37 results about "Vehicular Emissions" patented technology

The invention relates to the technical field of traffic transportation environment engineering and carbon emission evaluation, in particular to a construction method and equipment of a plateau gradient zone vehicle CO2 emission factor calculation model and a medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. And a specific and quantitative design basis is provided for low-carbon highway design. It is revealed for the first time that the CO2 emission rate of the plateau gradient zone has a remarkable altitude segmentation effect through empirical data, and a method for constructing a calculation model in a partition mode is innovatively provided. According to the method, the defects that a traditional single model is poor in adaptability and misaccurate in prediction in a complex plateau environment are overcome, and differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) are established for different altitude intervals; the finally formed partition model provides a core theoretical tool and decision basis for plateau highway carbon emission accurate accounting, low-carbon route optimization and green traffic construction.

Methods and systems are provided for optimizing a performance of a vehicle while maintaining vehicle emissions below a threshold. In one example, a method for a controller of a vehicle comprises adjusting an operating parameter of the vehicle in response to a margin between a moving average tailpipe NOx emissions and a threshold tailpipe NOx emissions decreasing below a threshold NOx emissions margin, the moving average tailpipe NOx emissions calculated at regular intervals over a plurality of overlapping moving average windows (MAWs) of measured tailpipe NOx emissions. The threshold tailpipe NOx margin may be predicted based on expected engine-out NOx emissions of the vehicle, current NOx emissions sensitivities, and / or a predicted catalyst conversion efficiency in relation to the expected engine-out NOx emissions.

The invention discloses a vehicle emission conformity online evaluation method and device, a storage medium, equipment and an evaluation method, and the method comprises the following steps: obtaining a remote monitoring data flow of a to-be-evaluated vehicle in a preset time period, carrying out the preprocessing of the remote monitoring data flow, and generating a valid data set; identifying and segmenting a plurality of independent driving cycles from the effective data set, and screening out the independent driving cycles with cold start characteristics and engine accumulated work not smaller than a preset cycle work threshold value as effective driving cycles; dividing the data of each effective driving cycle into a plurality of preset working condition partitions according to the operating characteristics; aiming at each preset working condition subarea, adopting a corresponding calculation model, and calculating a pollutant emission result of the target pollutant; and comparing the pollutant discharge result of the target pollutant in each subarea with a corresponding preset discharge limit value for judgment. By using the scheme of the invention, low-cost, large-scale and high-frequency emission supervision is realized.

This invention discloses an online evaluation method, apparatus, storage medium, and device for vehicle emission compliance. The evaluation method includes the following steps: acquiring remote monitoring data streams of the vehicle to be evaluated within a preset time period; preprocessing the remote monitoring data streams to generate a valid dataset; identifying and segmenting multiple independent driving cycles from the valid dataset, and selecting independent driving cycles with cold start characteristics and engine cumulative power not less than a preset cycle power threshold as valid driving cycles; dividing the data of each valid driving cycle into multiple preset operating condition zones according to operating characteristics; using a corresponding calculation model for each preset operating condition zone to calculate the pollutant emission results of the target pollutant; and comparing the pollutant emission results of the target pollutant in each zone with the corresponding preset emission limits for determination. This invention achieves low-cost, large-scale, and high-frequency emission supervision.

The subject of the present invention is the use, for reducing the emissions of nitrogen oxides and at least one type of polluting agent chosen from carbon monoxide and unburned hydrocarbons during the combustion of a liquid fuel in a compression-ignition internal combustion engine, of an additive composition comprising: (i) one or more additives chosen from compounds having in their structure at least one alkyl-phenol group; and (ii) one or more cetane index-improving additives; in which the mass ratio of the quantity of additive(s) (i) to the quantity of additive(s) (ii) is in the range from 1:3 to 3:1.The present invention also relates to a method for reducing emissions of nitrogen oxides and at least one type of polluting agent chosen from carbon monoxide and unburned hydrocarbons during the combustion of a liquid fuel in a compression-ignition internal combustion engine, using this additive composition.

