A Method and System for Assessing the Electrification Potential of Heavy-Duty Vehicles Based on Remote Monitoring Data

Abstract

This invention provides a method and system for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data. It utilizes trajectory time intervals, speed changes, and stopping behavior to segment the journey; and calculates fuel consumption, average speed, and NO based on data collected by high-precision sensors. x Emissions, as a crucial parameter for assessing electrification potential, are used to preserve the heterogeneity of cargo weight, driving behavior, and route selection across different freight tasks within the algorithm. A method is proposed to integrate multiple economic factors, including purchase costs, battery swapping infrastructure construction and operation expenses, fuel savings, carbon trading revenue, nitrogen oxide emission reduction subsidies, and detour time costs, into a unified measurement system. A four-dimensional state space—vehicle-time-route-battery—is established, leveraging a high-speed road network geographic information system to maximize the optimization potential of battery swapping detours while satisfying time window constraints. The model is transformed into a mixed-integer programming solution by using binary route selection variables and integer variables, providing strong support for electrification replacement.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, and in particular to a method and system for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data. Background Technology

[0002] Heavy-duty diesel trucks serve as the backbone of intercity freight transport, but they are also significant sources of crude oil consumption, carbon emissions, and pollutant emissions. Accelerating the electrification of trucks in the intercity freight sector is crucial for national energy security, addressing climate change, and mitigating health problems related to air pollution. The high costs of vehicle acquisition and the construction of supporting energy replenishment facilities, along with operational technical challenges, have made the electrification of this type of vehicle difficult. Therefore, there is an urgent need for methods to assess the electrification potential of diesel trucks to support the development of electrification plans and the planning of supporting energy replenishment facilities.

[0003] Even though charging infrastructure covers almost all highway service areas, battery swapping facilities still need to be built in some service areas to support the electrification of heavy freight. Intercity heavy freight transport is characterized by high energy consumption and strong time constraints. The current driving range of electric heavy trucks is insufficient to support a complete intercity freight journey. Current charging facilities, even with supercharging technology, cannot complete a full charge within one hour. Charging while traveling on highways, especially for refrigerated vehicles, is highly likely to cause logistical delays. On the one hand, battery swapping stations have high construction and battery storage costs. Charging stations should be built in service areas with high potential for high-frequency battery swapping demand to avoid resource misallocation and economic losses. On the other hand, due to differences in transportation frequency, cargo weight, routes, traffic conditions, and road gradients, there are significant heterogeneities in energy consumption characteristics among different vehicles, leading to significant differences in the spatiotemporal distribution of battery swapping demand and economic benefits after electrification. The freight routes of electric heavy trucks determine the distribution of battery swapping demand in service areas, and whether or not battery swapping stations are built in service areas also affects the route planning of electric heavy trucks. Under specific electrification goals and investment strategies, a method for assessing the electrification potential of diesel trucks that comprehensively considers route planning, economic benefits, and environmental benefits can optimize resource allocation and improve system operational efficiency.

[0004] Currently, there is a lack of research on electrification alternatives for fuel-powered trucks that consider the needs of logistics systems and the economic and environmental benefits throughout their entire lifecycle. From the perspective of enterprises, the high purchase cost, limited range, and inadequate charging infrastructure of electric trucks significantly increase operational uncertainty. Due to the lack of a scientific and systematic electrification replacement plan, enterprises struggle to accurately assess the economic feasibility of electric trucks based on existing logistics needs, resulting in insufficient motivation for replacement. From the perspective of the government, current policy-making relies heavily on pilot project experience, lacking a systematic analysis of the refined characteristics of freight demand, energy supply network layout, and the economic and environmental benefits throughout the entire lifecycle, making it difficult to formulate precise subsidy policies and enforcement measures. There is an urgent need to develop a scientific and systematic electrification replacement plan for freight fleets to provide economic decision support for enterprises and a theoretical basis for the government to formulate precise policy tools.

