Energy consumption monitoring method and device based on vehicle energy flow

Abstract

The present invention relates to the field of vehicle energy consumption monitoring, and provides an energy consumption monitoring method and device based on the energy flow of a whole vehicle, the method comprising: collecting energy flow signals and driving behavior data of target energy-consuming components of a target vehicle; inputting the energy flow signals into an energy consumption management module to perform data analysis on the energy flow signals to obtain energy consumption data corresponding to the target energy-consuming components; performing energy consumption abnormality diagnosis based on the energy consumption data; after the energy consumption abnormality diagnosis is passed, determining the target driving style and target driving condition based on the energy consumption data and driving behavior data and monitoring the energy consumption data. Thus, based on the real-time monitoring of energy consumption of the energy flow of the whole vehicle, an energy consumption management module is established to integrate and calculate the energy flow related signals of each energy-consuming component to realize a complete energy flow analysis closed loop of the vehicle, thereby improving the accuracy of the energy flow analysis and being able to more meticulously and accurately monitor the energy flow of each energy-consuming component.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle energy consumption monitoring, and in particular to an energy consumption monitoring method and device based on whole vehicle energy flow. Background Art

[0002] Energy consumption is one of the performance parameters that users are most concerned about during the operation of electric heavy trucks, and the energy consumption of the entire electric heavy truck is composed of the power consumption of each component. Therefore, the vehicle's energy flow monitoring and energy consumption abnormality warning functions can effectively manage vehicle energy consumption.

[0003] The existing technology is based on the energy consumption analysis of the vehicle-side software and the back-end Internet of Vehicles platform. It simply monitors and optimizes the measures based on the energy consumption status parameters of the battery and motor to determine the best control mode corresponding to the current battery status of the vehicle. Summary of the invention

[0004] The present invention provides an energy consumption monitoring method and device based on the energy flow of a whole vehicle, which is used to solve the defect in the prior art that the energy consumption of the energy-consuming components of the whole vehicle is not accurately monitored according to the actual working conditions and / or driving behavior and abnormal energy consumption warnings and interventions are not performed, and accurate energy flow analysis of the whole vehicle is achieved. The energy consumption of each energy-consuming component can be monitored more carefully and abnormal warnings and interventions can be performed.

[0005] The present invention provides an energy consumption monitoring method based on the energy flow of a whole vehicle, comprising:

[0006] Collecting energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle;

[0007] Inputting the energy flow signal into an energy consumption management module, and performing data analysis on the energy flow signal through the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming component;

[0008] diagnose abnormal energy consumption based on the energy consumption data;

[0009] After the energy consumption abnormality diagnosis is passed, determining a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data;

[0010] The energy consumption data corresponding to the target energy-consuming component is monitored based on the target driving style and the target driving condition.

[0011] In a possible implementation, the method further includes:

[0012] Calculating the energy consumption field of the target energy-consuming component under vehicle operating conditions and vehicle idling conditions;

[0013] The energy consumption management module is constructed based on the energy consumption field.

[0014] In a possible implementation, the method further includes:

[0015] Obtaining state parameters of the target energy-consuming component corresponding to the vehicle operating condition and the vehicle idling condition;

[0016] Determine the correlation degree between the energy consumption field and the corresponding state parameter through the correlation coefficient and determine the change trend between the energy consumption field and the corresponding state parameter in the time series;

[0017] A regression model between the energy consumption field and the corresponding state parameter is established based on the correlation degree and the change trend.

[0018] In a possible implementation, the method further includes:

[0019] The energy flow signal includes a current signal and a voltage signal;

[0020] The current signal and the voltage signal are input into an energy consumption management module, and energy consumption data corresponding to the target energy consumption component is obtained based on time integral calculation through the energy consumption field, the current signal and the voltage signal in the energy consumption management module.

[0021] In a possible implementation, the method further includes:

[0022] Building an abnormal warning mechanism based on the regression model;

[0023] Based on the regression model, energy consumption abnormality diagnosis is performed on the energy consumption data corresponding to the target energy-consuming component, and an early warning response is performed according to the diagnosis result.

[0024] In a possible implementation, the method further includes:

[0025] Determining standard energy consumption data and an energy consumption data deviation range based on the target driving style and the target driving condition;

[0026] The energy consumption data corresponding to the target energy consumption component is monitored based on the standard energy consumption data and the energy consumption data deviation range.

[0027] In a possible implementation, the method further includes:

[0028] The monitoring results are sent to the user end, and energy consumption warnings and energy-saving prompts are issued through the user end.

[0029] The present invention also provides an energy consumption monitoring device based on the energy flow of the whole vehicle, comprising:

[0030] A collection module, used to collect energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle;

[0031] An analysis module, used for inputting the energy flow signal into an energy consumption management module, and performing data analysis on the energy flow signal through the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming component;

[0032] A diagnosis module, used for diagnosing abnormal energy consumption based on the energy consumption data;

[0033] a determination module, configured to determine a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data after the energy consumption abnormality diagnosis is passed;

[0034] A monitoring module is used to monitor energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition.

[0035] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the energy consumption monitoring method based on the energy flow of the entire vehicle as described above are implemented.

[0036] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the energy consumption monitoring method based on the energy flow of the entire vehicle as described above are implemented.

[0037] The energy consumption monitoring method and device based on the energy flow of the whole vehicle provided by the present invention collect energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle; input the energy flow signals into an energy consumption management module, and perform data analysis on the energy flow signals through the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming components; perform energy consumption anomaly diagnosis based on the energy consumption data; after the energy consumption anomaly diagnosis is passed, determine a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming components and the driving behavior data; and monitor the energy consumption data corresponding to the target energy-consuming components based on the target driving style and the target driving condition. Compared with the current energy consumption analysis based on vehicle-side software and back-end Internet of Vehicles platform, which only monitors and optimizes measures based on the energy consumption status parameters of batteries and motors, it does not take into account the energy consumption destination and abnormal monitoring of auxiliary drive high-voltage components. When the vehicle has abnormal energy consumption, it does not consider timely warning and intervention measures, and cannot provide accurate solutions and energy-saving guidance. This solution, based on real-time monitoring of the energy consumption of the entire vehicle energy flow, establishes an energy consumption management module to integrate and calculate the energy flow related signals of each energy-consuming component to realize a complete energy flow analysis closed loop of the vehicle, thereby improving the accuracy of energy flow analysis and being able to more accurately monitor the energy flow of each energy-consuming component. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is one of the flow charts of the energy consumption monitoring method based on the whole vehicle energy flow provided by the present invention.