This invention relates to a method for calculating the actual road emissions of heavy-duty vehicles under remote monitoring. Based on the three-zone moving average window method, a three-zone moving average window model is established using a fixed duration of 300 seconds. The corresponding load ratio is calculated based on the carbon dioxide emissions of each window. The window, moving second by second, is divided into idling, low-load, and medium-high load zones according to the load ratio. Emission values ​​for the three zones are calculated, and it is verified whether the emissions in each zone exceed the limits. The beneficial effects are: This invention solves the current limitation of existing calculation methods for verifying the actual driving emission levels of vehicles after receiving emission data from networked vehicles on a remote platform. It can fully utilize networked vehicle emission data, taking into account emission data under idling and low-load conditions. It has no strict requirements on vehicle load and data continuity, does not distinguish between vehicle types, simplifies the monitoring procedure, and has strong adaptability and wide application range.

Particulate matter, such as fog, snow, rain, steam, vehicle exhaust, debris (plastic bags), etc. may cause one or more sensor types to generate false positive solid surface detections. In particular, various depth measurements may be impeded by particulate matter. Identifying false positive return(s) may comprise clustering lidar points, determining differences in range indicated by two different lidar devices having lidar points in the cluster, determining first differences that are more negative than a negative difference threshold and second differences that are more positive than a positive difference threshold, determining a first portion of lidar data in the cluster associated with the first differences and the second differences is associated with particulate matter or debris, and controlling a vehicle based at least in part on suppressing the first portion of the lidar data or indicating that the first portion of the lidar data is associated with particulate matter or debris.

This invention provides a zero-carbon regulation method and system for highways, relating to the field of energy regulation technology. The method includes: constructing a spatiotemporal neural network model based on historical traffic flow, weather, and time data to predict traffic density, environmental data, and carbon sink data for each section of the highway; constructing a photovoltaic output model based on photovoltaic information, incorporating dynamic pavement reflectivity and shading coefficient; calculating the carbon absorption of load-bearing structures based on carbon sink and environmental data; calculating vehicle carbon emissions based on vehicle emission type and traffic density, and combining the photovoltaic output model and the carbon absorption of load-bearing structures to calculate carbon intensity information; generating a preliminary collaborative scheduling strategy for load-bearing structures with the goal of minimizing carbon intensity information; and introducing carbon-sensitive parameters to generate the final collaborative scheduling strategy. This invention achieves full-process optimization of prediction-quantification-scheduling through multi-module innovation, improving data prediction accuracy and resource utilization efficiency.

The present disclosure provides "systems and methods for compression heating of air." The present disclosure provides methods and systems for compression heating of air. In one example, a method can include, during engine startup and prior to a first combustion event, deactivating cylinder exhaust valves while electrically and un-fueled rotating the engine until a threshold intake temperature is reached; and after the first combustion event, alternately activating and deactivating the exhaust valves of one or more cylinders to maintain an intake temperature above the threshold temperature. In this way, the temperature of the charge of air can be increased, resulting in improved fuel economy and reduced vehicle emissions.

A traceable calibration device of particle number (PN) analyzers for vehicle emission includes a particle generation system, used for generating different types of aerosol samples; a solenoid valve system, used for controlling various aerosol samples to quantitatively enter the particle growth system; a particle growth system, used for performing charge classification to obtain doubly-charged-particle aerosol samples, triply-charged-particle aerosol samples and monodisperse aerosol samples, and obtaining grown particle samples by controlling the growth speed of the monodisperse aerosol samples through temperature adjustment; and a particle measurement system, used for measuring electrical current and PN concentration of the grown particle samples. In the present disclosure, the problem of low calibration accuracy of a PN analyzer for vehicle emission in the related art is solved.

