Carbon emission monitoring method and system for new energy truck based on real-time on-board weighing

Abstract

The application relates to the technical field of carbon emission monitoring of new energy trucks, and discloses a new energy truck carbon emission monitoring method and system based on vehicle-mounted real-time weighing, which comprises the following steps: obtaining real-time weighing data of a truck through a vehicle-mounted real-time weighing device configured on the new energy truck, and constructing a load state vector; combining the load state vector and pre-acquired real-time road state data of the truck to simulate an instantaneous load state model of the truck; analyzing the mapping relationship between the load and energy consumption under corresponding working conditions, and calculating energy consumption sensitivity; correlatively analyzing the instantaneous load state model and the energy consumption sensitivity, and calculating a dynamic carbon emission factor; calculating a first carbon emission amount according to the real-time weighing data of each truck, correcting the first carbon emission amount according to the dynamic carbon emission factor, and combining all trucks to calculate an overall carbon emission amount; the application improves the precision of the carbon emission analysis process, corrects carbon emission through the dynamic carbon emission factor, and improves the accuracy of the carbon emission monitoring result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy truck carbon emission monitoring, more particularly to a new energy truck carbon emission monitoring method and system based on vehicle-mounted real-time weighing. BACKGROUND

[0002] At present, as one of the important sources of carbon emissions, the precise accounting and effective supervision of transportation have become the core issue of industry development. The traditional carbon emission monitoring method mostly uses a macro estimation model based on driving mileage and average load, and calculates based on fixed carbon emission factors, which cannot perceive the real-time dynamic changes of vehicle load and ignores the influence of road conditions on vehicle energy consumption. The energy consumption of new energy trucks is extremely sensitive to changes in load and road conditions, and the carbon emission evaluation results calculated by the traditional carbon emission monitoring method have low accuracy, which cannot meet the precision requirements of carbon emission data.

[0003] The existing technology has the following problems: based on static data to estimate the load, ignoring the change of load, unable to capture the dynamic changes of load caused by loading and unloading, cargo shifting, etc., resulting in inaccurate baseline data for carbon emission calculation; ignoring the influence of road condition changes on truck carbon emission, resulting in low calculation accuracy of truck carbon emission process; based on fixed carbon emission factors for calculation, unable to reflect the dynamic characteristic changes caused by changes in load and working conditions, resulting in low precision of the carbon emission monitoring process; to solve at least one of the above problems, the present application provides a new energy truck carbon emission monitoring method and system based on vehicle-mounted real-time weighing. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a new energy truck carbon emission monitoring method and system based on vehicle-mounted real-time weighing, which can effectively solve the problems in the background art. The specific technical solution of the present application is as follows:

[0005] The new energy truck carbon emission monitoring method based on vehicle-mounted real-time weighing comprises:

[0006] Through the vehicle-mounted real-time weighing device configured on the new energy truck, real-time weighing data of the truck is obtained, load characteristics are extracted from the real-time weighing data, and a load state vector is constructed;

[0007] The load state vector and the pre-acquired real-time road state data of the truck are combined to analyze the changes of the dynamic load of the truck with the road slope and curvature, and an instantaneous load state model of the truck is simulated;

[0008] According to the load state vector and the real-time energy consumption data of the truck, the mapping relationship between the load and the energy consumption under the corresponding working condition is analyzed through a pre-set energy consumption analysis model, and the energy consumption sensitivity is calculated;

[0009] Correlate the instantaneous load state model and the energy consumption sensitivity, analyze the carbon emission intensity generated by unit mass displacement, and calculate a dynamic carbon emission factor;

[0010] According to the real-time weighing data of each truck, the corresponding first carbon emission is calculated through the instantaneous load state model, the first carbon emission is corrected according to the dynamic carbon emission factor to obtain the second carbon emission, and the second carbon emission of all trucks is combined for feedback optimization to obtain the total carbon emission.

[0011] Specifically, the real-time weighing device configured on the new energy truck obtains real-time weighing data of the truck, extracts load characteristics from the real-time weighing data, and constructs a load state vector, including:

[0012] The real-time weighing device configured on the new energy truck obtains real-time weighing data of the truck;

[0013] The real-time state of the new energy truck is analyzed, and the real-time weighing data is filtered to obtain filtered data;

[0014] The filtered data is subjected to load characteristic extraction through a pre-set feature extraction model to construct a load state vector.

[0015] Specifically, in combination with the load state vector and the pre-acquired real-time road state data of the truck, the change of the dynamic load of the truck with the road slope and curvature is analyzed, and an instantaneous load state model of the truck is simulated, including:

[0016] In combination with the load state vector and the pre-acquired real-time road state data of the truck, the change of the dynamic load of the truck with the road slope and curvature is analyzed, and road slope data and road curvature data are obtained;

[0017] Based on the road slope data, a longitudinal load transfer mechanism is configured to calculate the load change rate of each axle of the truck, and the longitudinal load transfer mechanism calculates the load increment of the rear axle through a trigonometric function projection for uphill working conditions, and simultaneously calculates the load increment of the front axle for downhill working conditions;

[0018] Based on the road curvature data, the inclination of the center height of the truck is analyzed, and the inside and outside load difference of the truck wheel is calculated;

[0019] In combination with the load change rate and the inside and outside load difference, the load state change is simulated to obtain the instantaneous load state model of the truck.

[0020] Specifically, in combination with the load change rate and the inside and outside load difference, the load state change is simulated to obtain the instantaneous load state model of the truck, including:

[0021] Based on the load state vector, the road excitation main frequency band is identified;

[0022] According to the road excitation main frequency band, the coupling vibration situation of the load change rate and the inside-outside load difference is analyzed, the load state change is simulated, and a dynamic load response value is obtained;

[0023] According to the dynamic load response value, an envelope prediction corresponding to the dynamic load response is superimposed on the base load of the truck, a load fluctuation boundary is constructed, and a transient load state model of the truck is obtained.

[0024] Specifically, according to the load state vector and real-time energy consumption data of the truck, a preset energy consumption analysis model is used to analyze the mapping relationship between the load and the energy consumption under the corresponding working condition, and an energy consumption sensitivity is calculated, including:

[0025] According to the load state vector, a preset energy consumption analysis model is used to analyze the mapping relationship between the load and the energy consumption under different working conditions;

[0026] Based on the real-time energy consumption data, matching is performed in the corresponding mapping relationship to obtain a target mapping relationship;

[0027] According to the target mapping relationship, the ratio of the load change rate to the energy consumption change rate at the corresponding time is calculated to obtain a first sensitivity, and the energy consumption change amount corresponding to the unit load change within the corresponding working condition time is calculated to obtain a second sensitivity;

[0028] According to the first sensitivity, the second sensitivity is real-time corrected, and the energy consumption sensitivity is calculated.

[0029] Specifically, the preset energy consumption analysis model is used to analyze the mapping relationship between the load and the energy consumption under different working conditions according to the load state vector, including:

[0030] According to the load state vector, the membership degree of each base working condition is calculated, and the base working conditions include smooth running, acceleration climbing, deceleration braking, and road bumping;

[0031] An energy consumption analysis sub-model is set for each base working condition to analyze the energy consumption under the corresponding working condition, and the energy consumption analysis sub-models are weighted and fused according to the membership degrees to obtain an energy consumption analysis model;

[0032] A transition working condition in which the base working condition switches during the truck running process is identified, the switching speed and the energy consumption change in the transition process are analyzed, and a transition compensation factor is calculated;

[0033] The mapping relationship between the load and the energy consumption under different working conditions is obtained in combination with the transition compensation factor and the energy consumption analysis model.

[0034] Specifically, the transient load state model and the energy consumption sensitivity are associated and analyzed to analyze the carbon emission intensity generated by unit mass displacement, and a dynamic carbon emission factor is calculated, including:

[0035] The instantaneous load change of the truck is calculated by the instantaneous load state model to obtain a load sequence;

[0036] The coupling of the load fluctuation and the energy consumption sensitivity change is analyzed by a preset correlation analysis model, the carbon emission intensity generated by unit mass displacement is calculated, and a dynamic carbon emission factor is obtained.

