Plug-in hybrid vehicle actual pollution reduction and emission reduction benefit evaluation method considering user behaviors

Abstract

The invention belongs to the field of green traffic, and particularly relates to a plug-in hybrid vehicle actual pollution reduction and emission reduction benefit evaluation method considering user behaviors. Comprising the steps of 1, collecting actual road emission data of the plug-in hybrid electric vehicle; obtaining user travel data of the plug-in hybrid electric vehicle through field investigation; 2, an emission factor matrix of the plug-in hybrid electric vehicle responding to the SOC is constructed from the two dimensions of speed-acceleration joint distribution and the average vehicle speed; 3, describing user charging characteristics, and constructing a multi-level layered travel intensity matrix responding to user behaviors; step 4, the discharge factor matrix and the daily travel intensity matrix are combined, and the travel discharge amount of the plug-in hybrid electric vehicle considering the user behavior is calculated; and 5, calculating the total emission amount of tail gas pollutants of the plug-in hybrid electric vehicle. According to the method, the diversity of energy management strategies of the plug-in hybrid electric vehicle and the diversity of user behaviors are comprehensively considered, and the tail gas emission amount of the plug-in hybrid electric vehicle in the actual use process is calculated.

Description

Technical Field

[0001] The present invention belongs to the field of green transportation, and in particular relates to a method for evaluating the actual pollution reduction and emission reduction benefits of a plug-in hybrid vehicle taking user behavior into consideration. Background Art

[0002] As electricity sources become cleaner, the promotion of plug-in hybrid vehicles has become an important option for mitigating greenhouse gases and air pollutants in the global road transport sector. Globally, governments have widely formulated policies to promote the rapid growth of plug-in hybrid vehicle ownership. However, the charging infrastructure in many countries is lagging behind and it is difficult to meet the growing charging demand in the short term. However, many consumers purchase plug-in hybrid vehicle ownership mainly to take advantage of preferential policies such as tax exemptions and road access, even if there are no daily charging conditions, which has led to the underutilization of the proportion of electric drive in plug-in hybrid vehicles and its related environmental benefits. Therefore, it is of great significance to study the actual environmental benefits of plug-in hybrid vehicles taking into account user behavior (such as charging frequency, mileage, driving time, etc.).

[0003] At present, the emission reduction benefits of plug-in hybrid vehicles are mostly based on laboratory tests, without considering the actual charging frequency and driving intensity of users, and there is limited verification in actual driving. There are two main reasons for this phenomenon: first, the complexity of the energy management strategy of plug-in hybrid vehicles; second, the frequent changes in power distribution between the engine and the electric motor in the plug-in hybrid vehicle, frequent engine restarts, and fluctuations in catalytic converter performance make the exhaust emission generation mechanism of plug-in hybrid vehicles complicated. First, based on the state of charge (SOC) of the battery, plug-in hybrid vehicles are designed to operate in two driving modes: charge consumption mode (CD) and charge maintenance mode (CS). In CD mode, the electric motor mainly provides power to the vehicle, and the engine remains off when low power demand (such as idling or light acceleration), and the internal combustion engine will only start when higher power is required. Once the SOC reaches a predetermined threshold, the vehicle will switch to CS mode to maintain the SOC and prevent further discharge. Second, CO, HC and NOx emissions in the exhaust are mainly affected by the operating state of the internal combustion engine, rather than the total fuel consumption. Both CO and HC are derived from incomplete combustion. The production of CO depends mainly on the availability of oxygen, while HC emissions tend to increase under partial load conditions due to inefficient combustion. Frequent engine restarts and vehicle speed fluctuations, especially in urban driving, further exacerbate CO and HC emissions. NOx emissions are generated under high temperature and pressure, usually during cold start and sustained high temperature phases. Although catalytic converters play a certain role in reducing emissions, their efficiency is unstable and a considerable amount of NOx may still be emitted to the atmosphere. In addition, CO2 comes from complete combustion, and the emission is proportional to fuel consumption. In CS mode, the continuous operation of the internal combustion engine not only meets the increased power demand, but also maintains a high temperature and high pressure environment, which helps the complete combustion of the fuel, but also leads to increased CO2 and NOx emissions. A large number of studies have shown that under certain conditions, such as high-speed driving or rapid acceleration, the emissions of plug-in hybrid vehicles in CS mode may exceed those in CD mode, and may even exceed the emissions of comparable gasoline vehicles of the same model. Therefore, it is urgent to comprehensively consider the travel emission calculation problems of plug-in hybrid vehicles with diversified driving modes and user behaviors, and establish an actual environmental benefit evaluation method of plug-in hybrid vehicles taking user behaviors into consideration to verify the pollution reduction and emission reduction benefits of plug-in hybrid vehicles. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention proposes to consider the energy management strategy characteristics of plug-in hybrid vehicles, establish an emission factor matrix of plug-in hybrid vehicles that responds to SOC from two dimensions: speed-acceleration joint distribution and average vehicle speed, based on the actual road driving emission data of the vehicle; obtain the plug-in hybrid vehicle user travel data through field surveys, and construct a multi-level hierarchical travel intensity matrix that responds to user behavior; and jointly calculate the travel emissions of the plug-in hybrid vehicle that considers user behavior by combining the emission factor matrix and the travel intensity matrix.

[0005] Technical solution of the present invention:

[0006] A method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior, the specific steps are as follows:

[0007] Step 1: Collect actual road emission data of plug-in hybrid vehicles; obtain plug-in hybrid vehicle user travel data through field surveys;

[0008] The actual road emission data of the plug-in hybrid vehicle specifically includes: vehicle speed, acceleration, latitude and longitude, altitude, carbon dioxide CO2, carbon monoxide CO, hydrocarbons HC, nitrogen oxides NO in the exhaust gas x Emission coefficient, engine speed and vehicle SOC value, data acquisition accuracy is 1Hz;

[0009] The plug-in hybrid vehicle user trip data obtained through field research includes: trip timestamp, SOC value, cumulative mileage, vehicle operating status (pure electric, fuel, hybrid), and longitude and latitude, and data collection is carried out at intervals of no less than 30 seconds;

[0010] Step 2: Construct the emission factor matrix of plug-in hybrid vehicles responding to SOC from the two dimensions of speed-acceleration joint distribution and average vehicle speed;

[0011] Step 3: Characterize user charging characteristics and construct a multi-level hierarchical trip intensity matrix that responds to user behavior;

[0012] Step 4: Combine the emission factor matrix and the daily travel intensity matrix to calculate the PHEV travel emissions taking into account user behavior;

[0013] Step 5: Taking "maximizing new energy" and "eliminating new energy" as the two baseline scenarios, and the actual driving of plug-in hybrid vehicles by users as the real scenario, calculate the total amount of exhaust pollutant emissions of plug-in hybrid vehicles under the three scenarios, and quantitatively evaluate the environmental benefits of plug-in hybrid vehicles in actual pollution reduction and emission reduction.

