Vehicle dynamic characteristic MAP estimation method and device, medium and equipment

Abstract

The invention relates to the technical field of vehicles, and discloses a vehicle dynamic characteristic MAP estimation method and device, a medium and equipment. The method comprises the steps that the maximum power point rotating speed and the maximum torque point rotating speed of a target vehicle are obtained; acquiring state data of the target vehicle in real time, at least executing a working condition grouping operation and a data screening operation on the state data to obtain effective working condition data, and further performing data volume verification on the effective working condition data; after the data volume verification is passed, generating a verification data table, an external characteristic curve, a minimum consumption curve two-dimensional table and an equal consumption curve set according to the effective working condition data, the maximum power point rotating speed and the maximum torque point rotating speed so as to draw a dynamic characteristic MAP, and continuously collecting target vehicle data; and when the target vehicle data meets a preset condition, judging the effectiveness of the dynamic characteristic MAP based on the target vehicle data and the verification data table, and if the dynamic characteristic MAP is effective, exiting calculation and outputting an effective dynamic characteristic MAP.

Description

Methods, apparatus, media and equipment for estimating vehicle dynamic characteristics using MAP Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, apparatus, medium and equipment for estimating vehicle dynamic characteristics (MAP). Background Technology

[0002] The acquisition of existing dynamic characteristic MAPs mainly relies on bench testing, road testing, and offline data analysis (such as traditional calibration methods used by OEMs). These technologies have at least the following drawbacks:

[0003] 1. Limited data dependence: MAP maps are core confidential data of OEMs. Third-party systems (such as predictive cruise control, cloud-based assisted driving, etc.) need to obtain them through cooperation, resulting in a narrow scope of application.

[0004] 2. Cost and cycle issues: Bench testing requires specialized equipment and facilities, while road testing consumes a lot of manpower and time, with a single test cycle lasting from several weeks to several months.

[0005] 3. Insufficient accuracy and adaptability: Offline data analysis is difficult to cover complex actual working conditions, static mesh division cannot adapt to individual differences in the power system of a single vehicle, and the data error rate is too high.

[0006] 4. Lack of dynamic optimization: Traditional MAP maps are fixed after generation and cannot adapt to performance changes during long-term vehicle use. Existing vehicle terminal data solutions are only used for diagnosis and cannot generate high-precision MAP maps. Summary of the Invention

[0007] The purpose of this invention is to provide a method, apparatus, medium, and equipment for estimating vehicle dynamic characteristics (MAP), which can at least eliminate the limitation of OEM data dependence to achieve MAP estimation, while reducing testing costs, shortening testing cycles, and improving MAP accuracy, vehicle adaptability, and dynamic optimization capabilities.

[0008] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for estimating vehicle dynamic characteristics (MAP), comprising at least:

[0009] S1. Collect the model information of the target vehicle to obtain the maximum power point speed and the maximum torque point speed of the target vehicle;

[0010] S2. Collect the status data of the target vehicle in real time during operation, and perform at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then perform data volume verification on the valid working condition data.

[0011] S3. After the effective operating condition data passes the data quantity verification, a verification data table, an external characteristic curve, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves are generated based on the effective operating condition data, the maximum power point speed, and the maximum torque point speed.

[0012] S4. Draw a power characteristic MAP based on the external characteristic curve, the minimum consumption curve two-dimensional table, and the set of equal consumption curves, and continuously collect target vehicle data;

[0013] S5. When the target vehicle data meets the preset conditions, the validity of the power characteristic MAP is determined based on the target vehicle data and the verification data table.

[0014] S6. When the dynamic characteristic MAP is valid, exit the calculation and output the valid dynamic characteristic MAP.

[0015] Optionally, after step S2, at least the following steps are also included:

[0016] S7. When the valid operating condition data fails the data volume verification, the status data of the target vehicle during operation is re-collected, and at least the operating condition grouping operation and the data filtering operation are performed on the re-collected status data to obtain the valid operating condition data. Then, the data volume verification is performed on the valid operating condition data until the valid operating condition data passes the data volume verification.

[0017] Optionally, after step S5, at least the following steps are also included:

[0018] S8. When the power characteristic MAP is invalid, the target vehicle data is used as the state data, and steps S2 to S5 are re-executed until the power characteristic MAP is valid. Then, the calculation is exited and the valid power characteristic MAP is output.

[0019] Optionally, step S2 specifically includes at least:

[0020] S201. Collect the status data of the target vehicle in real time during operation, and perform the working condition grouping operation on the status data according to the vehicle operating status to obtain grouped data. Then, filter the grouped data for data glitch based on a preset speed change error threshold to obtain filtered grouped data.

[0021] S202. The filtered group data with a continuous duration not less than a preset duration threshold is retained as the valid working condition data, and the filtered group data with a continuous duration less than the preset duration threshold is removed.

[0022] S203. Perform data volume verification on the valid working condition data according to the preset data volume verification conditions.

[0023] Optionally, step S3 specifically includes at least:

[0024] S301. After the effective operating condition data passes the data quantity verification, the effective operating condition data is divided into dynamic grids according to preset rules, and the root mean square value is calculated based on the effective operating condition data falling into each grid intersection point and the surrounding set threshold range. Then, each root mean square value is used as the energy consumption rate of the corresponding grid intersection point to generate the verification data table.

