Electric vehicle user working condition circulation formulating method, device and equipment and medium

Abstract

The invention provides an electric vehicle user working condition cycle formulating method, device, equipment and medium, and the method comprises the steps: collecting historical driving data, analyzing and extracting historical driving characteristics of a target group, collecting the current driving characteristics of a test vehicle, and comprehensively comparing the current driving characteristics with the historical characteristics; the driving data of the tested vehicle and the historical driving data are acquired to obtain a driving deviation between the driving data of the tested vehicle and the historical driving data, and when the driving deviation is smaller than a preset threshold value, the current driving working condition is considered to be matched with the target group characteristics and is determined as a target working condition; according to the method, a new energy automobile test working condition curve of a certain specific automobile type or user group is established based on user driving behavior big data, so that the working condition curve is closer to an actual automobile use scene of a target group, and powerful support is provided for subsequent automobile research and development.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a method, device, equipment and medium for formulating an electric vehicle user operating cycle. Background Art

[0002] Automobile test conditions are crucial for evaluating automobile energy consumption, driving range and emissions. European, American and Japanese countries have already developed their own test conditions based on their own automobile usage characteristics, such as the EU's NEDC, the US Environmental Protection Agency's FTP75 test conditions, and the United Nations Economic Commission for Europe's WLTP conditions.

[0003] With the depletion of global oil resources and the intensification of energy shortages, new energy vehicles have become a key choice for the sustainable development of the automotive industry. Globally, the energy consumption and driving range tests of new energy vehicles mainly use four test methods: EPA rating, WLTP, NEDC and CLTC. Chinese automakers often use CLTC and WLTC to test energy consumption and driving range. The United States uses EPA ratings, Europe and Japan rely on WLTP standards, and Russia and Middle Eastern countries still use NEDC test conditions. In the early stages of the development of new energy vehicles in my country, energy consumption and driving range tests have always used foreign test conditions, such as NEDC for pure electric vehicles and WLTC for hybrid vehicles.

[0004] However, the current test conditions have shortcomings. For example, the WLTC condition has not been used to collect data on Chinese roads, and cannot fully represent China's actual road conditions and domestic users' driving habits; while the CLTC condition is based on a limited sample size and was collected at an early time, and has not been updated in a timely manner with the development of new energy vehicle technology, changes in user groups, vehicle usage habits, and the evolution of road traffic conditions. Therefore, the existing conditions are somewhat different from the actual driving of current Chinese new energy vehicle users, and cannot provide good data support for related research and development projects. Summary of the invention

[0005] In view of the above-mentioned shortcomings of the prior art, the present application provides a method, device, equipment and medium for formulating a tram user operating cycle to solve the above-mentioned technical problems.

[0006] The present application provides a method for formulating a tram user operating condition cycle, the method comprising: collecting historical driving data, and obtaining historical driving characteristics of a target group based on the historical driving data; collecting current driving characteristics of a test vehicle, and comparing the current driving characteristics with the historical driving characteristics to obtain a driving deviation; and determining the driving condition corresponding to the current driving characteristics with a driving deviation less than a preset threshold as a target condition.

[0007] In one embodiment of the present application, after determining the target operating condition, the method also includes: determining the historical power consumption distribution information of any target vehicle model based on the historical driving characteristics; dividing the vehicle power consumption into multiple power consumption intervals based on the historical power consumption distribution information, and calculating the probability of each power consumption interval; testing the target operating condition to obtain a verification power consumption value during the test; comparing the verification power consumption value with the multiple power consumption intervals to determine the target power consumption interval to which the verification power consumption value belongs, so as to obtain the test power consumption probability corresponding to the verification power consumption value; if the test power consumption probability meets the preset probability threshold range, it is determined that the target operating condition test has passed, and the target operating condition is determined as the user operating condition cycle of the target group.

[0008] In one embodiment of the present application, the target operating condition is tested, including: obtaining basic vehicle information of the target vehicle model, the basic vehicle information including at least vehicle mass, tire specifications, and power system characteristics; inputting data of the target operating condition into a chassis dynamometer control device; pre-setting a test vehicle based on the basic vehicle information of the target vehicle model to ensure that the basic vehicle information of the test vehicle is consistent with the basic vehicle information of the target vehicle model; obtaining a target resistance, the target resistance being a vehicle announced resistance or an actual road resistance of the vehicle obtained through an actual road test; and operating the chassis dynamometer control device based on the target resistance to test the target operating condition.

[0009] In one embodiment of the present application, historical driving data is collected, and historical driving characteristics of the target group are obtained based on the historical driving data, including: obtaining mileage data of all single trips, drawing a mileage data distribution map and calculating the average mileage to obtain a driving mileage characteristic, wherein a single trip is a process from starting to stopping a vehicle; recording the duration and stop time of each single trip, determining the driving time of a single trip, and calculating the average vehicle speed based on the driving mileage and driving time to obtain a driving time characteristic; collecting acceleration data, and counting the time and mileage proportion of acceleration and deceleration to obtain an acceleration characteristic; dividing the vehicle speed data into predetermined intervals, and counting the driving mileage, time and average vehicle speed in each interval to obtain interval driving characteristics in different vehicle speed intervals; determining the driving mileage characteristic, the driving time characteristic, the acceleration characteristic, and the interval driving characteristic as the historical driving characteristics of the entire target.

