Statistical calculation method for carbon emission of rail transit vehicle product operation
By refining operational scenarios and establishing an energy consumption data dictionary, and combining the emission factor method to calculate the carbon emissions of rail transit vehicles, the systematization and standardization issues of calculating operational carbon emissions of rail transit vehicles in existing technologies have been solved, and accurate energy consumption and carbon emission assessments throughout the entire life cycle have been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CRRC CHANGCHUN RAILWAY VEHICLES CO LTD
- Filing Date
- 2022-10-28
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies have failed to establish a systematic and standardized method for calculating carbon emissions from the operation of rail transit vehicles, making it difficult for companies to accurately assess and disclose carbon emission data.
By refining operational scenarios, a multi-dimensional energy consumption data dictionary and calculation model are established, including product design data, simulation models, experimental data, and measured data, and the carbon emissions of rail transit vehicles are calculated using the emission factor method.
It enables accurate calculation of energy consumption and carbon emissions of rail transit vehicles throughout their entire life cycle, and provides a standardized data comparison model applicable to products from different manufacturers and operating environments.
Smart Images
Figure CN115544799B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of urban transportation energy consumption statistics and calculation technology, and in particular to a statistical calculation method for carbon emissions from the operation of rail transit vehicles. Background Technology
[0002] The Chinese government has clearly stated its goals for achieving carbon peaking by 2030 and carbon neutrality by 2060. As a major mode of green transportation for society, rail transit necessitates the establishment and improvement of a related carbon emission accounting system. For rail transit equipment manufacturers, systematically and standardizedly calculating, evaluating, and disclosing the operational carbon emissions of their main products is a crucial corporate social responsibility. Therefore, establishing a comprehensive, multi-scenario statistical calculation method for the operational carbon emissions of rail transit vehicles is a vital requirement for relevant enterprises. Summary of the Invention
[0003] The present invention aims to solve the technical problems in the prior art by providing a statistical calculation method for carbon emissions from the operation of rail transit vehicles.
[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A statistical calculation method for carbon emissions from the operation of rail transit vehicles includes the following steps: Step i: Evaluate the reliability of the source of operational energy consumption data; Step ii: Define the standard operating scenario; Step iii: Calculate energy consumption data samples based on the defined standard operating scenario; Step iv: Calculate the carbon emissions for the integrated operation scenario based on the energy consumption data sample calculation results.
[0005] In the above technical solution, the reliability evaluation process in step i is as follows: Based on product design data and literature, energy consumption data is obtained through theoretical estimation, while product operation energy consumption data is sourced from estimation levels. Energy consumption data for product operation is obtained by establishing a model-based simulation environment and using a solver built from a simulation mathematical model. The source of energy consumption data is the simulation level. Through product launch trials, the actual energy consumption data obtained from the product's own sensors and data acquisition system, and the source of product operation energy consumption data, are experimental. The actual operating energy consumption data is obtained by collecting on-board data from the product, and the source of the product operating energy consumption data is actual measurement. The single-scenario energy consumption reliability index is S xWhen the product's operating energy consumption data comes from the estimation level, x=1, S1=0.25; when the product's operating energy consumption data comes from the simulation level, x=2, S2=0.5; when the product's operating energy consumption data comes from the experimental level, x=3, S3=0.75; when the product's operating energy consumption data comes from the measured level, x=4, S4=0.75; then: W=[S x (P1 / P)+S x (P2 / P) + ... + S x (P N / P)] 100; Among them, the reliability index of the comprehensive energy consumption data per 100 kilometers is W, the comprehensive energy consumption per 100 kilometers is P, the number of segments in a single scenario is N, and the energy consumption data of a single scenario segment is P1, P2...P N P = P1 + P2 + ... + P N .
[0006] In the above technical solution, the standard operating scenarios in step ii include: Product operating load scenarios include: no load, where the product does not carry passenger or cargo loads during operation; nominal load, where the product carries the standard load quota specified in the product manual or design manual during operation; and ultimate load, the maximum load that may occur in actual operation of the product that exceeds the nominal load. The product's operational kinetic energy scenarios include: flat terrain, with the starting and ending elevations being the same and traction fluctuations less than a given value during constant-speed driving; gentle slopes, with an average gradient of 1%; standard slope 1, with an average gradient of 2%; and standard slope 2, with an average gradient of 3%. Product operating speed scenarios include: standard speed, the standard operating speed explicitly specified in the product manual or design manual; specific standard speed, the nominal operating speed lower than the standard speed under specific conditions explicitly specified in the product manual or design manual during product operation; ultra-low speed, operating at 40% of the standard speed; low speed, operating at 60% of the standard speed; and extreme speed, the maximum design-permissible speed higher than the standard speed. The product's operating status scenarios include: constant speed, where the product maintains a constant speed in the corresponding speed scenario mode; standard acceleration, where the product accelerates from a standstill to its corresponding speed using standard acceleration (acceleration mode); extreme acceleration, where the product accelerates from a standstill to its corresponding speed using the maximum acceleration within the operational allowable range (acceleration mode); standard deceleration, where the product brakes from a constant speed to a standstill using standard acceleration (deceleration mode) in the corresponding speed scenario mode; extreme deceleration, where the product brakes from a constant speed to a standstill using the maximum acceleration within the operational allowable range (deceleration mode) in the corresponding speed scenario mode; and idling, where the product is in a stationary, paused state just before starting to move.