[0025] S302. Perform torque segmentation on the effective operating condition data according to the maximum power point speed and the maximum torque point speed, generate the external characteristic curve by linear fitting, and calculate the torque value according to the preset speed interval to form a two-dimensional table of external characteristics.

[0026] S303. Perform energy consumption segmentation on the effective operating condition data according to the maximum power point speed and the maximum torque point speed, generate the minimum consumption curve through linear fitting, and calculate the energy consumption value according to the set speed interval to form the two-dimensional table of the minimum consumption curve.

[0027] S304. Based on the verification data table, perform data clustering, select cluster center point clusters after obtaining cluster center points, and then use the energy consumption rate of the cluster center point clusters at the preset side edge as a benchmark to filter equal energy consumption points within a preset error range. After forming equal consumption curves based on all the equal energy consumption points, generate the equal consumption curve set based on the preset path algorithm and the equal consumption curves.

[0028] Based on the same concept, in a second aspect, the present invention also provides a vehicle dynamic characteristic MAP estimation device for performing the vehicle dynamic characteristic MAP estimation method described in any one of the first aspects.

[0029] The vehicle dynamic characteristics MAP estimation device includes at least:

[0030] The data acquisition module is used to collect the model information of the target vehicle in order to obtain the maximum power point speed and the maximum torque point speed of the target vehicle;

[0031] The data verification module is used to collect the status data of the target vehicle in real time during operation, and perform at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then perform data volume verification on the valid working condition data.

[0032] The line table generation module is used to generate a verification data table, an external characteristic curve, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the effective operating condition data, the maximum power point speed, and the maximum torque point speed after the effective operating condition data has passed the data quantity verification.

[0033] The MAP plotting module is used to plot a dynamic characteristic MAP based on the external characteristic curve, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and to continuously collect target vehicle data.

[0034] The MAP judgment module is used to determine the validity of the dynamic characteristic MAP based on the target vehicle data and the verification data table when the target vehicle data meets the preset conditions.

[0035] The MAP output module is used to exit the calculation and output a valid dynamic characteristic MAP when the dynamic characteristic MAP is valid.

[0036] Optionally, it may also include at least:

[0037] The re-verification module is used to re-collect the status data of the target vehicle during operation when the valid operating condition data fails the data volume verification, and to perform the operating condition grouping operation and the data filtering operation on the re-collected status data to obtain the valid operating condition data, and then perform data volume verification on the valid operating condition data until the valid operating condition data passes the data volume verification.

[0038] Optionally, it may also include at least:

[0039] The invalid operation module is used to take the target vehicle data as the status data when the power characteristic MAP is invalid, and re-execute the following steps of the data verification module, the line table generation module, the MAP drawing module and the MAP judgment module in sequence until the power characteristic MAP is valid, then exit the calculation and output the valid power characteristic MAP.

[0040] Based on the same concept, in a third aspect, the present invention also provides an electronic device, including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps in the MAP estimation method for vehicle dynamic characteristics according to any one of the first aspects.

[0041] Based on the same concept, in a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in the MAP estimation method for vehicle dynamic characteristics as described in any of the first aspects.

[0042] The technical solution provided by this invention first collects the model information of the target vehicle to obtain the maximum power point speed and maximum torque point speed of the target vehicle; further, it collects the status data of the target vehicle in real time during operation, and performs at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then performs data quantity verification on the valid working condition data; further, after the valid working condition data passes the data quantity verification, it generates a verification data table, external characteristic curves, a two-dimensional table of minimum consumption curves, and a set of isoconsumption curves based on the valid working condition data, the maximum power point speed, and the maximum torque point speed; further, it draws a power characteristic MAP diagram based on the external characteristic curves, the two-dimensional table of minimum consumption curves, and the set of isoconsumption curves, and continuously collects target vehicle data; further, when the target vehicle data meets preset conditions, it judges the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table; finally, when the power characteristic MAP diagram is valid, it exits the calculation and outputs the valid power characteristic MAP diagram. Therefore, this invention can at least overcome the data dependence limitation of the OEM to achieve MAP estimation, while reducing testing costs, shortening the testing cycle, and improving the accuracy of the MAP diagram, vehicle adaptability, and its dynamic optimization capabilities. Attached Figure Description

[0043] Figure 1 is a flowchart of a vehicle dynamic characteristic MAP estimation method provided by an embodiment of the present invention;

[0044] Figure 2 is a flowchart of another MAP estimation method for vehicle dynamic characteristics provided in an embodiment of the present invention;

[0045] Figure 3 is a schematic diagram of a verification data table representing the scatter distribution of fuel consumption characteristics corresponding to engine speed-torque, provided by an embodiment of the present invention.