[0040] Figure 2 This is the second flow chart of the energy consumption monitoring method based on the whole vehicle energy flow provided by the present invention.

[0041] Figure 3 It is a schematic diagram of the fluctuation of the rotation speed and current of the motor provided by the present invention over time.

[0042] Figure 4 It is a schematic diagram showing the change of energy consumption of the motor provided by the present invention over time.

[0043] Figure 5 It is a structural schematic diagram of the energy consumption monitoring device based on the whole vehicle energy flow provided by the present invention.

[0044] Figure 6It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] To facilitate understanding of the embodiments of the present invention, specific embodiments will be further explained below in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the embodiments of the present invention.

[0047] Combine the following Figures 1 to 4 The energy consumption monitoring method based on the whole vehicle energy flow of the present invention is described.

[0048] Figure 1 This is one of the flow charts of the energy consumption monitoring method based on the whole vehicle energy flow provided by the present invention, such as Figure 1 As shown, the method specifically includes:

[0049] S11, collecting energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle.

[0050] The embodiment of the present invention is preferably applicable to the energy consumption monitoring scenario of heavy trucks. The target energy-consuming components include but are not limited to batteries, motors, air-conditioning compressors, water-cooling units, air pumps, oil pumps, DCDC, etc. The target energy-consuming components may be one, multiple or all of them.

[0051] The energy flow signals of the above-mentioned target energy-consuming components of the target vehicle are collected. The energy flow signals are obtained by the vehicle controller (VCU software) receiving the bus signals of the controllers of each energy-consuming component on the target vehicle. The energy flow signals include but are not limited to current signals and voltage signals. The driving behavior data of the target vehicle include but are not limited to acceleration, braking, turning and other behaviors.

[0052] S12, inputting the energy flow signal into an energy consumption management module, and performing data analysis on the energy flow signal through the energy consumption management module to obtain energy consumption data corresponding to the target energy consumption component.

[0053] S13. Perform abnormal energy consumption diagnosis based on the energy consumption data.

[0054] In the embodiment of the present invention, an energy consumption management module needs to be established in advance, and the energy consumption management module also includes a regression model for analyzing energy flow signals and driving behavior data, obtaining energy consumption data corresponding to target energy-consuming components, and performing early abnormal diagnosis on the energy consumption data.

[0055] The role of the regression model is to analyze the relationship between the energy consumption data of each energy-consuming component and its working state (current, voltage, temperature, speed and other parameters) based on historical data, normal operation data and real-time collected component energy consumption status. Predict the energy that a component should consume under specific working conditions. The input of the model is the state parameters of each component (such as motor speed, temperature, load, voltage, current, etc.) and the overall operating environment of the vehicle (such as external temperature, road conditions, etc.). The output is the predicted energy consumption value, which indicates the reasonable energy consumption expected by the system under these conditions. If the real-time energy consumption deviates too much from the value predicted by the model, the system will determine that the energy consumption is abnormal.

[0056] The early abnormal diagnosis is based on the energy consumption abnormality judgment of the regression model. This step is to ensure that the vehicle's energy consumption data is accurate and to eliminate the cause of the system's own failure. If the fault diagnosis is not completed first, the energy consumption abnormality may be due to the failure of a certain component rather than the driving style or working conditions. Direct analysis of driving style and working conditions may mislead the judgment. Therefore, only after the diagnosis is completed can the energy consumption data corresponding to the target energy-consuming component be further monitored.

[0057] S14: after the abnormal energy consumption diagnosis is passed, determining a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data.

[0058] According to the diagnostic results of the regression model, after the abnormal energy consumption diagnosis is passed, the target driving style and target driving conditions are classified and determined in combination with the vehicle's acceleration, braking, turning and other driving behaviors. Among them, the driving conditions include five conditions (city, highway, mountain, national highway, and reverse short conditions) and the driving style includes three driving styles (aggressive, stable, and economical driving). Different operating scenario combinations are formed, and these combinations are used to match the energy consumption level and evaluate whether the energy consumption is reasonable.

[0059] S15. Monitoring energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition.

[0060] It is normal for energy consumption to fluctuate in different scenarios. For example, high energy consumption in urban conditions due to aggressive driving is reasonable. However, if energy consumption is still high in stable driving and high-speed conditions, it may indicate that there is an abnormality in the vehicle system. Therefore, the purpose of scene matching is to help the system analyze the actual energy consumption performance of the vehicle more accurately and make reasonable judgments based on driving style and operating data.

[0061] The energy consumption fluctuation range refers to a reasonable range in which the energy consumption deviates from the predicted value of the regression model. This range is usually set based on historical data and actual working conditions. Specifically, it is manifested as:

[0062] Mild anomaly: Energy consumption deviates slightly from the model prediction value, but has not yet reached a level that requires intervention.

[0063] Severe abnormality: Energy consumption deviates significantly from the predicted value, indicating that the system is faulty or the driving style seriously affects energy efficiency.

[0064] The abnormal range is set dynamically, and different components, different operating conditions, and different driving styles will have different energy consumption thresholds. For example, under high-speed conditions, slight fluctuations in energy consumption may be normal, while under idling conditions, abnormal fluctuations in energy consumption are more likely to be identified as faults.

[0065] Furthermore, an energy-saving suggestion option button can be established on the vehicle's central control screen. Through the human-computer interaction mode, the driver can be reminded of abnormal energy consumption points and given the option to turn on the energy-saving function. Through active matching of the status, the vehicle's energy-saving management and control can be achieved in different scenarios and under abnormal energy consumption conditions, thereby improving the vehicle's adaptability to working conditions.