[0014] Preferably, step 1 comprises the following steps:

[0015] Step 11: Select the exhaust emission data monitoring equipment of the plug-in hybrid vehicle, including: portable emissions measurement system (PEMS), on-board diagnostics system (OBD), environmental sensor, Beidou positioning satellite system and sports camera;

[0016] Among them, PEMS is responsible for recording timestamps, greenhouse gases and air pollutants in exhaust gas (carbon dioxide CO2, carbon monoxide CO, hydrocarbons HC, nitrogen oxides NOx )’s emission volume concentration and exhaust volume flow data, OBD is responsible for collecting the engine speed and vehicle speed data of the plug-in hybrid vehicle, Beidou positioning satellite system is responsible for collecting vehicle speed, latitude and longitude of vehicle location and altitude information, environmental sensors record the temperature and dew point in the atmospheric environment, and the sports camera is responsible for capturing information on the vehicle dashboard during the experiment, including SOC value, timestamp, vehicle speed, etc.;

[0017] Step 12: Design a real-road emission monitoring test plan for plug-in hybrid vehicles; the number of real-road emission monitoring tests for plug-in hybrid vehicles shall not be less than 4 times / vehicle, and the actual road driving time for each test shall not be less than 90 minutes, and the cumulative road driving time shall not be less than 10 hours / vehicle, of which the cumulative driving time on expressways shall not be less than 3 hours and the cumulative driving mileage shall not be less than 120 kilometers, and the cumulative driving time on non-expressways shall not be less than 6 hours and the cumulative driving mileage shall not be less than 150 kilometers; in at least 4 real-road emission monitoring tests, the initial SOC of the vehicle shall not be less than 60% in at least two of them;

[0018] Step 13: Preprocessing of experimental data of actual road emissions of plug-in hybrid vehicles and removal of outliers; Based on statistical principles, the “interquartile range method” is selected to locate the position of outliers in the experimental data, and the outliers are replaced by Nan values. At the same time, considering the data characteristics of the experimental parameters, the upper or lower bound parameters in the “interquartile range method” are modified; appropriate smoothing parameters are selected, and the CO2, CO, HC, and NO in the exhaust gas are respectively smoothed using smoothing curves. x The emission volume concentration, exhaust volume flow, Beidou vehicle speed, and OBD vehicle speed are smoothed to improve data quality; according to the national standard "Light-duty Vehicle Pollutant Emission Limits and Measurement Methods (China Phase VI)", namely GB18352.6, the altitude is corrected and the CO2, CO, HC, and NO in the exhaust are calculated. x The emission factor is in g / s; the Paddle deep learning framework is used to identify vehicle speed, SOC value, time and other information in the captured video;

[0019] Step 14: Alignment of experimental data of actual road emissions of plug-in hybrid vehicles; Considering that PMES, OBD, and sports cameras in the monitoring equipment for actual road emissions of plug-in hybrid vehicles are independent of each other, and the environmental sensor and Beidou positioning satellite system rely on PEMS for power supply and communication, time alignment is performed according to "PMES time-dashboard time", and speed alignment is performed according to "Beidou vehicle speed-OBD vehicle speed-dashboard vehicle speed" to ensure data alignment between different devices;

[0020] Step 15: Map matching based on Hidden Markov Model (HMM) and Viterbi algorithm; determine the geographic coordinate system according to the actual road emission monitoring test site of the plug-in hybrid vehicle, and import the road data and map files of the urban area where the test is located, such as shapefile files; encode the map data based on the binary tree structure to narrow the search space of the Markov model; map each trajectory point to a potential position on the center line of the road based on the principle of minimum vertical projection distance; set the matching parameters of the hidden Markov model, call the Viterbi algorithm for map matching, and output the road name and road type corresponding to each vehicle trajectory point;

[0021] Step 16: Sub-trip division; Based on the output results of step 15, the above road types are divided into expressways and non-expressways; With timestamp as feature and vehicle speed as target variable, a decision tree regression model is constructed, and the change position (or timestamp) of the predicted vehicle speed is calculated; By setting a threshold, the change points are screened and merged. If the difference between the mean of the front and rear vehicle speeds exceeds the threshold, the change point is marked; if the mean of the front and rear speeds of a certain change point is lower than 3 or the distance between the two is less than 400, the change point is marked as invalid; For each experimental data, the vehicle speed column data is traversed to identify the sections with speeds lower than 5, and the start and end timestamps of these low-speed sections are recorded; Check the change points of the trip and road type, add new change points, and finally divide a trip into multiple sub-trips, and distinguish between high-speed trips and non-high-speed trips.

[0022] Preferably, the step 2 constructs an emission factor matrix of the plug-in hybrid vehicle responding to SOC from two dimensions, namely, the speed-acceleration joint distribution and the average vehicle speed, and comprises the following steps:

[0023] Step 21: Construct the PHEV emission factor matrix E based on the speed-acceleration joint distribution in response to SOC p (v i ,a j ,m k ),as follows:

[0024] The actual road data of all plug-in hybrid vehicles processed in step 1 are divided into speed intervals of 1 km / h and acceleration intervals of 0.1 m / s2, and the speed v is established. i In the range of 0–100 km / h, acceleration a j The velocity-acceleration joint distribution in the interval -4-4m / s2;

[0025] According to the vehicle SOC value, the driving mode of the plug-in hybrid vehicle is divided into two types: power consumption mode (CD) and power maintenance mode (CS), and different driving modes are constructed. kThe speed-acceleration joint distribution under the CD mode and the CS mode are used to calculate the CO2, CO, HC, and NO in the exhaust gas of each speed-acceleration group. x The mean emission factor of , in g / s;

[0026] The emission factor matrix of plug-in hybrid vehicles responding to SOC based on the joint distribution of speed and acceleration is constructed as follows:

[0027]

[0028] v=(0,…,100) T

[0029] a=(-4,…,4) T

[0030]

[0031] Where speed bin represents the speed range, acc bin represents the acceleration range, ceil(v) and ceil(10*a) represent the 10 times of speed and acceleration rounded up, respectively, m represents the driving mode, m∈{CD,CS}, Indicates speed v i , acceleration a j and drive mode m k The emission factor of pollutant p under the condition of p (v i ,a j ,m k ) represents the emission factor matrix of pollutant p.