[0005] Existing technical solutions typically rely on traffic flow or speed to make rough estimates of energy consumption, emissions, and freight demand distribution. This leads to inaccurate economic and environmental benefit assessments and limited optimization potential, resulting in resource misallocation. Complex operating conditions such as heavy loads, uphill driving, and congestion have a significant impact on fuel consumption. A single truck can emit over 200 kg of carbon dioxide per day, and its dominance in pollutant emissions is even more pronounced. The "Technical Specifications for Remote Monitoring of Heavy-Duty Vehicle Emissions Part 3: Communication Protocol and Data Format" standard (hereinafter referred to as the "National VI Monitoring Standard") clearly stipulates that heavy-duty diesel vehicles must have the ability to collect and transmit data from OnBoard Diagnostics (OBD) systems, engine data, and exhaust aftertreatment devices primarily based on selective catalytic reduction technology. Summary of the Invention

[0006] The embodiments of the present invention provide a method and system for evaluating the electrification potential of heavy-duty vehicles based on remote monitoring data, which is used to solve the technical problems existing in the prior art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution.

[0008] Methods for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data include:

[0009] S1. Obtain engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles through a remote monitoring platform;

[0010] S2. Based on the acquired engine data, OBD data and SCR data of intercity heavy freight vehicles, perform freight OD identification and statistical analysis of the travel data of intercity heavy freight vehicles.

[0011] S3. By constructing and solving the electrification replacement model for heavy-duty diesel trucks in intercity transportation, the electrification potential of the target intercity heavy-duty freight vehicles is evaluated.

[0012] The evaluation results of step S3 are used for the electrification replacement of intercity heavy freight vehicles.

[0013] Preferably, step S2 specifically includes:

[0014] S21, Through-type

[0015] (1)

[0016] (2)

[0017] Frame sequence for calculating the instantaneous acceleration of intercity heavy freight vehicles; where: t i This refers to the data frame acquisition time. For time difference; v i For speed; a i For acceleration;

[0018] S22. Remove the idle frames in the frame sequence of instantaneous acceleration of intercity heavy freight vehicles where both speed and acceleration are 0, and then calculate the non-idle time difference using equation (1).

[0019] S23, If the non-idle time difference satisfies the equation

[0020] (3)

[0021] Then it is determined that the current data frame of instantaneous acceleration of the intercity heavy freight vehicle and the previous data frame of instantaneous acceleration of the intercity heavy freight vehicle belong to different trips; where: Define the threshold for the journey;

[0022] S24. Traverse all data frames of the frame sequence of instantaneous acceleration of intercity heavy freight vehicles, and use the formula...

[0023] (4)

[0024] The trip number is calculated; where: For the itinerary number;

[0025] S25. Take the acquisition time, longitude, and latitude of the first data frame of the instantaneous acceleration frame sequence of the heavy freight vehicle as the start time, start position, end time, and end position of the journey.

[0026] S26, Through-type

[0027] (5)

[0028] (6)

[0029] (7)

[0030] (8)

[0031] (9)

[0032] Calculate travel statistics; in equations (1) to (4): t i This refers to the data frame acquisition time. The time difference is in seconds. v i For speed; a i For acceleration; Define the threshold for the journey; For the trip number; in equations (5) to (9): This represents the average speed over the journey. n Number of frames in the trip data; L This refers to the mileage traveled. M n and M 1 These are the odometer readings for the last frame and the first frame of the trip, respectively. E fuel This refers to the diesel fuel consumption during the trip. f i Engine fuel flow rate; E nox NO x Emissions; u gas The ratio of exhaust component density to exhaust density; SCR i This is the output value of the NOx sensor downstream of the SCR. IA i This refers to the intake air volume; Diesel density; E carbon Carbon emissions; This refers to the carbon-fuel ratio.

[0033] Preferably, step S3 specifically includes:

[0034] S31, using the spatiotemporal trajectory model and energy constraint of electric heavy trucks

[0035] (10)

[0036] (11)

[0037] (12)

[0038] Flow balance constraint, origin-end point constraint, arrival time constraint, path uniqueness constraint

[0039] (13)

[0040] (14)

[0041] (15)

[0042] (16)

[0043] (17)

[0044] (18)

[0045] Electric heavy trucks battery swapping model at highway service areas

[0046] (19)

[0047] (20)

[0048] (twenty one)

[0049] and objective function

[0050] (twenty two)

[0051] (twenty three)

[0052] (twenty four)

[0053] (25)

[0054] (26)

[0055] (27)

[0056] (28)

[0057] Construct a model for the electrification of heavy-duty diesel trucks in intercity transportation;