[0007] In the above technical solution, step iii, the method for calculating energy consumption data samples for a single-mode operation scenario is as follows: The comprehensive energy consumption per 100 kilometers is based on a distance of 100 kilometers. It is calculated by dividing the driving process set in the comprehensive operation scenario into multiple single road segments according to a single operation scenario, calculating the energy consumption of each single road segment separately, and then summing them up. If the total distance set in the comprehensive operation scenario is not equal to 100 kilometers, the total distance set in the comprehensive operation scenario needs to be proportionally calculated with 100 kilometers to obtain a quantifiable comprehensive energy consumption per 100 kilometers.
[0008] In the above technical solution, in step iii, when the sample road segment is not equal to 100 kilometers, the conversion method for energy consumption data samples is as follows: K=M (100 / S) Among them, the sample road segment length is S, the measured energy consumption is M, and the energy consumption data sample for the single-mode operation scenario is K.
[0009] In the above technical solution, the method for calculating the distance value in step iii, where acceleration and deceleration occur, is as follows: S = (V2) V2-V1 V1) / 2a In the acceleration and deceleration scenario, the initial speed is V1, the final speed is V2, the acceleration is a, and the sample road segment length is S.
[0010] In the above technical solution, step iv specifically involves: calculating the carbon emissions of the comprehensive operation scenario using the general emission factor calculation method, multiplying the energy consumption per 100 kilometers of various energy sources of the vehicle product sample by the corresponding energy emission factor, and then summing the results to obtain the carbon emission data of the vehicle product sample.
[0011] In the above technical solution, step iv specifically involves: The carbon emission per 100 kilometers of the vehicle product sample is W; the vehicle product sample is driven by n energy sources, and the emission factors of the n energy sources are K1...Kn according to the emission factor method; calculate the comprehensive energy consumption per 100 kilometers of each energy source under the comprehensive operating scenario of the vehicle product sample, respectively, as Q1...Qn; then: W=Q1 K1+Q2 K2+.....Qn Kn.
[0012] In the above technical solution, after step iv, there is a step: calculating the carbon emissions of the vehicle product sample throughout its entire life cycle operation.
[0013] In the above technical solution, the specific method for calculating the carbon emissions of the vehicle product sample throughout its entire life cycle is as follows: Let M be the carbon emissions over the entire lifecycle of a sample vehicle sold annually, W be the carbon emissions per 100 kilometers, X be the annual operating mileage, Y be the annual sales, and F be the operating period. Then: M = W (X / 100) Y F.
[0014] The present invention has the following beneficial effects: The statistical calculation method for carbon emissions from the operation of rail transit vehicles of this invention is applicable to various types of energy-driven rail transit vehicles, calculating energy consumption and carbon emissions throughout their entire operational lifecycle. By refining operational scenarios and establishing energy consumption data dictionaries for different operational scenarios, it provides a methodology and calculation model for dynamically segmenting energy consumption and carbon emission data under comprehensive operational scenarios, providing a theoretical basis for enterprises to disclose carbon emission data for their products.
[0015] The statistical calculation method for carbon emissions from the operation of rail transit vehicles of the present invention realizes the calculation of carbon emissions of different rail transit vehicle products under standard subdivided scenarios by subdividing operating scenarios, which greatly reduces the influence of external factors on the data and provides a standardized data comparison mode for products from different manufacturers, regions and operating environments.
[0016] This invention is applicable to various types of energy-powered rail transit vehicles, calculating energy consumption and carbon emissions throughout their entire operational lifecycle. By refining operational scenarios and establishing energy consumption data dictionaries for different operating scenarios, it provides a methodology and calculation model for dynamically segmenting energy consumption and carbon emission data in comprehensive operational scenarios, providing a theoretical basis for enterprises to disclose carbon emission data for their products.
[0017] This invention enables carbon emission calculation for different rail transit vehicle products under standard sub-scenarios by segmenting operational scenarios. It refines and decomposes the impact of external influencing factors on comprehensive data, providing a standardized data comparison model for products from different manufacturers, regions, and operating environments. Attached Figure Description
[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0019] Figure 1 This is a flowchart illustrating the steps of the statistical calculation method for carbon emissions from the operation of rail transit vehicles according to the present invention.