[0066] The energy consumption monitoring method based on the energy flow of the whole vehicle provided by the present invention collects the energy flow signal of the target energy consuming component of the target vehicle and the driving behavior data of the target vehicle; inputs the energy flow signal into an energy consumption management module, and performs data analysis on the energy flow signal through the energy consumption management module to obtain energy consumption data corresponding to the target energy consuming component; performs energy consumption abnormality diagnosis based on the energy consumption data; after the energy consumption abnormality diagnosis is passed, determines the target driving style and the target driving condition based on the energy consumption data corresponding to the target energy consuming component and the driving behavior data; and monitors the energy consumption data corresponding to the target energy consuming component based on the target driving style and the target driving condition. Compared with the current energy consumption analysis based on vehicle-side software and back-end Internet of Vehicles platform, which only monitors and intervenes in optimization measures based on the energy consumption status parameters of batteries and motors, it does not take into account the energy consumption destination and abnormal monitoring of auxiliary drive high-voltage components. When the vehicle has abnormal energy consumption, it does not consider timely warning and intervention measures, and cannot provide accurate solutions and energy-saving guidance. This method, based on the real-time monitoring of the energy consumption of the whole vehicle energy flow, establishes an energy consumption management module to integrate and calculate the energy flow related signals of each energy-consuming component to realize the complete energy flow analysis closed loop of the vehicle, thereby improving the accuracy of the energy flow analysis and being able to more accurately monitor the energy flow of each energy-consuming component.

[0067] Figure 2 This is the second flow chart of the energy consumption monitoring method based on the whole vehicle energy flow provided by the present invention, such as Figure 2 As shown, the method specifically includes:

[0068] S21, collecting energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle.

[0069] The embodiment of the present invention is preferably applicable to the energy consumption monitoring scenario of heavy trucks. The target energy-consuming components include batteries, motors, air-conditioning compressors, water-cooling units, air pumps, oil pumps, DCDC, etc. The target energy-consuming components may be one, multiple or all of them.

[0070] The energy flow signals of the above-mentioned target energy-consuming components of the target vehicle are collected. The energy flow signals are obtained by the vehicle controller (VCU software) receiving the bus signals of the controllers of each energy-consuming component on the target vehicle. The energy flow signals include but are not limited to current signals and voltage signals. The driving behavior data of the target vehicle include but are not limited to acceleration, braking, turning and other behaviors.

[0071] For example, the energy flow signal of the motor is collected, which mainly includes current signal and voltage signal. Current signal collection: Current sensors are usually used to measure the three-phase current of the motor. The current sensor can be a Hall effect sensor or a hollow perforated sensor, which is usually integrated inside the motor controller. There are many current sampling schemes, including single resistor sampling, dual resistor sampling and three resistor sampling. The selection of sampling points is very critical. Sampling needs to be performed when the high end of the three-phase inverter is closed and the low end is open.

[0072] Voltage signal acquisition: The voltage signal is usually provided by the voltage source inverter inside the motor controller, which is responsible for converting DC power into AC power to drive the motor. The output voltage of the inverter can be adjusted by pulse width modulation (PWM) technology. The power supply part of the motor controller converts the fixed DC voltage into the required different levels of voltage for use by the microcontroller, motor temperature sensor and position feedback sensor.

[0073] The collected data needs to be parsed and processed to extract the energy consumption information of each component, and then analyzed and compared to understand the energy consumption distribution of each component.

[0074] S22, inputting the current signal and the voltage signal into an energy consumption management module, and obtaining energy consumption data corresponding to the target energy consumption component based on time integral calculation through the energy consumption field, the current signal and the voltage signal in the energy consumption management module.

[0075] In the embodiment of the present invention, firstly, the energy consumption fields of the target energy-consuming components under the vehicle operating condition and the vehicle idling condition are calculated; and an energy consumption management module is constructed based on the energy consumption fields.

[0076] Specifically, the energy flow related signals of the vehicle-side energy-consuming components including batteries, motors, air conditioning compressors, water cooling units, air pumps, oil pumps, DCDC, etc. are precisely subdivided. The energy consumption fields of the target energy-consuming components under vehicle operating conditions and vehicle idling conditions are calculated. The energy consumption field is the energy value consumed or generated by each component per unit time. The energy consumption fields uploaded to the cloud platform include:

[0077] ① Total cumulative charge and discharge volume of the battery (BatTotalAcmConsEnrg): The amount of electricity output or recovered by the battery over a period of time (unit: kWh).

[0078] ②Battery cumulative recovered electricity (BatAcmRegenEnrg): The amount of electricity recovered by the battery during driving braking (unit: kWh).

[0079] ③ Accumulated charging capacity of the charging pile (BatAcmChrgEnrg): The amount of electricity input to the battery by the charging pile during the charging process of the vehicle (unit: kWh).

[0080] ④ Motor cumulative energy consumption (MotAcmConsEnrg): The amount of electricity consumed by the motor during driving (unit: kWh).

[0081] ⑤Motor accumulated recovered electricity (MotAcmRegenEnrg): The amount of electricity recovered by the motor during vehicle braking (unit: kWh).

[0082] ⑥ Accumulated energy consumption of air pump (APCAcmConsEnrg): The amount of electricity consumed during the working process of the vehicle air pump (unit: kWh).

[0083] ⑦ Accumulated energy consumption of the fuel pump (EPSAcmConsEnrg): The amount of electricity consumed by the fuel pump during driving (unit: kWh).

[0084] ⑧DCDC energy consumption field (DCDCAcmConsEnrg): The amount of electricity consumed by DCDC during vehicle startup (unit: kWh).

[0085] ⑨Air conditioning accumulated energy consumption field (ACCMAcmConsEnrg): The amount of electricity consumed when the air conditioning is turned on during vehicle startup (unit: kWh).

[0086] ⑩Heat PTC energy consumption field (VCU6_PTCAcmConsEnrg): The amount of electricity consumed by the battery heating film when the battery is heated during driving (unit: kWh).

[0087] The steps of energy flow calculation include:

[0088] 1) Determine input energy: Input energy mainly refers to the energy provided by the battery, the energy input by the charging pile, and the energy recovered by the vehicle during braking.

[0089] 2) Determine output energy: Output energy refers to the consumption of each energy-consuming component, including motors, air conditioners, air pumps, oil pumps, DCDC, etc.

[0090] 3) Calculate energy balance: By comparing the difference between input and output, you can analyze the flow of energy and whether there is energy loss or anomalies.