[0046] Figure 4 is a schematic diagram of engine speed-torque operating condition distribution and characteristic analysis provided by an embodiment of the present invention;

[0047] Figure 5 is a clustering and grid partitioning analysis diagram of engine speed-torque data provided in an embodiment of the present invention;

[0048] Figure 6 is a clustering and operating condition partitioning analysis result of engine speed-torque data provided in an embodiment of the present invention;

[0049] Figure 7 is a MAP comparison analysis diagram of an engine provided by an embodiment of the present invention;

[0050] Figure 8 is a schematic diagram of a vehicle dynamic characteristic MAP estimation device provided in an embodiment of the present invention;

[0051] Figure 9 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0054] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0055] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0056] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0057] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0058] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0059] Figure 1 is a flowchart of a vehicle dynamic characteristic MAP estimation method provided by an embodiment of the present invention. This embodiment is applicable to at least any vehicle dynamic characteristic MAP autonomous estimation and dynamic optimization scenario. The vehicle dynamic characteristic MAP estimation method can be, but is not limited to, executed by the vehicle dynamic characteristic MAP estimation device in this embodiment of the present invention as the execution subject. The execution subject can be implemented in software and / or hardware. As shown in Figure 1, the vehicle dynamic characteristic MAP estimation method includes at least the following steps:

[0060] S1. Collect the model information of the target vehicle to obtain the maximum power point speed and the maximum torque point speed of the target vehicle.

[0061] The maximum power point speed (MPPS) can refer to the engine / motor speed at which the vehicle outputs maximum power, and the maximum torque point speed (MTPS) can refer to the engine / motor speed at which the vehicle outputs maximum torque. It is understandable that if the MPPS and MTP of the target vehicle are unavailable, both can be marked as 0.

[0062] S2. Collect the status data of the target vehicle in real time during operation, and perform at least the working condition grouping operation and data filtering operation on the status data to obtain valid working condition data, and then verify the data volume of the valid working condition data.

[0063] In step S2, the executing entity can be an in-vehicle intelligent terminal associated with the cloud control system. Additionally, the target vehicle's status data during operation may include, but is not limited to, time, speed, longitude, latitude, altitude, gradient, throttle opening, gear, brake opening, torque, engine / motor speed, instantaneous fuel consumption (for gasoline vehicles) or instantaneous electricity consumption (for electric vehicles), and frontal area.

[0064] In one specific implementation, step S2 may optionally include at least the following:

[0065] S201. Collect the status data of the target vehicle in real time during operation, and perform working condition grouping operation on the status data according to the vehicle's operating status to obtain grouped data. Then, filter the grouped data for data glitch based on a preset speed change error threshold to obtain filtered grouped data.

[0066] S202. Filter group data with a continuous duration not less than a preset duration threshold are retained as valid operating condition data, and filter group data with a continuous duration less than the preset duration threshold are removed.

[0067] S203. Perform data volume verification on the valid operating condition data according to the preset data volume verification conditions.

[0068] Specifically, the aforementioned steps S201 to S203 can be described as follows:

[0069] The collected status data is grouped according to vehicle operating status (e.g., acceleration, deceleration, constant speed). A speed change error threshold (e.g., 0.36 km / h; i.e., the aforementioned preset speed change error threshold) is set to filter data spikes and ensure the stability of continuous operating condition grouping. Furthermore, valid operating condition data with a continuous duration of not less than 10 seconds (i.e., the aforementioned preset duration threshold can be 10 seconds) is retained, while invalid data with short durations (i.e., filtered grouping data with a continuous duration of less than 10 seconds) is removed. Furthermore, when the cumulative amount of valid data (i.e., the cumulative number of valid operating condition data) is not less than 50,000 and the vehicle speed coverage range reaches the minimum speed (e.g., 600 r / min) to the maximum speed (engineSPDMax) of the engine / motor, it indicates that the valid operating condition data has met the preset data volume verification conditions and can pass the data volume verification, and can enter the subsequent data processing stage.

[0070] S3. After the effective operating condition data passes the data quantity verification, generate a verification data table, external characteristic curve, minimum consumption curve two-dimensional table and equal consumption curve set based on the effective operating condition data, maximum power point speed and maximum torque point speed.

[0071] Step S3 essentially involves feature data processing and map construction.

[0072] In another specific implementation, step S3 may optionally include at least the following:

[0073] S301. After the effective operating condition data passes the data quantity verification, the effective operating condition data is divided into dynamic grids according to preset rules, and the root mean square value is calculated based on the effective operating condition data falling within the set threshold range of each grid intersection point and its vicinity. Then, each root mean square value is used as the energy consumption rate of the corresponding grid intersection point to generate a verification data table.

[0074] S302. Perform torque segmentation on the effective operating condition data based on the maximum power point speed and the maximum torque point speed, generate external characteristic curves through linear fitting, and calculate torque values ​​according to preset speed intervals to form a two-dimensional table of external characteristics.

[0075] S303. Perform energy consumption segmentation operation on the effective operating condition data based on the maximum power point speed and the maximum torque point speed, generate the minimum consumption curve through linear fitting, and calculate the energy consumption value according to the set speed interval to form a two-dimensional table of minimum consumption curve.

[0076] S304. Based on the verification data table, perform data clustering, select cluster center point clusters after obtaining cluster center points, and then use the energy consumption rate of the cluster center point clusters at the preset side edge as a benchmark to filter equal energy consumption points within a preset error range. After forming equal energy consumption curves based on all equal energy consumption points, generate an equal energy consumption curve set based on the preset path algorithm and equal energy consumption curves.