[0020] Figure 2 A schematic diagram illustrating the encoding of energy consumption data parameters for a single-mode operation scenario of a product. Detailed Implementation
[0021] The present invention will now be described in detail with reference to the accompanying drawings.
[0022] like Figure 1 As shown, the statistical calculation method for carbon emissions from the operation of rail transit vehicles according to the present invention includes the following steps: Step i: Evaluate the reliability of the source of operational energy consumption data; Step ii: Define the standard operating scenario; Step iii: Calculate energy consumption data samples based on the defined standard operating scenario; Step iv: Calculate the carbon emissions for the integrated operation scenario based on the energy consumption data sample calculation results.
[0023] The statistical calculation method for carbon emissions from the operation of rail transit vehicles according to the present invention includes the following reliability evaluation process in step i: Based on product design data and literature, energy consumption data is obtained through theoretical estimation, while product operation energy consumption data is sourced from estimation levels. Energy consumption data for product operation is obtained by establishing a model-based simulation environment and using a solver built from a simulation mathematical model. The source of energy consumption data is the simulation level. Through product launch trials, the actual energy consumption data obtained from the product's own sensors and data acquisition system, and the source of product operation energy consumption data, are experimental. The actual operating energy consumption data is obtained by collecting on-board data from the product, and the source of the product operating energy consumption data is actual measurement. The single-scenario energy consumption reliability index is S x When the product's operating energy consumption data comes from the estimation level, x=1, S1=0.25; when the product's operating energy consumption data comes from the simulation level, x=2, S2=0.5; when the product's operating energy consumption data comes from the experimental level, x=3, S3=0.75; when the product's operating energy consumption data comes from the measured level, x=4, S4=0.75; then: W=[S x (P1 / P)+S x (P2 / P) + ... + S x (P N / P)] 100; Among them, the reliability index of the comprehensive energy consumption data per 100 kilometers is W, the comprehensive energy consumption per 100 kilometers is P, the number of segments in a single scenario is N, and the energy consumption data of a single scenario segment is P1, P2...P N P = P1 + P2 + ... + P N .
[0024] The statistical calculation method for carbon emissions from the operation of rail transit vehicles in this invention includes the following standard operating scenarios in step ii: Product operating load scenarios include: no load, where the product does not carry passenger or cargo loads during operation; nominal load, where the product carries the standard load quota specified in the product manual or design manual during operation; and ultimate load, the maximum load that may occur in actual operation of the product that exceeds the nominal load. The product's operational kinetic energy scenarios include: flat terrain, with the starting and ending elevations being the same and traction fluctuations less than a given value during constant-speed driving; gentle slopes, with an average gradient of 1%; standard slope 1, with an average gradient of 2%; and standard slope 2, with an average gradient of 3%. Product operating speed scenarios include: standard speed, the standard operating speed explicitly specified in the product manual or design manual; specific standard speed, the nominal operating speed lower than the standard speed under specific conditions explicitly specified in the product manual or design manual during product operation; ultra-low speed, operating at 40% of the standard speed; low speed, operating at 60% of the standard speed; and extreme speed, the maximum design-permissible speed higher than the standard speed. The product's operating status scenarios include: constant speed, where the product maintains a constant speed in the corresponding speed scenario mode; standard acceleration, where the product accelerates from a standstill to its corresponding speed using standard acceleration (acceleration mode); extreme acceleration, where the product accelerates from a standstill to its corresponding speed using the maximum acceleration within the operational allowable range (acceleration mode); standard deceleration, where the product brakes from a constant speed to a standstill using standard acceleration (deceleration mode) in the corresponding speed scenario mode; extreme deceleration, where the product brakes from a constant speed to a standstill using the maximum acceleration within the operational allowable range (deceleration mode) in the corresponding speed scenario mode; and idling, where the product is in a stationary, paused state just before starting to move.
[0025] In the statistical calculation method for carbon emissions from the operation of rail transit vehicles of the present invention, step iii, the method for calculating energy consumption data samples for a single-mode operation scenario is as follows: The comprehensive energy consumption per 100 kilometers is based on a distance of 100 kilometers. It is calculated by dividing the driving process set in the comprehensive operation scenario into multiple single road segments according to a single operation scenario, calculating the energy consumption of each single road segment separately, and then summing them up. If the total distance set in the comprehensive operation scenario is not equal to 100 kilometers, the total distance set in the comprehensive operation scenario needs to be proportionally calculated with 100 kilometers to obtain a quantifiable comprehensive energy consumption per 100 kilometers.
[0026] In step iii, when the sample road segment is not equal to 100 kilometers, the conversion method for energy consumption data samples is as follows: K=M (100 / S) Among them, the sample road segment length is S, the measured energy consumption is M, and the energy consumption data sample for the single-mode operation scenario is K.