[0037] Specifically, the first carbon emission amount corresponding to each truck is calculated according to the real-time weighing data of each truck by the instantaneous load state model, the first carbon emission amount is corrected according to the dynamic carbon emission factor to obtain a second carbon emission amount, the second carbon emission amount of all trucks is combined for feedback optimization to obtain the overall carbon emission amount, including:

[0038] The first carbon emission amount corresponding to each truck is calculated according to the real-time weighing data of each truck by the instantaneous load state model;

[0039] The load fluctuation characteristics are analyzed, the first carbon emission amount is divided into multiple stages, and each stage of the first carbon emission amount is corrected according to the dynamic carbon emission factor to obtain the second carbon emission amount;

[0040] The second carbon emission amount of all trucks is combined for feedback optimization to obtain the overall carbon emission amount.

[0041] Specifically, the load fluctuation characteristics are analyzed, the first carbon emission amount is divided into multiple stages, and each stage of the first carbon emission amount is corrected according to the dynamic carbon emission factor to obtain the second carbon emission amount, including:

[0042] The load fluctuation characteristics are analyzed, and the first carbon emission amount is divided into stages, including stable load, gradual load, sudden load and oscillation load;

[0043] For stable load, the steady-state component in the dynamic carbon emission factor is used for correction; for gradual load, the load change rate and the dynamic carbon emission factor are combined for correction; for sudden load, the dynamic carbon emission factor at the corresponding time before and after the mutation point is used for correction; for oscillation load, the corresponding dynamic carbon emission factor is used for correction for the upper envelope line and the lower envelope line of the oscillation, respectively;

[0044] The second carbon emission amount is obtained by combining the carbon emission amount corrected in each stage.

[0045] A new energy truck carbon emission monitoring system based on real-time vehicle weighing is used to implement the new energy truck carbon emission monitoring method based on real-time vehicle weighing, including:

[0046] The load analysis module acquires real-time weighing data of the truck through the on-board real-time weighing equipment configured in the new energy truck, extracts load characteristics from the real-time weighing data, and constructs a load state vector.

[0047] The state analysis module, combining the load state vector and the pre-acquired real-time road state data of the truck, analyzes the changes in the dynamic load of the truck with the road slope and curvature, and simulates the instantaneous load state model of the truck.

[0048] The load energy consumption mapping module analyzes the mapping relationship between load and energy consumption under the corresponding working conditions based on the load state vector and the real-time energy consumption data of the truck, and calculates the energy consumption sensitivity through a preset energy consumption analysis model.

[0049] The load energy consumption correlation module performs correlation analysis on the instantaneous load state model and energy consumption sensitivity, analyzes the carbon emission intensity generated per unit mass displacement, and calculates the dynamic carbon emission factor.

[0050] The carbon emission monitoring module calculates the corresponding first carbon emission based on the real-time weighing data of each truck using an instantaneous load state model. It then corrects the first carbon emission based on the dynamic carbon emission factor to obtain the second carbon emission. Finally, it combines the second carbon emission of all trucks for feedback optimization to obtain the total carbon emission.

[0051] The beneficial effects of this application are as follows: By integrating real-time weighing and road data, and combining longitudinal and lateral load transfer to construct an instantaneous load state model, dynamic load can be accurately analyzed; energy consumption sensitivity analysis based on real-time operating conditions can accurately quantify the impact of load on energy consumption under different operating conditions; by correlating instantaneous load and energy consumption sensitivity, a dynamic carbon emission factor corresponding to real-time operating conditions can be calculated, and targeted optimization of carbon emissions at different load fluctuation stages can be performed, thereby improving the accuracy of overall carbon emissions. By combining real-time weighing and road data, the accuracy of the carbon emission analysis process can be improved, and the calculated dynamic carbon emission factor reflects the carbon emission fluctuations caused by loading and unloading and changes in road conditions in real time, effectively addressing various complex road and driving conditions and improving the accuracy of carbon emission monitoring results. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the carbon emission monitoring method for new energy freight vehicles based on real-time on-board weighing in Implementation 1 of this application.

[0053] Figure 2 This is a schematic diagram of the basic working conditions of the truck in Embodiment 1 of this application;

[0054] Figure 3 This is a flowchart illustrating the overall carbon emission calculation process in Embodiment 1 of this application;

[0055] Figure 4 FIG. 1 is a structural schematic diagram of a new energy truck carbon emission monitoring system based on vehicle-mounted real-time weighing in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The present application is further described in detail below with reference to the accompanying drawings and embodiments.

[0057] In the embodiments of the present application, the words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design presented as "exemplary" or "for example" in the embodiments of the present application should not be construed as being preferred or superior over other embodiments or design solutions. Rather, the use of the words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0058] Hereinafter, the terms "first", "second", and the like are used in a general sense and only for the purpose of description, and should not be construed as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0059] Embodiment 1

[0060] Reference Figure 1 As shown, the specific embodiments of the new energy truck carbon emission monitoring method based on vehicle-mounted real-time weighing in the present application include:

[0061] S101, acquiring real-time weighing data of the truck through a vehicle-mounted real-time weighing device configured on the new energy truck, extracting load features from the real-time weighing data, and constructing a load state vector;

[0062] S102, combining the load state vector and pre-acquired real-time road state data of the truck, analyzing the change of the dynamic load of the truck with the road slope and curvature, and simulating to obtain an instantaneous load state model of the truck;

[0063] S103, according to the load state vector and real-time energy consumption data of the truck, analyzing the mapping relationship between the load and the energy consumption under the corresponding working condition through a pre-set energy consumption analysis model, and calculating the energy consumption sensitivity;

[0064] S104, performing correlation analysis on the instantaneous load state model and the energy consumption sensitivity, analyzing the carbon emission intensity generated by unit mass displacement, and calculating the dynamic carbon emission factor;

[0065] S105, according to the real-time weighing data of each truck, the corresponding first carbon emission is calculated through the instantaneous load state model, the first carbon emission is corrected according to the dynamic carbon emission factor, the second carbon emission is obtained, the feedback optimization is combined with the second carbon emission of all trucks, and the overall carbon emission is obtained.

[0066] In the embodiment, the real-time weighing data of the truck is obtained through the vehicle-mounted real-time weighing device configured by the new energy truck, the real-time weighing data is dynamically filtered, the filtering parameters are adaptively adjusted according to the real-time running state of the truck in the filtering process, the high-frequency noise and abnormal pulses irrelevant to the vehicle motion are effectively eliminated, the real trend of load change is retained, the filtered data is obtained, the load characteristics are extracted from the filtered real-time weighing data, and the load state vector is constructed; the signal-to-noise ratio of the load data can be improved through adaptive filtering, the interference of driving vibration on the weighing accuracy is effectively avoided, accurate data support is provided for subsequent calculation, the load state of the vehicle can be reflected through the construction of the load state vector, the dynamic stability and change trend of the load are analyzed, and the accuracy of the carbon emission monitoring process is improved.

[0067] Specifically, the actual load of the truck is not limited to the static weight, the road geometry shape can cause dynamic transfer of the vehicle mass, affect the real-time load of the wheel and the stress state of the vehicle, the load state vector and the pre-acquired real-time road state data of the truck are combined, the change of the truck dynamic load with the road slope and curvature is analyzed, the instantaneous load space distribution state of the vehicle under the road condition is simulated, and the instantaneous load state model of the truck is obtained; by quantifying the load transfer caused by the road slope and curvature, the instantaneous load state model constructed can accurately reflect the actual load and additional resistance borne by the vehicle when climbing, descending and turning, the equivalent load dynamically changing with the road environment is dynamically calculated in the energy consumption and carbon emission calculation process, and the accuracy and reliability of the carbon emission evaluation under complex road conditions are improved.

[0068] Specifically, according to the load state vector, the basic working condition of the current vehicle is identified, the real-time energy consumption data of the truck is combined, the mapping relationship between the load and the energy consumption under the corresponding working condition is analyzed through the preset energy consumption analysis model, and the energy consumption sensitivity is calculated; by analyzing the vehicle working condition and compensating the working condition, the mapping relationship between the load and the energy consumption obtained by analysis can accurately match the actual running state of the vehicle, the energy consumption sensitivity calculated can reflect the real-time road condition, accurate data support is provided for analyzing the carbon emission efficiency, and the accuracy and efficiency of the carbon emission monitoring process are improved.