[0058] In equations (10) to (12): and These represent the remaining battery power of the vehicles at two adjacent time steps; x itr It indicates a vehiclei Is it in time? t Select path r The binary variable for driving, 1 indicates selection, 0 indicates no selection; L r For path r Length; EF i For vehicles i Energy consumption factor; The ratio of the work efficiency of electric motors to that of diesel engines; This refers to the volumetric calorific value of a diesel engine. This is the lower limit of the State of Charge (SOC) for electric heavy-duty trucks on highways. For electric heavy trucks i Battery capacity; y i For diesel heavy trucks i The decision variable for whether to replace with an electric heavy truck is 0, which means no replacement and 1 means replacement; M is a positive number generated by a computer representing infinity; in equations (13) to (18): For vehicles i Drive through the path r Time; n , n’ , n’’ These are different nodes in the highway network; s i and d i These are the starting point and the ending point of vehicle i, respectively; T i max Let be the latest arrival time of vehicle i; in equations (19) to (21): For vehicles i Is it in time? t The decision variable for battery swapping is 1 for swapping batteries and 0 for not swapping batteries; z n Service area nodes n The decision variable for whether or not to build a battery swapping station is 1, where 1 indicates that a battery swapping station is built and 0 indicates that a battery swapping station is not built. For the battery swapping time; in equations (22) to (28): C f Annual fuel savings revenue; C nox NO for the year x Subsidy income; C C Annual carbon trading revenue; C bss This represents the equivalent annual cost of a battery swapping station. C eht The equivalent annual cost of purchasing electric heavy-duty trucks; CT For the time cost of detours; T bss The service life of the battery swapping station; For the first t Annual operating expense ratio; r The discount rate for the project; S bss The residual value rate of the battery swapping station equipment; For the construction cost of the battery swapping station; S eht The residual value rate of electric heavy-duty trucks; T eht This refers to the service life of electric heavy-duty trucks; Cost of purchasing electric heavy-duty trucks; NO x Emissions reduction subsidies; T i For vehicles i The daily equivalent number of trips is calculated using monitoring data; For the time cost of detours; For electricity price; For oil prices; For carbon trading prices;

[0059] S32. Solve the model for the electrification of heavy-duty diesel trucks in intercity transportation, and then use the formula...

[0060] (29)

[0061] Calculate the electrification benefits of all diesel trucks replaced by electric heavy-duty trucks.

[0062] Secondly, the present invention provides a heavy-duty vehicle electrification potential assessment system based on remote monitoring data, characterized in that it includes:

[0063] The data acquisition module is used to acquire engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles through a remote monitoring platform.

[0064] The modeling and evaluation module is used for:

[0065] Based on the acquired engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles, freight OD identification is performed, and the travel data of intercity heavy-duty freight vehicles is statistically analyzed.

[0066] The potential for electrification of target intercity heavy-duty freight vehicles is assessed by constructing and solving an electrification replacement model for intercity heavy-duty diesel trucks.

[0067] The output module is used to visualize the assessment results of the electrification potential of the target intercity heavy freight vehicles.

[0068] Preferably, it also includes a heavy-duty vehicle electrification plan development module, used to: develop an electrification plan for intercity heavy-duty freight vehicles based on the assessment results of the electrification potential of all intercity heavy-duty freight vehicles obtained.

[0069] As can be seen from the technical solutions provided by the embodiments of the present invention above, the present invention provides a method and system for evaluating the electrification potential of heavy-duty vehicles based on remote monitoring data, which uses trajectory time intervals, speed changes, and stopping behavior to segment the journey; and calculates fuel consumption, average speed, and NO based on data collected by high-precision sensors. x Emissions, as a crucial parameter for assessing electrification potential, are used to preserve the heterogeneity of cargo weight, driving behavior, and route selection for different freight tasks within the algorithm. A method is proposed that integrates multiple economic factors, including purchase costs, construction and operation expenses of battery swapping infrastructure, fuel savings, carbon trading revenue, nitrogen oxide emission reduction subsidies, and detour time costs, into a unified measurement system. A measurement framework is established through an equivalent annual cost conversion mechanism for scheme optimization. A four-dimensional state space—vehicle-time-route-electricity—is established. Relying on a highway network geographic information system, the method comprehensively considers route selection, battery swapping station deployment, battery swapping behavior, energy consumption heterogeneity, and task time constraints to achieve joint optimization of battery swapping station service area location and freight truck electrification schemes, maximizing the optimization potential of battery swapping detours while satisfying time window constraints. The model is transformed into a mixed-integer programming solution by using binary path selection variables and integer variables. The method and system provided by this invention have the following beneficial effects:

[0070] (1) Comprehensive consideration of factors in the assessment of electrification potential

[0071] This invention comprehensively considers vehicle purchase costs, investment in battery swapping station construction, fuel expenditure differences, transportation time costs, carbon revenue under the carbon trading mechanism, and NOx emission reduction subsidies. Detailed quantitative analysis of the electrification benefits from different aspects can more effectively support the formulation and implementation of electrification plans by various stakeholders.

[0072] (2) The economic and environmental benefits are accurately quantified.

[0073] Compared to calculation methods based on constant parameters and traffic survey data, the calculation method based on remote monitoring data can reflect the changes in indicators caused by the heterogeneity of freight tasks. It provides a more accurate assessment of the vehicle electrification potential and enhances the effectiveness of system electrification solutions.

[0074] (3) Maximize the potential for optimizing battery swapping routes while ensuring logistical efficiency.

[0075] This invention, relying on the geographic information system and spatiotemporal mapping framework of the highway network, can maximize the system optimization potential brought about by battery swapping detours and reduce the construction cost of battery swapping facilities. Based on the identified logistics time window constraints, it can ensure on-time arrival without delays. The above two points overcome the limitations of existing technologies that assume the trajectory remains unchanged before and after electrification.

[0076] (4) The system of the present invention uses the above-mentioned evaluation method, which can effectively save system resources and improve the evaluation accuracy.

[0077] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0078] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0079] Figure 1 The flowchart of the method for evaluating the electrification potential of heavy-duty vehicles based on remote monitoring data provided by the present invention;

[0080] Figure 2 A schematic diagram of a preferred embodiment of the method for evaluating the electrification potential of heavy-duty vehicles based on remote monitoring data provided by the present invention;

[0081] Figure 3 The logical block diagram of the heavy-duty vehicle electrification potential assessment system based on remote monitoring data provided by the present invention. Detailed Implementation

[0082] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0083] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0084] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0085] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0086] This invention provides a method and system for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data to address the following technical problems existing in the prior art:

[0087] (1) The evaluation dimension of electrification potential is too singular.

[0088] Current assessments of the electrification potential of heavy-duty diesel trucks typically only consider purchase costs and the price difference between electricity and diesel, neglecting other key economic factors. These include the matching of battery swapping infrastructure construction costs with service capacity, the impact of transportation timeliness on operating revenue, carbon emission trading policies, and local government NOx reduction subsidies. These factors significantly influence project investment payback periods and alternative strategies. This results in biased assessments that fail to reflect the true economic feasibility under various cost-benefit scenarios, potentially underestimating electrification potential or misleading investment deployment, thus impacting policy implementation and corporate decision-making.

[0089] (2) Inaccurate forecasting of energy replenishment demand

[0090] Traditional methods often rely on cross-sectional flow rate or vehicle speed data to assess the battery swapping demand for heavy-duty trucks in service areas. This fails to accurately reflect energy consumption variations caused by factors such as load, operating conditions, and traffic congestion during actual operation, leading to inaccurate estimates of battery swapping demand in electrification potential assessments. Furthermore, the locations of charging points are often based on static deployment models, lacking the ability to identify dynamic charging demand. Significant errors in battery swapping route design result in mid-mission interruptions or redundant deployments, reducing system reliability and resource utilization.

[0091] (3) Failure to consider freight time constraints and detours

[0092] Existing models are primarily based on two assumptions: 1) Transportation routes remain unchanged before and after vehicle electrification. This fails to consider route guidance caused by battery swapping station deployment, potentially leading to misjudgments of electrification potential (electrification impossible due to the lack of swapping stations on transportation routes) or misallocation of battery swapping infrastructure resources (redundant configuration / incorrect location). 2) Route planning does not affect transportation efficiency. This ignores the stringent timeliness requirements of heavy freight transport and the route delays and scheduling conflicts caused by battery swapping detours. Even if the battery swapping route theoretically has sufficient range, if detours to distant swapping stations are required or battery swapping takes too long, it will violate transportation contracts, miss loading and unloading time windows, and make it difficult to implement electrification strategies.