[0077] Specifically, steps S301 to S304 can be described as follows:

[0078] The engine / motor speed and torque parameters are divided into dynamic grids according to preset rules (e.g., default 20×30 grid). The root mean square value of the original data falling near the intersection of each grid (i.e. the effective operating condition data falling within the set threshold range of each grid intersection and its vicinity) is calculated and used as the fuel consumption rate or electricity consumption rate of that grid point (corresponding to fuel vehicles or electric vehicles respectively, i.e. the aforementioned energy consumption rate), and a primary data table (i.e. the aforementioned verification data table) is generated.

[0079] Furthermore, based on the maximum torque information registered in step S1 (i.e., the aforementioned maximum power point speed and maximum torque point speed), the maximum torque data {n1,n2,…,n} at different speeds are processed. k The system is segmented (for example, a segment is considered as two consecutive adjacent points with a difference of no more than 10%), and external characteristic curves are generated by linear fitting (for example, the curve corresponding to 100% throttle opening). The torque value is calculated at 100r / min speed intervals (i.e., the aforementioned preset speed intervals) to form a two-dimensional table of external characteristics.

[0080] Furthermore, for the non-zero minimum fuel consumption rate (or electricity consumption rate) data {m1,m2,…,m at different speeds} k Using the same segmentation and fitting method as the external characteristic curve, a two-dimensional table of minimum fuel consumption / electricity consumption curves (i.e., the aforementioned two-dimensional table of minimum consumption curves) is generated.

[0081] Furthermore, data clustering is performed based on the primary data table to obtain cluster center points; using the intersection of the 50% torque line and the external characteristic curve as the reference point, the cluster of points closest to the cluster center is selected. Based on the fuel consumption rate or electricity consumption rate of the right edge of the cluster (i.e., the energy consumption rate of the aforementioned cluster center point cluster at the preset side edge) as the benchmark, equal fuel consumption / electricity consumption points (i.e., the aforementioned equal energy consumption points) are selected within an error range of no more than 10% (i.e., the aforementioned preset error range) to form equal fuel consumption / electricity consumption curves (i.e., the aforementioned equal consumption curves); the curve trajectory is optimized through the optimal path algorithm (such as A*, Dijkstra's algorithm; i.e., the aforementioned preset path algorithm), and finally a complete set of equal fuel consumption / electricity consumption curves (i.e., the aforementioned equal consumption curve set) is generated.

[0082] S4. Draw a dynamic characteristic MAP based on the external characteristic curve, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collect target vehicle data.

[0083] Among them, the external characteristic curves, minimum fuel consumption / electricity consumption curves, and sets of equal fuel consumption / electricity consumption curves can be stored in the cloud control system database to draw a complete power characteristic MAP.

[0084] S5. When the target vehicle data meets the preset conditions, the validity of the dynamic characteristic MAP is determined based on the target vehicle data and the verification data table.

[0085] Specifically, when the number of newly added valid data in the target vehicle data is not less than 50,000 (i.e., the aforementioned preset condition), a verification is performed: if more than 90% of the new data covers the characteristic curve range of the generated MAP and the error between each data point and the primary data table is not higher than 5%, then the MAP is confirmed to be valid.

[0086] S6. When the dynamic characteristic MAP is valid, exit the calculation and output the valid dynamic characteristic MAP.

[0087] The technical solution provided in this embodiment first collects the model information of the target vehicle to obtain the maximum power point speed and the maximum torque point speed of the target vehicle; further, it collects the status data of the target vehicle in real time during operation, and performs at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then performs data quantity verification on the valid working condition data; further, after the valid working condition data passes the data quantity verification, it generates a verification data table, external characteristic curves, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the valid working condition data, the maximum power point speed, and the maximum torque point speed; further, it draws a power characteristic MAP diagram based on the external characteristic curves, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collects target vehicle data; further, when the target vehicle data meets preset conditions, it judges the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table; finally, when the power characteristic MAP diagram is valid, it exits the calculation and outputs the valid power characteristic MAP diagram.

[0088] Therefore, this embodiment, on the one hand, utilizes a collaborative architecture between the cloud control system and the in-vehicle intelligent terminal. The in-vehicle terminal collects dynamic parameters such as speed, torque, and fuel / electricity consumption in real time, and relies on the cloud platform to complete data aggregation and map generation, achieving autonomous construction of a dynamic characteristic MAP in non-bench calibration scenarios. On the other hand, this embodiment dynamically adjusts the grid density rules based on the distribution characteristics of actual vehicle operating data; it uses the root mean square value for aggregation of neighborhood data at grid intersections; and it achieves individual difference compensation for single-vehicle dynamic characteristics through dynamic adaptation of grid parameters. This technology, by combining grid dynamism with data aggregation algorithms, solves the problem of insufficient accuracy in traditional static grids, and its protection scope covers grid partitioning strategies, data aggregation calculation models, and individual difference adaptation mechanisms. On the other hand, this embodiment collaboratively constructs external characteristic curves, minimum energy consumption curves, and energy consumption curves optimized by clustering, including: a piecewise fitting rule for the external characteristic curve (segmentation based on continuous data difference ≤10%); a non-zero extreme value screening logic for the minimum energy consumption curve; an energy consumption curve generation method based on a 50% torque benchmark point and cluster analysis; and a topological fusion mechanism for the three types of curves in the MAP map. This embodiment differs from the traditional construction mode dominated by a single curve, achieving a complete description of power characteristics through the complementary features of multiple curves. Furthermore, this embodiment involves a MAP accuracy optimization system based on dynamic data accumulation, including: a threshold triggering mechanism for new data volume (≥50,000 valid data points); a dual-dimensional verification standard for coverage and error (90% data coverage, error ≤5%); an automatic regeneration process when verification fails; and a technical logic for dynamic map optimization based on long-term data iteration. This mechanism ensures that MAP accuracy dynamically improves throughout the vehicle's entire lifecycle through closed-loop iteration, and its protection scope covers verification rule design, iteration triggering conditions, and optimization execution processes.