[0091] For example, an electric car collects the following energy consumption fields in a certain period of time:

[0092] 1. Total battery charge and discharge volume (BatTotalAcmConsEnrg): 60 kWh;

[0093] 2. Battery cumulative recycled energy (BatAcmRegenEnrg): 10 kWh;

[0094] 3. Accumulated charging capacity of charging pile (BatAcmChrgEnrg): 50 kWh;

[0095] 4. Motor cumulative energy consumption (MotAcmConsEnrg): 40 kWh;

[0096] 5. Motor cumulative regenerated electricity (MotAcmRegenEnrg): 10 kWh;

[0097] 6. Air conditioning cumulative energy consumption field (ACCMAcmConsEnrg): 5 kWh;

[0098] 7. DCDC energy consumption field (DCDCAcmConsEnrg): 3 kWh, which indicates the amount of electricity consumed by the DCDC converter during operation, which is used to convert high voltage electricity into low voltage electricity to supply the electronic devices in the car.

[0099] Step 1: Calculate the input energy. The input energy includes: the energy obtained by the battery through the charging pile: 50 kWh; the energy recovered by the motor through braking: 10 kWh; therefore, the total input energy is: input energy = 50 kWh + 10 kWh = 60 kWh.

[0100] Step 2: Calculate the output energy. The output energy includes: energy consumed by the motor: 40 kWh; energy consumed by the air conditioner: 5 kWh; energy consumed by the DCDC converter: 3 kWh; therefore, the total output energy is: output energy = 40 kWh + 5 kWh + 3 kWh = 48 kWh.

[0101] Step 3: Calculate the energy flow balance. The total energy consumption of the battery (total charge and discharge) is 60 kWh, while the total energy consumption of the vehicle (the sum of the consumption of each component) is 48 kWh. Therefore, the remaining energy is the energy recovery part generated during the operation of the vehicle, which matches the accumulated recovery power of the battery.

[0102] In order to verify the rationality of the system energy flow, the following energy balance formula can be used:

[0103] Total battery charge and discharge capacity - motor recovery capacity = motor energy consumption + air conditioning energy consumption + DCDC energy consumption

[0104] Substitute the known values ​​into: total battery charge and discharge capacity - motor recovery capacity = 60 kWh - 10 kWh = 50 kWh;

[0105] Motor energy consumption + air conditioning energy consumption + DCDC energy consumption = 40 kWh + 5 kWh + 3 kWh = 48 kWh;

[0106] There is a 2 kWh energy difference here, which may be due to other energy-consuming components or system losses that were not taken into account. This difference can be marked as energy loss, indicating that there may be energy efficiency issues in the system or incomplete data collection.

[0107] Step 4: Diagnosis of abnormal energy consumption. In the energy flow calculation, a certain amount of energy loss (2 kWh) was found. This indicates that during this cycle, there may be energy that is not consumed or recovered through normal channels. This loss may be due to the following reasons: Low DCDC conversion efficiency: DCDC may have efficiency loss during the power conversion process. Mechanical system loss: The mechanical parts of the vehicle (such as tire friction, transmission system) may cause some energy loss. Battery self-discharge: The battery may have a small self-discharge during operation, resulting in some energy not being effectively utilized.

[0108] S23. Construct an abnormal warning mechanism based on the regression model.

[0109] S24, diagnose energy consumption anomalies on the energy consumption data corresponding to the target energy-consuming component based on the regression model, and make an early warning response according to the diagnosis result.

[0110] In an embodiment of the present invention, the state parameters corresponding to the target energy-consuming components under the vehicle operating conditions and the vehicle idling conditions are obtained; the degree of association between the energy consumption field and the corresponding state parameters is determined through the correlation coefficient, and the change trend between the energy consumption field and the corresponding state parameters in the time series is determined; and a regression model between the energy consumption field and the corresponding state parameters is established based on the degree of association and the change trend.

[0111] The role of the regression model is to predict energy consumption and determine whether there is a possibility of abnormal energy consumption, so as to provide early warning of energy consumption;

[0112] Build process:

[0113] ①Time series analysis:

[0114] In addition to calculating the correlation between the status parameters of each component and the energy consumption, the trend of energy consumption over time can also be monitored through time series analysis.

[0115] ②Time series modeling:

[0116] A time series model of the energy consumption data of each component is established. Commonly used methods include the ARIMA model (autoregressive integrated moving average model) to analyze the rules of time series data.

[0117] The purpose of time series modeling is to predict the energy consumption at a certain point in the future and compare it with the actual energy consumption to determine whether any abnormal situation has occurred.

[0118] ③Trend analysis and anomaly detection:

[0119] In normal vehicle operation, the energy consumption of each component usually changes with certain regularity along with the state parameters. For example, the energy consumption of the motor may change with the speed, and the energy consumption of the air conditioning compressor may fluctuate with the temperature.

[0120] Through trend analysis, if it is found that the energy consumption data has a sudden change that is inconsistent with the component status parameters (for example, the motor speed remains unchanged, but the energy consumption suddenly increases), the system can identify it as an energy consumption abnormality and issue an early warning.

[0121] ④Dynamically adjust the model:

[0122] The system can continuously adjust and optimize the model based on new energy consumption data to more accurately reflect the energy consumption patterns of each component. This also helps to improve the accuracy of early warnings and reduce false alarms.

[0123] Example: To establish a regression model between motor temperature (state parameter) and energy consumption, the steps are as follows:

[0124] Collect the time series data X1, X2, ..., Xn of the motor temperature and the corresponding energy consumption data Y1, Y2, ..., Yn.

[0125] Calculate the mean values ​​of motor temperature and energy consumption.

[0126] Calculate the covariance, standard deviation and finally the Pearson correlation coefficient r.

[0127] If the result is r=0.85, it means that there is a strong positive correlation between motor temperature and energy consumption, that is, as the motor temperature increases, the energy consumption also increases significantly.

[0128] Excessive motor temperature may cause abnormal energy consumption, and the system needs to be adjusted or issue an early warning in time.

[0129] Specifically, the state parameters and energy consumption data of major energy-consuming components such as motors, power batteries, air-conditioning compressors, and water cooling units are analyzed respectively, and the degree of correlation between the state of each component and the energy consumption, as well as the changing trend between the state of the electrical components and the energy consumption in a time series are determined through the correlation coefficient; according to the degree of correlation and the changing trend between the state of the component and the energy consumption, a regression model between the state of the component and the energy consumption is established.

[0130] The state parameters of the energy consumption of related components include the correlation analysis of the state parameters (such as temperature, current, power, etc.) of the main energy-consuming components (such as motors, power batteries, air-conditioning compressors, etc.) and their energy consumption data. The linear relationship between the state quantity of the component and the energy consumption is measured by the Pearson correlation coefficient.

