key component. The calculation method for the energy consumption distribution characteristic value of the key component includes: Obtain energy consumption data of a key component of a vehicle in a certain usage scenario; obtain the energy consumption distribution probability density feature set of the key component in the usage scenario from cloud big data; Determine the interval of the energy consumption distribution probability density feature set corresponding to the energy consumption data of the key component; calculate the sum of the energy consumption distribution probability densities corresponding to all other distribution intervals where the lower limit value of the corresponding energy consumption distribution probability density feature set is greater than the interval where the key component is located, which is the energy consumption distribution feature value of the key component.

2. The new energy vehicle energy chain diagnostic method based on cloud-based big data as described in claim 1, characterized in that, Also includes: When the energy consumption of critical components is abnormal, the diagnostic process also includes diagnosing vehicle operation. Diagnostic methods include: When the energy consumption of the electric drive system in a key component is abnormal, the vehicle speed and / or the duration of the window being open are obtained. If the vehicle speed is greater than the set speed and / or the duration of the window being open is greater than the set duration, it is considered that the high speed or the high-speed window opening is causing abnormal energy consumption. When the energy consumption of the air conditioning compressor or PTC in a key component is abnormal, the duration of the vehicle's windows being open and the degree of window opening are obtained. If the duration of the window being open is greater than a set duration and the degree of window opening is greater than a set ratio, it is considered that the window opening caused the compressor or PTC energy consumption to be abnormal. When the DCDC energy consumption in a key component is abnormal, the headlight on duration is obtained. If the light sensor determines it is daytime and the headlight on duration exceeds the set duration, it is considered that the headlights were mistakenly turned on, causing the DCDC energy consumption to be abnormal. When the electric drive system in a critical component experiences abnormal energy recovery, the vehicle's regenerative braking status is obtained. If the vehicle's regenerative braking is off, it is assumed that the off regenerative braking caused the abnormal energy recovery in the electric drive system.

3. The new energy vehicle energy chain diagnostic method based on cloud-based big data as described in claim 1 or 2, characterized in that, Also includes: When the energy consumption of the electric drive system in a critical component is abnormal, the following judgments are also included: The vehicle's acceleration index is calculated based on the vehicle's acceleration and travel distance over multiple set sampling periods; If the vehicle speed is less than the set speed and the acceleration index is greater than the first calibration value, or if the vehicle speed is greater than the set speed and the acceleration index is greater than the second calibration value, then it is considered that aggressive driving has caused abnormal energy consumption of the electric drive system.

4. The new energy vehicle energy chain diagnosis method based on cloud-based big data as described in claim 3, characterized in that, It also includes correcting the vehicle's acceleration and travel distance over multiple set sampling periods, the correction method including: If the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and travel distance at that sampling moment are discarded and not included in the calculation of the acceleration index.

5. The new energy vehicle energy chain diagnostic method based on cloud-based big data as described in claim 1, characterized in that, When the energy consumption of critical components is abnormal, the system also includes initiating performance diagnostics for these critical components. Diagnostic methods include: Calculate the sum of the target power consumption for each load under the key component 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 is greater than the calibration value, the load energy consumption of the key component is considered abnormal.

6. The new energy vehicle energy chain diagnosis method based on cloud-based big data as described in claim 1 or 5, characterized in that, When the energy consumption of critical components is abnormal, the system also includes initiating performance diagnostics for these critical components. Diagnostic methods include: When the energy consumption at the high-voltage end of the DC-DC converter in a critical component is abnormal: The average output power and operating efficiency of the DC-DC are calculated based on the input and output voltage and current information. The target efficiency of the DC-DC is calculated based on the maximum load power and the average output power. When the difference between the target efficiency and the operating efficiency of the DC-DC is greater than the calibrated value, it is considered that the DC-DC efficiency performance is abnormal, resulting in abnormal energy consumption at the high-voltage end of the DC-DC. When the compressor in a critical component experiences abnormal energy consumption: If the compressor discharge pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or if the compressor discharge 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 air conditioner compressor discharge pressure leads to abnormal compressor energy consumption. When the energy recovery consumption of the electric drive system in a critical component is abnormal: When the SOC is less than the set value and the braking system has not disabled regenerative braking: If the allowable negative power of the motor is less than the allowable input power of the battery, and the wheel-side braking power requested is less than the allowable input power of the battery, it is determined that the battery's allowable input power is insufficient, resulting in abnormal energy consumption recovery in the electric drive system. If the allowable negative power of the motor is greater than the allowable input power of the battery, and the wheel-side braking power requested is less than the allowable negative power of the motor, then it is determined that the allowable braking power of the motor is insufficient, resulting in abnormal energy consumption recovery of the electric drive system. When the electric drive system in a critical component experiences abnormal power consumption: Calculate the motor efficiency based on the input current and voltage of the motor controller, and the torque and speed at the motor output. Based on the calibrated table of motor torque and speed relationship, 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 and the actual efficiency of the motor is greater than the rated value, it is determined that the abnormal motor efficiency performance leads to abnormal drive energy consumption of the electric drive system.