[0089] It should be noted that the aforementioned "engine / motor" can refer to either an engine or a motor; MAP stands for Meaningful Area Plot.

[0090] Based on the above embodiments or implementation methods, Figure 2 is a flowchart of another vehicle dynamic characteristic MAP estimation method provided by an embodiment of the present invention. As shown in Figure 2, the vehicle dynamic characteristic MAP estimation method includes at least the following steps:

[0091] S1. Collect the model information of the target vehicle to obtain the maximum power point speed and the maximum torque point speed of the target vehicle.

[0092] S2. Collect the status data of the target vehicle in real time during operation, and perform at least the working condition grouping operation and data filtering operation on the status data to obtain valid working condition data, and then verify the data volume of the valid working condition data.

[0093] S7. When the valid operating condition data fails the data volume verification, the status data of the target vehicle during operation is re-collected, and at least the operating condition grouping operation and data filtering operation are performed on the re-collected status data to obtain valid operating condition data. Then, the data volume verification is performed on the valid operating condition data until the valid operating condition data passes the data volume verification.

[0094] S3. After the effective operating condition data passes the data quantity verification, generate a verification data table, external characteristic curve, minimum consumption curve two-dimensional table and equal consumption curve set based on the effective operating condition data, maximum power point speed and maximum torque point speed.

[0095] Figure 3 is a schematic diagram of a verification data table that characterizes the scatter distribution of fuel consumption characteristics corresponding to engine speed and torque, provided by an embodiment of the present invention. Referring to Figure 3, the engine speed and torque are divided into grids according to certain rules (the default is 20×30). The root mean square value of all data close to the grid intersection is taken as the fuel consumption rate of that point, and a verification data table is established.

[0096] Figure 4 is a schematic diagram of engine speed-torque operating condition distribution and characteristic analysis provided by an embodiment of the present invention. Referring to Figure 4, the method for extracting the external characteristic curve (the 100% throttle opening line, i.e., the torque at 100% throttle in Figure 4) is as follows: based on the vehicle model registration, the maximum torque is filtered, and the maximum torque data at different speeds is segmented; two consecutive adjacent points differing by 10% are set as a segment, and the maximum torque data is divided into n groups. The segmented data is linearly fitted (any existing linear fitting method can be used) to obtain the external characteristic diagram. The torque value is calculated at 100 rpm intervals, and a two-dimensional table is generated to obtain the engine external characteristics. The minimum fuel consumption curve (the white curve shown in Figure 4) is extracted by fitting the non-zero minimum fuel consumption rate at different speeds in the same way to obtain a two-dimensional table and the minimum fuel consumption curve.

[0097] Figure 5 is a clustering and grid partitioning analysis diagram of engine speed-torque data provided in an embodiment of the present invention. Referring to Figure 5, the colored dots are the speed-torque data collected during actual engine operation. Different colors represent the clustering results, that is, the algorithm groups data with similar operating characteristics (speed and torque correlation patterns) into one category. The red intersecting grids covering the data points represent the partitioning of the speed-torque parameter space.

[0098] Figure 6 shows the clustering and operating condition partitioning analysis results of engine speed-torque data provided in this embodiment of the invention. Referring to Figure 6, operating condition intervals are added to the basic clustering data for accurate analysis of engine operating characteristics. Black elliptical curve 1 (i.e., the equal fuel consumption curve): This is the optimal fuel consumption boundary constructed under a 10% error threshold, used to define the optimal fuel consumption operating condition range within this interval. Orange solid line (trajectory 2): Generated by the optimal path algorithm, representing the efficient operating trajectory of the engine during operating condition transitions. Yellow dashed line (region 3): This is the boundary of an operating condition sub-partition, used to distinguish the operating condition characteristics of this region from other regions. Black elliptical curve 4 (black elliptical curve 4 includes the aforementioned black elliptical curve 1): This corresponds to the characteristic distribution of the lowest fuel consumption region, reflecting the optimal speed-torque variation law within this operating condition interval.

[0099] Figure 7 is a MAP comparison analysis diagram of an engine provided by an embodiment of the present invention. Referring to Figure 7, the blue broken line (i.e., the external characteristic curve) outlines the full-load power boundary of the engine, representing the maximum torque that the engine can output at different speeds, which is the upper limit of power performance; the colored contour lines: closed curves are lines of equal fuel consumption / equal efficiency, and the same line represents operating points with similar fuel consumption rates (or efficiencies); the red / green marked points are actual test operating condition data, and different colors / shapes distinguish the results of two types of test schemes; the dense area is the common / high-efficiency operating condition cluster, reflecting the actual operating preferences of the engine or the direction of calibration optimization.