The invention relates to the technical field of automobile exhaust purification, in particular to an automobile exhaust purifier which comprises a heat preservation shell, a first pipeline and a second pipeline are fixedly installed in the heat preservation shell, a first spray head and a second spray head are installed outside the heat preservation shell, the first spray head communicates with the first pipeline, the second spray head communicates with the second pipeline, and the first pipeline communicates with an external urea pump. The second pipeline communicates with an external water pump, and a switching device is arranged on the heat preservation shell. Compared with an existing automobile exhaust purifier, after the first spray head for spraying urea is blocked, the standby second spray head is switched to continue to work through the switching device, and the blocked spray head is automatically cleaned through the water pump; the problem that the torque of a vehicle can only be limited to guarantee that tail gas discharged by the vehicle is qualified after an existing nozzle is blocked by urea crystallization is solved, safety accidents caused by the limited torque of the truck after the urea nozzle is crystallized in the uphill process of the truck are avoided, and the life safety of a driver is guaranteed while the tail gas treatment effect is guaranteed to reach the standard.

The embodiment of the invention provides a vehicle pollution emission supervision method and related equipment, and belongs to the technical field of vehicle monitoring. According to the method, a monitoring data time sequence of a target vehicle from a vehicle online monitoring system is collected, each element of the monitoring data time sequence comprises pollution gas emission concentration and position information, and the pollution gas emission amount of the target vehicle in unit time is determined according to the pollution gas emission concentration in the monitoring data time sequence. And then determining the number of times that the target vehicle appears in the pollution sensitive area in unit time according to the position information in the monitoring data time sequence, and determining the pollution risk of the target vehicle by considering the number of times of interaction between the target vehicle and the pollution sensitive area and the pollution gas emission. The pollution risk represents the pollution degree of the vehicle to the pollution sensitive area, and the target vehicle is supervised according to the pollution risk, so that the vehicle emission with great influence on the pollution sensitive area can be supervised, and the environmental protection effect of the pollution sensitive area is improved.

The present application relates to the field of transportation environment engineering and carbon emission assessment, and particularly relates to a method for constructing a highland gradient zone vehicle CO2 emission factor calculation model, equipment and medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. Specific and quantitative design basis is provided for low-carbon highway design. Through empirical data, it is first revealed that the CO2 emission rate of the highland gradient zone has a significant altitude segmentation effect, and a method of constructing a calculation model by zones is innovatively proposed. The method overcomes the defects of poor adaptability and inaccurate prediction of traditional single models in complex highland environments, and by establishing differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) for different altitude intervals, the final zoned model provides a core theoretical tool and decision basis for accurate accounting of highland highway carbon emissions, optimization of low-carbon routes and green transportation construction.

This invention relates to the field of coal mine safety monitoring and environmental sensing, and discloses a method and system for detecting and correcting harmful gases in underground mines based on vehicle emission monitoring. The method first acquires multi-source data on vehicle emission sources, spatial location, airflow field, and sensor observations, and performs time synchronization alignment. Next, it calculates the vehicle's flow field coupling factor to the sensor and calculates the exhaust gas transmission lag time based on the airflow field and location data. Based on emission source data, lag time, and diffusion parameters, it constructs an interference prediction model to quantitatively calculate the theoretical interference concentration generated by the vehicle. Furthermore, it calculates the dynamic confidence weight coefficient and combines the observed data with the theoretical value to calculate the corrected real environmental gas concentration. Finally, it compares the real concentration with a safety threshold and executes alarm triggering or suppression control based on the result. This invention can effectively isolate vehicle exhaust gas interference, restore the true background concentration in the roadway, reduce the false alarm rate, and improve the accuracy of coal mine monitoring and production efficiency.

The present invention relates to the field of transportation technology, and in particular to a method, device, and electronic device for detecting and tracing vehicle emissions anomalies, comprising: inputting the current operating conditions and current operating data into a dynamic threshold model to obtain a first detection result, and inputting them into an emission prediction model to obtain a second detection result; when the first detection result and the second detection result do not meet the preset conditions for determining vehicle emissions anomalies, fusing the first detection result and the second detection result to obtain a final detection result; when the final detection result is greater than or equal to a preset threshold, determining that the vehicle emissions are abnormal; and using a Cosine similarity calculation strategy based on a vehicle emissions anomaly knowledge graph library to obtain the cause and probability of the emissions anomaly. This method solves the problems of traditional vehicle emissions anomaly detection and tracing methods, such as low emission anomaly detection efficiency, high misjudgment rate, and difficulty in tracing, achieves accurate detection of emissions anomalies, completes closed-loop management from anomaly triggering to root cause tracing, and improves intelligent operation and maintenance.

