Comprehensive energy consumption evaluation method and system under vehicle multi-scene working conditions

Abstract

The invention provides a comprehensive energy consumption evaluation method and system under vehicle multi-scene working conditions, and belongs to the technical field of vehicle energy management, and the method comprises the steps: 1, obtaining the driving working condition data of a vehicle in the driving process, and obtaining the driving environment data of the vehicle in the driving process; 2, analyzing the driving condition data and the driving environment data to determine scene condition data of the vehicle; 3, determining scene power consumption data of the vehicle based on the scene working condition data and the energy consumption evaluation model; and step 4, generating a comprehensive energy consumption evaluation report of the vehicle under the multi-scene working condition based on the scene power consumption data. Working conditions and environmental factors can be comprehensively analyzed, efficient association of the environment and the working conditions is achieved, personalized energy consumption analysis is provided for different driving scenes, a refined comprehensive energy consumption evaluation report is generated, a vehicle owner and a vehicle management system are helped to make more scientific energy-saving decisions, and intelligent travel and green traffic development are promoted.

Description

Technical Field

[0001] This invention relates to the field of vehicle energy management technology, and in particular to a method and system for comprehensive energy consumption assessment under multiple vehicle operating conditions. Background Technology

[0002] With increasing environmental awareness and a worsening energy crisis, the automotive industry is focusing on improving vehicle energy efficiency. Traditional vehicle energy consumption assessment methods primarily rely on standard tests under laboratory conditions, failing to adequately consider the complex environments and operating conditions encountered in real-world driving, resulting in significant discrepancies between assessment results and actual driving realities. Traditional energy consumption assessment models depend on analysis under single scenarios or operating conditions, making it difficult to comprehensively reflect a vehicle's energy consumption performance under various combinations of environments and operating conditions. Therefore, accurately assessing vehicle energy consumption across multiple scenarios and operating conditions has become a pressing issue.

[0003] Therefore, the present invention provides a method and system for comprehensive energy consumption assessment of vehicles under multiple operating conditions. Summary of the Invention

[0004] This invention provides a method and system for comprehensive energy consumption assessment of vehicles under multiple operating conditions. By acquiring and analyzing driving condition data and driving environment data during vehicle operation, the system determines the vehicle's scenario-specific operating condition data. Combined with an energy consumption assessment model, the system determines the vehicle's scenario-specific power consumption data and generates a comprehensive energy consumption assessment report under multiple operating conditions. This allows for a comprehensive analysis of operating conditions and environmental factors, achieving efficient correlation between environment and operating conditions. It provides personalized energy consumption analysis for different driving scenarios, generates detailed comprehensive energy consumption assessment reports, and helps vehicle owners and vehicle management systems make more scientific energy-saving decisions, promoting the development of intelligent mobility and green transportation.

[0005] On the one hand, the present invention provides a comprehensive energy consumption assessment method for vehicles under multiple operating conditions, including: Step 1: Obtain driving condition data and driving environment data of the vehicle during driving; Step 2: Analyze driving condition data and driving environment data to determine the vehicle's scenario condition data; Step 3: Based on scenario operating condition data and energy consumption assessment model, determine the vehicle's scenario power consumption data; Step 4: Generate a comprehensive energy consumption assessment report for the vehicle under multiple operating conditions based on scenario power consumption data.

[0006] According to the present invention, a comprehensive energy consumption assessment method for vehicles under multiple operating conditions is provided, which acquires driving condition data of the vehicle during driving, including: Obtain the driving range of the vehicle during its journey, acquire and analyze the traffic flow data within the vehicle's driving range to determine a specified time period; Vehicle-based on-board storage devices, the vehicle's driving condition data is obtained within each specified time period across multiple natural days. The driving condition data is determined based on the driving condition sub-data within all specified time periods.

[0007] According to the present invention, a comprehensive energy consumption assessment method for vehicles under multiple operating conditions acquires driving environment data of the vehicle during driving, including: Based on all driving locations in the driving condition sub-data within each specified time period, driving environment sub-data for each specified time period is obtained. The driving environment sub-data includes traffic flow data, weather data, and road condition data. The driving environment data is determined based on the driving environment sub-data within all specified time periods.