[0100] S4. Draw a dynamic characteristic MAP based on the external characteristic curve, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collect target vehicle data.

[0101] S5. When the target vehicle data meets the preset conditions, the validity of the dynamic characteristic MAP is determined based on the target vehicle data and the verification data table.

[0102] S6. When the dynamic characteristic MAP is valid, exit the calculation and output the valid dynamic characteristic MAP.

[0103] S8. When the dynamic characteristic MAP is invalid, use the target vehicle data as the status data and re-execute steps S2 to S5 until the dynamic characteristic MAP is valid. Then, exit the calculation and output a valid dynamic characteristic MAP.

[0104] In summary, this embodiment leverages online real-time data processing and cloud control system computing power, resulting in short data accumulation and map generation cycles, enabling rapid response to vehicle optimization and control needs. Furthermore, based on dynamic grid partitioning, root mean square value aggregation, and single-vehicle data accumulation, the generated MAP map accurately adapts to the inconsistencies in individual vehicle powertrain systems, providing a more precise description. Moreover, through adaptive verification and iteration mechanisms, this embodiment continuously optimizes the MAP map accuracy based on data updates during vehicle use, adapting to performance changes over long-term vehicle operation. Finally, this embodiment eliminates reliance on OEM-protected data, allowing optimization schemes such as economic speed control to be applied to more vehicle models, improving overall traffic economy and contributing to energy conservation and emission reduction.

[0105] It should be noted that the following are several alternative solutions to the embodiments of the present invention:

[0106] 1. Static mesh + calibration coefficient method: This method uses a fixed mesh and compensates for individual differences through subsequent calibration coefficients. It enhances preprocessing but has limited accuracy improvement (error ≤8%) and is less adaptable than dynamic mesh.

[0107] 2. Generation of non-clustered equal energy consumption curves: Linear interpolation is used instead of cluster analysis to screen equal energy consumption points. The process is simplified, but the continuity of the curves is insufficient and the integrity of the graph is reduced.

[0108] 3. Timed iteration mechanism: Triggering iterations at a fixed period (e.g., 3 months) instead of a data volume threshold reduces the amount of real-time computation but may miss the opportunity to optimize accuracy and has insufficient dynamic responsiveness.

[0109] Figure 8 is a schematic diagram of a vehicle dynamic characteristic MAP estimation device provided in an embodiment of the present invention. This embodiment is applicable to at least any vehicle dynamic characteristic MAP autonomous estimation and dynamic optimization scenario. The vehicle dynamic characteristic MAP estimation device can be implemented in software and / or hardware. As shown in Figure 8, the vehicle dynamic characteristic MAP estimation device is used to execute the vehicle dynamic characteristic MAP estimation method of any of the foregoing embodiments or implementation methods.

[0110] A vehicle dynamic characteristics MAP estimation device includes at least the following:

[0111] The data acquisition module 110 is used to collect the model information of the target vehicle in order to obtain the maximum power point speed and the maximum torque point speed of the target vehicle.

[0112] The data verification module 120 is used to collect the status data of the target vehicle in real time during operation, and perform at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then perform data volume verification on the valid working condition data.

[0113] The line table generation module 130 is used to generate a verification data table, an external characteristic curve, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the effective operating condition data, the maximum power point speed, and the maximum torque point speed after the effective operating condition data has passed the data quantity verification.

[0114] The MAP plotting module 140 is used to plot a dynamic characteristic MAP based on the external characteristic curve, the minimum consumption curve two-dimensional table, and the set of equal consumption curves, and to continuously collect target vehicle data.

[0115] The MAP judgment module 150 is used to judge the validity of the dynamic characteristic MAP based on the target vehicle data and the verification data table when the target vehicle data meets the preset conditions.

[0116] The MAP output module 160 is used to exit the calculation and output a valid dynamic characteristic MAP when the dynamic characteristic MAP is valid.

[0117] Optionally, it may also include at least:

[0118] The re-verification module 170 is used to re-collect the status data of the target vehicle during operation when the valid operating condition data fails the data volume verification, and to perform the operating condition grouping operation and the data filtering operation on the re-collected status data to obtain the valid operating condition data, and then perform data volume verification on the valid operating condition data until the valid operating condition data passes the data volume verification.

[0119] Optionally, it may also include at least:

[0120] The invalid operation module 180 is used to, when the power characteristic MAP is invalid, take the target vehicle data as the status data and re-execute the following steps of the data verification module, the line table generation module, the MAP drawing module and the MAP judgment module in sequence until the power characteristic MAP is valid, then exit the calculation and output the valid power characteristic MAP.

[0121] Optionally, the data verification module 120 is specifically used for at least:

[0122] The status data of the target vehicle during operation is collected in real time, and the status data is grouped according to the vehicle's operating status to obtain grouped data. Then, the grouped data is filtered for data spikes based on a preset speed change error threshold to obtain filtered grouped data.

[0123] The filtered group data with a continuous duration not less than a preset duration threshold is retained as the valid operating condition data, and the filtered group data with a continuous duration less than the preset duration threshold is removed.

[0124] The valid operating condition data is validated according to preset data volume validation conditions.