[0027] In the statistical calculation method for carbon emissions from the operation of rail transit vehicles of the present invention, step iii, the method for calculating the distance value in acceleration and deceleration scenarios is as follows: S = (V2) V2-V1 V1) / 2a In the acceleration and deceleration scenario, the initial speed is V1, the final speed is V2, the acceleration is a, and the sample road segment length is S.
[0028] The statistical calculation method for carbon emissions of rail transit vehicle products in this invention, specifically step iv, involves calculating the carbon emissions of the comprehensive operation scenario using the general emission factor calculation method. This is achieved by multiplying the energy consumption per 100 kilometers of various energy sources of the vehicle product sample by the corresponding energy emission factor and then summing the results to obtain the carbon emission data of the vehicle product sample.
[0029] The carbon emission per 100 kilometers of the vehicle product sample is W. The vehicle product sample is powered by n energy sources, and the emission factors of the n energy sources are K1...Kn according to the emission factor method. Calculate the comprehensive energy consumption per 100 kilometers of each energy source under the comprehensive operating scenario of the vehicle product sample, and assign them Q1...Qn respectively. Then: W=Q1 K1+Q2 K2+.....Qn Kn.
[0030] The statistical calculation method for carbon emissions from the operation of rail transit vehicles of the present invention includes, after step iv, a further step: calculating the carbon emissions from the entire life cycle of the vehicle product sample during operation, specifically: Let M be the carbon emissions over the entire lifecycle of a sample vehicle sold annually, W be the carbon emissions per 100 kilometers, X be the annual operating mileage, Y be the annual sales, and F be the operating period. Then: M=W (X / 100) Y F.
[0031] The following provides a more detailed explanation of the statistical calculation method for carbon emissions from the operation of rail transit vehicles according to the present invention.
[0032] like Figure 1 As shown, the statistical calculation method for carbon emissions from the operation of rail transit vehicles according to the present invention includes the following steps: I. Reliability Evaluation Methods for Product Operation Energy Consumption Data Sources 1. Reliability classification of product operation energy consumption data sources As shown in Table 1, this invention standardizes data levels by classifying the sources of product operation energy consumption data into four levels, ensuring the accuracy of product operation energy consumption and carbon emission data, and evaluates the reliability of data through a data reliability attribute identification method.
[0033] 2. Calculation method for reliability index of product operation energy consumption data The comprehensive energy consumption data for the product is based on the energy consumption corresponding to a 100-kilometer operating distance (when the distance of the target scenario is not equal to 100 kilometers, all data need to be proportionally converted to 100 kilometers before calculation using this method). The 100-kilometer journey is divided into segments according to a single scenario, and the data from all segments is weighted and summed. By assigning a reliability classification attribute to each segment of data and combining it with the proportion of total energy consumption over 100 kilometers, the reliability index of the product's operational energy consumption data can be calculated using the following formula. The index range is 25-100.
[0034] Let P be the overall energy consumption per 100 kilometers, N be the number of segments in a single scenario, and P1 be the energy consumption data for each segment in a single scenario...P N .
[0035] Formula: P = P1 + P2 + ... + P N The reliability index of comprehensive energy consumption data per 100 kilometers is W, and the reliability index of energy consumption in a single scenario is S. x The attributes are: estimation level x=1, S1=0.25; simulation level x=2, S2=0.5; experimental level x=3, S3=0.75; and measured level x=4, S4=0.75.
[0036] Formula: W=[S x (P1 / P)+S x (P2 / P) + ... + S x (P N / P)] 100 II. Definition of Standard Operating Scenario for Individual Product Samples Rail transit vehicles operate in complex scenarios, with significant differences in energy consumption across different scenarios. This invention differentiates typical operating scenarios for rail transit vehicles across four dimensions: load, kinetic energy, constant speed travel, and acceleration / deceleration travel. By employing permutation and combination methods, it obtains detailed energy consumption data for each individual scenario, thereby classifying and quantitatively describing energy consumption during product operation. This provides data samples for various individual scenarios to accurately calculate energy consumption and carbon emissions throughout the product's operating cycle.
[0037] 1. Product operating load scenarios 2. Product operating kinetic energy scenario 3. Product operating speed scenarios 4. Product operating status scenarios III. Multi-dimensional Matrix Model of Comprehensive Product Operation Scenarios This invention fits the standard operating scenarios of individual product samples into comprehensive energy consumption and carbon emission data under the comprehensive operating scenario of the product by combining and summing them. Through a comprehensive operating scenario calculation model that can be quickly defined and adjusted, a method for calculating the operating energy consumption and carbon emission of the product under comprehensive conditions is obtained.