[0069] Specifically, the load sequence reflecting the actual force change is output through the instantaneous load state model, the load sequence and energy consumption sensitivity of the instantaneous load state model are associated and coupled, the carbon emission intensity generated by unit mass displacement is analyzed, and the dynamic carbon emission factor is calculated. Through the calculation of the dynamic carbon emission factor, the carbon emission efficiency of the vehicle under different loads and different road conditions can be reflected. For example, when heavy load is on the uphill, more electric energy is needed to complete the same transportation task, and the dynamic carbon emission factor will increase, indicating that the carbon emission intensity increases at this time. When the load is light and the road is flat or downhill, the dynamic carbon emission factor will decrease. Combining the dynamic carbon emission factor with the total energy consumption and transportation efficiency can improve the accuracy of the carbon emission monitoring result.

[0070] Specifically, according to the real-time weighing data of each truck, the corresponding first carbon emission is calculated through the instantaneous load state model, the load fluctuation characteristics in the journey are analyzed, the entire carbon emission process is divided into different stages such as stable load, gradual load, sudden load and oscillation load, the first carbon emission of each stage is corrected according to the dynamic carbon emission factor to obtain the second carbon emission, and the overall carbon emission is obtained through feedback optimization of the second carbon emission of all trucks. Through the segmented correction strategy based on the load fluctuation characteristics, the accuracy of the single journey carbon emission calculation result can be improved, and the error problem caused by a single correction strategy can be avoided. Through overall feedback optimization, the carbon emission data can be optimized in real time, and the accuracy of the overall carbon emission can be improved.

[0071] The present application fuses real-time weighing and road data, and constructs an instantaneous load state model by combining longitudinal and lateral load transfer, which can accurately analyze dynamic load. Based on real-time working conditions, energy consumption sensitivity analysis can accurately quantify the influence of load on energy consumption under different operating conditions. By associating instantaneous load and energy consumption sensitivity, the dynamic carbon emission factor corresponding to the real-time working condition is calculated, and the carbon emission of different load fluctuation stages is optimized, which can improve the accuracy of the overall carbon emission. By combining real-time weighing and road data, the accuracy of the carbon emission analysis process can be improved, the dynamic carbon emission factor can reflect the carbon emission fluctuation caused by loading and unloading, road condition change, and effectively cope with various complex roads and driving conditions, and improve the accuracy of the carbon emission monitoring result.

[0072] Further, the real-time weighing data of the truck is obtained through the vehicle-mounted real-time weighing device configured on the new energy truck, the load characteristics are extracted from the real-time weighing data, and the load state vector is constructed, including:

[0073] S201, obtaining the real-time weighing data of the truck through the vehicle-mounted real-time weighing device configured on the new energy truck;

[0074] S202, analyze the real-time state of the new energy truck, filter the real-time weighing data to obtain filtered data;

[0075] S203, load feature extraction is performed on the filtered data through a preset feature extraction model to construct a load state vector.

[0076] In this embodiment, the truck frame will produce corresponding deformation or stress change when bearing load. Through the weighing sensor array arranged at the key load-bearing parts of the truck, which include but are not limited to the axle and the suspension system, the real-time mechanical state change is sensed. The vehicle-mounted real-time weighing device contains multiple high-precision sensor units, each of which continuously collects real-time pressure or strain signals at its location to obtain real-time weighing data of the truck. Through data collection covering the main load-bearing structure of the vehicle, the load distribution of each part of the vehicle can be comprehensively obtained, effectively avoiding measurement deviation caused by uneven load distribution, improving the quality and reliability of the basic weight data, and providing accurate data support for the carbon emission monitoring and analysis process.

[0077] During vehicle driving, the output signal of the weighing sensor is the superposition of useful load signal and various interference signals. The real-time state of the new energy truck is analyzed, the real-time weighing data is filtered, and the real-time state parameters of the truck include but are not limited to longitudinal / lateral acceleration, vehicle speed, and steering angle. Based on the state parameters, the real-time state of the vehicle is judged in real time, and the type and parameters of the filtering algorithm are dynamically selected. For example, a low-cut filter with low cut-off frequency can be used to fully suppress high-frequency vibration during smooth driving; when driving on a bumpy road or accelerating or braking, the filtering parameters are adaptively adjusted to balance between noise suppression and true load change trend retention. The data is filtered according to the filtering parameters to obtain filtered data. By introducing real-time state information of the vehicle for adaptive filtering, various dynamic disturbances introduced by vehicle vibration and external road excitation can be distinguished and effectively filtered out, while the true load change characteristics are retained to the maximum extent, providing accurate data support for feature extraction and carbon emission analysis process.

[0078] Specifically, load feature extraction is performed on the filtered data through a preset feature extraction model. The feature extraction model is specific in the time domain, calculates the load mean value in the sliding time window to represent the static load level, calculates the variance or standard deviation to represent the load fluctuation intensity, and obtains the change trend slope of the load through linear fitting. In the frequency domain, the signal is subjected to fast Fourier transform, the frequency spectrum structure is analyzed, and the amplitude of the vibration frequency component and other frequency characteristics are extracted. The extracted feature values are combined in order to obtain a load state vector. By constructing the load state vector and combining multi-dimensional feature information such as load size, stability, change direction, and vibration characteristics, the feature diversity of the carbon emission monitoring and analysis process is improved, and the accuracy of the carbon emission monitoring process is improved.

[0079] Further, in combination with the load state vector and the pre-acquired real-time road state data of the truck, the change of the dynamic load of the truck with the road slope and curvature is analyzed, and a transient load state model of the truck is simulated, including:

[0080] S301, in combination with the load state vector and the pre-acquired real-time road state data of the truck, the change of the dynamic load of the truck with the road slope and curvature is analyzed, and the road slope data and the road curvature data are obtained;

[0081] S302, based on the road slope data, a longitudinal load transfer mechanism is configured to calculate the load change rate of each axle of the truck, and the longitudinal load transfer mechanism is used to calculate the load increment of the rear axle through a trigonometric function projection for uphill working conditions, and to calculate the load increment of the front axle for downhill working conditions;

[0082] S303, based on the road curvature data, the inclination of the center height of the truck is analyzed, and the inside and outside load difference of the truck wheel is calculated;

[0083] S304, in combination with the load change rate and the inside and outside load difference, the load state change is simulated, and a transient load state model of the truck is obtained.

[0084] In this embodiment, the longitudinal slope and the transverse curvature of the road change the distribution of the gravity component of the vehicle and the force balance state, resulting in dynamic transfer of the load; in combination with the load state vector and the pre-acquired real-time road state data of the truck, the road state data of the current position of the vehicle is obtained through the vehicle-mounted combined navigation system, including the longitudinal slope angle and the transverse curvature radius, the total weight of the vehicle at the current time and the distribution characteristics are extracted from the load state vector, and the change of the gravity acceleration in the longitudinal and transverse directions of the vehicle under the current road state condition is analyzed; the influence degree of the slope angle on the front and rear axle loads of the vehicle is determined through kinematic analysis, the influence range of the curvature radius on the left and right side wheel loads of the vehicle through centrifugal force is analyzed, and the road slope data and the road curvature data are obtained. By correlating the road environment data and the vehicle load state, accurate input parameters and analysis directions are provided for establishing a transient load state model, and the accuracy of dynamic load calculation is improved.

[0085] Specifically, when the vehicle is on a slope, the gravity can be decomposed into two components, one perpendicular to the road surface and the other parallel to the road surface, where the component parallel to the road surface will generate a moment of force that makes the vehicle rotate around the center of the wheel ground, causing the front and rear axle loads to change. Based on the road slope data, a mechanical model of the vehicle is constructed, which regards the truck as a rigid mass block, and determines key parameters such as wheelbase, center of mass height, etc. according to the truck data; for uphill working conditions, the component of gravity along the slope direction is calculated by a trigonometric function, which acts on the vehicle's center of mass and forms a moment of force on the front wheel ground, causing the rear axle load to increase and the front axle load to decrease accordingly; for downhill working conditions, the same mechanical principle is used to calculate the increase of front axle load and the decrease of rear axle load simultaneously, and through this longitudinal load transfer mechanism, the change rate of each axle load of the truck relative to the horizontal road surface state is calculated. Through the mechanical model based on vehicle parameters and the projection calculation of trigonometric function, the axle load change caused by climbing or descending can be calculated in real time, providing accurate longitudinal force state data for the energy consumption analysis process, and improving the accuracy of vehicle energy consumption and carbon emission monitoring under slope working conditions.