[0125] Optionally, the line table generation module 130 is specifically used for at least:

[0126] After the effective operating condition data passes the data quantity verification, the effective operating condition data is divided into dynamic grids according to preset rules, and the root mean square value is calculated based on the effective operating condition data falling into each grid intersection point and the surrounding set threshold range. Then, each root mean square value is used as the energy consumption rate of the corresponding grid intersection point to generate the verification data table.

[0127] Based on the maximum power point speed and the maximum torque point speed, the effective operating condition data is subjected to torque segmentation, and the external characteristic curve is generated by linear fitting. The torque value is calculated according to the preset speed interval to form a two-dimensional table of external characteristics.

[0128] Based on the maximum power point speed and the maximum torque point speed, the effective operating condition data is subjected to energy consumption segmentation operation, and the minimum consumption curve is generated by linear fitting. The energy consumption value is calculated according to the set speed interval to form a two-dimensional table of the minimum consumption curve.

[0129] Data clustering is performed based on the verification data table. After obtaining the cluster center points, cluster center point clusters are selected. Then, based on the energy consumption rate of the cluster center point clusters at the preset side edges, equal energy consumption points are selected within a preset error range. After forming equal energy consumption curves based on all the equal energy consumption points, the set of equal energy consumption curves is generated according to the preset path algorithm and the equal energy consumption curves.

[0130] The technical solution provided in this embodiment firstly collects the model information of the target vehicle through a data acquisition module to obtain the maximum power point speed and the maximum torque point speed of the target vehicle. Further, a data verification module collects the status data of the target vehicle in real time during operation, and performs at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then verifies the data volume of the valid working condition data. Further, after the valid working condition data passes the data volume verification, a line table generation module generates a verification data table, external characteristic curves, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the valid working condition data, the maximum power point speed, and the maximum torque point speed. Further, a MAP drawing module draws a power characteristic MAP diagram based on the external characteristic curves, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collects target vehicle data. Further, when the target vehicle data meets preset conditions, a MAP judgment module judges the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table. Finally, when the power characteristic MAP diagram is valid, the MAP output module exits the calculation and outputs the valid power characteristic MAP diagram. Therefore, this embodiment can at least get rid of the data dependence of the OEM to realize MAP estimation, while reducing test costs and shortening the test cycle, and improving the accuracy of MAP map, vehicle adaptability and its dynamic optimization capabilities.

[0131] This embodiment provides an electronic device. Figure 9 is a schematic diagram of the structure of an electronic device provided in this embodiment. Referring to Figure 9, the electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the above-mentioned vehicle power characteristic MAP estimation methods are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanism (not shown). The memory 1002 stores a computer program that can be executed by the processor. When the electronic device 1000 is running, the processor 1001 executes the computer program to perform the vehicle power characteristic MAP estimation method in any of the optional implementations of the above embodiments, so as to at least achieve the following functions: collecting the model information of the target vehicle to obtain the maximum power point speed and maximum torque point speed of the target vehicle; collecting the status data of the target vehicle in real time during operation, and at least performing operating condition grouping and data filtering on the status data. The system selects an operation to obtain valid operating condition data, and then performs data quantity verification on the valid operating condition data. After the valid operating condition data passes the data quantity verification, it generates a verification data table, external characteristic curves, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the valid operating condition data, the maximum power point speed, and the maximum torque point speed. It then draws a power characteristic MAP diagram based on the external characteristic curves, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collects target vehicle data. When the target vehicle data meets preset conditions, it judges the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table. When the power characteristic MAP diagram is valid, it exits the calculation and outputs the valid power characteristic MAP diagram.

[0132] This embodiment provides a computer-readable storage medium storing a computer program. When executed by a processor, the program implements the vehicle power characteristic MAP estimation method provided in all embodiments of this application: collecting model information of the target vehicle to obtain the maximum power point speed and maximum torque point speed of the target vehicle; collecting the state data of the target vehicle in real time during operation, and performing at least a working condition grouping operation and a data filtering operation on the state data to obtain valid working condition data, and then performing data quantity verification on the valid working condition data; after the valid working condition data passes the data quantity verification, generating a verification data table, external characteristic curves, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the valid working condition data, the maximum power point speed, and the maximum torque point speed; drawing a power characteristic MAP diagram based on the external characteristic curves, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collecting target vehicle data; when the target vehicle data meets preset conditions, judging the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table; when the power characteristic MAP diagram is valid, exiting the calculation and outputting the valid power characteristic MAP diagram.

[0133] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0134] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0135] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0136] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating vehicle dynamic characteristics using a MAP (Motor Performance Mapping) algorithm, characterized in that, The process includes at least the following steps: S1. Collecting the model information of the target vehicle to obtain the maximum power point speed and maximum torque point speed of the target vehicle; S2. Real-time acquisition of the status data of the target vehicle during operation, and performing at least a working condition grouping operation and a data filtering operation on the status data to obtain valid working condition data, and then performing data quantity verification on the valid working condition data; S3. After the valid working condition data passes the data quantity verification, generating a verification data table, an external characteristic curve, a two-dimensional table of minimum consumption curves, and a set of equal consumption curves based on the valid working condition data, the maximum power point speed, and the maximum torque point speed; S4. Drawing a power characteristic MAP diagram based on the external characteristic curve, the two-dimensional table of minimum consumption curves, and the set of equal consumption curves, and continuously collecting target vehicle data; S5. When the target vehicle data meets preset conditions, determining the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table; S6. When the power characteristic MAP diagram is valid, exiting the calculation and outputting the valid power characteristic MAP diagram.