[0131] First, collect the state parameters and corresponding energy consumption data within a period of time to form two time series. Assume that there are n pairs of data, representing the state parameters X1, X2, ..., Xn and the corresponding energy consumption data Y1, Y2, ..., Yn.

[0132] Calculate the average:

[0133] Calculate the average value of status parameters and energy consumption data:

[0134]

[0135] Compute the covariance:

[0136] Covariance represents the common variation of two variables, and the formula is:

[0137]

[0138] Calculate the standard deviation:

[0139] Calculate the standard deviation of X and Y separately:

[0140]

[0141] The standard deviation indicates the distribution of the data, that is, how much the data deviate from the mean.

[0142] Calculate the Pearson correlation coefficient:

[0143] The formula for the Pearson correlation coefficient r is:

[0144]

[0145] It indicates the linear correlation between state parameter X and energy consumption data Y.

[0146] Furthermore, the calculated correlation coefficient r can explain the degree of association between the two variables:

[0147] If r is close to 1, it means that the state parameter and the energy consumption data are highly positively correlated, that is, when the state parameter increases, the energy consumption data will also increase.

[0148] If r is close to -1, it means that the state parameter and energy consumption data are highly negatively correlated, that is, when the state parameter increases, the energy consumption data will decrease.

[0149] If r is close to 0, it means that there is no obvious linear relationship between the state parameters and the energy consumption data.

[0150] In the embodiment of the present invention, the calculation of the correlation coefficient is an important part of the diagnosis and early warning function. By calculating the correlation coefficient of the state parameters and energy consumption data of each key component (such as the motor, air conditioning compressor, etc.), the system can: find out which state parameters have a significant correlation with energy consumption. Determine whether the energy consumption anomaly is related to the state change of a specific component. When the energy consumption deviates from the normal range, the driver or engineer is reminded to take necessary measures through the early warning mechanism. Finally, the correlation coefficient helps to establish a mathematical model between the state parameters and energy consumption, thereby supporting functions such as intelligent monitoring, diagnosis and energy-saving optimization.

[0151] Further, such as Figure 3 and 4 The figure shows how the motor speed, current and energy consumption change over time, and simulates abnormal energy consumption.

[0152] Figure 3 The speed and current of the motor are shown to fluctuate over time. Normally, they vary periodically.

[0153] Figure 4 The figure shows the change of the motor energy consumption over time. Under normal circumstances, the energy consumption has a relatively stable correlation with the motor speed and current, but in the 70th to 80th time period, the energy consumption in the area marked with dotted lines suddenly increased significantly, indicating that the motor energy efficiency was abnormal during this period.

[0154] Through these visualized data, potential energy consumption anomalies can be detected and marked, reminding users to conduct further diagnosis and early warning.

[0155] Furthermore, an early warning mechanism is constructed based on the regression model to determine whether there is a fault in the energy consumption corresponding to the current component state of the target vehicle and issue an early warning, and the component state, energy consumption and early warning response are displayed.

[0156] Specifically, the component status includes parameters that reflect the current operating conditions of the main energy-consuming components of the vehicle. Different components have different status parameters. Common status parameters include:

[0157] a. Motor status parameters

[0158] Motor speed (RPM): reflects the working speed of the motor.

[0159] Current (A): The current consumption of the motor, which directly affects its power.

[0160] Voltage (V): The supply voltage of the motor.

[0161] Motor temperature (°C): High temperatures may cause motor efficiency to decrease.

[0162] Working mode: The motor is in driving mode or energy recovery mode.

[0163] b. Battery status parameters

[0164] Battery voltage (V): The output voltage of the battery, which varies under different load conditions.

[0165] Battery current (A): The current output by the battery, reflecting the electrical energy provided by the battery at a certain moment.

[0166] Battery temperature (°C): Temperature can significantly affect the efficiency and life of the battery. Too high or too low a temperature can cause abnormal energy consumption.

[0167] Charge and discharge rate: The speed at which a battery charges and discharges, usually expressed as C-rate (charge and discharge rate), determines the battery's discharge capacity and charging efficiency.

[0168] Battery SOC (State of Charge): The remaining power of the battery, which affects energy consumption and performance.

[0169] c. Air conditioning compressor status parameters

[0170] Compressor speed: The operating speed of the air conditioner compressor determines its cooling / heating efficiency.

[0171] Compressor power (W): The power consumption of the compressor in different temperature control modes.

[0172] Air conditioning system temperature: Air conditioning inlet and outlet temperatures, used to evaluate the cooling or heating effect of the system.

[0173] System pressure (Pa): The refrigerant pressure in the air-conditioning system, reflecting its workload.

[0174] d. Water cooling unit status parameters

[0175] Water cooling pump speed: The operating speed of the water pump in the cooling system determines its cooling efficiency.

[0176] Coolant temperature: The difference between the inlet and outlet temperatures of the coolant, used to evaluate the performance of the water cooling system.

[0177] System pressure (Pa): The liquid pressure in the water-cooling unit determines the cooling intensity.

[0178] Fan power (W): The working power of the auxiliary fan in the water-cooling unit.

[0179] e. Other component status parameters

[0180] Air pump speed: air pump working state parameter, used in pneumatic system.

[0181] Oil pump speed: The working state of the oil pump affects the working efficiency of the hydraulic system.

[0182] S25. After the abnormal energy consumption diagnosis is passed, determine a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data.

[0183] In order to cope with changes in energy consumption caused by sudden changes in operating conditions and avoid user confusion about energy consumption fluctuations, the changes in energy consumption are analyzed based on the vehicle's load, speed and driving behavior, and these changes are classified into different driving scenarios (i.e. five operating conditions) and different driving styles (i.e. three driving styles), ultimately achieving reasonable matching and management of energy consumption.

[0184] 1. Working condition characteristic analysis: The current driving condition can be compared and analyzed based on the regression model and actual data, such as:

[0185] Urban conditions: frequent starting and stopping, high energy consumption;

[0186] High-speed operation: continuous and stable driving with relatively low energy consumption;

[0187] Mountainous conditions: high energy consumption due to factors such as climbing;

[0188] National highway conditions: vehicle speed is below 80, with frequent starts and stops, and relatively stable energy consumption;

[0189] Short-distance transport conditions: For short-distance transport conditions in transport scenarios such as ports and steel mills, there are frequent starts and stops, heavy loads, and unstable energy consumption.