[0093] In view of this, the object of the present invention is:

[0094] (1) Construct a multi-dimensional economic evaluation model that comprehensively considers vehicle purchase cost, investment in battery swapping station construction, fuel expenditure differences, transportation task time cost, carbon revenue under the carbon trading mechanism, and NOx emission reduction subsidies, so as to improve the ability to judge the full cycle and all factors of the electrification path of heavy-duty diesel trucks.

[0095] (2) Based on remote monitoring trajectory and energy consumption data, construct a dynamic energy consumption calculation and battery swapping demand estimation mechanism based on task path to support high-precision matching and feasibility verification of transportation path and battery swapping station coverage under limited battery swapping station resources.

[0096] (3) In the electric heavy truck route planning model, the arrival time limit constraint and detour cost of the freight task are explicitly introduced to ensure that the generated alternative route is not only energy feasible and economically reasonable, but also has practical operational feasibility.

[0097] See Figure 1 This invention provides a method for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data, comprising the following steps:

[0098] S1. Obtain engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles through a remote monitoring platform;

[0099] S2. Based on the acquired engine data, OBD data and SCR data of intercity heavy freight vehicles, perform freight OD identification and statistical analysis of the travel data of intercity heavy freight vehicles.

[0100] S3. By constructing and solving the electrification replacement model for heavy-duty diesel trucks in intercity transportation, the electrification potential of target intercity heavy-duty freight vehicles is evaluated.

[0101] The evaluation results of step S3 are used for the electrification replacement of intercity heavy freight vehicles, such as providing data support for the development of electrification replacement schemes for intercity heavy freight vehicles.

[0102] In the preferred embodiment provided by the present invention, the specific execution process of steps S1 and S2 includes:

[0103] (1) Freight OD identification

[0104] According to the National VI platform and National VI monitoring standards, heavy-duty diesel trucks need to collect and transmit data such as vehicle speed, collection time, engine fuel flow, and NOx sensor output values ​​of the upstream / downstream of the selective catalytic reduction system (SCR) at a frequency of 1HZ. This invention proposes a trip division method based on frame-by-frame monitoring data. The instantaneous acceleration of the vehicle is calculated according to formulas (1) and (2), and idling frames with both speed and acceleration of 0 are removed. The non-idling time difference is recalculated according to formula (1). When formula (3) is satisfied, the current data frame and the previous data frame belong to different trips. The data frames are traversed and the trip number is calculated according to formula (4).

[0105] For example, in some feasible embodiments, the following sub-steps are performed:

[0106] S21, Through-type

[0107] (1)

[0108] (2)

[0109] Frame sequence for calculating the instantaneous acceleration of intercity heavy freight vehicles; where: t i This refers to the data frame acquisition time. For time difference; v i For speed; a i For acceleration;

[0110] S22. Remove the idle frames in the frame sequence of instantaneous acceleration of intercity heavy freight vehicles where both speed and acceleration are 0, and then calculate the non-idle time difference using equation (1).

[0111] S23, If the non-idle time difference satisfies the equation

[0112] (3)

[0113] Then it is determined that the current data frame of instantaneous acceleration of the intercity heavy freight vehicle and the previous data frame of instantaneous acceleration of the intercity heavy freight vehicle belong to different trips; where: Define the threshold for the journey;

[0114] S24. Traverse all data frames of the frame sequence of instantaneous acceleration of intercity heavy freight vehicles, and use the formula...

[0115] (4)

[0116] The trip number is calculated; where: This is the itinerary number.

[0117] In the formula: t i This refers to the data frame acquisition time. The time difference is in seconds. v i Speed ​​(km / h); a i acceleration (m / s²) 2 ); Set the threshold for trip segmentation (s); This is the itinerary number.

[0118] The acquisition time, longitude, and latitude of the first data frame are taken as the start time, start position, end time, and end position of the trip. Trip statistics are calculated for electrification potential assessment, as shown in formulas (5) to (9).

[0119] S26, Through-type

[0120] (5)

[0121] (6)

[0122] (7)

[0123] (8)

[0124] (9)

[0125] Calculate travel statistics.