[0008] According to the present invention, a comprehensive energy consumption assessment method for vehicles under multiple operating conditions analyzes driving condition data and driving environment data to determine the vehicle's scenario operating condition data, including: Preprocess the driving condition data and driving environment data, analyze all driving positions and driving environment sub-data of the driving condition sub-data within each specified time period in the preprocessed driving condition data, and determine the position division rules of each driving condition sub-data in the driving condition data. Based on the location division rules of each driving condition sub-data in the driving condition data, multiple driving areas of each driving condition sub-data are determined, and based on the driving condition data and driving environment data, the regional environment data and regional condition data of each driving area within each specified time period are determined. Feature extraction is performed on the regional environmental data of each driving area within each specified time period to determine the regional environmental vector of each driving area. At the same time, feature extraction is performed on the regional operating condition data of each driving area within each specified time period to determine the regional operating condition vector of each driving area. Cluster analysis is performed on the regional environmental vectors of all driving areas within all specified time periods to determine multiple environmental category data, which include environmental category labels and multiple regional environmental vectors. Cluster analysis is performed on the regional operating condition vectors of all driving areas within all specified time periods to determine multiple operating condition category data, which include operating condition category labels and multiple operating condition environment vectors. Based on all environmental category data and all operating condition category data, determine the environment-operating condition association value for each environmental category label and each operating condition category label; The scenario operating condition data is determined based on all environment-operating condition correlation values, the first preset correlation threshold, and the second preset correlation threshold.

[0009] According to the present invention, a comprehensive energy consumption assessment method for vehicles under multiple operating conditions determines the environment-operating condition correlation value for each environment category label and each operating condition category label based on all environmental category data and all operating condition category data, including: Based on all environmental category data and all operating condition category data, determine the association value for each environmental category label and each operating condition category label; ; ; ; ; ; ; This represents the environment-operating condition association value between the i-th environment category label and the j-th operating condition category label. Indicates the number of specified time periods. This represents the number of driving areas within the specified time period (a). This represents the regional operating condition vector for the b-th driving area within the a-th specified time period. and the j-th working condition category data The working condition correlation value, This represents the regional environment vector of the b-th driving area within the a-th specified time period. and the data of the i-th working condition category Environmental correlation values, Indicates the correlation weight of operating conditions. Indicates the environmental association weight. This represents an indication function for the b-th driving area within the a-th specified time period, based on the i-th environmental category data and the j-th operating condition category data. This represents the fitted environment vector for the i-th environment category data. This represents the number of regional environment vectors in the i-th environment category data. This represents the i-th environmental category data. This represents the regional environment vector of the b-th driving area within the a-th specified time period. This represents the regional environment vector of the c-th driving area within the a-th specified time period. This represents the fitted working condition vector for the j-th working condition category data. This indicates that the parameters are being adjusted. This represents the number of regional working condition vectors in the j-th working condition category data. This represents the data for the j-th working condition category. This represents the regional operating condition vector for the b-th driving area within the a-th specified time period. This represents the clustering vector of the j-th work condition category data. This represents the clustering condition vector for the i-th environmental category data.

[0010] According to the present invention, a comprehensive energy consumption assessment method for vehicles under multiple operating conditions determines scenario operating condition data based on all environment-operating condition correlation values, a first preset correlation threshold, and a second preset correlation threshold, including: Based on all environment-operating condition correlation values, determine the environment-operating condition matrix; ; in, This represents the initialization of the environment-condition matrix, where N1 represents the number of environment category data points and N2 represents the number of condition category data points. These represent the environment-operating condition association values ​​for the first environment category label, the first operating condition category label, the j-th operating condition category label, and the N1-th operating condition category label, respectively. These represent the environment-operating condition association values ​​of the i-th environment category label, the 1st operating condition category label, and the N1th operating condition category label, respectively. These represent the environment-operating condition association values ​​of the N2nd environment category label, the 1st operating condition category label, the jth operating condition category label, and the N1st operating condition category label, respectively. The environment-operational condition matrix is ​​determined based on the correlation values ​​of all environmental category labels and all operating condition category labels; Each correlation value in the environment-operating condition matrix is ​​compared with a preset correlation threshold. The environment category label and operating condition category label corresponding to each environment-operating condition correlation value that is greater than or equal to the first preset correlation threshold are determined as strong environment-operating condition category labels, and strong environment-operating condition category data is determined. The environmental category label and operating condition label corresponding to each environmental-operating condition association value that is less than the first preset association threshold and greater than the second preset association threshold are determined as weak environmental-operating condition category labels, and the weak environmental-operating condition category data is determined. The environmental category labels and operating condition category labels corresponding to environmental-operating condition association values ​​that are less than the second preset association threshold are merged to determine the merged environmental-operating condition category labels and the merged environmental-operating condition category data. The vehicle's scenario operating condition data is determined based on all strong environment-operating condition category data, all weak environment-operating condition category data, and merged environment-operating condition category data.