[0190] 2. Driving style analysis: The system will classify different driving styles based on the diagnostic data of the regression model and the vehicle's acceleration, braking, turning and other behaviors. Driving styles may include:

[0191] Aggressive driving: Frequent acceleration and braking, resulting in large fluctuations in energy consumption.

[0192] Smooth driving: less sudden acceleration and braking, and stable energy consumption.

[0193] Economical driving: Maintain reasonable speed and energy consumption.

[0194] For example, if the vehicle is driving on a highway and the driver maintains a steady driving style, the system's operating condition classification will classify the current driving environment as "high-speed conditions" and the driving style as "steady driving". The system expects that energy consumption should be relatively stable and low. However, if the system detects that the energy consumption at this time is higher than expected, it may be necessary to prompt the user that the vehicle is overloaded or to check other systems, such as tire pressure or motor status.

[0195] On the contrary, in urban conditions, the system will allow greater fluctuations in energy consumption, especially during aggressive driving (such as frequent acceleration and braking). In this case, the system will not pay too much attention to the sudden increase in energy consumption because it is a reasonable scenario match.

[0196] S26. Determine standard energy consumption data and an energy consumption data deviation range based on the target driving style and the target driving condition.

[0197] S27: Monitor the energy consumption data corresponding to the target energy consumption component based on the standard energy consumption data and the energy consumption data deviation range.

[0198] S28. Send the monitoring result to the user terminal, and perform energy consumption warning and energy-saving prompt through the user terminal.

[0199] The following is a unified description of S26-S28:

[0200] Through active matching of states, energy-saving control of vehicles in different scenarios and under abnormal energy consumption conditions can be achieved.

[0201] Specifically, state matching means that the system determines the specific scenario the vehicle is in based on the real-time monitored vehicle status (such as current speed, load, energy consumption, driving behavior, etc.) and driving conditions (such as city, highway, mountain, etc.), and actively adopts corresponding strategies to optimize energy consumption.

[0202] The key to state matching is that the system can identify the energy consumption characteristics of the vehicle under specific operating conditions and driving styles, compare them with the expected energy consumption levels, and adopt energy-saving control strategies when abnormalities are found.

[0203] Energy-saving control in different scenarios and abnormal energy consumption conditions includes: Different scenarios: including five operating conditions (city, highway, mountain, national highway, and reverse short-distance conditions) and three driving styles (aggressive, stable, and economical driving). The system regulates the vehicle's energy consumption according to the current operating scenario and driving style.

[0204] For example, under high-speed conditions + smooth driving, the system will optimize the power distribution of the power system and air-conditioning system to minimize energy consumption during high-speed driving.

[0205] In urban conditions + aggressive driving, the system will allow short-term fluctuations in energy consumption, but at the same time may suggest that the driver drive more steadily to save energy.

[0206] Abnormal energy consumption: If the system detects that the actual energy consumption of the vehicle is significantly different from the expected energy consumption (such as energy consumption far higher than the standard under high-speed conditions), it will trigger the abnormal detection mechanism and take proactive energy-saving measures. For example:

[0207] If the vehicle is driving in urban conditions but the energy consumption is abnormally high, it may indicate insufficient tire pressure or other problems. The system will remind the driver to check and even actively adjust the power consumption of certain components.

[0208] When the battery temperature is too high, the system may limit the battery output power to reduce energy consumption and protect the battery.

[0209] Energy-saving control refers to the system actively optimizing energy use through intelligent adjustments. For example:

[0210] Powertrain optimization: When the system recognizes high-speed conditions, it may optimize the efficiency of the motor to avoid unnecessary high power output, while controlling the energy consumption of the DCDC converter and other electrical devices.

[0211] Air conditioning system control: When the air conditioning compressor energy consumption is abnormal, the system may reduce the compressor's power output or suggest the driver to temporarily turn off the air conditioning to reduce unnecessary energy consumption.

[0212] Operating condition adaptability means that the system can flexibly adjust the working status of each energy-consuming component according to the different operating conditions of the vehicle (city, highway, mountain, etc.) to ensure the reasonable distribution of energy consumption under different conditions.

[0213] By actively adapting to different driving conditions, the vehicle can maintain higher energy efficiency in different driving conditions. For example, in mountain conditions, the system will optimize the output power of the motor to ensure more efficient energy consumption when going uphill, and make reasonable use of the brake energy recovery system when going downhill.

[0214] Abnormal conditions of different components are detected through energy consumption monitoring. Based on the severity of the abnormality, the emergency level of the abnormality is automatically determined to determine whether immediate intervention is required. Based on the degree of urgency, notifications are sent to users and backend engineers to provide real-time feedback on abnormal energy consumption. The system proposes corresponding countermeasures based on the abnormal status, such as suggesting to check a certain component, restrict functions, or stop operation immediately.

[0215] For example: 1. Battery overheat (status)

[0216] Status description: The system detects that the battery temperature exceeds the safe operating range and the temperature rises too quickly, which may be caused by excessive discharge or high ambient temperature.

[0217] Urgency: High urgency.

[0218] Countermeasures:

[0219] User Tips: Emergency alerts are pushed in real time via the mobile app to remind the driver to immediately stop charging or reduce battery load to avoid further heating.

[0220] Engineer feedback: The cloud platform uploads information such as battery temperature, current current and voltage to the backend. Engineers can retrieve the data and analyze whether it is a cooling system failure or other problem.

[0221] The system automatically handles: It may limit the discharge power of the battery, reduce the load, and reduce the charging speed.

[0222] Final measure: If the temperature cannot drop quickly, the system will recommend stopping the vehicle and shutting down the vehicle power system to prevent further damage to the battery.

[0223] 2. Abnormal increase in motor energy consumption (status)

[0224] Status description: Under stable driving conditions, the system detects a sudden increase in motor energy consumption, which may be due to increased mechanical friction inside the motor, winding failure, etc., which leads to a decrease in motor efficiency.

[0225] Urgency: Medium urgency.

[0226] Countermeasures:

[0227] User Tips: The APP pushes notifications to remind the driver of abnormal energy consumption and recommends avoiding violent acceleration or excessive load and maintaining smooth driving.

[0228] Engineer feedback: The cloud platform sends a notification to the responsible engineer, providing key data such as motor speed, current, and temperature for the engineer to analyze the source of the problem. It can further recommend checking the mechanical transmission system or motor windings.