1. Name of the product in this design: Teaching aid (automobile emission). 2. Purpose of this design: As an educational tool to demonstrate vehicle emissions. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the key design points: Design 1 3D view. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted. 6. Design 1 is designated as the basic design.

Systems and methods described herein relate to evaluating sensor data from a first vehicle to determine a vehicle template corresponding to a second vehicle, obtaining a set of emissions-related data from sensors of the first vehicle, recording a first subset of emissions-related data to a vehicle record corresponding to the second vehicle based on the vehicle template, and recording a second subset of emissions-related data to a geographical record based on a location of the first vehicle.

The utility model provides an automobile exhaust pipe, which relates to the technical field of automobile exhaust pipes, and comprises an exhaust pipe, one end of the exhaust pipe is fixedly communicated with a catalytic converter, and one end of the catalytic converter far away from the exhaust pipe is fixedly communicated with a tail pipe; the surface of the catalytic converter is sleeved with a protective shell, a limiting structure is arranged in the protective shell, and the top of the limiting structure is fixedly connected with an automobile bottom plate. Through the arrangement of the catalytic converter, the exhaust pipe, the tail pipe, the protective shell and the limiting structure, the catalytic converter can effectively convert harmful substances in exhausted gas, reduce pollution of vehicle emission to the environment and improve the environmental protection performance, the protective shell can protect the catalytic converter and prevent the catalytic converter from being collided or damaged in the driving process, and the service life of the exhaust pipe is prolonged. The service life of the catalytic converter is prolonged, and the problem that an existing automobile exhaust pipe is possibly physically damaged due to uneven roads, gravel collision and the like in the automobile running process is solved.

This invention relates to the field of carbon emission control technology, and particularly to a method for predicting and assessing vehicle carbon emissions in tunnel sections of highways in high-altitude mountainous areas. The method includes: conducting real-vehicle emission tests on tunnel sections and adjacent open road sections of highways in high-altitude mountainous areas to collect operational and environmental data; analyzing the correlation between tunnel length and changes in temperature and humidity within the tunnel to establish a quantitative model of tunnel length-temperature and humidity changes; constructing a carbon emission prediction model for tunnel sections using tunnel length, average altitude within the tunnel, and average slope within the tunnel as input features, and vehicle CO2 emission factors as output targets; predicting and assessing the carbon emissions of the tunnel throughout its entire life cycle based on the model, and outputting the assessment results. This invention is the first to construct a dedicated carbon emission prediction model for tunnel sections, solving the problem of low prediction accuracy in existing technologies, achieving accurate quantitative prediction of carbon emissions from tunnel sections, and providing a scientific basis for the low-carbon design of highway tunnels in high-altitude mountainous areas.

The embodiment of the application provides a kind of vehicle pollution emission supervision method and related equipment, belong to vehicle monitoring technical field.The method is by collecting the monitoring data time series of target vehicle from vehicle online monitoring system, each element of monitoring data time series includes pollution gas emission concentration and position information, according to pollution gas emission concentration in monitoring data time series, determine the pollution gas emission amount of target vehicle in unit time, then according to the position information in monitoring data time series, determine the number of times that target vehicle appears in pollution sensitive area in unit time, by considering the interaction number of target vehicle and pollution sensitive area and pollution gas emission amount, determine the pollution risk of target vehicle, the pollution risk characterizes the pollution degree of vehicle to pollution sensitive area, according to the pollution risk, the target vehicle is supervised, can be to pollution sensitive area influence big vehicle emission supervision, improve the environmental protection effect of pollution sensitive area.