2. The MAP estimation method for vehicle dynamic characteristics according to claim 1, characterized in that, After step S2, the method further includes at least: S7, when the valid operating condition data fails the data volume verification, re-collecting the status data of the target vehicle during operation, and performing the operating condition grouping operation and the data filtering operation on the re-collected status data to obtain the valid operating condition data, and then performing data volume verification on the valid operating condition data until the valid operating condition data passes the data volume verification.

3. The MAP estimation method for vehicle dynamic characteristics according to claim 1, characterized in that, After step S5, the process includes at least the following step: S8, when the dynamic characteristic MAP is invalid, the target vehicle data is used as the state data, and steps S2 to S5 are re-executed until the dynamic characteristic MAP is valid, then the calculation is exited and the valid dynamic characteristic MAP is output.

4. The MAP estimation method for vehicle dynamic characteristics according to claim 1, characterized in that, Step S2 specifically includes at least the following: S201, collecting the status data of the target vehicle during operation in real time, and performing the working condition grouping operation on the status data according to the vehicle's operating status to obtain grouped data, and then filtering the grouped data for data spikes based on a preset speed change error threshold to obtain filtered grouped data; S202, retaining the filtered grouped data with a continuous duration not less than a preset duration threshold as the valid working condition data, and removing the filtered grouped data with a continuous duration less than the preset duration threshold; S203, performing data volume verification on the valid working condition data according to preset data volume verification conditions.

5. The MAP estimation method for vehicle dynamic characteristics according to claim 1, characterized in that, Step S3 specifically includes at least the following: S301, after the effective operating condition data passes the data quantity verification, dividing the effective operating condition data into dynamic grids according to preset rules, and calculating the root mean square value based at least on the effective operating condition data falling within a set threshold range of each grid intersection point and its vicinity, and then using each root mean square value as the energy consumption rate of the corresponding grid intersection point to generate the verification data table; S302, performing torque segmentation on the effective operating condition data according to the maximum power point speed and the maximum torque point speed, generating the external characteristic curve through linear fitting, and calculating the torque value according to the preset speed interval to form the external characteristic curve. S303. Perform energy consumption segmentation on the effective operating condition data according to the maximum power point speed and the maximum torque point speed, generate the minimum consumption curve through linear fitting, and calculate the energy consumption value according to the set speed interval to form the minimum consumption curve two-dimensional table; S304. Perform data clustering based on the verification data table, select cluster center point clusters after obtaining the cluster center points, and then use the energy consumption rate of the cluster center point clusters at the preset side edge as a benchmark to filter equal energy consumption points within a preset error range. After forming equal consumption curves based on all the equal energy consumption points, generate the equal consumption curve set according to the preset path algorithm and the equal consumption curves.

6. A vehicle dynamic characteristic MAP estimation device, characterized in that, Used to perform the MAP estimation method for vehicle dynamic characteristics as described in any one of claims 1-5; The vehicle power characteristic MAP estimation device includes at least: a data acquisition module for collecting model information of the target vehicle to obtain the maximum power point speed and maximum torque point speed of the target vehicle; a data verification module for real-time acquisition of the state data of the target vehicle during operation, and performing at least a working condition grouping operation and a data filtering operation on the state data to obtain valid working condition data, and then verifying the data volume of the valid working condition data; a line table generation module for generating a verification data table, an external characteristic curve, a minimum consumption curve two-dimensional table, and an isoconsumption curve set based on the valid working condition data, the maximum power point speed, and the maximum torque point speed after the valid working condition data passes the data volume verification; a MAP drawing module for drawing a power characteristic MAP diagram based on the external characteristic curve, the minimum consumption curve two-dimensional table, and the isoconsumption curve set, and continuously collecting target vehicle data; a MAP judgment module for judging the validity of the power characteristic MAP diagram based on the target vehicle data and the verification data table when the target vehicle data meets preset conditions; and a MAP output module for exiting the calculation and outputting a valid power characteristic MAP diagram when the power characteristic MAP diagram is valid.

7. The vehicle dynamic characteristics MAP estimation device according to claim 6, characterized in that, It also includes at least: a re-verification module, used to re-collect the status data of the target vehicle during operation when the valid operating condition data fails the data volume verification, and to perform the operating condition grouping operation and the data filtering operation on the re-collected status data to obtain the valid operating condition data, and then perform data volume verification on the valid operating condition data until the valid operating condition data passes the data volume verification.

8. The vehicle dynamic characteristics MAP estimation device according to claim 6, characterized in that, It also includes at least: an invalid operation module, used to take the target vehicle data as the state data when the power characteristic MAP is invalid, and re-execute the subordinate steps of the data verification module, the line table generation module, the MAP drawing module and the MAP judgment module in sequence until the power characteristic MAP is valid, then exit the calculation and output the valid power characteristic MAP.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the vehicle dynamic characteristics MAP estimation method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the MAP estimation method for vehicle dynamic characteristics as described in any one of claims 1 to 5.

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