[0032] Step 22: Construct a plug-in hybrid vehicle emission factor matrix based on the average vehicle speed and the response SOC;

[0033] The vehicles are grouped according to speed, with 0-2.5km / h as one group, and within the range of 2.5km / h-102.5km / h, they are grouped at intervals of 5km / h, i.e. 2.5-7.5km / h, 7.5-12.5km / h, 12.5-17.5km / h, ..., 97.5-102.5km / h;

[0034] The plug-in hybrid vehicle emission factor matrix based on the speed-acceleration joint distribution output in step 21 is multiplied by the speed-acceleration distribution matrix in each vehicle speed group to obtain the plug-in hybrid vehicle emission factor matrix based on the average vehicle speed, which is as follows:

[0035]

[0036] Among them, avg speed bin represents the average speed range. represents the average vehicle speed, Indicates the average speed Speed ​​in interval v i , acceleration a j The frequency, Indicates based on average speed The velocity-acceleration distribution matrix

[0037]

[0038] Among them, ° represents matrix dot product, represents the emission factor matrix of the PHEV pollutant p in response to SOC based on the average vehicle speed, Represents the average speed Plug-in hybrid vehicle driving mode m k The emission factor of pollutant p is E p (v i ,a j ,CD) and E p (v i ,a j , CS) represent E p (v i ,a j ,m k ) in the matrix m k Submatrix whose columns are equal to CD or CS.

[0039] Step 3: Characterize user charging characteristics and construct a multi-level hierarchical trip intensity matrix that responds to user behavior;

[0040] The user behavior includes: the user's daily charging frequency and daily mileage.

[0041] Step 3 specifically includes the following steps:

[0042] Step 31: Collect and improve the user's travel data for each hour: obtain the plug-in hybrid vehicle user travel data through field research, and supplement the user's 24-hour daily cumulative mileage and SOC value through interpolation; the improved user travel data is provided to step 32;

[0043] Step 32: Based on the improved user travel data, the user's daily mileage and average driving speed are calculated using the timestamp and accumulated mileage data; taking a single user as the object, the user's daily average mileage is calculated, thereby determining the user's daily average mileage grouping label;

[0044] Preferably, the average daily mileage is divided into mileage ranges of 0-30km, 30-90km, 90-210km, and greater than 210km at equal intervals of 3km, 6km, 12km, and 24km, respectively, and the average driving speed is grouped according to step 22, i.e., avgspeed bin;

[0045] Furthermore, plug-in hybrid vehicles with a battery range of 80 km or more are classified as long-range plug-in hybrid vehicles, and the rest are classified as short-range plug-in hybrid vehicles. User travel data is grouped, and the group calculation determines the average daily mileage group label.

[0046] Step 33: Determine whether the user charges the vehicle based on the latitude and longitude information of the vehicle location and the change in the SOC value; obtain the user's daily charging status, and characterize the user's daily average charging frequency characteristics according to the long cruise and short endurance groups;

[0047] Specifically, if the change in the longitude and latitude of the vehicle is less than or equal to one millionth of a degree for more than 15 consecutive minutes, and the SOC value gradually increases, the vehicle is considered to be charging, otherwise it is not charging;

[0048] Step 34: Establishing an SOC distribution matrix based on the average daily mileage; according to the improved user travel data, grouping labels by average daily mileage and distinguishing the driving mode state of the hybrid vehicle by hour, i.e., CD mode or CS mode;

[0049] Step 35: According to the range grouping, the mileage-vehicle speed distribution matrix that responds to the user behavior is obtained.

[0050] Preferably, the step 4 combines the emission factor matrix and the daily travel intensity matrix to calculate the plug-in hybrid vehicle travel emissions taking into account user behavior, including the following steps:

[0051] Step 41: Calculate the plug-in hybrid vehicle travel emissions taking into account user behavior; multiply the plug-in hybrid vehicle emission factor matrix based on the average vehicle speed output in step 2 and the vehicle speed distribution matrix in response to user behavior in step 3 to calculate the CO2, CO, HC, NO in the plug-in hybrid vehicle exhaust during the user's daily travel. x The trip emissions are as follows:

[0052] The vehicle speed distribution matrix S in response to user behavior, the hourly average vehicle speed matrix S of plug-in hybrid vehicle i with range group n on driving day d n，i，d (h) as an example, S n，i，d (h) is a submatrix of S, as follows,

[0053]

[0054] Where i represents a plug-in hybrid vehicle; n represents the plug-in hybrid vehicle range group number, n∈{N1, N2}, N1 and N2 represent the short-range and long-range plug-in hybrid vehicle groups, respectively; d represents the driving day; m h Indicates the driving mode for h hours, m h ∈{CD,CS};S n，i，d (h) represents the hourly average vehicle speed matrix of plug-in hybrid vehicle i with range group n on driving day d; h represents hours (0 to 23), v h represents the vehicle speed in h hours, V h Indicates the speed grouping label for h hours, corresponding to the avg speedbin in step 22;

[0055]

[0056] in, Indicates based on shared columns (specifically S n，i，d (h) V in the matrix h and m h ,and In the matrix and m k , which means vehicle speed grouping and driving mode) are combined. The specific steps are as follows: First, for the matrix S n，i，d Each row (h, v h , V h , m h )Search Is there a matching V h and m h , and secondly, if there is a matching V h and m h , then merge the corresponding emission factors If there is no matching V h and m h , then fill the row with 0; U n，i，d，p represents the pollutant p emission matrix of plug-in hybrid vehicle user i with range group n during the trip on driving day d;