[0011] According to the present invention, a comprehensive energy consumption assessment method for vehicles under multiple operating conditions is provided. Based on scenario operating condition data and an energy consumption assessment model, the method determines the scenario power consumption data of the vehicle, including: Input each severe environment-condition category data in the scenario operating condition data into the energy consumption assessment model to determine the severe environment-condition power consumption data for each severe environment-condition category data; Input each weak environment-condition category data in the scenario operating condition data into the energy consumption assessment model to determine the weak environment-condition power consumption data for each weak environment-condition category data; Input the merged environment-operation category data from the scenario operating condition data into the energy consumption assessment model to determine the merged environment-operation category data of the merged environment-operation power consumption data. The vehicle's scenario operating condition data is determined based on all strong environment-operating condition power consumption data, all weak environment-operating condition power consumption data, and the merged environment-operating condition power consumption data.

[0012] On the other hand, the present invention also provides a comprehensive energy consumption assessment system for vehicles under multiple operating conditions, comprising: Acquisition module: Acquires driving condition data and driving environment data of the vehicle during driving. Analysis module: Analyzes driving condition data and driving environment data to determine the vehicle's scenario condition data; Determine module: Based on scenario operating condition data and energy consumption assessment model, determine the vehicle's scenario power consumption data; Generation module: Generates a comprehensive energy consumption assessment report for vehicles under multiple operating conditions based on scenario power consumption data.

[0013] Compared with the prior art, the beneficial effects of this application are as follows: By acquiring and analyzing driving condition data and environmental data during vehicle operation, the system determines the vehicle's scenario-based operating conditions. Combined with an energy consumption assessment model, it determines the vehicle's scenario-based power consumption data and generates a comprehensive energy consumption assessment report under multiple scenario conditions. This allows for a comprehensive analysis of operating conditions and environmental factors, achieving efficient correlation between the two. It provides personalized energy consumption analysis for different driving scenarios, generates detailed comprehensive energy consumption assessment reports, and helps vehicle owners and vehicle management systems make more scientific energy-saving decisions, promoting the development of intelligent mobility and green transportation. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a comprehensive energy consumption assessment method for vehicles under multiple operating conditions provided in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the structure of a comprehensive energy consumption assessment system for vehicles under multiple operating conditions provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1: This invention provides a method for comprehensive energy consumption assessment of vehicles under multiple operating conditions, such as... Figure 1 Shown, including: Step 1: Obtain driving condition data and driving environment data of the vehicle during driving; Step 2: Analyze driving condition data and driving environment data to determine the vehicle's scenario condition data; Step 3: Based on scenario operating condition data and energy consumption assessment model, determine the vehicle's scenario power consumption data; Step 4: Generate a comprehensive energy consumption assessment report for the vehicle under multiple operating conditions based on scenario power consumption data.

[0019] In this embodiment, driving condition data, including vehicle speed, acceleration, braking status, and engine load, is collected through onboard equipment (such as GPS and sensors). Simultaneously, driving environment data related to vehicle operation is also collected, such as traffic flow, weather conditions, and road conditions.

[0020] In this embodiment, different scenario operating condition data are analyzed and determined based on the collected operating condition and environmental data.

[0021] In this embodiment, a trained energy consumption assessment model is used to calculate the vehicle's scene power consumption data for each scene based on the input scene operating condition data.

[0022] In this embodiment, power consumption data from all scenarios are integrated to generate a detailed comprehensive energy consumption assessment report. This report will show the energy consumption performance of the vehicle under different operating conditions and environmental conditions, providing data support for driving optimization, energy-saving strategies and vehicle performance improvement.

[0023] The beneficial effects of the above technical solution are as follows: By acquiring and analyzing driving condition data and driving environment data during vehicle operation, the system determines the vehicle's scenario-based operating condition data. Combined with an energy consumption assessment model, it determines the vehicle's scenario-based power consumption data and generates a comprehensive energy consumption assessment report under multiple scenario conditions. This allows for a comprehensive analysis of operating conditions and environmental factors, achieving efficient correlation between environment and operating conditions. It provides personalized energy consumption analysis for different driving scenarios, generates refined comprehensive energy consumption assessment reports, and helps vehicle owners and vehicle management systems make more scientific energy-saving decisions, promoting the development of intelligent mobility and green transportation.

[0024] Example 2: This invention provides a method for comprehensive energy consumption assessment of vehicles under multiple operating conditions, which acquires driving condition data of the vehicle during driving, including: Obtain the driving range of the vehicle during its journey, acquire and analyze the traffic flow data within the vehicle's driving range to determine a specified time period; Vehicle-based on-board storage devices, the vehicle's driving condition data is obtained within each specified time period across multiple natural days. The driving condition data is determined based on the driving condition sub-data within all specified time periods.

[0025] In this embodiment, the vehicle's driving range, i.e., the geographical range of the vehicle's driving within a historical time period, is obtained through GPS or other positioning technologies.