The present invention proposes a method and system for predicting NOx emissions of hybrid electric vehicles based on dictionary learning, which belongs to the field of vehicle emission prediction. Emission-related parameters, an initial high-dimensional driving emission data set, and incremental test data are obtained; the initial high-dimensional driving emission data set is mapped to an initial low-dimensional embedding through t-SNE; based on the initial low-dimensional embedding, a high-dimensional dictionary and a low-dimensional dictionary are constructed through dictionary learning and iterative optimization is performed; a sparse coding matrix of the incremental test data on the high-dimensional dictionary is calculated, and an incremental low-dimensional embedding is generated by coupling with the low-dimensional dictionary; the incremental low-dimensional embedding is input into a pre-trained SuperLearner regression model to obtain an incremental NOx emission factor correction coefficient; NOx emissions are calculated based on the emission-related parameters and the incremental NOx emission factor correction coefficient. The present invention utilizes t-SNE and dictionary learning dimensionality reduction, combined with the SuperLearner regression model, to improve the prediction accuracy of NOx emissions of hybrid electric vehicles and efficiently process incremental data.

The invention relates to the technical field of carbon emission monitoring, and discloses a multi-source information carbon emission early warning method and system, and the method comprises the steps: carrying out the monitoring of carbon emission data, calculating a carbon emission index, judging whether the early warning needs to be given out or not according to the carbon emission index, transmitting an early warning signal when the early warning needs to be given out, and transmitting an analysis signal at the same time. Receiving the analysis signal, obtaining current production emission data, vehicle emission data and power supply emission data, analyzing a production emission index, a vehicle emission index and a power supply emission index, carrying out abnormity positioning according to the production emission index, the vehicle emission index and the power supply emission index, and feeding back a positioning result. Full-process efficient management from anomaly recognition to rectification is achieved, pertinence and reliability of carbon emission reduction measures are improved, a problem source is accurately positioned, response efficiency is improved, manual troubleshooting time is shortened, and a problem repairing period is shortened.

Determination and alleviation of root causes for the emission of pollutants from vehicles includes receiving emission data associated with emission of a set of pollutants by the vehicle. The emission data is received from at least one of the vehicle or a set of data sources. A machine learning (ML) model is applied to the emission data. A set of root causes is determined based on the application of the ML model to the emission data. A set of robots is controlled to execute a set of actions within the vehicle to alleviate the set of root causes associated with the emission of the set of pollutants.

The present invention provides a system and a method for determining the particulate matter mass in vehicle emissions tests, seeking to improve weighing resolution and reduce experimental uncertainties. The invention presents two embodiments, in which in the first embodiment there is a branch in the vehicle emissions collection equipment that allows to integrate the particulate matter generated during the entire test into a single filter. In the second embodiment, there are two branches in which one branch has a filter for the accumulation of the particulates produced in phases 1 and 2 of the test and the other branch has another filter for the accumulation of the particulates in phases 2 and 3.

The invention provides an emission sensing equipment precision collaborative evaluation method based on remote online OBD monitoring, and relates to the technical field of vehicle testing, and the method comprises the steps: building a multi-target optimization model with emission species and vehicle attributes as targets based on the historical detection data of vehicle multi-element emission sensing equipment, and determining an evaluated vehicle sample; a vehicle sample is monitored in real time through a multi-element emission sensing device, multi-element pollutant emission data of the sample are obtained, and the multi-element pollutant emission data are grouped according to the vehicle speed and specific power of a vehicle; and respectively calculating weighted average absolute errors, weighted relative errors, comprehensive error indexes based on environmental factor coupling and multivariable regression coefficients under different groups, and carrying out clustering analysis to obtain a precision evaluation result of the multivariate emission sensing equipment. According to the invention, by adding a collaborative evaluation method for the detection precision of the same pollutants among different test devices, the accuracy, comprehensiveness and scientificity of the whole vehicle emission detection and evaluation are comprehensively improved.

The invention relates to the technical field of vehicle emission prediction, and discloses a vehicle actual road driving CO2 emission prediction method based on travel data, which comprises the following steps: collecting vehicle emission data and travel data second by second, carrying out preprocessing and short travel segmentation on the collected data, and extracting short travel driving condition characteristics and average emission rate; key driving condition features are screened based on the maximum mutual information coefficient, normalization and reconstruction are sequentially carried out on data after feature screening, and a training set, a verification set and a test set are divided in proportion; constructing an emission prediction model based on a channel attention mechanism and a long-short term memory neural network, and training, adjusting and testing the model by using the training set, the verification set and the test set; the technical problem that the emission prediction precision cannot be considered while the application cost of a prediction model is reduced in the prior art is solved.