[0057] Preferably, step 5 comprises the following steps:

[0058] Step 51: In the “new energy maximization” benchmark scenario (S1, scenario 1), the plug-in hybrid vehicle maintains the CD mode for the entire journey; in the “new energy removal” benchmark scenario (S2, scenario 2), the plug-in hybrid vehicle maintains the CS mode for the entire journey; the actual driving of the plug-in hybrid vehicle is scenario 3 (S3); calculate the CO2, CO, HC, and NO in the exhaust gas of the plug-in hybrid vehicle under the three scenarios. x Total emissions are used to assess the environmental benefits of plug-in hybrid vehicles in terms of actual pollution reduction and emission reduction, as follows:

[0059] The “new energy maximization” benchmark scenario (scenario 1, S1) is the user hourly average vehicle speed matrix S n，i，d (h)m h All are set to CD mode, that is In the “de-renewable energy” benchmark scenario (scenario 2, S2), the user hourly average vehicle speed matrix S n，i，d (h)m h All are set to CS mode, that is

[0060] Therefore, the user travel emissions under the S1 and S2 scenarios are calculated as follows:

[0061]

[0062] Among them, U′ n，i，d，p and U″ n，i，d，p The table shows the pollutant p emission matrix of plug-in hybrid vehicle user i with range group n during the trip on driving day d under the S1 and S2 scenarios respectively;

[0063] The total emissions of plug-in hybrid vehicles under S1, S2 and actual scenarios are calculated as follows:

[0064] total S1，p =∑ n ∑ i ∑ d U′ n，i，d，p

[0065] total S2，p =∑ n ∑ i ∑ d U″ n，i，d，p

[0066] total S3，p =∑ n ∑ i ∑ d U n，i，d，p

[0067] Among them, total S1，p 、total S2,p and total actual，p Respectively represent the total amount of pollutant p emitted by plug-in hybrid vehicles under scenarios S1, S2 and S3.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The method of the present invention uses the actual road emission data of plug-in hybrid vehicles to establish a plug-in hybrid vehicle emission factor matrix that responds to SOC in multiple dimensions; uses field survey user travel data to establish a multi-level hierarchical travel intensity matrix that responds to user behavior; combines the emission factor matrix and the daily travel intensity matrix to calculate the travel emissions of plug-in hybrid vehicles that take into account user behavior; and quantitatively evaluates the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles through the "new energy maximization" and "new energy removal" benchmark scenario settings. The present invention breaks through the limitations of traditional laboratory testing from the multi-dimensional integration of user behavior, vehicle characteristics, and emission generation mechanisms, and provides a highly adaptable solution for the calculation of travel emissions of plug-in hybrid vehicles, and further quantitatively evaluates the environmental benefits of pollution reduction and emission reduction of plug-in hybrid vehicles in daily use. This provides important technical support for the formulation of environmental management policies, the promotion of green travel, and the optimal design of plug-in hybrid vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0071] In conjunction with the accompanying drawings and embodiments, the present invention is described in detail in detail the method for calculating the travel emissions of a plug-in hybrid vehicle at a real SOC, and the present invention is not limited to this single example; any other changes, modifications, substitutions, combinations, and simplifications that deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

[0072] Embodiment 1:

[0073] A method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior comprises the following steps:

[0074] Step 1: Design an experimental plan, select experimental equipment, and collect actual road emission data of plug-in hybrid vehicles; obtain plug-in hybrid vehicle user travel data through field surveys;

[0075] Step 2: Construct the emission factor matrix of plug-in hybrid vehicles responding to SOC from the two dimensions of speed-acceleration joint distribution and average vehicle speed;

[0076] Step 3: Characterize user charging characteristics and construct a multi-level hierarchical trip intensity matrix that responds to user behavior;

[0077] Step 4: Combine the emission factor matrix in step 2 and the daily travel intensity matrix in step 3 to calculate the PHEV travel emissions taking into account user behavior;

[0078] Step 5: Set "maximizing new energy" and "eliminating new energy" as two baseline scenarios respectively, and take the actual driving of plug-in hybrid vehicles by users as the real scenario. Calculate the total amount of exhaust pollutant emissions of plug-in hybrid vehicles under the three scenarios, and quantitatively evaluate the environmental benefits of plug-in hybrid vehicles in actual pollution reduction and emission reduction.

[0079] Embodiment 2:

[0080] On the basis of Example 1, in step 1, an experimental plan is designed, experimental equipment is selected, and actual road emission data of plug-in hybrid vehicles are collected, including: vehicle speed, acceleration, longitude and latitude, altitude, exhaust gas (carbon dioxide CO2, carbon monoxide CO, hydrocarbons HC, nitrogen oxides NO x ) emission coefficient, engine speed and SOC value of the vehicle, with a data collection accuracy of 1Hz; obtain the travel data of plug-in hybrid vehicle users through field surveys, including the timestamp of the travel, SOC value, accumulated mileage, vehicle operating status (pure electric, fuel, hybrid) and other information, and the data collection is carried out at intervals of no less than 30 seconds, including the following steps:

[0081] Step 11: Select the exhaust emission data monitoring equipment of the plug-in hybrid vehicle, including the portable emission measurement system (PEMS), on-board diagnostics system (OBD), environmental sensors, Beidou positioning satellite system and sports camera; among them, PEMS is responsible for recording timestamps, greenhouse gases and air pollutants in exhaust gas (carbon dioxide CO2, carbon monoxide CO, hydrocarbons HC, nitrogen oxides NO x )’s emission volume concentration and exhaust volume flow data, OBD is responsible for collecting the engine speed and vehicle speed data of the plug-in hybrid vehicle, Beidou positioning satellite system is responsible for collecting vehicle speed, latitude and longitude of vehicle location and altitude information, environmental sensors record the temperature and dew point in the atmospheric environment, and the sports camera is responsible for capturing information on the vehicle dashboard during the experiment, including SOC value, timestamp, vehicle speed, etc.;

[0082] In this embodiment, step 11 selects equipment for the vehicle actual road driving emission monitoring experiment. The specific equipment selection and data collection in this embodiment are shown in Table 1;

[0083] Table 1 is a summary of experimental equipment and data content

[0084]

[0085]