[0038] 1. Multi-dimensional matrix model for product operation scenarios Under standard operating conditions for individual product samples across four dimensions, 300 single modes can be generated using permutation and combination enumeration methods. When the measurement conditions are met, each sub-mode can provide a single-mode product energy consumption value. To standardize calculation units and facilitate clear and intuitive data comparisons, the energy consumption data for all 300 single modes are assigned as energy consumption per 100 kilometers.
[0039] 2. Encoding method for energy consumption data parameters in single-mode product operation scenarios The parameter is encoded in five digits, such as Figure 2 As shown, the first digit is a number, and the last four digits are letters. The first digit represents the four levels of reliability grading for the product's operational energy consumption data source: value "1" represents "Estimated Level," value "2" represents "Simulation Level," value "3" represents "Experimental Level," and value "4" represents "Measured Level." The second digit represents the product's operating load scenario: value "A" represents "No-load," value "B" represents "Nominal Load," and value "C" represents "Ultimate Load." The third digit represents the product's operating kinetic energy scenario: value "A" represents "Flat," value "B" represents "Gentle Slope," value "C" represents "Standard Slope 1," and value "D" represents "Standard Slope 2." The fourth digit represents the product's operating speed scenario: value "A" represents "Standard Speed," value "B" represents "Specific Standard Speed," value "C" represents "Ultra-Low Speed," value "D" represents "Low Speed," and value "E" represents "Ultimate Speed." The fifth digit represents the product's operating state and scenario. The code value "A" represents "constant speed", the code value "B" represents "standard acceleration", the code value "C" represents "maximum acceleration", the code value "D" represents "standard deceleration", and the code value "E" represents "maximum deceleration".
[0040] IV. Energy consumption data sample calculation based on standard operating scenarios 1. When it is impossible to provide a data conversion method for the movement distance of vehicle product samples in a given scenario, in an arbitrary manner. Of the four levels of reliability grading, the data for Level 1 (Estimation Level) and Level 2 (Simulation Level) are theoretical calculations. Except for acceleration and deceleration scenarios, energy consumption data must be quantified based on a 100-kilometer journey when performing sample calculations. The data for Level 3 (Experimental Level) and Level 4 (Measured Level) are all real-world collected data. If the sample road segment cannot be measured at a distance of 100 kilometers, the following formula should be used for conversion: The sample road segment length is S, the measured energy consumption is M, and the energy consumption data sample for the single-mode operation scenario with the corresponding code XXXXX is K.
[0041] K=M (100 / S) 2. Energy consumption calculation method for acceleration and deceleration scenarios Due to the speed limit of the vehicle product sample, the acceleration and deceleration scenarios cannot last for 100 kilometers. The distance in the acceleration-limited scenario is determined by the product's acceleration value and the initial and final velocities. Let the initial velocity be V1, the final velocity be V2, the acceleration be a, and the distance be S in the acceleration and deceleration scenario. Then the distance formula is as follows: S = (V2) V2-V1 V1) / 2a After obtaining the S value, the energy consumption per 100 kilometers under the corresponding scenario code is calculated by proportional conversion.
[0042] Products equipped with kinetic energy recovery or regenerative braking are allowed to have negative energy consumption during deceleration.
[0043] 3. Energy consumption calculation method for slope scenarios When the vehicle product sample operates on a slope, the energy consumption differs between uphill and downhill sections. Therefore, the energy consumption parameters for slope scenarios need to be set using a two-dimensional array. The first element of the array represents the uphill energy consumption, and the second element represents the downhill energy consumption. The two energy consumption data points for uphill and downhill sections need to be calculated or measured separately.
[0044] 4. Energy consumption calculation method for idling scenarios In rail transit vehicle operation, there are scenarios such as waiting to enter stations or waiting for stops along the way. These scenarios should be considered when setting up integrated operation scenario combinations. Since the vehicle is stationary when idling, parameters cannot be calibrated based on energy consumption per 100 kilometers. This invention calibrates the vehicle's idling energy consumption data in seconds ( / S), dividing it into two parameters: low-power idling energy consumption and normal idling energy consumption. The duration of the corresponding idling scenario can be set when setting up integrated operation scenario combinations.
[0045] 5. Method for calculating energy consumption data samples in single-mode operation scenarios For each single-mode operation scenario (300 in total), four attribute data values can be defined. For ease of calculation and comparison, the data samples are calculated with 100 kilometers as the corresponding distance range. The sample calculation method for energy consumption of each single-mode operation scenario is divided into two categories: conventional calculation and special calculation. Conventional calculation means that if the scenario mode can provide a distance of 100 kilometers, the energy consumption value generated by the 100-kilometer distance can be directly calculated. Special calculation means that if the scenario mode cannot provide a distance of 100 kilometers, the corresponding energy consumption value needs to be obtained according to the actual distance data under the actual calculation scenario. Then, the energy consumption value is magnified or reduced according to the distance ratio (the ratio of 100 kilometers to the actual distance in kilometers) to obtain the energy consumption value of the corresponding single scenario under the 100-kilometer distance.