[0010] In one embodiment of the present application, the current driving characteristics are compared with the historical driving characteristics to obtain a driving deviation, including: calculating the mileage deviation degree between the current mileage and the historical mileage distribution, and determining the mileage deviation value of the test vehicle's mileage relative to the historical driving data based on the mileage deviation degree; calculating the deviation between the current average speed and the historical average speed to obtain the speed deviation value of the test vehicle's driving speed relative to the historical driving data; calculating the deviation between the time and mileage proportion of acceleration and deceleration and the historical driving data to obtain the acceleration deviation value of the test vehicle's driving acceleration relative to the historical driving data; comparing the current mileage, time and average speed in each speed interval with the historical driving data to obtain the partitioned driving deviation value of the test vehicle in each speed interval relative to the historical driving data; performing weighted calculation on the mileage deviation value, the speed deviation value, the acceleration deviation value, and the partitioned driving deviation value according to a preset weight ratio to obtain the driving deviation between the current test mileage and the historical driving data of the test vehicle.

[0011] In one embodiment of the present application, the driving condition corresponding to the current driving characteristics in which the driving deviation is less than a preset threshold is determined as the target condition, including: based on the historical driving characteristics, identifying the key driving behaviors that affect the driving deviation; establishing a driving behavior model to quantify the degree of influence of various driving behaviors on the driving deviation, and formulating an optimization strategy for the key driving behaviors; predicting the effect of the optimization strategy after implementation and generating simulation results, evaluating the change state of the driving deviation based on the simulation results, and adjusting the optimization strategy based on the evaluation conclusions until the simulation results reach the expected driving deviation reduction target; and applying the finalized optimization strategy to the condition optimization.

[0012] In one embodiment of the present application, after the target operating condition is determined as the user operating condition cycle of the target group, it also includes: determining cycle data based on the user operating condition cycle, the cycle data including a time-speed curve, a total cycle mileage, a total cycle time, and an average cycle speed; the time-speed curve represents the relationship between the speed of the vehicle under the target operating condition and time, the total cycle mileage represents the total distance traveled during the entire operating condition cycle, the total cycle time represents the total time required to complete the entire operating condition cycle, and the average cycle speed represents the average driving speed during the entire operating condition cycle.

[0013] The present application provides a device for formulating a tram user operating condition cycle, the device comprising: a historical data acquisition module, used to collect historical driving data, and obtain historical driving characteristics of a target group based on the historical driving data; a driving characteristic determination module, used to collect current driving characteristics of a test vehicle, and compare the current driving characteristics with the historical driving characteristics to obtain a driving deviation; a target operating condition determination module, used to determine the driving condition corresponding to the current driving characteristics with a driving deviation less than a preset threshold as a target operating condition.

[0014] The present application provides an electronic device, including a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the tram user operating cycle formulation method as described above.

[0015] The present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is used to enable a computer to execute the tram user operating cycle formulation method as described above.

[0016] Beneficial effects of the present application: The electric vehicle user operating condition cycle system method proposed in the present application first collects historical driving data, analyzes and extracts the historical driving characteristics of the target group; secondly, collects the current driving characteristics of the test vehicle and compares them with the historical characteristics to obtain the driving deviation between the driving data of the test vehicle and the historical driving data; then, when the driving deviation is less than a preset threshold, it is determined that the current driving condition is relatively consistent with the characteristics of the target group, and it is regarded as the proposed operating condition, and the proposed operating condition is further optimized and adjusted, and finally the optimized operating condition is determined as the target operating condition; the method can timely discover and evaluate changes in driving behavior by real-time collection of the current driving characteristics of the test vehicle and comparing them with historical data, thereby ensuring that the selected operating condition cycle can always accurately reflect the current driving conditions and environment, and improve the timeliness and accuracy of the operating condition data; in addition, after determining the proposed operating condition, further optimizing it can better simulate the driving conditions in the real world, which not only improves the quality of the operating condition data, but also provides more accurate support for the design, testing and verification of electric vehicles. Moreover, compared with directly using the existing general operating condition data, the operating condition data obtained by this method can provide more accurate and personalized operating condition cycles, which helps to improve the accuracy and applicability of electric vehicles in design, testing and performance evaluation.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the accompanying drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, it is not necessary to spend creative work before the invention.

[0019] Figure 1 is a schematic diagram of an implementation environment of a method for formulating a tram user operating cycle according to an exemplary embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for formulating a tram user operating cycle shown in an exemplary embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of the overall steps of a method for formulating a tram user operating cycle shown in an exemplary embodiment of the present application;

[0022] Figure 4 is a block diagram of a tram user operating cycle formulation device shown in an exemplary embodiment of the present application;

[0023] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The following will describe the implementation methods of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for illustrating the present application, not for limiting the scope of protection of the present application.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0026] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0027] First of all, it should be explained that working conditions refer to the working state of the equipment under conditions directly related to its action, focusing on the actual working state of the equipment under specific conditions rather than the ideal state.

[0028] A cycle is a series of operations or conditions designed to simulate the operation of a device or system under specific operating conditions. In the automotive field, the cycle is particularly used to evaluate the performance of a car under different operating conditions, especially the emission performance. By simulating various operating conditions in actual driving, the cycle can help manufacturers understand the performance of the car under different conditions, so as to carry out targeted optimization and improvement.