[0086] Step 12: Design a real-road emission monitoring test plan for plug-in hybrid vehicles; the number of real-road emission monitoring tests for plug-in hybrid vehicles shall not be less than 4 times / vehicle, and the actual road driving time for each test shall not be less than 90 minutes, and the cumulative road driving time shall not be less than 10 hours / vehicle, of which the cumulative driving time on expressways shall not be less than 3 hours and the cumulative driving mileage shall not be less than 120 kilometers, and the cumulative driving time on non-expressways shall not be less than 6 hours and the cumulative driving mileage shall not be less than 150 kilometers; in at least 4 real-road emission monitoring tests, the initial SOC of the vehicle shall not be less than 60% in at least two of them;

[0087] Step 13: Preprocessing of experimental data of actual road emissions of plug-in hybrid vehicles and removal of outliers; Based on statistical principles, the “interquartile range method” is selected to locate the position of outliers in the experimental data, and the outliers are replaced by Nan values. At the same time, considering the data characteristics of the experimental parameters, the upper or lower bound parameters in the “interquartile range method” are modified; appropriate smoothing parameters are selected, and the CO2, CO, HC, and NO in the exhaust gas are respectively smoothed using smoothing curves. x The emission volume concentration, exhaust volume flow, Beidou vehicle speed, and OBD vehicle speed are smoothed to improve data quality; according to the national standard "Light-duty Vehicle Pollutant Emission Limits and Measurement Methods (China Phase VI)", namely GB18352.6, the altitude is corrected and the CO2, CO, HC, and NO in the exhaust are calculated. x The emission factor is in g / s; the Paddle deep learning framework is used to identify vehicle speed, SOC value, time and other information in the captured video;

[0088] Step 14: Alignment of experimental data of actual road emissions of plug-in hybrid vehicles; Considering that PMES, OBD, and sports cameras in the monitoring equipment for actual road emissions of plug-in hybrid vehicles are independent of each other, and the environmental sensor and Beidou positioning satellite system rely on PEMS for power supply and communication, time alignment is performed according to "PMES time-dashboard time", and speed alignment is performed according to "Beidou vehicle speed-OBD vehicle speed-dashboard vehicle speed" to ensure data alignment between different devices;

[0089] Step 15: Map matching based on Hidden Markov Model (HMM) and Viterbi algorithm; determine the geographic coordinate system according to the actual road emission monitoring test site of the plug-in hybrid vehicle, and import the road data and map files of the urban area where the test is located, such as shapefile files; encode the map data based on the binary tree structure to narrow the search space of the Markov model; map each trajectory point to a potential position on the center line of the road based on the principle of minimum vertical projection distance; set the matching parameters of the hidden Markov model, call the Viterbi algorithm for map matching, and output the road name and road type corresponding to each vehicle trajectory point;

[0090] Step 16: Sub-trip division; Based on the output results of step 15, the above road types are divided into expressways and non-expressways; With timestamp as feature and vehicle speed as target variable, a decision tree regression model is constructed, and the change position (or timestamp) of the predicted vehicle speed is calculated; By setting a threshold, the change points are screened and merged. If the difference between the mean of the front and rear vehicle speeds exceeds the threshold, the change point is marked; if the mean of the front and rear speeds of a certain change point is lower than 3 or the distance between the two is less than 400, the change point is marked as invalid; For each experimental data, the vehicle speed column data is traversed to identify the sections with speeds lower than 5, and the start and end timestamps of these low-speed sections are recorded; Check the change points of the trip and road type, add new change points, and finally divide a trip into multiple sub-trips, and distinguish between high-speed trips and non-high-speed trips.

[0091] Embodiment 3:

[0092] On the basis of Example 2, in step 2, an emission factor matrix of a plug-in hybrid vehicle responding to SOC is constructed from multiple dimensions such as speed-acceleration joint distribution and average vehicle speed, and specifically includes the following steps:

[0093] Step 21: Construct the plug-in hybrid vehicle emission factor matrix that responds to the speed-acceleration joint distribution of SOC;

[0094] The actual road data of all plug-in hybrid vehicles processed in step 1 are divided into speed intervals of 1 km / h and acceleration intervals of 0.1 m / s2, and the speed v is established. i In the range of 0-100km / h, acceleration a j The velocity-acceleration joint distribution in the interval -4-4m / s2;

[0095] According to the vehicle SOC value, the driving mode of the plug-in hybrid vehicle is divided into two types: power consumption mode (CD) and power maintenance mode (CS), and different driving modes are constructed. k The speed-acceleration joint distribution under the CD mode and the CS mode are used to calculate the CO2, CO, HC, and NO in the exhaust gas of each speed-acceleration group. x The mean emission factor of , in g / s;

[0096] Construct the plug-in hybrid vehicle emission factor matrix E that responds to the speed-acceleration joint distribution of SOC p (v i , a j , m k ),as follows:

[0097]

[0098] v = (0, ..., 100) T

[0099] a=(-4,...,4) T

[0100]

[0101] Where speed bin represents the speed range, aCc bin represents the acceleration range, ceil(v) and ceil(10*a) represent the 10 times of speed and acceleration rounded up, respectively, m represents the driving mode, m∈{CD, CS}, Indicates speed v i , acceleration a j and drive mode m k The emission factor of pollutant p under the condition of p (v i , a j , m k ) represents the emission factor matrix of pollutant p

[0102] Step 22: Construct the plug-in hybrid vehicle emission factor matrix based on average vehicle speed;

[0103] The vehicles are grouped according to speed, with 0-2.5km / h as one group, and within the range of 2.5km / h-102.5km / h, they are grouped at intervals of 5km / h, i.e. 2.5-7.5km / h, 7.5-12.5km / h, 12.5-17.5km / h, ..., 97.5-102.5km / h;

[0104] The plug-in hybrid vehicle emission factor matrix based on the speed-acceleration joint distribution output in step 21 is multiplied by the speed-acceleration distribution matrix in each vehicle speed group to obtain the plug-in hybrid vehicle emission factor matrix based on the average vehicle speed, which is as follows:

[0105]

[0106] Among them, avg speed bin represents the average speed range. represents the average vehicle speed, Indicates the average speed Speed ​​in interval v i , acceleration a j The frequency, Indicates based on average speed The velocity-acceleration distribution matrix