[0229] System automatic processing: If the problem persists, the system may limit the maximum power output of the motor and reduce the power consumption of the vehicle.

[0230] Final measures: If the problem cannot be solved in a short period of time, the engineer will recommend the user to go to a maintenance point for inspection.

[0231] 3. Air conditioning compressor failure (status)

[0232] Status description: The system detects that the energy consumed by the air conditioning compressor is too high and the air conditioning cooling / heating effect is poor, which may be due to problems such as compressor jam or refrigerant leakage.

[0233] Urgency: Low urgency.

[0234] Countermeasures:

[0235] User Tips: The APP push notification reminds users that the air conditioning system's energy consumption is abnormal, which may affect the comfort in the car. It is recommended to check the air conditioning system as soon as possible.

[0236] Engineer feedback: The system uploads the compressor energy consumption data, working pressure, etc. to the background, so that engineers can check the working condition of the compressor and determine whether maintenance or refrigerant replenishment is needed.

[0237] The system automatically handles: the air conditioning compressor may be temporarily shut down, or the compressor workload may be reduced to prevent further damage.

[0238] Final measures: If the problem persists, the system will recommend that the user go to a service center for inspection, but there is no need to stop the vehicle immediately.

[0239] 4. Abnormal DCDC energy consumption (status)

[0240] Status description: The energy consumption of the DCDC converter increases, causing the battery power to be consumed abnormally quickly. This may be caused by decreased DCDC efficiency or excessive load on the electrical system.

[0241] Urgency: Medium urgency.

[0242] Countermeasures:

[0243] User Tips: The APP notifies the driver of abnormal DCDC converter energy consumption and recommends turning off unnecessary power-consuming devices (such as the in-car entertainment system, seat heating, etc.) to reduce the load on the low-voltage system.

[0244] Engineer feedback: Upload the DCDC input, output voltage, current and other data to the backend, and the engineer will analyze whether it is necessary to check the converter's cooling system or electrical circuit.

[0245] Automatic system processing: If the problem persists, the system may reduce the output of DCDC to protect the battery and maintain the normal operation of necessary low-voltage electrical equipment.

[0246] Final measure: If abnormal energy consumption persists, the system may recommend that the vehicle enter maintenance mode and limit some of the vehicle's functions.

[0247] 5. Vehicle speed and energy consumption do not match (status)

[0248] Status description: The vehicle consumes too much energy under low-speed conditions (such as urban conditions). The driving style is smooth, but the system detects abnormally high energy consumption, which may be caused by insufficient tire pressure or excessive vehicle load.

[0249] Urgency: Medium urgency.

[0250] Countermeasures:

[0251] User Tips: The APP reminds the driver to check tire pressure or vehicle load, and recommends avoiding overloading or driving on unstable roads for a long time.

[0252] Engineer feedback: The system will send the vehicle's real-time speed, load and energy consumption data to engineers, who can use the data to determine whether there are problems with the tires or other mechanical systems.

[0253] Automatic system processing: The system may actively detect tire pressure through sensors and prompt the driver to add air pressure or limit the maximum vehicle speed in an overloaded state.

[0254] Final Action: If the problem is not resolved, the system may recommend that the user go to a nearby service point for a comprehensive inspection.

[0255] Summarize:

[0256] Status refers to abnormal conditions of a vehicle component or energy consumption detected by the system, such as battery overheating, abnormal motor energy consumption, air conditioning compressor failure, etc.

[0257] Countermeasures are divided into user operation prompts, engineer diagnosis and data analysis, and automatic system response measures according to different states. Emergency situations (such as battery overheating) will lead to strong intervention measures, while low-emergency anomalies (such as air conditioning failure) may only notify the user to check.

[0258] Through the combination of cloud and Tbox, the system transmits all abnormal information to the background and mobile phone, helping users to respond to energy consumption abnormalities in a timely manner and ensure the safety and energy efficiency of the vehicle.

[0259] The energy consumption monitoring method based on the energy flow of the whole vehicle provided by the present invention collects the energy flow signal of the target energy consuming component of the target vehicle and the driving behavior data of the target vehicle; inputs the energy flow signal into an energy consumption management module, and performs data analysis on the energy flow signal through the energy consumption management module to obtain energy consumption data corresponding to the target energy consuming component; performs energy consumption abnormality diagnosis based on the energy consumption data; after the energy consumption abnormality diagnosis is passed, determines the target driving style and the target driving condition based on the energy consumption data corresponding to the target energy consuming component and the driving behavior data; and monitors the energy consumption data corresponding to the target energy consuming component based on the target driving style and the target driving condition. This method, based on the real-time monitoring of the energy consumption of the whole vehicle energy flow, establishes an energy consumption management module to integrate and calculate the energy flow related signals of each energy-consuming component to realize the complete energy flow analysis closed loop of the vehicle, thereby improving the accuracy of the energy flow analysis and being able to more accurately monitor the energy flow of each energy-consuming component; based on the diagnosis and early warning function of abnormal energy consumption, based on the correlation analysis of state parameters and energy consumption data and the time series regression model, accurate energy consumption abnormality diagnosis and early warning functions are realized; by classifying working conditions and driving styles, energy consumption optimization for different driving conditions is realized; through the interaction between the vehicle-side central control and the driver, real-time energy-saving suggestions are provided, which improves the practicality and flexibility of vehicle energy consumption management.

[0260] The energy consumption monitoring device based on the energy flow of the whole vehicle provided by the present invention is described below. The energy consumption monitoring device based on the energy flow of the whole vehicle described below and the energy consumption monitoring method based on the energy flow of the whole vehicle described above can be referred to each other.

[0261] Figure 5 It is a structural schematic diagram of the energy consumption monitoring device based on the whole vehicle energy flow provided by the present invention, which specifically includes:

[0262] The acquisition module 501 is used to acquire the energy flow signal of the target energy-consuming component of the target vehicle and the driving behavior data of the target vehicle. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0263] The analysis module 502 is used to input the energy flow signal into the energy consumption management module, and the energy consumption management module performs data analysis on the energy flow signal to obtain energy consumption data corresponding to the target energy-consuming component. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0264] The diagnosis module 503 is used to perform energy consumption abnormality diagnosis based on the energy consumption data. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0265] The determination module 504 is used to determine the target driving style and target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data after the energy consumption abnormality diagnosis is passed. Detailed descriptions refer to the relevant descriptions corresponding to the above method embodiments, which will not be repeated here.