[0029] NEDC: New European Driving Cycle is a test condition used in Europe to evaluate vehicle energy consumption and emissions.

[0030] FTP75: Federal Test Procedure 75 is a test condition developed by the U.S. Environmental Protection Agency (EPA) for evaluating automobile emissions.

[0031] WLTP: Worldwide Harmonized Light Vehicles Test Procedure is a globally unified automobile test procedure used to evaluate the energy consumption and emissions of light vehicles.

[0032] EPA: Environmental Protection Agency is an agency of the U.S. government responsible for formulating and implementing environmental protection policies, including the formulation of automobile emissions and energy consumption standards.

[0033] CLTC: China Light-Vehicle Test Cycle is a test condition developed by China to evaluate automobile energy consumption and emissions.

[0034] OBD-II interface, full name On-Board Diagnostics II interface, is a standardized automobile diagnostic interface.

[0035] Figure 1 It is a schematic diagram of the implementation environment of the tram user operating cycle formulation method shown in an exemplary embodiment of the present application.

[0036] like Figure 2As shown, the implementation environment of the tram user operating cycle formulation method includes a data acquisition module 101 and a computer module 102. Among them, the data acquisition module 101 is responsible for collecting raw data during the operation of the tram, including but not limited to the speed, acceleration, battery power, motor temperature, driving mode selection, road condition information, etc. of the tram. The data acquisition device can be various sensors installed on the tram (such as GPS sensors, accelerometers, gyroscopes, temperature sensors, battery management systems, etc.), or it can be data collected by external devices after being connected to the tram by wireless or wired means. This application does not impose any restrictions on the specific implementation form of the data acquisition module. The computer device 102 is used to process the raw data collected by the data acquisition module 101, analyze the driving habits and usage scenarios of the tram users, and establish a user operating model based on these data. The device can also further process the data using a machine learning algorithm to identify a typical user operating cycle, and optimize the various performance parameters of the tram accordingly, such as battery management strategy, energy recovery efficiency, etc. The computer device 102 can be a high-performance GPU computer, a GPU computing cluster, a dedicated edge computing device, or an embedded system integrated in the tram. This application also does not impose any restrictions on the specific type of computer device.

[0037] Figure 2 It is a flow chart of a method for formulating a tram user operating cycle, shown as an exemplary embodiment of the present application.

[0038] like Figure 2 As shown, in an exemplary embodiment, the tram user operating cycle formulation method includes at least steps S210 to S240, which are described in detail as follows:

[0039] Step S210 , collecting historical driving data, and obtaining historical driving characteristics of the target group based on the historical driving data.

[0040] In one embodiment of the present application, historical driving data is collected, and historical driving characteristics of a target group are obtained based on the historical driving data, including: obtaining mileage data of all single trips, drawing a mileage data distribution diagram and calculating the average mileage to obtain a driving mileage characteristic, where a single trip is a process from starting to stopping a vehicle; recording the duration and stop time of each single trip, determining the driving time of a single trip, and calculating the average vehicle speed based on the mileage and driving time to obtain a driving time characteristic; collecting acceleration data, and counting the time and mileage proportion of acceleration and deceleration to obtain an acceleration characteristic; dividing the vehicle speed data into predetermined intervals, and counting the mileage, time and average vehicle speed in each interval to obtain interval driving characteristics in different vehicle speed intervals; determining the mileage characteristic, driving time characteristic, acceleration characteristic, and the interval driving characteristic as the historical driving characteristics of the entire target.

[0041] In a specific embodiment of the present application, a GPS module is used to record the vehicle position coordinates to calculate the distance between two points to obtain the mileage, and the recording starts after each vehicle is started and ends when the vehicle stops. Each record contains the start time, end time and mileage; a vehicle state sensor is used to identify the working state of the engine, distinguish between the driving state and the parking state, record the start and end time of each driving, and record the parking time; an accelerometer is used to record the change in vehicle acceleration, and the timestamp and acceleration value of each acceleration change are recorded; a GPS module is used to calculate the instantaneous speed or directly read the vehicle's OBD-II interface data, and the timestamp and speed value of each speed change are recorded to obtain historical driving data. Then, all the historical driving data obtained above are cleaned and preliminarily sorted, such as removing invalid data points such as abnormal mileage records caused by the loss of GPS signals, and ensuring the integrity and consistency of the data by checking whether the driving time and mileage match. Then, the cleaned data is analyzed in depth to extract useful features, including: summarizing the mileage data of all single trips, calculating the total mileage and average mileage, making a histogram or density map of the mileage data, and understanding the mileage distribution; calculating the average speed based on the mileage and driving time, recording the total time of each trip, and analyzing the distribution of driving time; calculating the total time of acceleration and deceleration and the corresponding mileage based on the acceleration data, and obtaining its proportion in the total driving time; dividing the speed into a low-speed zone (0-30km / h), a medium-speed zone (30-60km / h), and a high-speed zone (>60km / h) or other speed segments, and this application does not impose any restrictions on the value of its speed interval, and calculates the mileage, time and average speed in each interval. Finally, the historical driving characteristics are obtained based on the sorted data. It should be noted that the above-mentioned data cleaning method and speed division method are only used as exemplary explanations, and this application does not impose any specific restrictions on its data cleaning method and division method.