[0026] In this embodiment, traffic flow data (such as traffic volume and traffic density on roads) is collected and analyzed within the vehicle's driving range. Long-term traffic flow data within the vehicle's driving range is analyzed to identify the peak and trough times of traffic flow on a daily, weekly, or monthly basis. For example, on weekdays, traffic flow in the city center typically peaks between 7-9 AM and 5-7 PM, and drops to its lowest point between 2-4 AM. For example, a specified time period can be defined as follows: Peak Hour Period: Based on the peak traffic flow, a period is extended forward and backward, defining a specified time period. For example, 6-10 AM and 4-8 PM can be defined as peak hours, during which traffic flow is high, vehicles move slowly, and there are frequent stops and starts, representing typical congestion periods; Low Hour Period: Similar periods are defined based on the lowest traffic flow, such as 1-5 AM, during which roads are clear, vehicle speeds are relatively stable, and the conditions differ significantly from peak hours; Off-Peak Hour Period: Time outside of peak and low hours can be classified as off-peak hours. For example, from 10 a.m. to 4 p.m., traffic flow is relatively stable, neither as congested as during peak hours nor as smooth as during off-peak hours, with vehicle operating conditions in an intermediate state.

[0027] In this embodiment, the vehicle's on-board storage device (such as an on-board computer, data recorder, etc.) saves the vehicle's driving data over multiple natural days, divides this data according to a specified time period, and records the vehicle's driving conditions (such as vehicle speed, acceleration, engine load, etc.) over multiple natural days within each time period.

[0028] In this embodiment, the driving condition sub-data within all specified time periods are summarized and analyzed to obtain the overall driving condition data of the vehicle, representing the comprehensive performance of the vehicle under different time periods and different traffic conditions.

[0029] The beneficial effects of the above technical solution are: acquiring driving condition data of the vehicle during driving can provide data basis for determining scenario condition data, so as to achieve a refined comprehensive energy consumption assessment.

[0030] Example 3: This invention provides a method for comprehensive energy consumption assessment of vehicles under multiple operating conditions, which acquires driving environment data of the vehicle during driving, including: Based on all driving locations in the driving condition sub-data within each specified time period, driving environment sub-data for each specified time period is obtained. The driving environment sub-data includes traffic flow data, weather data, and road condition data. The driving environment data is determined based on the driving environment sub-data within all specified time periods.

[0031] In this embodiment, within each specified time period of vehicle travel, driving environment sub-data related to the vehicle's location is obtained based on the vehicle's location (via GPS positioning data, etc.). This driving environment sub-data includes multiple factors: traffic flow data (such as vehicle density and flow on the road), meteorological data (such as weather information such as temperature, humidity, and wind speed), and road condition data (such as road smoothness and traffic signal status).

[0032] In this embodiment, the collected driving environment sub-data for each specified time period is summarized and analyzed to ultimately generate driving environment data. This data integrates the vehicle's driving conditions under traffic conditions and environmental factors over multiple time periods, providing a basis for subsequent energy consumption assessments and driving optimization.

[0033] The beneficial effects of the above technical solution are: acquiring driving environment data of the vehicle during driving can provide data basis for determining scenario operating conditions, so as to achieve a refined comprehensive energy consumption assessment.

[0034] Example 4: This invention provides a method for comprehensive energy consumption assessment of vehicles under multiple operating conditions, which analyzes driving condition data and driving environment data to determine the vehicle's scenario operating condition data, including: Preprocess the driving condition data and driving environment data, analyze all driving positions and driving environment sub-data of the driving condition sub-data within each specified time period in the preprocessed driving condition data, and determine the position division rules of each driving condition sub-data in the driving condition data. Based on the location division rules of each driving condition sub-data in the driving condition data, multiple driving areas of each driving condition sub-data are determined, and based on the driving condition data and driving environment data, the regional environment data and regional condition data of each driving area within each specified time period are determined. Feature extraction is performed on the regional environmental data of each driving area within each specified time period to determine the regional environmental vector of each driving area. At the same time, feature extraction is performed on the regional operating condition data of each driving area within each specified time period to determine the regional operating condition vector of each driving area. Cluster analysis is performed on the regional environmental vectors of all driving areas within all specified time periods to determine multiple environmental category data, which include environmental category labels and multiple regional environmental vectors. Cluster analysis is performed on the regional operating condition vectors of all driving areas within all specified time periods to determine multiple operating condition category data, which include operating condition category labels and multiple operating condition environment vectors. Based on all environmental category data and all operating condition category data, determine the environment-operating condition association value for each environmental category label and each operating condition category label; The scenario operating condition data is determined based on all environment-operating condition correlation values, the first preset correlation threshold, and the second preset correlation threshold.