[0126] In the formula: The average speed during the journey (km / h); n Number of frames in the trip data; L Mileage (km);M n and M 1 These are the odometer readings (km) for the last frame and the first frame of the trip, respectively. E fuel The diesel fuel consumption per trip (L); f i Engine fuel flow rate (L / h) E nox NO x Emissions (g); u gas The ratio of exhaust component density to exhaust density; SCR i The output value of the NOx sensor downstream of the SCR (ppm); IA i Intake volume (kg / h); Diesel fuel density (kg / L); E carbon Carbon emissions (kg); This refers to the carbon-fuel ratio.

[0127] Step S3 specifically includes the following processes:

[0128] (2) Electrification replacement model for heavy-duty diesel trucks in intercity transportation

[0129] This invention proposes a method that integrates consideration of vehicle purchase cost, investment in battery swapping infrastructure, backup battery configuration cost, changes in energy replenishment cost, freight route detour time cost, and carbon emissions and NOx. x A joint optimization model for electrification substitution and infrastructure deployment of emission reduction subsidy benefits. The model aims to minimize the total system cost, and the decision variables include the site selection of battery swapping stations, the proportion of electric trucks to be replaced, and the selection of task paths. By constructing a mixed integer programming model, the substitution strategies under different scenarios are comprehensively evaluated, and the global optimal solution or near-optimal solution is obtained based on a commercial optimization solver, outputting a highly operable electrification substitution and battery swapping network construction scheme. Formulas (10) to (12) are the spatiotemporal trajectory model and energy constraints of electric heavy trucks. To consider the impact of vehicle weight and driving behavior on energy consumption, the energy consumption factor is calculated by the measured fuel efficiency of the vehicle, the volumetric calorific value of the diesel engine, and the ratio of the power efficiency of the motor and the diesel engine. By introducing electrification substitution variables and penalty term M, the energy structure and constraint effectiveness of the fleet are controlled.

[0130] (10)

[0131] (11)

[0132] (12)

[0133] In equations (10) to (12): and The remaining battery power (kWh) of the vehicle is for two adjacent time steps. x itr It indicates a vehicle i Is it in time? t Select path r The binary variable for driving, 1 indicates selection, 0 indicates no selection; L r For path r Length (km); EF i For vehicles i Energy consumption factor (kWh / km); The ratio of the work efficiency of electric motors to that of diesel engines; The volumetric calorific value of the diesel engine (kWh / L); This is the lower limit of the SOC (State of Charge) for electric heavy-duty trucks on highways. For electric heavy trucks i Battery capacity (kWh) y i Let be the decision variable for whether diesel heavy truck i should be replaced by electric heavy truck, where 0 indicates no replacement and 1 indicates replacement; M is a positive number generated by a computer representing infinity.

[0134] The above model establishes a spatiotemporal trajectory model of trucks on the highway network by introducing path selection variables. However, it is still necessary to introduce flow balance constraints, origin-end point constraints, arrival time constraints, and path uniqueness constraints to ensure the validity of the trajectory. Flow balance constraints are established for each node of the highway network according to formulas (13) to (15) to ensure the validity of the spatiotemporal trajectory and the origin-end point. Formula (16) ensures timely arrival of vehicles by constraining the sum of path selections later than the arrival time to 0. The travel time is calculated using formula (18). Formula (17) is the path uniqueness constraint.

[0135] (13)

[0136] (14)

[0137] (15)

[0138] (16)

[0139] (17)

[0140] (18)

[0141] In equations (13) to (18): For vehicles i Drive through the path r Time (h); n , n’ , n’’ These are different nodes in the highway network; s i and d i These are the starting point and the ending point of vehicle i, respectively; T i max Let be the latest arrival time of vehicle i.

[0142] The battery swapping model for electric heavy-duty trucks at highway service areas is shown below. Formula (19) ensures that the vehicle can only swap batteries if it passes through a service area and the service area has a battery swapping station. The battery capacity after swapping is the battery capacity, i.e., 100% SOC.

[0143] (19)

[0144] (20)

[0145] (twenty one)

[0146] In equations (19) to (21): For vehicles i Is it in time? t The decision variable for battery swapping is 1 for swapping batteries and 0 for not swapping batteries; z n Service area nodes n The decision variable for whether or not to build a battery swapping station is 1, where 1 indicates that a battery swapping station is built and 0 indicates that a battery swapping station is not built. This refers to the battery swapping time.