[0266] The monitoring module 505 is used to monitor the energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition. For detailed description, please refer to the relevant description corresponding to the above method embodiment, which will not be repeated here.

[0267] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the energy consumption monitoring method based on the energy flow of the whole vehicle, the method comprising: collecting the energy flow signal of the target energy-consuming component of the target vehicle and the driving behavior data of the target vehicle; inputting the energy flow signal into the energy consumption management module, performing data analysis on the energy flow signal through the energy consumption management module to obtain the energy consumption data corresponding to the target energy-consuming component; performing energy consumption abnormality diagnosis based on the energy consumption data; after the energy consumption abnormality diagnosis is passed, determining the target driving style and the target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data; monitoring the energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition.

[0268] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0269] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the energy consumption monitoring method based on the energy flow of the whole vehicle provided by the above-mentioned methods, and the method includes: collecting energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle; inputting the energy flow signals into an energy consumption management module, and performing data analysis on the energy flow signals through the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming components; performing energy consumption abnormality diagnosis based on the energy consumption data; after the energy consumption abnormality diagnosis is passed, determining a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming components and the driving behavior data; and monitoring the energy consumption data corresponding to the target energy-consuming components based on the target driving style and the target driving condition.

[0270] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the above-mentioned energy consumption monitoring methods based on the energy flow of the whole vehicle, the method comprising: collecting energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle; inputting the energy flow signals into an energy consumption management module, performing data analysis on the energy flow signals through the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming components; performing energy consumption abnormality diagnosis based on the energy consumption data; after the energy consumption abnormality diagnosis is passed, determining a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming components and the driving behavior data; and monitoring the energy consumption data corresponding to the target energy-consuming components based on the target driving style and the target driving condition.

[0271] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0272] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0273] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring energy consumption based on vehicle energy flow, characterized in that: include: Collecting energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle, wherein the energy flow signals include current signals and voltage signals; Calculating the energy consumption field of the target energy-consuming component under vehicle operating conditions and vehicle idling conditions; Building an energy consumption management module based on the energy consumption field; Obtaining state parameters of the target energy-consuming component corresponding to the vehicle operating condition and the vehicle idling condition; Determine the correlation degree between the energy consumption field and the corresponding state parameter through the correlation coefficient and determine the change trend between the energy consumption field and the corresponding state parameter in the time series; Establishing a regression model between the energy consumption field and the corresponding state parameter based on the correlation degree and the change trend; Inputting the energy flow signal into an energy consumption management module, and performing data analysis on the energy flow signal through the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming component; The step of inputting the energy flow signal into an energy consumption management module, and performing data analysis on the energy flow signal by the energy consumption management module to obtain energy consumption data corresponding to the target energy-consuming component includes: Inputting the current signal and the voltage signal into an energy consumption management module, and obtaining energy consumption data corresponding to the target energy consumption component based on time integral calculation through the energy consumption field, the current signal and the voltage signal in the energy consumption management module; diagnose abnormal energy consumption based on the energy consumption data; The abnormal energy consumption diagnosis based on the energy consumption data includes: Building an abnormal warning mechanism based on the regression model; Performing energy consumption abnormality diagnosis on the energy consumption data corresponding to the target energy-consuming component based on the regression model, and performing an early warning response according to the diagnosis result; After the energy consumption abnormality diagnosis is passed, determining a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data; Monitoring energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition; The monitoring of the energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition includes: Determining standard energy consumption data and an energy consumption data deviation range based on the target driving style and the target driving condition; The energy consumption data corresponding to the target energy consumption component is monitored based on the standard energy consumption data and the energy consumption data deviation range.

2. The method according to claim 1, characterized in that The method further comprises: The monitoring results are sent to the user end, and energy consumption warnings and energy-saving prompts are issued through the user end.

3. An energy consumption monitoring device based on vehicle energy flow, characterized in that: include: A collection module, used to collect energy flow signals of target energy-consuming components of a target vehicle and driving behavior data of the target vehicle, wherein the energy flow signals include current signals and voltage signals; Calculating the energy consumption fields of the target energy-consuming components under vehicle operating conditions and vehicle idling conditions; and constructing an energy consumption management module based on the energy consumption fields; Obtaining the state parameters corresponding to the target energy-consuming component under the vehicle operating condition and the vehicle idling condition; determining the degree of correlation between the energy consumption field and the corresponding state parameter through the correlation coefficient and determining the change trend between the energy consumption field and the corresponding state parameter in the time series; Establishing a regression model between the energy consumption field and the corresponding state parameter based on the correlation degree and the change trend; An analysis module, used for inputting the energy flow signal into an energy consumption management module, performing data analysis on the energy flow signal through the energy consumption management module, and obtaining energy consumption data corresponding to the target energy consumption component; the step of inputting the energy flow signal into the energy consumption management module, performing data analysis on the energy flow signal through the energy consumption management module, and obtaining energy consumption data corresponding to the target energy consumption component comprises: inputting the current signal and the voltage signal into the energy consumption management module, and obtaining energy consumption data corresponding to the target energy consumption component based on time integral calculation through the energy consumption field in the energy consumption management module, the current signal, and the voltage signal; A diagnosis module, configured to perform energy consumption anomaly diagnosis based on the energy consumption data; the energy consumption anomaly diagnosis based on the energy consumption data includes: building an abnormal warning mechanism based on the regression model; performing energy consumption anomaly diagnosis on the energy consumption data corresponding to the target energy-consuming component based on the regression model, and performing an early warning response according to the diagnosis result; a determination module, configured to determine a target driving style and a target driving condition based on the energy consumption data corresponding to the target energy-consuming component and the driving behavior data after the energy consumption abnormality diagnosis is passed; A monitoring module is used to monitor the energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition; the monitoring of the energy consumption data corresponding to the target energy-consuming component based on the target driving style and the target driving condition includes: determining standard energy consumption data and a deviation range of energy consumption data based on the target driving style and the target driving condition; and monitoring the energy consumption data corresponding to the target energy-consuming component based on the standard energy consumption data and the deviation range of energy consumption data.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the energy consumption monitoring method based on the whole vehicle energy flow as described in any one of claims 1 to 2 are implemented.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy consumption monitoring method based on the whole vehicle energy flow as described in any one of claims 1 to 2 are implemented.

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