[0042] Step S220, collecting the current driving characteristics of the test vehicle, and comparing the current driving characteristics with the historical driving characteristics to obtain the driving deviation.

[0043] In one embodiment of the present application, current driving characteristics are compared with historical driving characteristics to obtain driving deviations, including: calculating the mileage deviation degree between the current mileage and the historical mileage distribution, and determining the mileage deviation value of the test vehicle's mileage relative to the historical driving data based on the mileage deviation degree; calculating the deviation between the current average speed and the historical average speed to obtain the speed deviation value of the test vehicle's driving speed relative to the historical driving data; calculating the deviation between the time and mileage proportion of acceleration and deceleration and the historical driving data to obtain the acceleration deviation value of the test vehicle's driving acceleration relative to the historical driving data; comparing the current mileage, time and average speed in each speed interval with the historical driving data to obtain the partitioned driving deviation value of the test vehicle in each speed interval relative to the historical driving data; weighted calculation of the mileage deviation value, speed deviation value, acceleration deviation value, and partitioned driving deviation value according to a preset weight ratio to obtain the driving deviation of the current test mileage of the test vehicle from the historical driving data.

[0044] In a specific embodiment of the present application, calculating the driving deviation between the current test mileage of the test vehicle and the historical driving data includes the following:

[0045] First, calculate the mileage deviation value.

[0046] First, collect the mileage of the current test vehicle and obtain the mileage distribution of all trips in the historical driving data, and then calculate the deviation between the mileage of the current test vehicle and the historical mileage distribution. Assume that the mileage of the current test vehicle is M current , the average value of historical mileage distribution is M average , with a standard deviation of δ M , then the calculation formula for the mileage deviation degree is as follows:

[0047]

[0048] Among them, D M Indicates the mileage deviation value, M current Indicates the mileage of the current test vehicle, M average represents the average value of historical mileage distribution, δ M Represents standard deviation.

[0049] Second, calculate the vehicle speed deviation value.

[0050] First, calculate the average speed of the current test vehicle and obtain the average speed of all trips in the historical driving data. Then calculate the deviation between the average speed of the current test vehicle and the historical average speed. Assume that the average speed of the current test vehicle is V current , the historical average speed is V average , then the calculation formula of the vehicle speed deviation value is as follows:

[0051]

[0052] Among them, D V Indicates the vehicle speed deviation value, V current Indicates the average speed of the current test vehicle, V average Historical average speed.

[0053] Third, calculate the acceleration deviation value.

[0054] First, analyze the acceleration and deceleration behavior of the current test vehicle, record the time and mileage ratio of acceleration and deceleration, and obtain the acceleration and deceleration behavior data in the historical driving data. Finally, calculate the deviation between the acceleration of the current test vehicle and the historical data. Assume that the time and mileage ratio of acceleration and deceleration of the current test vehicle are T acc and T dec , the corresponding proportions in historical data are T acc,avg and T dec,avg , then the acceleration deviation value is expressed as follows:

[0055]

[0056] Among them, D A Indicates the acceleration deviation value, T acc Indicates the acceleration time and mileage ratio of the current test vehicle, T dec Indicates the time and mileage ratio of the current test vehicle deceleration, T acc,avg Indicates the time and mileage ratio of vehicle acceleration in historical data, T dec,avg Indicates the time and mileage ratio of vehicle deceleration in historical data.

[0057] Fourth, calculate the partition driving deviation value.

[0058] First, the vehicle speed is divided into several different ranges, such as low speed (0-40km / h), medium speed (40-80km / h) and high speed (>80km / h), and the mileage, time and average speed of the current test vehicle in each speed range are compared with the historical data, and the driving deviation value in each speed range is calculated. Assume that the mileage of the current test vehicle in a certain partition is S current , the mileage of this interval in the historical data is S average , for each interval i, the partition driving deviation value D Si It can be expressed by the following formula:

[0059]

[0060] Among them, D Si Indicates the zone driving deviation value, S current,irepresents the mileage of the current test vehicle corresponding to interval i, S average,i Indicates the mileage corresponding to historical data interval i.

[0061] Fifth, the total driving deviation is calculated.

[0062] According to the preset weight ratio, the mileage deviation value D M , speed deviation value D V , acceleration deviation value D A , and the partition driving deviation value D Si Perform weighted calculation to get the final driving deviation. Set the weight ratio to as well as The final driving deviation D total It can be expressed by the following formula:

[0063]

[0064] Among them, D total Indicates driving deviation, D M Indicates mileage deviation value, D V Indicates the speed deviation value, D A Indicates the acceleration deviation value, D Si Indicates the zone driving deviation value, represents the weight of the mileage deviation value, represents the weight of the vehicle speed deviation value, represents the weight of the acceleration deviation value, The weight representing the travel deviation value of the partition.

[0065] It should be noted that the above embodiment is only a specific embodiment to illustrate how to calculate the driving deviation according to the given steps and formulas. In actual use, the above calculation can be implemented by any other feasible algorithm or by using a computer program. This application does not impose any specific restrictions on its calculation method and calculation process.