[0147] This invention converts the costs of battery swapping stations and electric trucks into equivalent annual costs, taking into account fuel savings, detour time costs, and NOx. x Using emissions reduction subsidies and carbon trading revenue, an optimization model for electrification schemes is established to assess the electrification potential of various diesel trucks within the region. Objective function C This indicates the change in annual benefits before and after system optimization. CA value greater than 0 indicates a profit from optimization, while a value less than 0 indicates a loss, calculated according to formula (22). The calculation formulas for each part are shown in (23) to (28). The annual cost of the battery swapping station and electric heavy truck is converted into the annual cost using the discount rate of the project, while other costs are converted into RMB 10,000 by introducing price coefficients and daily average travel discount coefficients, with a statistical period of one year. The detour time cost needs to consider the path changes before and after system optimization. The fuel saving revenue needs to consider the path changes and whether the vehicle is electrified. Whether the vehicle is electrified also determines the NO x Emissions reduction subsidies and carbon trading revenue.

[0148] (twenty two)

[0149] (twenty three)

[0150] (twenty four)

[0151] (25)

[0152] (26)

[0153] (27)

[0154] (28)

[0155] In equations (22) to (28): C f Annual fuel savings (ten thousand yuan); C nox NO for the year x Subsidy income (ten thousand yuan); C C Annual carbon trading revenue (ten thousand yuan); C bss The equivalent annual cost of a battery swapping station (ten thousand yuan); C eht Annual cost (ten thousand yuan) for purchasing electric heavy-duty trucks; C T Cost of detour time (ten thousand yuan); T bss The service life of the battery swapping station; For the first t Annual operating expense ratio; r The discount rate for the project; S bss The residual value rate of the battery swapping station equipment; The construction cost of the battery swapping station (ten thousand yuan); S eht The residual value rate of electric heavy-duty trucks;T eht This refers to the service life of electric heavy-duty trucks; Cost of purchasing electric heavy-duty trucks (ten thousand yuan); NO x Emission reduction subsidy (ten thousand yuan / g); T i For vehicles i The daily equivalent number of trips is calculated using monitoring data; The cost of detour time (ten thousand yuan / h); Electricity price (ten thousand yuan / kWh); Oil price (ten thousand yuan / liter); This refers to the price of carbon trading.

[0156] The parameters involved in this invention and their recommended values ​​are shown in the table below.

[0157] Table 1 Parameter Reference Values

[0158]

[0159] (3) Model solution and electrification potential assessment

[0160] The mathematical model established in this invention is a mixed-integer linear programming model with integer variables and linear constraints. The optimization objective is to maximize the overall system benefit. The model constraints cover multiple aspects, including path selection flow balancing, dynamic power updates, battery swapping behavior logic, vehicle task time limits, and battery swapping facility capacity. For instances with a small number of vehicles, moderate path discrete granularity, and sparse distribution of battery swapping stations, commercial mathematical optimization software (such as Gurobi and CPLEX) can be used to perform global optimization of the model. Considering that this invention solves a planning problem and does not require real-time dynamic output, a high-performance computer with at least 32 cores and a CPU with a clock speed greater than 3.5 GHz and more than 64 GB of memory can be used to solve the problem. For situations with large scale, an expanding set of feasible paths, and variable explosion due to finer time granularity, approximate optimization strategies (Lagrangian relaxation, adaptive large neighborhood search, reinforcement learning, etc.) are recommended.

[0161] After solving the model, the electrification benefits of all diesel trucks replaced by electric heavy-duty trucks can be calculated to assess their electrification potential, providing data support for vehicle owners, battery swapping station operators, and government departments to conduct electrification feasibility assessments. The electrification potential quantification method proposed in this invention is shown in formula (29), which is based on the vehicle's annual fuel savings, NO x It consists of subsidy income, carbon trading income, annual cost equivalent to the purchase of electric heavy trucks, detour time cost, and annual cost equivalent to the average number of battery swapping stations per vehicle.

[0162] (29)

[0163] Secondly, this invention provides a system for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data, such as... Figure 3 As shown, it includes:

[0164] The data acquisition module 301 communicates with an external remote monitoring platform and is used to acquire engine data, OBD data and SCR data of intercity heavy freight vehicles through the remote monitoring platform.