[0086] When the vehicle turns, a centrifugal force pointing to the outside of the curve is generated, which acts on the center of mass of the vehicle and forms a moment of force with gravity, causing the vehicle to tilt to the outside, resulting in an increase in load on the outside wheel and a decrease in load on the inside wheel. Based on the road curvature data and the current vehicle speed, the centrifugal acceleration and centrifugal force acting on the vehicle are calculated, combined with the wheelbase, center of mass height and suspension characteristics of the vehicle, the roll moment generated by the centrifugal force is analyzed through the moment balance equation, the difference in vertical load change caused by the moment on the left and right wheels is calculated, and the inside and outside load difference is obtained. By calculating the load difference of the inside and outside wheels, the lateral load transfer of the vehicle during turning can be accurately analyzed, and the additional energy consumption caused by the increase of rolling resistance due to the increase of load on the outside tire during curve driving in the carbon emission process is calculated, improving the comprehensiveness and accuracy of the carbon emission monitoring process.

[0087] Specifically, the instantaneous load state of the vehicle on the road is the vector superposition of the static basic load and the dynamic load transfer amount caused by the road slope and curvature, combined with the load change rate and the inside and outside load difference, the load state change is simulated, the longitudinal calculation of each axle load change and the lateral calculation of the left and right wheel load change are spatially synthesized and superimposed according to the geometric layout and mechanical relationship of the vehicle, and the instantaneous load state model of the truck is obtained. By constructing the instantaneous load state model, the real force state of the vehicle under complex and comprehensive road conditions can be reflected, the detailed distribution and dynamic change of the load on each load point of the vehicle can be reflected, accurate data support can be provided for calculating the energy consumption and carbon emission of the vehicle, and the accuracy and reliability of the carbon emission monitoring result can be improved.

[0088] Further, in combination with the load change rate and the inner-outer load difference, simulate the load state change to obtain a transient load state model of the truck, including:

[0089] S401, identify the road excitation main frequency band based on the load state vector;

[0090] S402, analyze the coupling vibration of the load change rate and the inner-outer load difference according to the road excitation main frequency band, simulate the load state change, and obtain a dynamic load response value;

[0091] S403, superimpose the envelope prediction of the corresponding dynamic load response on the basic load of the truck according to the dynamic load response value, construct a load fluctuation boundary, and obtain a transient load state model of the truck.

[0092] In this embodiment, when the vehicle travels on different levels of road surface, the road unevenness will transmit vibration excitation of a specific frequency range to the vehicle body through the tire suspension system. The vibration excitation is reflected in the dynamic component of the load state vector and is concentrated in the corresponding characteristic frequency band. Based on the load state vector, the time domain signal sequence capable of representing the load high frequency fluctuation is separated out, the dynamic load signal is converted to the frequency domain by fast Fourier transform, the frequency spectrum of the signal is obtained, the characteristic analysis of the frequency spectrum is performed, and multiple local extreme points with energy significantly higher than the background noise are identified. The frequency corresponding to the extreme point is taken as the main frequency band of the road excitation. By extracting the main frequency band, the complex road excitation is converted into multiple characteristic frequency parameters, which provides accurate frequency input for analyzing the dynamic response characteristics of the load, can perform frequency domain analysis on the main vibration mode caused by the current road, and improves the accuracy and analysis efficiency of the analysis result of the transient state of the truck.

[0093] When the vehicle is subjected to road excitation of a specific frequency, the load will produce forced vibration of the corresponding frequency. The amplitude of the vibration is related to the strength of the road excitation and the dynamic characteristics of the system at the frequency. According to the road excitation main frequency band, the damping characteristics and mass distribution of the vehicle suspension system are combined to analyze the resonance or amplification effect of the vehicle load system in the excitation main frequency band under the joint action of the load excitation source; through coupling vibration analysis, the maximum dynamic deviation of the load relative to the static value under the composite excitation is calculated to obtain a dynamic load response value. By calculating the dynamic load response value, the dynamic fluctuation amplitude of the load caused by the road excitation is quantified, and the vibration characteristics of the longitudinal and lateral load changes are analyzed. The calculated dynamic load response value can fully reflect the dynamic behavior of the vehicle under complex excitation, and improve the accuracy of the analysis result of the transient state of the truck.

[0094] Specifically, the real load of the vehicle in driving changes dynamically, fluctuates around the reference value within a certain range, and the upper and lower boundaries of the fluctuation range constitute the instantaneous envelope of the load; according to the dynamic load response value, an offset related to the dynamic load response value is respectively added to the basic load of the truck through the envelope prediction algorithm, and the upper and lower envelope lines of the load fluctuation are obtained, and the load fluctuation boundary is constructed, which will be adjusted in real time according to the road excitation frequency and the vehicle dynamic response, and the instantaneous load state model of the truck is constructed in combination with the load fluctuation boundary. By constructing the load fluctuation boundary, the instantaneous load state model can reflect the instantaneous change range of the load of the vehicle in the vibration environment, provide accurate data support for energy consumption calculation and carbon emission monitoring, and effectively avoid errors caused by ignoring the dynamic fluctuation of the load when analyzing the peak power demand or the energy consumption under extreme working conditions, thereby improving the accuracy and reliability of the carbon emission monitoring process.

[0095] Further, according to the load state vector and the real-time energy consumption data of the truck, the mapping relationship between the load and the energy consumption under the corresponding working condition is analyzed through a preset energy consumption analysis model, and the energy consumption sensitivity is calculated, including:

[0096] S501, according to the load state vector, the mapping relationship between the load and the energy consumption under different working conditions is analyzed through a preset energy consumption analysis model;

[0097] S502, based on the real-time energy consumption data, matching is performed in the corresponding mapping relationship to obtain a target mapping relationship;

[0098] S503, according to the target mapping relationship, the ratio of the load change rate to the energy consumption change rate at the corresponding time is calculated to obtain a first sensitivity, and the energy consumption change amount corresponding to the unit load change within the corresponding working condition time is calculated to obtain a second sensitivity;

[0099] S504, according to the first sensitivity, the second sensitivity is corrected in real time, and the energy consumption sensitivity is calculated.

[0100] In this embodiment, according to the dynamic characteristics in the load state vector, combined with the running parameters of the truck, the mapping relationship between load and energy consumption under different working conditions is analyzed through a preset energy consumption analysis model; the energy consumption analysis model includes but is not limited to a hybrid structure model based on fuzzy reasoning and sub-model weighted fusion pre-trained by a large number of historical load state vectors and running parameters, and defines four basic working conditions, including smooth running with low acceleration and low load fluctuation, acceleration climbing with positive acceleration and positive slope, deceleration braking with negative acceleration and non-road bumping, and road bumping with high-frequency load vibration; the input of the model is the load state vector and the auxiliary state parameters obtained from the CAN bus, including but not limited to vehicle speed, acceleration, navigation slope. A working condition recognition fuzzy inference machine is run inside the model, which calculates the membership degree of the current state to the above four basic working conditions according to the input characteristics. Each basic working condition corresponds to a preset energy consumption analysis sub-model, which can be a lightweight regression model trained based on historical data of the basic working condition. The final output of the model is the mapping relationship from load to energy consumption obtained by weighted average of the outputs of the four energy consumption analysis sub-models, where the weight is the corresponding membership degree; the model can adjust the mapping relationship according to the actual running state of the vehicle by constructing the mapping relationship, providing a mapping relationship that adapts to real-time working conditions for precise calculation of energy consumption sensitivity, and improving the accuracy of energy consumption analysis.

[0101] Specifically, the real-time energy consumption data of the truck is continuously collected, including but not limited to the current, voltage and power information of the power battery. Based on the real-time energy consumption data, the measured energy consumption value near the current time is matched with the predicted energy consumption value of the mapping relationship in that time period to calculate the matching degree. The matching degree is calculated using the residual sum of squares, and the mapping relationship with the highest matching degree is selected as the target mapping relationship. By selecting the target mapping relationship, the real energy consumption characteristics of the vehicle in the current specific environment can be obtained, effectively compensating for the inherent deviation of a single mapping and the interference of external uncertain factors, and improving the precision and accuracy of the mapping process.