[0046] After obtaining the energy consumption values of all (or part of) 300 single operating scenarios for a certain product, a single-mode operating scenario energy consumption data dictionary for the vehicle product sample can be formed according to its parameter coding. This data dictionary can provide basic data for subsequent energy consumption calculation of comprehensive operating scenarios.
[0047] V. Energy Consumption Data Dictionary for Single-Mode Operation Scenarios When energy consumption is measured or calculated for all single-mode operating scenarios of a vehicle product sample, and energy consumption parameter values are generated based on a distance of 100 kilometers, these values can be used to generate a read-only data table according to energy consumption data encoding logic. This invention defines this data table as a single-mode operating scenario energy consumption data dictionary. The dictionary contains all data locations and values within the scope of energy consumption measurement for a single operating scenario of the product. After the dictionary is published, it can provide basic data for subsequent breakdown and calculation of comprehensive operating scenarios.
[0048] 1. Basic energy consumption parameter data in the dictionary By further categorizing 300 operational scenarios into four data reliability levels—estimation, simulation, experimentation, and actual measurement—and following the "Product Single-Mode Operation Scenario Energy Consumption Data Parameter Encoding Method," 1200 different coded data storage locations can be generated. These storage locations are used to store the corresponding measured energy consumption values. This invention defines the aforementioned 1200 data items as basic energy consumption transmission data within the dictionary. The data within the dictionary, after satisfying 300 scenarios, can meet the energy consumption data requirements for energy consumption calculations in all single scenarios. That is, for each single scenario, having at least one data value in one of the four reliability dimensions (estimation, simulation, experimentation, and actual measurement) is sufficient to meet the minimum data requirements. Therefore, when certain detection capabilities are unavailable, it is permissible to publish the data dictionary in cases where data for certain reliability levels is missing.
[0049] 2. Single-scene associated parameter data within the dictionary When performing comprehensive energy consumption calculations for the product in subsequent scenarios, it is also necessary to obtain some other parameters for the corresponding vehicle product samples. This invention defines these parameters as single-scenario associated parameter data within a dictionary. These include, but are not limited to, the following list (Table 6).
[0050] Sample form of energy consumption data dictionary for single-mode operation scenario VI. Product Comprehensive Energy Consumption Calculation Method Based on Single Product Operation Scenario Data 1. A method for calculating the comprehensive energy consumption of products with a definable calculation model. This invention uses a 100-kilometer distance as a benchmark. It subdivides the driving process set in the comprehensive operation scenario into multiple single road segments according to a single operation scenario, calculates the energy consumption of each single road segment separately, and then sums them up. The total mileage set in the comprehensive operation scenario is then proportionally calculated with 100 kilometers to obtain a quantifiable comprehensive energy consumption per 100 kilometers (when the total mileage of the target scenario is not equal to 100 kilometers, the corresponding parameters need to be converted according to the ratio).
[0051] Let P be the comprehensive energy consumption of the product when it travels S kilometers; S kilometers of travel distance consists of N segments of single-mode operation scenarios of the product; and Q be the comprehensive energy consumption per 100 kilometers. P = P1 + P2 + ... + PN Q=P (100 / S) 2. Example 1: Case Study of Model Setting for Comprehensive Operation Scenario The total distance is 100 kilometers. The vehicle is under nominal load throughout the entire route. The starting point and the end point are both at a constant speed. There is one stop in the middle. The vehicle accelerates and decelerates at both the exit and entry points of the stop. The road section is 10% uphill and 10% downhill. The vehicle travels at a constant speed on both uphill and downhill sections. The rest of the road section is flat. The vehicle is under nominal load throughout the 100-kilometer route. The road section is divided into 11 sections according to the single scenario mode. The road section descriptions are shown in Table 8 below.
[0052] The parameters required for the calculation are as follows (the following data should be provided and included by the product development department when establishing the system for actual application): Let the standard speed be VA, the low speed be VD, the acceleration during standard acceleration be GA1, the acceleration during standard deceleration be GA2, the stop time be T, the total distance of the comprehensive energy consumption scenario model be S, the distance of the nth segment be Sn, and the energy consumption of the nth segment be Pn. The energy consumption calculation method after segment subdivision is shown in the following table (Table 9).