[0107]

[0108] in, represents matrix dot product, The emission factor matrix of the plug-in hybrid vehicle pollutant p based on the average vehicle speed in the response driving mode is represented by

[0109] The emission factor matrices of the two plug-in hybrid vehicles based on the average vehicle speed in this embodiment are shown in Tables 2 and 3;

[0110]

[0111]

[0112] Embodiment 4:

[0113] Based on Example 3, step 3 specifically includes the following steps:

[0114] Step 31: Collect and improve the user's travel data for each hour: obtain the plug-in hybrid vehicle user travel data through field research, and supplement the user's 24-hour daily cumulative mileage and SOC value through interpolation; the improved user travel data is provided to step 32;

[0115] Step 32: Based on the improved user travel data, the user's daily mileage and average driving speed are calculated using the timestamp and accumulated mileage data; taking a single user as the object, the user's daily average mileage is calculated, thereby determining the user's daily average mileage grouping label;

[0116] Preferably, the average daily mileage is divided into mileage ranges of 0-30km, 30-90km, 90-210km, and greater than 210km at equal intervals of 3km, 6km, 12km, and 24km, respectively, and the average driving speed is grouped according to step 22, i.e., avgspeed bin;

[0117] Furthermore, plug-in hybrid vehicles with a battery range of 80 km or more are classified as long-range plug-in hybrid vehicles, and the rest are classified as short-range plug-in hybrid vehicles. User travel data is grouped, and the group calculation determines the average daily mileage group label.

[0118] The distribution characteristics of the average daily mileage of the plug-in hybrid vehicle in this embodiment are shown in Table 4:

[0119] Average daily mileage (km / day) Distribution of short-range group (%) Long-range battery life group distribution (%) (0,6] 12.26 15.54 (6,12] 6.25 1.67 (12,27] 23.2 32.07 (27,42] 3.85 0.88 (42,57] 2.57 0.49 (57,72] 1.85 0.36 (72,87] 1.46 0.17 (87,102] 12.8 14.85 (102,142] 2.35 0.38 (142,182] 6.74 8.2 (182,222] 4.23 3.17 (222,262] 15.87 19.26 (262,302] 3.6 1.83 >302 2.97 1.13

[0120] Taking the average speed of user A for 24 hours in a driving day as an example, the table example is as follows (in order to correspond to S n,i,d (h) matrix):

[0121]

[0122]

[0123] Step 33: Determine whether the user charges the vehicle based on the latitude and longitude information of the vehicle location and the change in the SOC value; obtain the user's daily charging status, and characterize the user's daily average charging frequency characteristics according to the long cruise and short endurance groups;

[0124] Specifically, if the change in the longitude and latitude of the vehicle is less than or equal to one millionth of a degree for more than 15 consecutive minutes, and the SOC value gradually increases, the vehicle is considered to be charging, otherwise it is not charging;

[0125] The charging frequency distribution characteristics of plug-in hybrid vehicle users in this embodiment are shown in Table 5:

[0126] Average daily charging frequency (times / day) Distribution of short-range group (%) Long-range battery life group distribution (%) ＝0 24.33 4.00 (0,0.25) 26.24 9.14 [0.25,0.5) 8.75 21.71 [0.5,0.75) 12.55 24.57 [0.75,1) 13.69 18.29 [1,1.25) 6.84 7.43 [1.25,1.5) 4.56 6.29 [1.5,1.75) 1.14 4.00 [1.75,2) 0.38 2.86 ≥2 1.52 1.71

[0127] Step 34: Establishing an SOC distribution matrix based on the average daily mileage; according to the improved user travel data, grouping labels by average daily mileage and distinguishing the driving mode state of the hybrid vehicle by hour, i.e., CD mode or CS mode;

[0128] Step 35: Construct a multi-level hierarchical travel intensity matrix that responds to user behavior from mileage, vehicle speed, and SOC driving mode; aggregate users with the same daily average mileage grouping label, use the daily average mileage grouping as the factor base, and statistically obtain a mileage-speed distribution matrix that responds to user behavior.

[0129] Embodiment 5:

[0130] Based on Example 4, step 4 specifically includes the following steps:

[0131] Step 41: Calculate the plug-in hybrid vehicle travel emissions taking into account user behavior; multiply the plug-in hybrid vehicle emission factor matrix based on average vehicle speed output in step 23 by the mileage-vehicle speed distribution matrix in response to user behavior in step 35 to calculate the CO2, CO, HC, NO in the plug-in hybrid vehicle exhaust during the user's daily travel. x of trip emissions.

[0132] Embodiment 6:

[0133] Based on Example 5, step 5 specifically includes the following steps:

[0134] Step 51: Set up the following 3 scenarios:

[0135] Scenario 1: In the “New Energy Maximization” baseline scenario, the plug-in hybrid vehicle maintains CD mode throughout the journey.

[0136] Scenario 2: In the "de-new energy" baseline scenario, the plug-in hybrid vehicle maintains CS mode throughout the journey.

[0137] Scenario 3: Actual driving scenario of plug-in hybrid vehicle

[0138] Calculate the CO2, CO, HC, and NO in the exhaust of plug-in hybrid vehicles under three scenarios x Total emissions are used to assess the environmental benefits of plug-in hybrid vehicles in reducing pollution and emissions.

[0139] The comparison of the total exhaust emissions of plug-in hybrid vehicles under three different scenarios in this embodiment is shown in Table 6:

[0140] scene <![CDATA[CO2 (tons)]]> CO(tons) HC(tons) NOx (tons) Scenario 1 124.607 0.015 0.046 0.161 Scenario 2 295.252 0.070 0.379 2.989 Scenario 3 220.229 0.048 0.234 1.750

[0141] The above description is only a description of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any changes or modifications made by any person skilled in the art based on the above disclosed technical contents shall be deemed as equivalent effective embodiments and shall fall within the scope of protection of the technical solution of the present application.