[0035] In this embodiment, the driving condition data and driving environment data are preprocessed to remove noise, outliers, and perform time alignment. Time alignment means that since the driving condition sub-data and driving environment sub-data may be collected at different frequencies, they need to be aligned to the same time scale. Using the driving time corresponding to the driving location in the driving condition sub-data as a benchmark, time matching and interpolation are performed on the driving environment sub-data to ensure that both types of data have corresponding values ​​at the same time point. For example, if the driving condition sub-data is collected every 1 second, while the meteorological data is collected every 5 minutes, linear interpolation or other methods can be used to interpolate the meteorological data at a frequency of one second, aligning it with the driving condition sub-data in time.

[0036] In this embodiment, the driving position and driving environment sub-data of the driving condition sub-data within each specified time period in the preprocessed driving condition data are analyzed, including the analysis of the magnitude of driving position change, the direction of driving position change, road type change, geographical area change, and stop point density analysis.

[0037] In this embodiment, based on location segmentation rules, the driving condition sub-data within each time period is divided into multiple driving regions. For each driving region, the system combines the driving condition data and driving environment data to extract regional environment data and regional driving condition data.

[0038] In this embodiment, feature extraction is performed on the regional environmental data and regional operating condition data within each driving area to generate corresponding regional environmental vectors and regional operating condition vectors.

[0039] In this embodiment, cluster analysis is performed on the environmental vectors and operating condition vectors of the driving area within all time periods to obtain multiple environmental category data and operating condition category data. Each category contains a label and multiple vectors.

[0040] In this embodiment, based on the environmental category data and operating condition category data obtained from clustering, the environmental-operating condition association value between each environmental category label and the operating condition category label is calculated. Then, combined with a preset association threshold, the vehicle's scenario operating condition data is finally determined.

[0041] The beneficial effects of the above technical solution are: analyzing driving condition data and driving environment data to determine the vehicle's scenario operating condition data can achieve efficient correlation between environment and operating condition, providing a more accurate decision-making basis for determining scenario power consumption data.

[0042] Example 5: This invention provides a comprehensive energy consumption assessment method for vehicles under multiple operating conditions. Based on all environmental category data and all operating condition category data, it determines the environment-operating condition correlation value for each environmental category label and each operating condition category label, including: Based on all environmental category data and all operating condition category data, determine the association value for each environmental category label and each operating condition category label; ; ; ; ; ; ; This represents the environment-operating condition association value between the i-th environment category label and the j-th operating condition category label. Indicates the number of specified time periods. This represents the number of driving areas within the specified time period (a). This represents the regional operating condition vector for the b-th driving area within the a-th specified time period. and the j-th working condition category data The working condition correlation value, This represents the regional environment vector of the b-th driving area within the a-th specified time period. and the data of the i-th working condition category Environmental correlation values, Indicates the correlation weight of operating conditions. Indicates the environmental association weight. This represents an indication function for the b-th driving area within the a-th specified time period, based on the i-th environmental category data and the j-th operating condition category data. This represents the fitted environment vector for the i-th environment category data. This represents the number of regional environment vectors in the i-th environment category data. This represents the i-th environmental category data. This represents the regional environment vector of the b-th driving area within the a-th specified time period. This represents the regional environment vector of the c-th driving area within the a-th specified time period. This represents the fitted working condition vector for the j-th working condition category data. This indicates that the parameters are being adjusted. This represents the number of regional working condition vectors in the j-th working condition category data. This represents the data for the j-th working condition category. This represents the regional operating condition vector for the b-th driving area within the a-th specified time period. This represents the clustering vector of the j-th work condition category data. This represents the clustering condition vector for the i-th environmental category data.

[0043] In this embodiment, This represents the average working condition vector for the j-th working condition category. This represents the average environment vector for the i-th environment category data.

[0044] In this embodiment, This indicates that the regional environment vector of the b-th driving area within the a-th specified time period belongs to the i-th environment category data.

[0045] In this embodiment, This indicates that the regional condition vector of the b-th driving area within the a-th specified time period belongs to the i-th condition category data.

[0046] The beneficial effects of the above technical solution are as follows: Based on all environmental category data and all operating condition category data, the environmental-operating condition correlation value of each environmental category label and each operating condition category label is determined, which can provide a more accurate decision basis for quantifying the correlation between environment and operating condition and further determining scenario power consumption data.