[0053] The energy consumption data for segments 1 to 11 can be calculated from the table above. Summing these energy consumption data gives the total energy consumption P for this comprehensive operating scenario in S. The comprehensive energy consumption Q per 100 kilometers for the vehicle product sample in the comprehensive operating scenario can be derived as follows: Q=P (S / 100) P = P1 + P2 + ... + P11 VII. Calculation Method for Carbon Emissions in Comprehensive Operating Scenarios of Vehicle Product Samples 1. Carbon emission calculation method for a 100-kilometer integrated operation scenario Using the general emission factor calculation method, the energy consumption per 100 kilometers of various energy sources of the vehicle product sample is multiplied by the corresponding emission factor of the energy source, and then the results are summed to obtain the carbon emission data of the vehicle product sample under a specific comprehensive operating scenario.
[0054] Let the carbon emission per 100 kilometers of a certain vehicle product sample be W, and the vehicle product sample be powered by n energy sources. According to the emission factor method, the emission factors for the n energy sources are K1...Kn. Calculate the comprehensive energy consumption per 100 kilometers for each energy source under the comprehensive operating scenario of the vehicle product sample, and their values are Q1...Qn. The calculation formula is as follows: W=Q1 K1+Q2 K2+.....Qn Kn 2. Carbon emission conversion of vehicle product samples from different dimensions For various carbon emission calculation needs based on vehicle product samples, carbon emission values for vehicle product samples can be obtained from multiple dimensions by externally defining relevant variables and then converting them with carbon emission data from a comprehensive operating scenario per 100 kilometers. For example, defining the annual operating carbon emission of a product according to three dimensions—time, sales volume, and sales category—can be calculated by multiplying the annual operating mileage with the carbon emission from a comprehensive operating scenario per 100 kilometers.
[0055] Example: Let the carbon emissions of a certain vehicle product throughout its entire life cycle be M; the carbon emissions per 100 kilometers be W; the annual operating mileage be X; and the annual sales be Y (column). Using the operating duration method to calculate the product's entire life cycle, with an operating period of F (years), the formula for calculating the carbon emissions throughout the product's entire life cycle is as follows: M=W (X / 100) Y F This invention is applicable to various types of energy-powered rail transit vehicles, calculating energy consumption and carbon emissions throughout their entire operational lifecycle. By refining operational scenarios and establishing energy consumption data dictionaries for different operating scenarios, it provides a methodology and calculation model for dynamically segmenting energy consumption and carbon emission data in comprehensive operational scenarios, providing a theoretical basis for enterprises to disclose carbon emission data for their products.
[0056] This invention enables carbon emission calculation for different rail transit vehicle products under standard sub-scenarios by segmenting operational scenarios. It refines and decomposes the impact of external influencing factors on comprehensive data, providing a standardized data comparison model for products from different manufacturers, regions, and operating environments.
[0057] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A statistical calculation method of rail transit vehicle product operation carbon emission, characterized in that, Includes the following steps: Step i: Evaluate the reliability of the source of operational energy consumption data; Step ii: Define the standard operating scenario; The standard operating scenarios include: Product operating load scenarios include: no load, where the product does not carry passenger or cargo loads during operation; nominal load, where the product carries the standard load quota specified in the product manual or design manual during operation; and ultimate load, the maximum load that may occur in actual operation of the product that exceeds the nominal load. The product's operational kinetic energy scenarios include: flat terrain, with the starting and ending elevations being the same and traction fluctuations less than a given value during constant-speed driving; gentle slopes, with an average gradient of 1%; standard slope 1, with an average gradient of 2%; and standard slope 2, with an average gradient of 3%. Product operating speed scenarios include: standard speed, the standard operating speed explicitly specified in the product manual or design manual; specific standard speed, the nominal operating speed lower than the standard speed under specific conditions explicitly specified in the product manual or design manual during product operation; ultra-low speed, operating at 40% of the standard speed; low speed, operating at 60% of the standard speed; and extreme speed, the maximum design-permissible speed higher than the standard speed. The product's operating status scenarios include: constant speed, where the product maintains a constant speed in the corresponding speed scenario mode; standard acceleration, where the product accelerates from a standstill to the corresponding speed using standard acceleration; extreme acceleration, where the product accelerates from a standstill to the corresponding speed using the maximum acceleration within the operational allowable range; standard deceleration, where the product brakes from a constant speed to a standstill using standard acceleration in the corresponding speed scenario mode; extreme deceleration, where the product brakes from a constant speed to a standstill using the maximum acceleration within the operational allowable range in the corresponding speed scenario mode; and idling, where the product is in a stationary, paused state just before starting to move. Step iii: Calculate energy consumption data samples based on the defined standard operating scenario, including the following steps: 1) When the sample road segment is not equal to 100 kilometers, the conversion method for energy consumption data samples is as follows: K = M (100 / S) In the formula, the sample road segment length is S, the measured energy consumption is M, and the corresponding coded single-mode operation scenario energy consumption data sample is K; 2) Energy consumption calculation method for acceleration and deceleration scenarios Let the initial velocity be V1, the final velocity be V2, the acceleration be a, and the distance be S in an acceleration / deceleration scenario. Then the formula for the distance value is as follows: S = (V2 V2-V1 V1 ) / 2a After obtaining the S value, the energy consumption per 100 kilometers under the corresponding scenario code is calculated by proportional conversion; for products with kinetic energy recovery or regenerative braking, the energy consumption during deceleration is allowed to be negative. 3) Energy consumption calculation method for slope scenarios When the vehicle product sample is running on a slope, the energy consumption parameters for the slope scenario need to be set using a two-dimensional array. The first element of the array represents the energy consumption uphill, and the second element represents the energy consumption downhill. The two energy consumption data for uphill and downhill need to be calculated or measured separately. 4) Energy consumption calculation method for idling scenarios The energy consumption data of the vehicle at idle speed is calibrated in seconds and divided into two parameters: low power idling energy consumption and normal idling energy consumption. When setting up a combination of comprehensive operation scenarios, the duration of the corresponding idling scenario is set. Step iv: Calculate the carbon emissions for the integrated operation scenario based on the energy consumption data sample calculation results.