[0102] Specifically, according to the target mapping relationship, the target mapping relationship is differentiated to calculate the ratio between the instantaneous change rate of the load and the instantaneous change rate of the energy consumption at the current load point, obtaining the first sensitivity, which reflects the instantaneous response characteristics of the system to the load micro-change. In the time segment representing the characteristics of the current basic working condition, the quotient of the total change amount of the load and the total change amount of the energy consumption in that time period is calculated to obtain the second sensitivity, which reflects the average energy consumption effect caused by unit load change under this working condition. By calculating the first sensitivity, the dynamic and instantaneous characteristics of the load and energy consumption correlation can be quickly analyzed to analyze the influence of load fluctuation on instantaneous power; by calculating the second sensitivity, the overall energy consumption of the load change in the corresponding working condition period can be reflected; by double-dimensional quantification, the comprehensiveness of energy consumption sensitivity analysis can be improved, and the accuracy of the energy consumption analysis process can be improved.

[0103] Specifically, the second sensitivity is corrected in real time according to the first sensitivity. When the first sensitivity is significantly higher or lower than the average change rate of the second sensitivity, it indicates that the transition process is in a dynamic change, the weight of the first sensitivity is increased, the weight is distributed according to the average proportion of the first sensitivity and the second sensitivity, and the synthesized sensitivity is more biased to the instantaneous response according to the weighted sum. When the first sensitivity and the second sensitivity are close, the running is relatively stable, and the energy consumption sensitivity is obtained by calculating the average of the first sensitivity and the second sensitivity. By combining the first sensitivity and the second sensitivity to correct the energy consumption sensitivity, the noise interference problem caused by only using the instantaneous sensitivity can be avoided, the problem of slow response to rapid change of working conditions caused by only using the average sensitivity can be avoided, the accuracy of the energy consumption sensitivity can be improved, and the accuracy and adaptability of the carbon emission monitoring process under various dynamic operating conditions can be improved.

[0104] Further, according to the load state vector, the mapping relationship between the load and the energy consumption under different working conditions is analyzed through a preset energy consumption analysis model, including:

[0105] S601, according to the load state vector, the membership degree corresponding to each basic working condition is calculated, and the basic working condition includes stable running, acceleration climbing, deceleration braking and road bumping;

[0106] S602, an energy consumption analysis sub-model is set for each basic working condition to analyze the energy consumption under the corresponding working condition, and the energy consumption analysis sub-model is weighted and fused according to the membership degree to obtain the energy consumption analysis model;

[0107] S603, the transition working condition in the basic working condition switching process of the truck running process is identified, the energy consumption change of the switching speed and the transition process is analyzed, and the transition compensation factor is calculated;

[0108] S604, the mapping relationship between the load and the energy consumption under different working conditions is obtained by combining the transition compensation factor and the energy consumption analysis model.

[0109] As Figure 2As shown, according to the load state vector, the membership degree corresponding to each basic working condition is calculated, the composite performance of multiple basic working conditions in the running state of the vehicle is described, and the basic working conditions include smooth running, accelerating climbing, decelerating braking and road bumping; the feature parameters of each basic working condition are extracted from the load state vector and the vehicle bus data, including but not limited to load change rate, longitudinal acceleration, vehicle inclination estimation value, road vibration characteristic frequency, etc., the Pearson similarity between the load state vector and the corresponding working condition feature parameter vector is calculated, and the degree of belonging to the working condition is calculated according to the Pearson similarity of each basic working condition, and the membership degree of each basic working condition is obtained. By calculating the membership degree of the basic working condition, the composite running state of the vehicle can be accurately reflected, the model switching mutation problem caused by working condition division can be avoided, and the accuracy of working condition recognition and the continuity of truck running can be improved.

[0110] Specifically, for each basic working condition, an energy consumption analysis sub-model is set based on the physical characteristics of the corresponding working condition, including but not limited to the main resistance to gravitational potential energy change in climbing working condition, the main correlation with kinetic energy increment in accelerating working condition, the energy recovery efficiency involved in braking working condition, and the sustained power fluctuation concerned in bumping working condition, the energy consumption in the corresponding working condition is analyzed, the energy consumption analysis sub-model is weighted and fused according to the corresponding membership degree, and an energy consumption analysis model is obtained; the energy consumption analysis sub-model can be selected in different forms according to its physical characteristics, the smooth running sub-model mainly resists rolling resistance and air resistance, which can be modeled as a quadratic polynomial about load mass and vehicle speed; the accelerating climbing sub-model mainly overcomes inertia force and gravity, and the core is the physical formula about transmission efficiency; the deceleration braking sub-model involves braking energy recovery, which can be modeled as a function about load mass, deceleration and vehicle speed; the road bumping sub-model reflects the additional power loss caused by sustained vibration, which can be modeled as a linear or nonlinear relationship about load high-frequency vibration energy and vehicle speed; the parameters in the sub-model are obtained by training after separating the working condition fragments from the massive historical data, including but not limited to polynomial coefficients, efficiency factors. By constructing the energy consumption analysis model, dynamic and accurate matching with the real-time running state of the vehicle can be realized, weighted fusion according to the membership degree can be realized, smooth transition between different basic working conditions can be realized, the model output mutation problem caused by working condition judgment change can be effectively avoided, and the continuity and stability of the load and energy consumption mapping relationship analysis can be improved.

[0111] Specifically, the transition working condition in which the truck is in the basic working condition switching process is identified. In the working condition switching process, the vehicle power system has response lag, control strategy adjustment and other dynamic processes. The energy consumption change law in the transition working condition is different from that in any stable working condition. The rate of change of the membership degree vector with time is monitored, the stage in which the membership degree dominant term changes rapidly is identified as the transition working condition, the membership degree change gradient is calculated, the working condition switching speed is analyzed, the rate of change of the transient energy consumption characteristic in the working condition switching process is analyzed, the membership degree change gradient and the rate of change of the transient energy consumption characteristic are averaged, and the transition compensation factor is calculated. Through the calculation of the transition compensation factor, the additional energy consumption characteristics in the transient process of working condition switching can be quantified, and the real relationship between load and energy consumption in the acceleration, deceleration, road condition change and other dynamic processes can be accurately reflected, thereby improving the response accuracy and tracking ability of the model in the dynamic operation of the vehicle and improving the accuracy of the load and energy consumption analysis process.

[0112] Specifically, the transition compensation factor and the energy consumption analysis model are combined, the calculated transition compensation factor is used as a dynamic correction term to correct the energy consumption analysis model, and the mapping relationship between load and energy consumption under different working conditions is obtained by using the transition compensation factor to weight and correct the output of the energy consumption analysis model according to the transition compensation factor when the transition working condition is identified. Through real-time correction of the energy consumption analysis model, the mapping relationship obtained can include various typical stable running working conditions and effectively reflect the energy consumption characteristics of the transient process of working condition switching. Based on the mapping relationship, the energy consumption prediction and sensitivity correlation process can be more accurately analyzed, and the accuracy and reliability of the carbon emission monitoring result in the dynamic driving environment can be improved.

[0113] Further, the instantaneous load state model and the energy consumption sensitivity are analyzed in correlation, the carbon emission intensity per unit mass displacement is analyzed, and the dynamic carbon emission factor is calculated, including:

[0114] S701, the instantaneous load change of the truck is calculated through the instantaneous load state model, and a load sequence is obtained.

[0115] S702, the coupling between load fluctuation and energy consumption sensitivity change is analyzed through a preset correlation analysis model, the carbon emission intensity per unit mass displacement is calculated, and a dynamic carbon emission factor is obtained.

[0116] In this embodiment, the instantaneous load change of the truck is calculated by the instantaneous load state model, which receives the latest sensor and state data at each sampling time and outputs an instantaneous load value representing the total load level of the vehicle at that time. This value is usually the equivalent total load after model calculation, which integrates static load, quasi-static load transfer caused by road slope and curvature, and dynamic fluctuations caused by road excitation. It reflects the virtual static load corresponding to the driving demand of the vehicle on a flat road under the same driving demand as in the actual complex road conditions. The instantaneous load values are arranged in chronological order to obtain the load sequence. By calculating the load sequence, the correlation between the load fluctuation pattern and the energy consumption response can be analyzed, providing accurate data support for calculating the carbon emission intensity of unit transportation work.