[0047] Example 6: This invention provides a comprehensive energy consumption assessment method for vehicles under multiple operating conditions, which determines scenario operating condition data based on all environment-operating condition correlation values, a first preset correlation threshold, and a second preset correlation threshold, including: Based on all environment-operating condition correlation values, determine the environment-operating condition matrix; ; in, This represents the initialization of the environment-condition matrix, where N1 represents the number of environment category data points and N2 represents the number of condition category data points. These represent the environment-operating condition association values ​​for the first environment category label, the first operating condition category label, the j-th operating condition category label, and the N1-th operating condition category label, respectively. These represent the environment-operating condition association values ​​of the i-th environment category label, the 1st operating condition category label, and the N1th operating condition category label, respectively. These represent the environment-operating condition association values ​​of the N2nd environment category label, the 1st operating condition category label, the jth operating condition category label, and the N1st operating condition category label, respectively. The environment-operational condition matrix is ​​determined based on the correlation values ​​of all environmental category labels and all operating condition category labels; Each correlation value in the environment-operating condition matrix is ​​compared with a preset correlation threshold. The environment category label and operating condition category label corresponding to each environment-operating condition correlation value that is greater than or equal to the first preset correlation threshold are determined as strong environment-operating condition category labels, and strong environment-operating condition category data is determined. The environmental category label and operating condition label corresponding to each environmental-operating condition association value that is less than the first preset association threshold and greater than the second preset association threshold are determined as weak environmental-operating condition category labels, and the weak environmental-operating condition category data is determined. The environmental category labels and operating condition category labels corresponding to environmental-operating condition association values ​​that are less than the second preset association threshold are merged to determine the merged environmental-operating condition category labels and the merged environmental-operating condition category data. The vehicle's scenario operating condition data is determined based on all strong environment-operating condition category data, all weak environment-operating condition category data, and merged environment-operating condition category data.

[0048] In this embodiment, all the calculated environment-operating condition association values ​​are organized into a matrix, where the rows of the matrix represent different operating condition category labels and the columns represent different environment category labels.

[0049] In this embodiment, each correlation value in the environment-operating condition matrix is ​​compared with a preset correlation threshold: correlation values ​​that are greater than or equal to the first preset correlation threshold are considered as strong environment-operating condition category labels and determined as strong environment-operating condition category data.

[0050] In this embodiment, the associated values ​​that are less than the first preset association threshold and greater than the second preset association threshold are considered as weak environment-operational condition category labels and determined as weak environment-operational condition category data.

[0051] In this embodiment, the environmental category label and the operating condition category label corresponding to the association value that is less than the second preset association threshold are merged and referred to as merged environmental-operating condition category label, and are determined as merged environmental-operating condition category data.

[0052] In this embodiment, based on the indicator functions of each environmental category data and each operating condition category data for all driving areas within all specified time periods, the data is determined as strong environmental-operating condition category data, weak environmental-operating condition category data, and merged environmental-operating condition category data.

[0053] In this embodiment, comprehensive scenario operating condition data is generated by combining all strong environment-operating condition category data, weak environment-operating condition category data, and merged environment-operating condition category data, representing the comprehensive state of the vehicle under different combinations of environment and operating conditions.

[0054] The beneficial effects of the above technical solution are as follows: Based on all environment-operating condition correlation values, the first preset correlation threshold and the second preset correlation threshold, the scenario operating condition data can be determined, which can improve the accuracy and adaptability of the analysis, accurately determine the complex and ever-changing scenario operating condition data, and provide personalized energy consumption analysis for different driving scenarios.

[0055] Example 7: This invention provides a comprehensive energy consumption assessment method for vehicles under multiple operating conditions. Based on scenario operating condition data and an energy consumption assessment model, the method determines the vehicle's scenario power consumption data, including: Input each severe environment-condition category data in the scenario operating condition data into the energy consumption assessment model to determine the severe environment-condition power consumption data for each severe environment-condition category data; Input each weak environment-condition category data in the scenario operating condition data into the energy consumption assessment model to determine the weak environment-condition power consumption data for each weak environment-condition category data; Input the merged environment-operation category data from the scenario operating condition data into the energy consumption assessment model to determine the merged environment-operation category data of the merged environment-operation power consumption data. The vehicle's scenario operating condition data is determined based on all strong environment-operating condition power consumption data, all weak environment-operating condition power consumption data, and the merged environment-operating condition power consumption data.

[0056] In this embodiment, data for each severe environment-operating condition category is input into the energy consumption assessment model. Based on the input environment and operating condition data, the model derives the severe environment-operating condition power consumption data for each category. These data represent the vehicle's energy consumption under specific severe environments and operating conditions.

[0057] In this embodiment, the data for each weak environment-operating condition category is input into the energy consumption assessment model to obtain the weak environment-operating condition power consumption data for each category, which represents the energy consumption under a weakly correlated environment and operating condition.

[0058] In this embodiment, the merged environment-operating condition category data is also input into the energy consumption assessment model to obtain merged environment-operating condition power consumption data, that is, the energy consumption under low correlation environment and operating condition.

[0059] In this embodiment, complete vehicle scenario operating condition data is generated based on all strong environment-operating condition power consumption data, weak environment-operating condition power consumption data, and merged environment-operating condition power consumption data, reflecting the comprehensive energy consumption performance of the vehicle under various environmental and operating conditions.