2. The statistical calculation method for carbon emissions from the operation of rail transit vehicles according to claim 1, characterized in that, The reliability evaluation process in step i is as follows: Based on product design data and literature, energy consumption data is obtained through theoretical estimation, while product operation energy consumption data is sourced from estimation levels. Energy consumption data for product operation is obtained by establishing a model-based simulation environment and using a solver built from a simulation mathematical model. The source of energy consumption data is the simulation level. Through product launch trials, the actual energy consumption data obtained from the product's own sensors and data acquisition system, and the source of product operation energy consumption data, are experimental. The actual operating energy consumption data is obtained by collecting on-board data from the product, and the source of the product operating energy consumption data is actual measurement. The single-scenario energy consumption reliability index is S x When the product operation energy consumption data comes from the estimation level, x=1, S1=0.25; when the product operation energy consumption data comes from the simulation level, x=2, S2=0.5; when the product operation energy consumption data comes from the experimental level, x=3, S3=0.75; when the product operation energy consumption data comes from the actual measurement level, x=4, S4=0.
75. but: W=[S x (P1 / P)+S x (P2 / P)+……+S x (P N / P)] 100; Among them, the reliability index of the comprehensive energy consumption data per 100 kilometers is W, the comprehensive energy consumption per 100 kilometers is P, the number of segments in a single scenario is N, and the energy consumption data of a single scenario segment is P1, P2...P N P = P1 + P2 + ... + P N .
3. The method for statistical calculation of carbon emissions of rail transit vehicle product operation according to claim 1, characterized in that, In step iii, the method for calculating energy consumption data samples for a single-mode operation scenario is as follows: The comprehensive energy consumption per 100 kilometers is based on a distance of 100 kilometers. It is calculated by dividing the driving process set in the comprehensive operation scenario into multiple single road segments according to a single operation scenario, calculating the energy consumption of each single road segment separately, and then summing them up. If the total distance set in the comprehensive operation scenario is not equal to 100 kilometers, the total distance set in the comprehensive operation scenario needs to be proportionally calculated with 100 kilometers to obtain a quantifiable comprehensive energy consumption per 100 kilometers.
4. The statistical calculation method for carbon emissions from the operation of rail transit vehicles according to claim 1, characterized in that, Step iv specifically involves calculating the carbon emissions for the comprehensive operation scenario using the general emission factor calculation method. This involves multiplying the energy consumption per 100 kilometers of various energy sources of the vehicle product sample by the corresponding energy emission factor and then summing the results to obtain the carbon emission data for the vehicle product sample.
5. The method for statistical calculation of carbon emissions of rail transit vehicle product operation according to claim 4, characterized in that, Step iv specifically involves: the carbon emission per 100 kilometers of the vehicle product sample is W, the vehicle product sample is driven by n energy sources, and the emission factors of the n energy sources are K1...Kn according to the emission factor method; calculate the comprehensive energy consumption per 100 kilometers of each energy source under the comprehensive operation scenario of the vehicle product sample, which are Q1...Qn respectively; but: W = Q1 K1+Q2 K2+.....Qn Kn.
6. The statistical calculation method for carbon emissions from the operation of rail transit vehicles according to claim 1, characterized in that, Step iv is followed by a step: calculating the carbon emissions of the vehicle product sample throughout its entire life cycle operation.
7. The method for statistical calculation of carbon emissions of rail transit vehicle product operation according to claim 6, characterized in that, The specific method for calculating the carbon emissions throughout the entire life cycle of a sample vehicle sold annually is as follows: Let M be the carbon emission throughout the entire life cycle of a vehicle product sample, W be the carbon emission per 100 kilometers, X be the annual operating mileage, Y be the annual sales, and F be the operating period. Then: M = W (X / 100) Y F.