[0117] Specifically, the load sequence and the real-time calculated energy consumption sensitivity are input into the preset correlation analysis model. The correlation analysis model specifically analyzes the coupling relationship between the fluctuation characteristics of the load sequence and the change of the energy consumption sensitivity in time, calculates the corresponding phase difference and correlation strength, and based on the law of conservation of energy and vehicle driving dynamics, comprehensively calculates the load fluctuation, the corresponding energy consumption sensitivity, and the vehicle displacement, calculates the energy consumed or saved for overcoming the dynamic load change and completing the unit mass unit displacement, multiplies the calculated energy value by the average carbon emission factor of the power grid to obtain the carbon emission intensity per unit mass displacement at that time, i.e. the dynamic carbon emission factor. By calculating the dynamic carbon emission factor, the transportation efficiency of the vehicle can be reflected in real time. When the vehicle is heavily loaded and uphill or the road conditions are poor, the dynamic carbon emission factor value increases, reflecting the decrease in carbon emission efficiency; when the vehicle is lightly loaded and smoothly driven, the dynamic carbon emission factor value decreases; the total energy consumption and transportation efficiency are considered in the calculation of the carbon emission accounting result, improving the accuracy of the calculated carbon emission.

[0118] As shown in Figure 3 , according to the real-time weighing data of each truck, the corresponding first carbon emission is calculated by the instantaneous load state model, the first carbon emission is corrected according to the dynamic carbon emission factor to obtain the second carbon emission, and the second carbon emission of all trucks is combined for feedback optimization to obtain the total carbon emission, including:

[0119] S801, according to the real-time weighing data of each truck, the corresponding first carbon emission is calculated by the instantaneous load state model;

[0120] S802, analyze the load fluctuation characteristics, divide the first carbon emission into multiple stages, and correct each stage of the first carbon emission according to the dynamic carbon emission factor to obtain the second carbon emission;

[0121] S803, combine the second carbon emissions of all trucks for feedback optimization to obtain the total carbon emission.

[0122] In this embodiment, according to the real-time weighing data of each truck, the corresponding first carbon emission is calculated by the instantaneous load state model; the equivalent load data output by the instantaneous load state model is continuously obtained, the equivalent load data fuses the static load and the dynamic load transfer effect, the equivalent load value at each sampling time is multiplied by the driving displacement of the vehicle at that time, the transport work completed in that period is obtained, the transport work is combined with the preset reference carbon emission factor, the reference carbon emission factor is set according to the average emission level of the power grid, and the theoretical carbon emission of the period is calculated by the carbon accounting formula. The calculation results of all periods are accumulated to obtain the first carbon emission based on fixed efficiency and without considering the influence of load fluctuation on energy efficiency. By calculating the first carbon emission, a carbon emission reference value corresponding to the real dynamic load of the vehicle under the assumption of standard emission efficiency is provided, which provides accurate data support for the efficiency correction process and improves the accuracy of the carbon emission correction result.

[0123] Specifically, the load fluctuation characteristics are analyzed, the first carbon emission is divided into multiple stages, and each stage of the first carbon emission is corrected according to a dynamic carbon emission factor to obtain a second carbon emission. By performing differentiated correction for stages with different load fluctuation characteristics, the subtle influence of load dynamic change on carbon emission efficiency can be obtained, and in complex working conditions with severe load fluctuations, the deviation problem caused by single correction can be effectively avoided, and the accuracy of carbon emission accounting in transient working conditions is improved.

[0124] Specifically, feedback optimization is performed in combination with the second carbon emissions of all trucks to obtain a total carbon emission; the second carbon emission data of all trucks in the fleet is aggregated, the statistical characteristics of carbon emissions under different vehicle types, different routes and different working conditions are analyzed, and patterns deviating from theoretical prediction are identified; a feedback optimization algorithm based on group data is established, the algorithm performs correlation analysis on the total carbon emission data of the fleet and the operating parameters of each vehicle, dynamically adjusts the key parameters in the instantaneous load state model and the energy consumption analysis model, including but not limited to the load transfer calculation coefficient, feeds back the optimized model parameters to the carbon emission calculation of each vehicle for optimization, and obtains the optimized total carbon emission. By using the overall data of the fleet, the model parameters can be continuously corrected, the system error can be eliminated, the calculation accuracy in operation can be improved, the reliability of single carbon emission accounting can be enhanced, and the adaptability and generalization of the entire monitoring process to different fleet compositions and different operating environments can be improved.

[0125] Further, the load fluctuation characteristics are analyzed, the first carbon emission is divided into multiple stages, and each stage of the first carbon emission is corrected according to a dynamic carbon emission factor to obtain a second carbon emission, including:

[0126] S901, analyze the load fluctuation characteristics, and divide the first carbon emission into stages, including stable load, gradual load, sudden load, and oscillation load;

[0127] S902, for the stable load, the steady-state component in the dynamic carbon emission factor is used for correction; for the gradual load, the load change rate and the dynamic carbon emission factor are combined for correction; for the sudden load, the dynamic carbon emission factor at the corresponding time before and after the mutation point is used for correction; for the oscillation load, the corresponding dynamic carbon emission factor is used for correction for the upper envelope and the lower envelope of the oscillation, respectively;

[0128] S903, combine the corrected carbon emission of each stage to obtain the second carbon emission.

[0129] In this embodiment, the load fluctuation characteristics are analyzed, and the first carbon emission is divided into stages, including stable load, gradual load, sudden load, and oscillation load. The multi-dimensional feature analysis is performed on the sequence of the load state vector changing with time, the sliding variance of the load value is calculated, the stage with continuously decreasing variance is identified as the stable load stage, the first derivative of the load change trend is analyzed to identify the stage with unchanged change rate as the gradual load stage, the second derivative mutation point of the load sequence is detected to identify the stage with change rate mutation as the sudden load stage, and the frequency domain analysis and envelope detection are used to identify the stage with periodic fluctuation characteristics as the oscillation load stage. Through the stage division, different modes of load fluctuation can be accurately identified, corresponding correction methods are taken for the energy efficiency characteristics of each mode, the error problem caused by using a single correction strategy in processing complex dynamic load is avoided, and the precision and accuracy of the carbon emission correction process are improved.

[0130] Specifically, for stable load, the steady-state component in the dynamic carbon emission factor is corrected; for gradual load, the load change rate and the dynamic carbon emission factor are combined for correction; for sudden load, the dynamic carbon emission factor at the corresponding time before and after the mutation point is used for correction; for oscillating load, the corresponding dynamic carbon emission factor is used for correction for the upper envelope and lower envelope of the oscillation; for the stable load stage, the steady-state component in the dynamic carbon emission factor is obtained by filtering, and the first carbon emission is proportionally corrected; for the gradual load stage, on the basis of the dynamic carbon emission factor, the load change rate is used as an adjustment parameter, and a linear function is used to establish a mapping relationship between the change rate and the correction intensity to correct the carbon emission; for the sudden load stage, the dynamic carbon emission factor at the time before and after the mutation point is used to independently correct the carbon emission of the two sub-stages before and after the mutation point; for the oscillating load stage, the upper envelope and lower envelope of the oscillation process are identified, and a higher dynamic carbon emission factor is used to correct the upper envelope, and a lower dynamic carbon emission factor is used to correct the lower envelope, so as to cover the efficiency fluctuation range in the oscillation process; the corrected carbon emission in each stage is combined to obtain the second carbon emission. By using the corresponding correction strategy for different fluctuation modes, the influence of the dynamic change of the load on the carbon emission efficiency can be accurately captured, the accuracy and reliability of the carbon emission accounting result can be improved in complex working conditions with sharp load change or continuous oscillation, the limitation of a single correction coefficient is avoided, the accuracy of the second carbon emission is improved, and the emission characteristics of the vehicle in different operating states can be accurately reflected, providing reliable full-trip carbon emission data support for green freight management.