[0060] The beneficial effects of the above technical solution are as follows: Based on scenario operating condition data and energy consumption assessment models, the scenario power consumption data of the vehicle can be determined, and energy consumption can be accurately analyzed and predicted. Personalized energy efficiency analysis can be provided for different driving scenarios, thereby providing strong data support for driving behavior optimization, energy-saving strategy formulation and vehicle performance improvement.

[0061] Example 8: This invention provides a comprehensive energy consumption assessment system for vehicles under multiple operating conditions, such as... Figure 2 Shown, including: Acquisition module: Acquires driving condition data and driving environment data of the vehicle during driving. Analysis module: Analyzes driving condition data and driving environment data to determine the vehicle's scenario condition data; Determine module: Based on scenario operating condition data and energy consumption assessment model, determine the vehicle's scenario power consumption data; Generation module: Generates a comprehensive energy consumption assessment report for vehicles under multiple operating conditions based on scenario power consumption data.

[0062] The beneficial effects of the above technical solution are as follows: By acquiring and analyzing driving condition data and driving environment data during vehicle operation, the system determines the vehicle's scenario-based operating condition data. Combined with an energy consumption assessment model, it determines the vehicle's scenario-based power consumption data and generates a comprehensive energy consumption assessment report under multiple scenario conditions. This allows for a comprehensive analysis of operating conditions and environmental factors, achieving efficient correlation between environment and operating conditions. It provides personalized energy consumption analysis for different driving scenarios, generates refined comprehensive energy consumption assessment reports, and helps vehicle owners and vehicle management systems make more scientific energy-saving decisions, promoting the development of intelligent mobility and green transportation.

[0063] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0065] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for comprehensive energy consumption assessment of vehicles under multiple operating conditions, characterized in that, include: Step 1: Obtain driving condition data and driving environment data of the vehicle during driving; Step 2: Analyze driving condition data and driving environment data to determine the vehicle's scenario condition data; Step 3: Based on scenario operating condition data and energy consumption assessment model, determine the vehicle's scenario power consumption data; Step 4: Generate a comprehensive energy consumption assessment report for the vehicle under multiple operating conditions based on scenario power consumption data.

2. The comprehensive energy consumption assessment method for vehicles under multiple operating conditions according to claim 1, characterized in that, Acquire driving condition data of the vehicle during operation, including: Obtain the driving range of the vehicle during its journey, acquire and analyze the traffic flow data within the vehicle's driving range to determine a specified time period; Vehicle-based on-board storage devices, the vehicle's driving condition data is obtained within each specified time period across multiple natural days. The driving condition data is determined based on the driving condition sub-data within all specified time periods.

3. The comprehensive energy consumption assessment method for vehicles under multiple operating conditions according to claim 2, characterized in that, Acquire driving environment data of the vehicle during driving, including: Based on all driving locations in the driving condition sub-data within each specified time period, driving environment sub-data for each specified time period is obtained. The driving environment sub-data includes traffic flow data, weather data, and road condition data. The driving environment data is determined based on the driving environment sub-data within all specified time periods.

4. The comprehensive energy consumption assessment method for vehicles under multiple operating conditions according to claim 1, characterized in that, Analyze driving condition data and driving environment data to determine the vehicle's scenario-based operating data, including: Preprocess the driving condition data and driving environment data, analyze all driving positions and driving environment sub-data of the driving condition sub-data within each specified time period in the preprocessed driving condition data, and determine the position division rules of each driving condition sub-data in the driving condition data. Based on the location division rules of each driving condition sub-data in the driving condition data, multiple driving areas of each driving condition sub-data are determined, and based on the driving condition data and driving environment data, the regional environment data and regional condition data of each driving area within each specified time period are determined. Feature extraction is performed on the regional environmental data of each driving area within each specified time period to determine the regional environmental vector of each driving area. At the same time, feature extraction is performed on the regional operating condition data of each driving area within each specified time period to determine the regional operating condition vector of each driving area. Cluster analysis is performed on the regional environmental vectors of all driving areas within all specified time periods to determine multiple environmental category data, which include environmental category labels and multiple regional environmental vectors. Cluster analysis is performed on the regional operating condition vectors of all driving areas within all specified time periods to determine multiple operating condition category data, which include operating condition category labels and multiple operating condition environment vectors. Based on all environmental category data and all operating condition category data, determine the environment-operating condition association value for each environmental category label and each operating condition category label; The scenario operating condition data is determined based on all environment-operating condition correlation values, the first preset correlation threshold, and the second preset correlation threshold.