7. A cloud-based big data-based new energy vehicle energy chain diagnostic system employing the cloud-based big data-based new energy vehicle energy chain diagnostic method of claim 1, characterized in that, include: Vehicle usage scenario analysis module: used to determine various vehicle usage scenarios based on the vehicle's driving environment and vehicle condition; Energy consumption distribution probability density feature set acquisition module: used to filter out energy consumption data of key vehicle components under each vehicle usage scenario from big data in the cloud, and obtain the energy consumption distribution probability density feature set of each key component under 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. Energy consumption anomaly judgment module: used to judge whether the vehicle's energy consumption is abnormal. The judgment method includes: acquiring energy consumption data during vehicle operation; obtaining the energy consumption distribution feature value of the vehicle based on the vehicle's usage scenario and the corresponding energy consumption distribution probability density feature set; and considering the energy consumption of the key component of the vehicle to be abnormal when the energy consumption distribution feature value of the key component is lower than a set threshold.

8. The new energy vehicle energy chain diagnostic system based on cloud-based big data as described in claim 7, characterized in that, Also includes: The first vehicle operation judgment module is used to analyze the cause of abnormal energy consumption in 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 a key component is abnormal, the vehicle speed and / or the duration of the window being open are obtained. If the vehicle speed is greater than the set speed and / or the duration of the window being open is greater than the set duration, it is considered that the high speed or the high-speed window opening is causing abnormal energy consumption. When the energy consumption of the air conditioning compressor or PTC in a key component is abnormal, the duration of the vehicle's windows being open and the degree of window opening are obtained. If the duration of the window being open is greater than a set duration and the degree of window opening is greater than a set ratio, it is considered that the window opening caused the compressor or PTC energy consumption to be abnormal. When the DCDC energy consumption in a key component is abnormal, the headlight on duration is obtained. If the light sensor determines it is daytime and the headlight on duration exceeds the set duration, it is considered that the headlights were mistakenly turned on, causing the DCDC energy consumption to be abnormal. When the electric drive system in a critical component experiences abnormal energy recovery, the vehicle's regenerative braking status is obtained. If the vehicle's regenerative braking is off, it is assumed that the off regenerative braking caused the abnormal energy recovery in the electric drive system.

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

10. The new energy vehicle energy chain diagnostic system based on cloud-based big data as described in claim 9, characterized in that, It also includes a correction module: used to correct the vehicle's acceleration and travel distance within a set number of sampling periods, the correction method including: If the acceleration at a certain sampling moment is less than the set acceleration, the acceleration and travel distance at that sampling moment are discarded and not included in the calculation of the acceleration index.

11. The new energy vehicle energy chain diagnostic method based on cloud-based big data as described in claim 7, characterized in that, It also includes a first critical component performance diagnostic module, used to diagnose the performance of critical components when energy consumption is abnormal, in order to locate the critical components causing the abnormal vehicle energy consumption. The diagnostic methods include: 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, the load energy consumption of the key component is considered abnormal.

12. The new energy vehicle energy chain diagnostic method based on cloud-based big data as described in claim 7 or 11, characterized in that, It also includes a second critical component performance diagnostic module, used to diagnose the performance of critical components when energy consumption is abnormal, in order to locate the critical components causing the abnormal vehicle energy consumption. The diagnostic methods include: When the energy consumption at the high-voltage end of the DC-DC converter in a critical component is abnormal: The average output power and operating efficiency of the DC-DC are calculated based on the input and output voltage and current information. The target efficiency of the DC-DC is calculated based on the maximum load power and the average output power. When the difference between the target efficiency and the operating efficiency of the DC-DC is greater than the calibrated value, it is considered that the DC-DC efficiency performance is abnormal, resulting in abnormal energy consumption at the high-voltage end of the DC-DC. When the compressor in a critical component experiences abnormal energy consumption: If the compressor discharge pressure is greater than the first calibration value within the first set time after the air conditioner is turned on, or if the compressor discharge 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 air conditioner compressor discharge pressure leads to abnormal compressor energy consumption. When the energy recovery consumption of the electric drive system in a critical component is abnormal: When the SOC is less than the set value and the braking system has not disabled regenerative braking: If the allowable negative power of the motor is less than the allowable input power of the battery, and the wheel-side braking power requested is less than the allowable input power of the battery, it is determined that the battery's allowable input power is insufficient, resulting in abnormal energy consumption recovery in the electric drive system. If the allowable negative power of the motor is greater than the allowable input power of the battery, and the wheel-side braking power requested is less than the allowable negative power of the motor, then it is determined that the allowable braking power of the motor is insufficient, resulting in abnormal energy consumption recovery of the electric drive system. When the electric drive system in a critical component experiences abnormal power consumption: Calculate the motor efficiency based on the input current and voltage of the motor controller, and the torque and speed at the motor output. Based on the calibrated table of motor torque and speed relationship, 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 and the actual efficiency of the motor is greater than the rated value, it is determined that the abnormal motor efficiency performance leads to abnormal drive 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, it implements 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.

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