[0066] Step S230: determining the driving condition corresponding to the current driving characteristic with a driving deviation less than a preset threshold as the target condition.

[0067] In one embodiment of the present application, one or more driving deviation thresholds are set according to system requirements and safety considerations. Then, all collected driving data are traversed, the driving deviation of each data point is calculated, and the calculated driving deviation is compared with the preset deviation threshold, so that the driving conditions corresponding to the data points with driving deviations less than the preset threshold are regarded as the intended selected conditions. It should be noted that there are various ways to set the threshold of driving deviation, which can be based on comprehensive consideration of historical data, industry standards, expert advice and other factors, and the present application does not impose any restrictions on its specific setting method.

[0068] In one embodiment of the present application, taking reducing driving deviation as an optimization goal as an example, the proposed working condition is optimized by filtering. First, the collected data is filtered to remove noise and outliers, extract more useful information, and the data under each proposed working condition is filtered separately for comparison and analysis. Then, based on the filtered data, each proposed working condition is evaluated and all the proposed working conditions are ranked based on the evaluation results to select the working condition with the highest score or the top ranking as the target working condition. It should be noted that in the filtering process, a low-pass filter can be used to smooth the data to remove high-frequency noise, or a Kalman filter can be used to estimate and predict the state of the equipment. The present application does not impose any specific restrictions on its filtering method.

[0069] In addition, it should be noted that the above embodiments are only for illustrative purposes. In the technical solution proposed in this application, the proposed working conditions can be optimized by filtering, or other feasible methods can be selected, such as simulation adjustment, statistical analysis, etc., as long as the optimization goal can be achieved. Therefore, this application does not impose any restrictions on the specific method of optimization.

[0070] In one embodiment of the present application, a driving condition corresponding to a current driving characteristic in which a driving deviation is less than a preset threshold is determined as a target condition, including: identifying key driving behaviors that affect driving deviation based on the historical driving characteristics; establishing a driving behavior model to quantify the degree of influence of various driving behaviors on driving deviation, and formulating optimization strategies for key driving behaviors; predicting the effect of the optimization strategy after implementation and generating simulation results, evaluating the change state of the driving deviation based on the simulation results, and adjusting the optimization strategy based on the evaluation conclusions until the simulation results reach the expected driving deviation reduction target; and applying the finalized optimization strategy to condition optimization.

[0071] In a specific embodiment of the present application, first, historical driving data of the same model of vehicles over a period of time is collected, including characteristics such as average vehicle speed and power consumption, and power consumption data is extracted from the historical driving data, for example: [10.0, 10.5, 11.0, 11.5, ..., 20.0] kW·h / 100km, and its power consumption data is divided into three intervals: low power consumption (10.0-14.0 kW·h / 100km), medium power consumption (14.5-16.5 kW·h / 100km), and high power consumption (16.5-20.0 kW·h / 100km). According to the historical data, the probability of each power consumption interval is calculated: the probability of low power consumption interval: 3 / 10=0.3; the probability of medium power consumption interval: 5 / 10=0.5; the probability of high power consumption interval: 2 / 10=0.2. Secondly, the test is carried out according to the target working condition, and the power consumption data during the test is recorded. Assuming that the power consumption data obtained during the test is [10.0, 10.5, 11.0, 11.5, 11.0] kW·h / 100km, the average power consumption is calculated as the verification power consumption value, which is 10.7kW·h / 100km. Then, the verification power consumption value 10.7kW·h / 100km is compared with the power consumption interval in the historical power consumption distribution information. The verification power consumption value is located in the middle power consumption interval (10.0-14.0kW·h / 100km), and the probability of this interval is 0.3, that is, the test power consumption probability is 30%. Assuming that the preset probability threshold is 40%. The test power consumption probability is 30%, which is less than the preset probability threshold of 40%, and it is determined that the target working condition test has not passed. Finally, since the test power consumption probability (30%) does not reach the preset probability threshold (40%), the target working condition test has not passed, and further adjustment or re-evaluation of the target working condition is required.

[0072] It should be noted that the above embodiment shows how to determine the historical power consumption distribution information of the target vehicle model based on the historical driving characteristics, and test the target working condition based on this information to determine the user working condition cycle. However, in actual applications, the division of power consumption intervals and the setting of probability thresholds may be adjusted according to specific circumstances and needs, and this application does not impose any restrictions on the specific values ​​and division methods of the division of power consumption and the setting of probability thresholds.

[0073] In one embodiment of the present application, a target operating condition is tested, including: obtaining basic vehicle information of a target vehicle model, the basic vehicle information including at least vehicle mass, tire specifications, and power system characteristics; inputting data of the target operating condition into a chassis dynamometer control device; pre-setting a test vehicle based on the basic vehicle information of the target vehicle model to ensure that the basic vehicle information of the test vehicle is consistent with the basic vehicle information of the target vehicle model; obtaining a target resistance, the target resistance being the vehicle's declared resistance or the vehicle's actual road resistance obtained through actual road testing; and operating the chassis dynamometer control device based on the target resistance to test the target operating condition.