5. The comprehensive energy consumption assessment method for vehicles under multiple operating conditions according to claim 4, characterized in that, Based on all environmental category data and all operating condition category data, determine the environment-operating condition association value for each environmental category label and each operating condition category label, including: Based on all environmental category data and all operating condition category data, determine the association value for each environmental category label and each operating condition category label; ； ； ； ； ； ； This represents the environment-operating condition association value between the i-th environment category label and the j-th operating condition category label. Indicates the number of specified time periods. This represents the number of driving areas within the specified time period (a). This represents the regional operating condition vector for the b-th driving area within the a-th specified time period. and the j-th working condition category data The working condition correlation value, This represents the regional environment vector of the b-th driving area within the a-th specified time period. and the data of the i-th working condition category Environmental correlation values, Indicates the correlation weight of operating conditions. Indicates the environmental association weight. This represents an indication function for the b-th driving area within the a-th specified time period, based on the i-th environmental category data and the j-th operating condition category data. This represents the fitted environment vector for the i-th environment category data. This represents the number of region environment vectors in the i-th environment category data. This represents the i-th environmental category data. This represents the regional environment vector of the b-th driving area within the a-th specified time period. This represents the region environment vector of the c-th driving area within the a-th specified time period. This represents the fitted working condition vector for the j-th working condition category data. This indicates that the parameters are being adjusted. This represents the number of regional working condition vectors in the j-th working condition category data. This represents the data for the j-th working condition category. This represents the regional operating condition vector for the b-th driving area within the a-th specified time period. This represents the clustering vector of the j-th work condition category data. This represents the clustering condition vector for the i-th environmental category data.

6. The comprehensive energy consumption assessment method for vehicles under multiple operating conditions according to claim 4, characterized in that, Scenario condition data is determined based on all environment-condition correlation values, a first preset correlation threshold, and a second preset correlation threshold, including: Based on all environment-operating condition correlation values, determine the environment-operating condition matrix; ； in, This represents the initialization of the environment-condition matrix, where N1 represents the number of environment category data points and N2 represents the number of condition category data points. These represent the environment-operating condition association values ​​for the first environment category label, the first operating condition category label, the j-th operating condition category label, and the N1-th operating condition category label, respectively. These represent the environment-operating condition association values ​​of the i-th environment category label, the 1st operating condition category label, and the N1th operating condition category label, respectively. These represent the environment-operating condition association values ​​of the N2nd environment category label, the 1st operating condition category label, the jth operating condition category label, and the N1st operating condition category label, respectively. The environment-operational condition matrix is ​​determined based on the correlation values ​​of all environmental category labels and all operating condition category labels; Each correlation value in the environment-operating condition matrix is ​​compared with a preset correlation threshold. The environment category label and operating condition category label corresponding to each environment-operating condition correlation value that is greater than or equal to the first preset correlation threshold are determined as strong environment-operating condition category labels, and strong environment-operating condition category data is determined. The environmental category label and operating condition label corresponding to each environmental-operating condition association value that is less than the first preset association threshold and greater than the second preset association threshold are determined as weak environmental-operating condition category labels, and the weak environmental-operating condition category data is determined. The environmental category labels and operating condition category labels corresponding to environmental-operating condition association values ​​that are less than the second preset association threshold are merged to determine the merged environmental-operating condition category labels and the merged environmental-operating condition category data. The vehicle's scenario operating condition data is determined based on all strong environment-operating condition category data, all weak environment-operating condition category data, and merged environment-operating condition category data.

7. The comprehensive energy consumption assessment method for vehicles under multiple operating conditions according to claim 6, characterized in that, Based on scenario operating condition data and energy consumption assessment models, the vehicle's scenario power consumption data is determined, including: Input each severe environment-condition category data in the scenario operating condition data into the energy consumption assessment model to determine the severe environment-condition power consumption data for each severe environment-condition category data; Input each weak environment-condition category data in the scenario operating condition data into the energy consumption assessment model to determine the weak environment-condition power consumption data for each weak environment-condition category data; Input the merged environment-operation category data from the scenario operating condition data into the energy consumption assessment model to determine the merged environment-operation category data of the merged environment-operation power consumption data. The vehicle's scenario operating condition data is determined based on all strong environment-operating condition power consumption data, all weak environment-operating condition power consumption data, and the merged environment-operating condition power consumption data.

8. A comprehensive energy consumption assessment system for vehicles under multiple operating conditions, characterized in that, include: Acquisition module: Acquires driving condition data and driving environment data of the vehicle during driving. Analysis module: Analyzes driving condition data and driving environment data to determine the vehicle's scenario condition data; Determine module: Based on scenario operating condition data and energy consumption assessment model, determine the vehicle's scenario power consumption data; Generation module: Generates a comprehensive energy consumption assessment report for vehicles under multiple operating conditions based on scenario power consumption data.

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