The invention provides a method for evaluating the emission reduction of atmospheric pollutants and carbon dioxide by transportation structure adjustment, and the method comprises the steps: obtaining the operation data of a vehicle and the load of the vehicle based on a remote monitoring platform; calculating the actual carrying capacity of the vehicle based on the vehicle load; when the vehicle load is larger than or equal to the preset ratio of the actual carrying capacity, the pollutant and carbon dioxide emission intensity of the unit transportation turnover is calculated according to the operation data and the vehicle load; on the basis of the emission intensity data, obtaining emission intensity distribution of each load interval; the vehicle emission intensity distribution and the actual carrying capacity are matched for the target transportation turnover and the weight of the transported cargo to determine a target vehicle carrying capacity combination. The method is based on a remote monitoring platform, the evaluation process adapts to different areas and cargo types, the emission intensity of vehicle atmospheric pollutants and carbon dioxide can be accurately evaluated, transport capacity distribution is optimized, the emission reduction amount of road atmospheric pollutants and carbon dioxide by transportation structure adjustment is calculated, transport capacity configuration is optimized, overall emission is reduced, and the traffic safety is improved. The problem of limitation of an existing evaluation method is solved.

The application provides a kind of precision collaborative evaluation method of emission sensing device based on remote online OBD monitoring, relating to vehicle testing technical field, comprising: based on the historical detection data of vehicle multi-element emission sensing device, a multi-objective optimization model is constructed with emission species and vehicle attributes as targets to determine the evaluation vehicle sample;Real-time monitoring of vehicle samples is carried out through multi-element emission sensing device, and multi-element pollutant emission data of the sample is obtained, and the multi-element pollutant emission data is grouped according to the vehicle speed and specific power, and the weighted average absolute error, weighted relative error, comprehensive error index based on environmental factor coupling and multivariate regression coefficient under different groups are calculated respectively and cluster analysis is carried out to obtain the precision evaluation result of multi-element emission sensing device.The application improves the accuracy, comprehensiveness and scientificity of the whole vehicle emission detection and evaluation by increasing the collaborative evaluation method of the detection precision of the same pollutant between different test devices.

The invention provides a driver car following behavior and vehicle emission influence analysis method and system, and relates to the related technical field of traffic engineering and environmental sciences, and the method comprises the steps: S1, extracting driver feature indexes according to the vehicle trajectory data of a car following fleet, and carrying out the visualization of the vehicle space-time trajectory according to the vehicle trajectory data; s2, a heterogeneity parameter of a driver is obtained through measurement of a vehicle space-time trajectory diagram, and the number of vehicle following times of the driver is obtained through statistics by combining an improved Newell model and a dynamic time warping algorithm; s3, acquiring driving environment parameters, vehicle technical condition parameters and vehicle operation condition parameters according to the vehicle track data of the vehicle following fleet, inputting the parameters into the localized MOVES model, and estimating to obtain emission factors of road traffic pollutants; and S4, constructing a semi-parameter generalized additive model to analyze a non-linear association relationship between the vehicle following behavior of the driver and the emission factors of the road traffic pollutants.

The application discloses a kind of methods and systems for predicting the influence of different driving behaviors on emissions, comprising: constructing fuzzy cellular automaton model of signal control section pedestrian crossing and establishing localized vehicle emission factor library;Further, different driving behavior scenarios are simulated to obtain driving behavior and corresponding vehicle second-by-second running parameters;Combined with the localized vehicle emission factor library, the first emission of the vehicle is calculated according to the vehicle second-by-second running parameters;According to the first emission of all vehicles, the total emission of all vehicles in the preset time period is calculated;According to the first emission and the total emission, the influence of driving behavior on emissions is predicted.The application can evaluate and predict the influence of different driving behaviors on emissions, and since fuzzy cellular automaton model and localized vehicle emission factor library are constructed for evaluation and prediction, the decision of a single driver can be considered, with high prediction accuracy and good results, and can be widely applied in the field of computer technology.