Claims

1. A method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior, characterized in that: The following steps are involved: Step 1: Collect actual road emission data of plug-in hybrid vehicles; Obtaining PHEV user travel data through field research; The actual road emission data of the plug-in hybrid vehicle specifically includes: vehicle speed, acceleration, latitude and longitude, altitude, carbon dioxide CO2, carbon monoxide CO, hydrocarbons HC, nitrogen oxides NO in the exhaust gas x Emission coefficient, engine speed and vehicle SOC value, data acquisition accuracy is 1Hz; The plug-in hybrid vehicle user trip data obtained through field research includes: trip timestamp, SOC value, cumulative mileage, vehicle operating status (pure electric, fuel, hybrid), and longitude and latitude, and data collection is carried out at intervals of no less than 30 seconds; Step 2: Construct the emission factor matrix of plug-in hybrid vehicles responding to SOC from the two dimensions of speed-acceleration joint distribution and average vehicle speed; Step 3: Characterize user charging characteristics and construct a multi-level hierarchical trip intensity matrix that responds to user behavior; Step 4: Combine the emission factor matrix and the daily travel intensity matrix to calculate the PHEV travel emissions taking into account user behavior; Step 5: Taking "maximizing new energy" and "eliminating new energy" as the two baseline scenarios, and the actual driving of plug-in hybrid vehicles by users as the real scenario, calculate the total amount of exhaust pollutants emitted by plug-in hybrid vehicles under the three scenarios, and quantitatively evaluate the environmental benefits of plug-in hybrid vehicles in terms of actual pollution reduction and emission reduction.

2. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior as claimed in claim 1, characterized in that: Step 1 includes the following steps: Step 11: Select the plug-in hybrid vehicle exhaust emission data monitoring equipment, including portable emission measurement system PEMS, on-board diagnostic system OBD, environmental sensors, Beidou positioning satellite system and sports camera; PEMS is responsible for recording timestamps, greenhouse gases and air pollutants in exhaust gas including carbon dioxide CO2, carbon monoxide CO, hydrocarbons HC, nitrogen oxides NO x The emission volume concentration and exhaust volume flow data of the plug-in hybrid vehicle are collected by OBD. The Beidou positioning satellite system is responsible for collecting the vehicle speed, longitude and latitude of the vehicle location, and altitude information. The environmental sensor records the temperature and dew point in the atmospheric environment. The sports camera is responsible for capturing the information on the vehicle dashboard during the experiment, including SOC value, timestamp, vehicle speed, etc. Step 12: Design a real-road emission monitoring test plan for plug-in hybrid vehicles; the number of real-road emission monitoring tests for plug-in hybrid vehicles shall not be less than 4 times / vehicle, and the actual road driving time for each test shall not be less than 90 minutes, and the cumulative road driving time shall not be less than 10 hours / vehicle, of which the cumulative driving time on expressways shall not be less than 3 hours and the cumulative driving mileage shall not be less than 120 kilometers, and the cumulative driving time on non-expressways shall not be less than 6 hours and the cumulative driving mileage shall not be less than 150 kilometers; in at least 4 real-road emission monitoring tests, the initial SOC of the vehicle shall not be less than 60% in at least two of them; Step 13: Preprocessing of experimental data of actual road emissions of plug-in hybrid vehicles and removal of outliers; Based on statistical principles, the "interquartile range method" is selected to locate the position of outliers in the experimental data, and the outliers are replaced by Nan values. At the same time, considering the data characteristics of the experimental parameters, the upper or lower bound parameters in the "interquartile range method" are modified; appropriate smoothing parameters are selected, and the CO2, CO, HC, NO in the exhaust gas are respectively smoothed using smoothing curves. x The emission volume concentration, exhaust volume flow, Beidou vehicle speed, and OBD vehicle speed are smoothed to improve data quality; according to the national standard "Light-duty Vehicle Pollutant Emission Limits and Measurement Methods (China Phase VI)", namely GB18352.6, the altitude is corrected and the CO2, CO, HC, and NO in the exhaust are calculated. x The emission factor is in g / s; the Paddle deep learning framework is used to identify vehicle speed, SOC value, time and other information in the captured video; Step 14: Alignment of experimental data of actual road emissions of plug-in hybrid vehicles; Considering that PMES, OBD, and sports cameras in the monitoring equipment for actual road emissions of plug-in hybrid vehicles are independent of each other, and the environmental sensor and Beidou positioning satellite system rely on PEMS for power supply and communication, time alignment is performed according to "PMES time-dashboard time", and speed alignment is performed according to "Beidou vehicle speed-OBD vehicle speed-dashboard vehicle speed" to ensure data alignment between different devices; Step 15: Map matching based on hidden Markov model and Viterbi algorithm; determine the geographic coordinate system according to the actual road emission monitoring test site of the plug-in hybrid vehicle, and import the road data and map files of the urban area where the test is located, such as shapefile files; encode the map data based on the binary tree structure to narrow the search space of the Markov model; map each trajectory point to the potential position on the center line of the road based on the principle of minimum vertical projection distance; set the matching parameters of the hidden Markov model, call the Viterbi algorithm for map matching, and output the road name and road type corresponding to each vehicle trajectory point; Step 16: Sub-trip division; Based on the output results of step 15, the above road types are divided into expressways and non-expressways; With timestamps as features and vehicle speed as target variables, a decision tree regression model is constructed, and the change position or timestamp of the predicted vehicle speed is calculated; By setting a threshold, the change points are screened and merged. If the difference between the mean speeds of the front and rear vehicles exceeds the threshold, the change point is marked; if the mean speeds of the front and rear vehicles of a certain change point are both lower than 3 or the distance between the two is less than 400, the change point is marked as invalid; For each experimental data, the vehicle speed column data is traversed to identify sections with speeds lower than 5, and the start and end timestamps of these low-speed sections are recorded; Check the change points of the trip and road type, add new change points, and finally divide a trip into multiple sub-trips, and distinguish between high-speed trips and non-high-speed trips.

3. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior as claimed in claim 1, characterized in that: The step 2 comprises the following steps: Step 21: Construct the PHEV emission factor matrix E based on the speed-acceleration joint distribution in response to SOC p (v i ,a j ,m k ),as follows: The actual road data of all plug-in hybrid vehicles processed in step 1 are divided into speed intervals of 1 km / h and acceleration intervals of 0.1 m / s2, and the speed v is established. i In the range of 0–100 km / h, acceleration a j The velocity-acceleration joint distribution in the interval -4-4m / s2; According to the vehicle SOC value, the driving mode of the plug-in hybrid vehicle is divided into two types: power consumption mode CD and power maintenance mode CS, and different driving modes m are constructed. k The speed-acceleration joint distribution under the CD mode and the CS mode are used to calculate the CO2, CO, HC, and NO in the exhaust gas of each speed-acceleration group. x The mean emission factor of , in g / s; The emission factor matrix of plug-in hybrid vehicles responding to SOC based on the joint distribution of speed and acceleration is constructed as follows: v＝(0,…,100) T a＝(-4,…,4) T Where speed bin represents the speed range, acc bin represents the acceleration range, ceil(v) and ceil(10*a) represent the 10 times of speed and acceleration rounded up, respectively, m represents the driving mode, m∈{CD,CS}, Indicates speed v i , acceleration a j and drive mode m k The emission factor of pollutant p under the condition of p (v i ,a j ,m k ) represents the emission factor matrix of pollutant p; Step 22: Construct a plug-in hybrid vehicle emission factor matrix based on the average vehicle speed response SOC; The vehicles are grouped according to speed, with 0-2.5km / h as one group, and within the range of 2.5km / h-102.5km / h, they are grouped at intervals of 5km / h, i.e. 2.5-7.5km / h, 7.5-12.5km / h, 12.5-17.5km / h, ..., 97.5-102.5km / h; The plug-in hybrid vehicle emission factor matrix based on the speed-acceleration joint distribution output in step 21 is multiplied by the speed-acceleration distribution matrix in each vehicle speed group to obtain the plug-in hybrid vehicle emission factor matrix based on the average vehicle speed, which is as follows: Among them, avg speed bin represents the average vehicle speed range, represents the average vehicle speed, Indicates the average speed Speed ​​in interval v i , acceleration a j The frequency, Indicates based on average speed The velocity-acceleration distribution matrix Among them, ° represents matrix dot product, represents the emission factor matrix of the PHEV pollutant p in response to SOC based on the average vehicle speed, Represents the average speed Plug-in hybrid vehicle driving mode m k The emission factor of pollutant p is E p (v i ,a j ,CD) and E p (v i ,a j , CS) represent E p (v i ,a j ,m k ) in the matrix m k Submatrix whose columns are equal to CD or CS.

4. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior as claimed in claim 1, characterized in that: Step 3 includes the following steps: Step 31: Collect and improve the user's travel data for each hour: obtain the plug-in hybrid vehicle user travel data through field research, and supplement the user's 24-hour daily cumulative mileage and SOC value through interpolation; the improved user travel data is provided to step 32; Step 32: Based on the improved user travel data, the user's daily mileage and average driving speed are calculated using the timestamp and accumulated mileage data; taking a single user as the object, the user's daily average mileage is calculated, thereby determining the user's daily average mileage grouping label; Step 33: Determine whether the user charges the vehicle based on the latitude and longitude information of the vehicle location and the change in the SOC value; obtain the user's daily charging status, and characterize the user's daily average charging frequency characteristics according to the long cruise and short endurance groups; Step 34: Establishing an SOC distribution matrix based on the average daily mileage; according to the improved user travel data, grouping labels by average daily mileage and distinguishing the driving mode state of the hybrid vehicle by hour, i.e., CD mode or CS mode; Step 35: According to the range grouping, the mileage-vehicle speed distribution matrix that responds to the user behavior is obtained.

5. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles taking into account user behavior as claimed in claim 4, characterized in that: In step 32, the average daily mileage is divided into mileage ranges of 0-30km, 30-90km, 90-210km, and greater than 210km at equal intervals of 3km, 6km, 12km, and 24km respectively, and the average driving speed is grouped in the same manner as step 22.

6. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior as claimed in claim 4, characterized in that: If the change in the vehicle's longitude and latitude is less than or equal to one millionth of a degree for more than 15 consecutive minutes, and the SOC value gradually increases, the vehicle is considered to be charging, otherwise it is not charging.

7. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior as claimed in claim 1, characterized in that: Step 4 is as follows: Step 41: Calculate the plug-in hybrid vehicle travel emissions taking into account user behavior; multiply the plug-in hybrid vehicle emission factor matrix based on the average vehicle speed output in step 2 and the vehicle speed distribution matrix in response to user behavior in step 3 to calculate the CO2, CO, HC, NO in the plug-in hybrid vehicle exhaust during the user's daily travel. x of trip emissions.

8. The method for evaluating the actual pollution reduction and emission reduction benefits of plug-in hybrid vehicles considering user behavior as claimed in claim 1, characterized in that: Step 5 is as follows: Step 51: Set up the following 3 scenarios: Scenario 1: In the "New Energy Maximization" baseline scenario, the plug-in hybrid vehicle maintains CD mode throughout the journey. Scenario 2: In the "de-new energy" baseline scenario, the plug-in hybrid vehicle maintains CS mode throughout the journey. Scenario 3: Actual driving scenario of plug-in hybrid vehicle; Calculate the CO2, CO, HC, and NO in the exhaust of plug-in hybrid vehicles under three scenarios x Total emissions are used to assess the environmental benefits of plug-in hybrid vehicles in terms of actual pollution reduction and emission reduction, as follows: The "new energy maximization" benchmark scenario (scenario 1, S1) is the user hourly average vehicle speed matrix S n,i,d (h)m h All are set to CD mode, that is In the "de-renewable energy" benchmark scenario (scenario 2, S2), the user hourly average vehicle speed matrix S n,i,d (h)m h All are set to CS mode, that is Therefore, the user travel emissions under the S1 and S2 scenarios are calculated as follows: Among them, U n ' ,i,d,p and U n " ,i,d,p Tables show the pollutant p emission matrix of plug-in hybrid vehicle user i with range group n during the trip on driving day d under scenarios S1 and S2 respectively; The total emissions of plug-in hybrid vehicles under S1, S2 and actual scenarios are calculated as follows: total S1,p =∑ n S i S d U′ n,i,d,p total S2,p =S n ∑ i ∑ d "U" n,i,d,p total S3,p =∑ n ∑ i S d U n,i,d,p Among them, total S1,p 、total S2,p and total actual,p Respectively represent the total amount of pollutant p emitted by plug-in hybrid vehicles under scenarios S1, S2 and S3.

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