[0074] In one embodiment of the present application, first, the unloaded mass and fully loaded mass of the target vehicle model are obtained through data provided by the vehicle manufacturer or actual measurement, and the specifications of the tires used by the target vehicle model are recorded, including tire size, type (such as radial tires), tread width, etc., and the power system related information of the target vehicle model is collected, including engine type (such as gasoline, diesel or electric), maximum power, maximum torque, etc. Then, the specific requirements of the target working condition (such as speed, acceleration, driving time, etc.) are input into the control system of the chassis dynamometer. And the mass, tire specifications and power system characteristics of the test vehicle are adjusted to ensure that the vehicle basic information of the test vehicle is consistent with the target vehicle model, and the target resistance is obtained through vehicle announcements or actual road tests. Finally, the target resistance value is set in the chassis dynamometer control system, the chassis dynamometer is started, the test vehicle is operated according to the requirements of the target working condition, and the test data, including the speed, acceleration, power output, etc. of the vehicle, are recorded and analyzed to evaluate the vehicle performance under the target working condition.

[0075] In a specific embodiment of the present application, the specific steps of testing the target working condition are as follows:

[0076] First, obtain the basic information of the target vehicle:

[0077] Vehicle mass: 1500kg

[0078] Tire size: 205 / 55R16

[0079] Power system characteristics: maximum output power 150kW, maximum torque 300Nm

[0080] Secondly, prepare the chassis dynamometer control equipment to ensure that the target working condition can be simulated and the data of the target working condition is input into the chassis dynamometer control equipment:

[0081] Enter the key parameters of the target working condition: the second-by-second speed curve of the working condition.

[0082] Then, the test vehicle is pre-set based on the basic vehicle information of the target vehicle model to ensure that the basic vehicle information of the test vehicle is consistent with that of the target vehicle model. The mass of the test vehicle is set to 1500kg, the tire specification is 205 / 55R16, and the power system characteristics are maximum output power of 150kW and maximum torque of 300Nm.

[0083] Then, obtain the target resistance by any of the following methods:

[0084] Method 1: Determine the target resistance by using the vehicle's declared resistance. Assume that the resistance coefficient obtained from the manufacturer's data is C d=0.3, air density ρ=1.225kg / m3, windward area A=2.0m2.

[0085] The resistance formula is as follows:

[0086]

[0087] Among them, F d represents the vehicle's declared resistance, ρ represents the air density, C d represents the drag coefficient, A represents the frontal area of ​​the vehicle, and v represents the vehicle speed.

[0088] Taking the vehicle speed of 60km / h as an example, the corresponding declared resistance is calculated as 225.75N according to Formula 6.

[0089] Method 2: Determine the target resistance based on the actual road resistance of the vehicle. Assume that the resistance obtained through actual road testing is 230N.

[0090] Finally, the chassis dynamometer control device is operated based on the target resistance, specifically including: setting the target resistance on the chassis dynamometer, for example, using 230N as the simulated resistance; and starting the chassis dynamometer to perform the test according to the target working condition.

[0091] It should be noted that the test of the target working condition is completed through the above steps, the consistency of the basic vehicle information of the test vehicle and the target vehicle model is ensured, and the test is performed based on the target resistance. In addition, the above embodiment shows how to test the target working condition. However, in actual applications, the specific implementation of the test may require more detailed parameter settings and technical support, and this application does not impose any specific restrictions on it.

[0092] In one embodiment of the present application, the proposed operating conditions are optimized, including: identifying key driving behaviors that affect driving deviations based on historical driving characteristics; establishing a driving behavior model to quantify the degree of influence of various driving behaviors on driving deviations, and formulating optimization strategies for key driving behaviors; using simulation software or models to predict the effect of the optimization strategy after implementation, evaluating the change state of driving deviations based on simulation results, and adjusting the optimization strategy based on the evaluation conclusions until the expected driving deviation reduction goal is achieved; and applying the finalized optimization strategy to operating condition optimization.

[0093] In one embodiment of the present application, after the target operating condition is determined as the user operating condition cycle of the target group, it also includes: determining cycle data based on the user operating condition cycle, the cycle data including a time-speed curve, a total cycle mileage, a total cycle time, and an average cycle speed; the time-speed curve represents the relationship between the speed of the vehicle under the target operating condition and time, the total cycle mileage represents the total distance traveled during the entire operating condition cycle, the total cycle time represents the total time required to complete the entire operating condition cycle, and the average cycle speed represents the average driving speed during the entire operating condition cycle.

[0094] Figure 3 It is a schematic diagram of the overall steps of a method for formulating a tram user operating cycle, shown as an exemplary embodiment of the present application.

[0095] like Figure 3 As shown, the complete steps of the tram user operating cycle formulation method include: first, extracting the single driving mileage data of a specific number of users during actual use, extracting the single driving time data of a specific number of users during actual use, extracting the single driving speed data of a specific number of users during actual use and calculating the formal mileage and driving time proportion of each speed interval, extracting the single driving acceleration data of a specific number of users during actual use and calculating the formal mileage and driving time proportion of process acceleration / deceleration, and extracting the single driving average power consumption of a certain model of a specific number of users during actual use; then, driving the vehicle on the road to reproduce the operating condition, collecting the vehicle speed data, verifying the speed data conformity, and optimizing the time-speed curve finally determined by the above steps through filtering software to verify the speed data conformity; finally, driving the speed-time curve finally determined by the above steps on the chassis dynamometer to verify the average power consumption of the vehicle, and outputting the user operating condition after the verification is completed.