[0131] As shown in Figure 4 The new energy truck carbon emission monitoring system based on real-time vehicle weighing is used to realize the new energy truck carbon emission monitoring method based on real-time vehicle weighing, which comprises:

[0132] The load analysis module obtains real-time weighing data of the truck through the real-time vehicle weighing device configured on the new energy truck, extracts load characteristics from the real-time weighing data, and constructs a load state vector.

[0133] The state analysis module analyzes the change of the dynamic load of the truck with the road slope and curvature in combination with the load state vector and the pre-acquired real-time road state data of the truck, and simulates to obtain an instantaneous load state model of the truck.

[0134] The load energy consumption mapping module analyzes the mapping relationship between the load and the energy consumption under the corresponding working condition through a preset energy consumption analysis model according to the load state vector and the real-time energy consumption data of the truck, and calculates the energy consumption sensitivity.

[0135] The load energy consumption correlation module correlates the instantaneous load state model and the energy consumption sensitivity, analyzes the carbon emission intensity generated by unit mass displacement, and calculates a dynamic carbon emission factor;

[0136] The carbon emission monitoring module calculates a corresponding first carbon emission amount according to real-time weighing data of each truck through the instantaneous load state model, corrects the first carbon emission amount according to the dynamic carbon emission factor to obtain a second carbon emission amount, feeds back and optimizes the second carbon emission amount of all trucks to obtain a total carbon emission amount.

[0137] In this embodiment, the load analysis module constructs a feature vector capable of comprehensively representing the load state of the vehicle through filtering and noise reduction and feature extraction, provides high-quality and reliable load information input for the system, ensures the accuracy of the calculation, and effectively avoids the influence of driving vibration and other interference factors on load perception. The state analysis module analyzes the dynamic load distribution of the vehicle under the slope and curve working conditions based on the load state vector and real-time road data, upgrades the traditional static weight monitoring to dynamic force analysis, can accurately quantify the load transfer effect caused by the change of road geometry, and provides a load input more consistent with the actual running state of the vehicle for the energy consumption calculation process.

[0138] Specifically, the load energy consumption mapping module identifies the vehicle operating conditions, and establishes a quantitative mapping relationship between the load change and the energy consumption change under the corresponding working conditions, calculates the energy consumption sensitivity parameter, realizes the dynamic self-adaptation of the load and energy consumption relationship, and can accurately analyze the influence degree of load variation on energy consumption under different driving situations. The load energy consumption correlation module couples the instantaneous load state and the energy consumption sensitivity, calculates the dynamic carbon emission factor reflecting the carbon emission intensity of unit transportation work, and constructs the dynamic carbon emission factor based on variable transportation efficiency, which can reflect the carbon emission intensity under different efficiency scenarios such as heavy load uphill and light load flat road. The carbon emission monitoring module performs initial carbon accounting, segmented correction based on load fluctuation characteristics, and feedback optimization of the overall result using fleet data. Through the multi-stage differential correction strategy, the correction accuracy can be improved, and the accuracy of the carbon emission result can be improved.

[0139] Embodiment 2:

[0140] This embodiment describes the overall process of the technical solution in combination with a specific application scenario. A new electric van truck with a total mass of 10 tons, of which the self-weight is 6 tons, carries 4 tons of goods, starts from warehouse A, passes through a mountainous highway containing long uphill, downhill and curve, and goes to distribution center B. The vehicle is equipped with a vehicle-mounted real-time weighing sensor, a high-precision positioning and inertial measurement unit, and is connected with the vehicle CAN bus.

[0141] After the vehicle starts, the system begins to run, and the load analysis module acquires the original weight signal through the weighing sensor at a frequency of 10 times per second. At the initial static state, the signal shows 9.5 tons, but under the condition of vehicle starting and slight bumps on the road, the original weight signal jumps between 9.2 tons and 9.8 tons. According to the acceleration state of the vehicle starting, the module starts a low-pass filter to filter out high-frequency noise caused by vibration, and obtains a smoothed weight data sequence, which stabilizes around 9.5 tons. The feature extraction model analyzes this smoothed data stream, calculates the mean value in a time window as the static load feature, and calculates the standard deviation in the time window as the load fluctuation feature, and combines these two features with the real-time change trend slope of the load to construct a load state vector, for example, the vector at a certain time is [static load: 9.5 tons, fluctuation intensity: 0.1 tons, trend: 0 tons / sec].

[0142] When the vehicle enters the mountain highway and starts a 2-kilometer uphill section with a slope of 5%, the state analysis module works simultaneously, and the slope data of the current road is obtained from the high-precision map service interface as 5%, and there is no significant bend. Combine this slope data with the current load state vector, based on known parameters such as the wheelbase and center of mass height of the vehicle, the longitudinal load transfer mechanism starts. According to the mechanical model, due to uphill, the rear axle load of the vehicle will increase, and through the calculation of the trigonometric function, the component force of gravity along the slope is about the total weight multiplied by the sine of the slope angle. This component force forms a moment on the front axle contact point, causing the rear axle load to increase by several kilograms, and the front axle load to decrease accordingly. The instantaneous load state model output is no longer just the total weight of 9.5 tons, but an equivalent dynamic load value, for example, due to the need to overcome additional gravity component when climbing, the system equivalent load value is calculated as 11.2 tons, which more truly reflects the actual resistance that the vehicle driving system needs to overcome.

[0143] At the same time, the load energy consumption mapping module is working continuously, according to the current load state vector (showing that the load is stable but in climbing) and the real-time motor power data read from the CAN bus, it judges that the vehicle is currently in a mixed working condition of accelerating climbing and smooth running. The built-in fuzzy logic of the module calculates the membership degree of the accelerating climbing working condition as 0.7, and the membership degree of the smooth running as 0.3. Correspondingly, the climbing working condition sub-model (mainly calculating the energy consumption of overcoming gravity) is given a weight of 0.7, and the smooth running sub-model (mainly calculating the energy consumption of rolling and air resistance) is given a weight of 0.3, and the two are fused to output the current load and energy consumption mapping relationship. Based on this mapping relationship, the module calculates that the current energy consumption sensitivity is high, for example, 1.5, which means that in the current climbing state, every additional 100 kilograms of load will consume about 1.5 kilowatt-hours of electricity per hour more.

[0144] When the vehicle enters a curve with a radius of 200 meters, the state analysis module initiates lateral load transfer calculation based on real-time road curvature data. Combined with vehicle speed, it calculates that the centrifugal force causes the inner wheels to have reduced load and the outer wheels to have increased load, resulting in an inner-outer load difference. This lateral load information is integrated into the instantaneous load state model, enabling the model to more accurately reflect the potential impact of tire force changes on rolling resistance.

[0145] The load energy consumption correlation module begins core calculation, receiving dynamic load sequences from the instantaneous load state model, such as starting from an equivalent load value of 11.2 tons when climbing uphill, and energy consumption sensitivity sequences from the load energy consumption mapping module, such as fluctuating around 1.5 when climbing. Through correlation analysis model, analysis finds that the current load is high and the sensitivity is high, meaning that the transportation efficiency is low. The model calculates that in this state, the consumption of electric energy corresponding to the carbon emission intensity significantly increases for every 1 ton of goods moved 1 kilometer. Based on the current average carbon emission factor of the power grid, for example, 0.5 kilograms of carbon dioxide per kilowatt-hour, the module calculates a dynamic carbon emission factor, for example, 0.12 kilograms of carbon dioxide per ton-kilometer at this moment, which is much higher than when the vehicle is lightly loaded on flat roads, such as possibly only 0.07 kilograms of carbon dioxide per ton-kilometer.

[0146] The carbon emission monitoring module performs comprehensive calculation, first calculating a preliminary first carbon emission based on the dynamic load and driving distance output by the instantaneous load state model, using a basic carbon emission factor such as the average carbon emission factor of the power grid. The module analyzes the load fluctuation characteristics of the entire journey: dividing the smooth uphill section into a gradual load phase and the moment of entering the curve into a sudden load phase; for the gradual load phase, the module corrects and adjusts the first carbon emission by combining the higher dynamic carbon emission factor (0.12 kilograms of carbon dioxide per ton-kilometer) of this phase. For the short sudden load phase, the dynamic carbon emission factors before and after the sudden point are used for accurate correction. All the corrected carbon emissions are added up to obtain the second carbon emission of this truck for this transportation task, which is a more accurate carbon emission estimate. The system backend collects the second carbon emissions of all trucks in the fleet, and through cross-validation and model parameter feedback optimization, finally obtains the overall carbon emission of the fleet during a specific time period, providing managers with accurate carbon accounting reports down to the truck and task level.