[0096] In a complete embodiment of the present application, the method for formulating a tram user operating cycle is divided into the following steps:

[0097] Step 1: extract the single-trip mileage data of vehicles of a specific number of users during actual use (a single trip is defined as the vehicle starting to run at a speed greater than 0 for a period of time, and the vehicle stops running for more than 5 minutes or the vehicle is powered off at high voltage, then the mileage is terminated), determine the distribution of single-trip mileage data during actual use by users and the average mileage of single trips by users, which is used as a reference for formulating the total mileage of the user's operating cycle.

[0098] Step 2: extract the single driving time data of the vehicles of a specific number of users during actual use, including driving time and parking time, determine the distribution of single driving time data during actual use by users, and calculate the average speed of a single trip in combination with the single driving mileage data.

[0099] Step 3, extract the single driving speed data of vehicles of a specific number of users during actual use (the sampling frequency can be 1HZ), divide the speed data according to a certain speed range, calculate the total mileage, total driving time, and average driving speed of each speed range, and obtain the mileage ratio and driving time ratio of each speed range.

[0100] Step 4, extract the single-time vehicle acceleration data of vehicles with a specific number of users during actual use (the sampling frequency can be 1HZ), determine the acceleration and deceleration data of the users during actual use (acceleration values ​​in the range of (-1, +1) are defined as uniform speed driving), calculate the total acceleration time and the total deceleration time, and obtain the acceleration time ratio, deceleration time ratio, acceleration mileage ratio, and deceleration mileage ratio.

[0101] Step 5, extract the average power consumption of a certain model of vehicle during actual use by a specific number of users, and filter it according to certain conditions, fit the filtered average power consumption data with a normal distribution, analyze the degree of data dispersion, and obtain the probability of a certain power consumption range.

[0102] Step 6: Based on the determined theoretical data such as total mileage, total driving time, interval speed time ratio / mileage ratio, etc., drive the vehicle on the road to reproduce the working conditions, simultaneously collect vehicle speed data, and compare the collected data with the theoretical data.

[0103] Step 7: According to the comparison result, if the collected data does not match the theoretical data, repeat step 6 until the deviation between the collected data and the theoretical data is controlled within a given range.

[0104] Step 8, optimize the speed-time curve of the collected data finally determined in step 7 through filtering software, compare the optimized data with the theoretical data to ensure that the deviation is controlled within a given range, if not satisfied, repeat step 8.

[0105] Step 9, input the speed-time user working condition data finally determined in step 8 into the chassis dynamometer control device, and pre-set the test vehicle (consistent with the model in step 5) according to the conditions screened in step 5, and run the working condition to carry out energy consumption and driving range test. The target resistance value used in the test can be the vehicle's announced resistance or the vehicle's actual road resistance. After the test is completed, compare the test results with the data in step 5 to determine the power consumption range in which the actual test result of the average power consumption is located.

[0106] Step 10, after the verification is completed, the user operating cycle is obtained, which includes: time-speed curve, total cycle mileage, total cycle time, and average cycle speed.

[0107] Figure 4 is a block diagram of a tram user operating cycle formulation device shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The device may also be applicable to other exemplary implementation environments and specifically configured in other devices, and this embodiment does not limit the implementation environment to which the device is applicable.

[0108] like Figure 4 As shown, the exemplary tram user operating cycle formulation device includes: a historical data collection module 410, a driving characteristic determination module 420, and a target operating condition determination module 430.

[0109] Among them, the historical data collection module 410 is used to collect historical driving data and obtain the historical driving characteristics of the target group based on the historical driving data; the driving characteristic determination module 420 is used to collect the current driving characteristics of the test vehicle and compare the current driving characteristics with the historical driving characteristics to obtain the driving deviation; the target operating condition determination module 430 is used to determine the driving condition corresponding to the current driving characteristics with a driving deviation less than a preset threshold as the target operating condition.

[0110] It should be noted that the tram user operating cycle formulation device provided in the above embodiment and the tram user operating cycle formulation method provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In actual application, the tram user operating cycle formulation device provided in the above embodiment can distribute the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0111] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the tram user operating condition cycle formulation method provided in the above-mentioned embodiments.

[0112] Figure 5 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0113] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503, such as executing the method described in the above embodiment. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, the ROM 502 and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0114] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0115] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 509, and / or installed from a removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0116] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0117] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0118] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0119] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor of a computer, causes the computer to execute the above-mentioned method for formulating a tram user operating cycle. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0120] Another aspect of the present application also provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the electric vehicle user operating cycle formulation method provided in each of the above embodiments.

[0121] The above embodiments are merely illustrative of the principles and effects of the present application, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A method for formulating a tram user operating cycle, characterized in that: The method comprises: Collecting historical driving data, and obtaining historical driving characteristics of the target group based on the historical driving data; Collecting current driving characteristics of the test vehicle, and comparing the current driving characteristics with the historical driving characteristics to obtain driving deviations; The driving condition corresponding to the current driving characteristic with a driving deviation less than a preset threshold is determined as the target condition.