[0147] Through the above specific examples, the entire process from real-time data collection, dynamic load modeling, energy consumption mapping analysis, dynamic carbon emission factor calculation to segmented correction and overall optimization is fully demonstrated. Based on the description of this embodiment, those skilled in the art can combine known vehicle-mounted sensor technology, map service interfaces, and data processing algorithms to implement this technical solution.

[0148] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical scheme falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A method for monitoring carbon emission of a new energy truck based on real-time on-board weighing, characterized in that, include: By using the on-board real-time weighing equipment configured on new energy trucks, real-time weighing data of the trucks is obtained, load characteristics are extracted from the real-time weighing data, and a load state vector is constructed. By combining the load state vector and the pre-acquired real-time road state data of trucks, the dynamic load of trucks is analyzed as a function of road slope and curvature, and road slope data and road curvature data are obtained. Based on the road slope data, a longitudinal load transfer mechanism is configured to calculate the load change rate of each axle of the truck. The longitudinal load transfer mechanism calculates the rear axle load increment through trigonometric function projection for uphill conditions and simultaneously calculates the front axle load increment for downhill conditions. Based on the road curvature data, analyze the tilt of the truck's center height and calculate the load difference between the inner and outer sides of the truck wheels; Based on the load state vector, the main frequency band of road excitation is identified; Based on the main frequency band of the road excitation, the coupled vibration of the load change rate and the load difference between the inner and outer sides is analyzed, the load state change is simulated, and the dynamic load response value is obtained. Based on the dynamic load response value, the envelope prediction of the corresponding dynamic load response is superimposed on the basic load of the truck to construct the load fluctuation boundary and obtain the instantaneous load state model of the truck. Based on the load state vector and the real-time energy consumption data of the truck, the mapping relationship between load and energy consumption under the corresponding working conditions is analyzed through a preset energy consumption analysis model, and the energy consumption sensitivity is calculated. A correlation analysis was performed on the instantaneous load state model and energy consumption sensitivity to analyze the carbon emission intensity generated per unit mass displacement and calculate the dynamic carbon emission factor. Based on the real-time weighing data of each truck, the corresponding first carbon emission is calculated through the instantaneous load state model. The first carbon emission is then corrected according to the dynamic carbon emission factor to obtain the second carbon emission. The second carbon emission of all trucks is then combined with feedback optimization to obtain the total carbon emission.

2. The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing according to claim 1, characterized in that, The method involves using an onboard real-time weighing device configured on the new energy truck to acquire real-time weighing data of the truck, extracting load characteristics from the real-time weighing data, and constructing a load state vector, including: Real-time weighing data of the trucks is obtained through onboard real-time weighing equipment configured on new energy trucks; Analyze the real-time status of new energy trucks, filter the real-time weighing data, and obtain the filtered data; Load features are extracted from the filtered data using a pre-defined feature extraction model to construct a load state vector.

3. The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing according to claim 1, characterized in that, Based on the load state vector and the truck's real-time energy consumption data, the mapping relationship between load and energy consumption under the corresponding operating conditions is analyzed using a preset energy consumption analysis model, and energy consumption sensitivity is calculated, including: Based on the load state vector, the mapping relationship between load and energy consumption under different operating conditions is analyzed through a preset energy consumption analysis model. Based on the real-time energy consumption data, a matching process is performed in the corresponding mapping relationship to obtain the target mapping relationship; According to the target mapping relationship, calculate the ratio of the load change rate to the energy consumption change rate at the corresponding time to obtain the first sensitivity, and calculate the energy consumption change corresponding to the unit load change within the corresponding working time to obtain the second sensitivity. The second sensitivity is corrected in real time based on the first sensitivity, and the energy consumption sensitivity is calculated.

4. The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing according to claim 3, characterized in that, The step of analyzing the mapping relationship between load and energy consumption under different operating conditions based on the load state vector and using a preset energy consumption analysis model includes: Based on the load state vector, calculate the membership degree corresponding to each basic working condition. The basic working conditions include smooth operation, acceleration and climbing, deceleration and braking, and road bumps. For each basic operating condition, an energy consumption analysis sub-model is set up to analyze the energy consumption under the corresponding operating condition. According to the membership degree, the energy consumption analysis sub-models are weighted and fused to obtain the energy consumption analysis model. Identify the transitional operating conditions during the truck's operation, where it switches between basic operating conditions, analyze the switching speed and energy consumption changes during the transition process, and calculate the transition compensation factor. By combining the aforementioned transition compensation factor and energy consumption analysis model, the mapping relationship between load and energy consumption under different operating conditions is obtained.

5. The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing according to claim 1, characterized in that, The correlation analysis between the instantaneous load state model and energy consumption sensitivity, the analysis of carbon emission intensity per unit mass displacement, and the calculation of the dynamic carbon emission factor include: The instantaneous load change of the truck is calculated using an instantaneous load state model to obtain the load sequence; By using a pre-defined correlation analysis model, the coupling between load fluctuations and changes in energy consumption sensitivity is analyzed, the carbon emission intensity generated per unit mass displacement is calculated, and the dynamic carbon emission factor is obtained.

6. The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing according to claim 1, characterized in that, The process involves calculating the first carbon emission based on the real-time weighing data of each truck using an instantaneous load state model. The first carbon emission is then corrected using the dynamic carbon emission factor to obtain the second carbon emission. Finally, the second carbon emissions from all trucks are combined for feedback optimization to obtain the total carbon emissions, including: Based on the real-time weighing data of each truck, the corresponding first carbon emission is calculated using an instantaneous load state model; By analyzing the load fluctuation characteristics, the first carbon emission is divided into multiple stages. The first carbon emission is then corrected for each stage based on the dynamic carbon emission factor to obtain the second carbon emission. By combining the second carbon emissions of all trucks for feedback optimization, the total carbon emissions are obtained.

7. The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing according to claim 6, characterized in that, The analysis of load fluctuation characteristics divides the first carbon emission into multiple stages, and corrects the first carbon emission for each stage according to the dynamic carbon emission factor to obtain the second carbon emission, including: Analyze the load fluctuation characteristics and divide the first carbon emission into stages, including stable load, gradual load, sudden load and oscillating load. For stable loads, corrections are made using the steady-state component of the dynamic carbon emission factor; for gradually changing loads, corrections are made by combining the load change rate and the dynamic carbon emission factor; for abrupt loads, corrections are made using the dynamic carbon emission factor at the corresponding time before and after the abrupt point; for oscillating loads, corrections are made using the corresponding dynamic carbon emission factor for the upper and lower envelopes of the oscillation. The second carbon emission figure is obtained by combining the revised carbon emissions from each stage.

8. A carbon emission monitoring system for new energy freight vehicles based on real-time on-board weighing, characterized in that, The method for monitoring carbon emissions of new energy freight vehicles based on real-time on-board weighing as described in any one of claims 1 to 7 includes: The load analysis module acquires real-time weighing data of the truck through the on-board real-time weighing equipment configured in the new energy truck, extracts load characteristics from the real-time weighing data, and constructs a load state vector. The state analysis module, combining the load state vector and the pre-acquired real-time road state data of the truck, analyzes the changes in the dynamic load of the truck with the road slope and curvature, and simulates the instantaneous load state model of the truck. The load energy consumption mapping module analyzes the mapping relationship between load and energy consumption under the corresponding working conditions based on the load state vector and the real-time energy consumption data of the truck, and calculates the energy consumption sensitivity through a preset energy consumption analysis model. The load energy consumption correlation module performs correlation analysis on the instantaneous load state model and energy consumption sensitivity, analyzes the carbon emission intensity generated per unit mass displacement, and calculates the dynamic carbon emission factor. The carbon emission monitoring module calculates the corresponding first carbon emission based on the real-time weighing data of each truck using an instantaneous load state model. It then corrects the first carbon emission based on the dynamic carbon emission factor to obtain the second carbon emission. Finally, it combines the second carbon emission of all trucks for feedback optimization to obtain the total carbon emission.

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