2. The method for formulating a tram user operating cycle according to claim 1, characterized in that: After determining the target operating condition, the method further includes: Determine the historical power consumption distribution information of any target vehicle model according to the historical driving characteristics; Dividing the vehicle power consumption into a plurality of power consumption intervals based on the historical power consumption distribution information, and calculating the probability of each power consumption interval; Testing the target working condition to obtain a verification power consumption value during the test; Comparing the verification power consumption value with the multiple power consumption intervals, determining the target power consumption interval to which the verification power consumption value belongs, so as to obtain the test power consumption probability corresponding to the verification power consumption value; If the test power consumption probability meets the preset probability threshold range, it is determined that the target operating condition test has passed, and the target operating condition is determined as the user operating condition cycle of the target group.

3. The method for formulating the tram user operating cycle according to claim 2, characterized in that: The target operating conditions are tested, including: Obtaining basic vehicle information of the target vehicle model, wherein the basic vehicle information at least includes vehicle mass, tire specifications, and power system characteristics; inputting the data of the target working condition into a chassis dynamometer control device; Presetting the test vehicle based on the basic vehicle information of the target vehicle model to ensure that the basic vehicle information of the test vehicle is consistent with the basic vehicle information of the target vehicle model; Obtaining a target resistance, where the target resistance is a vehicle declared resistance or a vehicle actual road resistance obtained through an actual road test; The chassis dynamometer control device is operated based on the target resistance to test the target operating condition.

4. The method for formulating a tram user operating cycle according to claim 1, characterized in that: Collect historical driving data, and obtain historical driving characteristics of the target group based on the historical driving data, including: Obtaining mileage data of all single trips, drawing a mileage data distribution diagram and calculating the average mileage to obtain a mileage feature, wherein a single trip is a process from starting to stopping a vehicle; Record the duration and stop time of each single trip, determine the driving time of a single trip, and calculate the average vehicle speed based on the mileage and driving time to obtain the driving time characteristics; Collect acceleration data and count the time and mileage ratios of acceleration and deceleration to obtain acceleration characteristics; The vehicle speed data is divided into predetermined intervals, and the mileage, time and average speed in each interval are counted to obtain interval driving characteristics in different speed intervals; The driving mileage feature, the driving time feature, the acceleration feature, and the interval driving feature are determined as the historical driving characteristics of the entire target.

5. The method for formulating the tram user operating cycle according to claim 4, characterized in that: Comparing the current driving characteristics with the historical driving characteristics to obtain a driving deviation includes: Calculating the degree of mileage deviation between the current mileage and the historical mileage distribution, and determining the mileage deviation value of the mileage of the test vehicle relative to the historical driving data based on the degree of mileage deviation; Calculate the deviation between the current average speed and the historical average speed to obtain the speed deviation value of the test vehicle relative to the historical driving data; Calculate the deviation of the acceleration and deceleration time and mileage ratio from the historical driving data to obtain the acceleration deviation value of the test vehicle's driving acceleration relative to the historical driving data; Compare the current mileage, time and average speed in each speed range with the historical driving data to obtain the partitioned driving deviation value of the test vehicle in each speed range relative to the historical driving data; The mileage deviation value, the vehicle speed deviation value, the acceleration deviation value, and the partition driving deviation value are weightedly calculated according to a preset weight ratio to obtain a driving deviation between the current test mileage of the test vehicle and the historical driving data.

6. The method for formulating a tram user operating cycle according to claim 1, characterized in that: The driving condition corresponding to the current driving characteristics with a driving deviation less than a preset threshold is determined as a target condition, including: Based on the historical driving characteristics, identifying key driving behaviors that affect driving deviations; Establish a driving behavior model to quantify the impact of various driving behaviors on driving deviations and develop optimization strategies for key driving behaviors; Predicting the effect of the optimization strategy after implementation and generating simulation results, evaluating the change state of driving deviation according to the simulation results, and adjusting the optimization strategy according to the evaluation conclusions until the simulation results achieve the expected driving deviation reduction target; The finalized optimization strategy is applied to operating condition optimization.

7. The method for formulating a tram user operating cycle according to any one of claims 2 or 3, characterized in that: After determining the target operating condition as the user operating condition cycle of the target group, the method further includes: Determining cycle data based on the user operating cycle, the cycle data including a time-speed curve, a total cycle mileage, a total cycle time, and an average vehicle speed of the cycle; The time-speed curve represents the relationship between the speed of the vehicle under the target working condition and time, the total cycle mileage represents the total distance traveled during the entire working condition cycle, the total cycle time represents the total time required to complete the entire working condition cycle, and the average cycle speed represents the average driving speed during the entire working condition cycle.

8. A tram user operating cycle formulation device, characterized in that: The device comprises: A historical data collection module, used to collect historical driving data and obtain historical driving characteristics of the target group based on the historical driving data; A driving characteristic determination module, used for collecting the current driving characteristics of the test vehicle, and comparing the current driving characteristics with the historical driving characteristics to obtain a driving deviation; The target operating condition determination module is used to determine the driving condition corresponding to the current driving characteristics with a driving deviation less than a preset threshold as the target operating condition.

9. An electronic device, characterized in that: It comprises a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute the computer program stored in the memory to implement the tram user operating cycle formulation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program is used to enable a computer to execute the method for formulating a tram user operating condition cycle as described in any one of claims 1-7.

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