[0165] Modeling and evaluation module 302 is used for:

[0166] Based on the acquired engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles, freight OD identification is performed, and the travel data of intercity heavy-duty freight vehicles is statistically analyzed.

[0167] The potential for electrification of target intercity heavy-duty freight vehicles is assessed by constructing and solving an electrification replacement model for intercity heavy-duty diesel trucks.

[0168] Output module 303 is used to visualize the evaluation results of the electrification potential of the target intercity heavy freight vehicles.

[0169] In some preferred embodiments, the system also has a heavy-duty vehicle electrification plan formulation module, which is used to: formulate a corresponding electrification plan for intercity heavy-duty freight vehicles based on the assessment results of the electrification potential of all intercity heavy-duty freight vehicles (within the jurisdiction).

[0170] In summary, this invention provides a method and system for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data. It utilizes trajectory time intervals, speed changes, and stopping behavior to segment the journey; and calculates fuel consumption, average speed, and NO based on data collected by high-precision sensors. x Emissions, as a crucial parameter for assessing electrification potential, are used to preserve the heterogeneity of cargo weight, driving behavior, and route selection for different freight tasks within the algorithm. A method is proposed that integrates multiple economic factors, including purchase costs, construction and operation expenses of battery swapping infrastructure, fuel savings, carbon trading revenue, nitrogen oxide emission reduction subsidies, and detour time costs, into a unified measurement system. A measurement framework is established through an equivalent annual cost conversion mechanism for scheme optimization. A four-dimensional state space—vehicle-time-route-electricity—is established. Relying on a highway network geographic information system, the method comprehensively considers route selection, battery swapping station deployment, battery swapping behavior, energy consumption heterogeneity, and task time constraints to achieve joint optimization of battery swapping station service area location and freight truck electrification schemes, maximizing the optimization potential of battery swapping detours while satisfying time window constraints. The model is transformed into a mixed-integer programming solution by using binary path selection variables and integer variables. The method and system provided by this invention have the following beneficial effects:

[0171] (1) Comprehensive consideration of factors in the assessment of electrification potential

[0172] This invention comprehensively considers vehicle purchase costs, investment in battery swapping station construction, fuel expenditure differences, transportation time costs, carbon revenue under the carbon trading mechanism, and NOx emission reduction subsidies. Detailed quantitative analysis of the electrification benefits from different aspects can more effectively support the formulation and implementation of electrification plans by various stakeholders.

[0173] (2) The economic and environmental benefits are accurately quantified.

[0174] Compared to calculation methods based on constant parameters and traffic survey data, the calculation method based on remote monitoring data can reflect the changes in indicators caused by the heterogeneity of freight tasks. It provides a more accurate assessment of the vehicle electrification potential and enhances the effectiveness of system electrification solutions.

[0175] (3) Maximize the potential for optimizing battery swapping routes while ensuring logistical efficiency.

[0176] This invention, relying on the geographic information system and spatiotemporal mapping framework of the highway network, can maximize the system optimization potential brought about by battery swapping detours and reduce the construction cost of battery swapping facilities. Based on the identified logistics time window constraints, it can ensure on-time arrival without delays. The above two points overcome the limitations of existing technologies that assume the trajectory remains unchanged before and after electrification.

[0177] (4) The system of the present invention uses the above-mentioned evaluation method, which can effectively save system resources and improve the evaluation accuracy.

[0178] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0179] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0180] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0181] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the electrification potential of heavy-duty vehicles based on remote monitoring data, characterized in that, include: S1. Obtain engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles through a remote monitoring platform; S2. Based on the acquired engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles, perform freight OD identification and statistical analysis of the intercity heavy-duty freight vehicle travel data; specifically including: S21, Through-type （1） （2） Frame sequence for calculating the instantaneous acceleration of intercity heavy freight vehicles; where: t i This refers to the data frame acquisition time. For time difference; v i For speed; a i For acceleration; S22. Remove the idle frames in the frame sequence of instantaneous acceleration of intercity heavy freight vehicles where both speed and acceleration are 0, and then calculate the non-idle time difference using equation (1). S23, If the non-idle time difference satisfies the equation （3） Then it is determined that the current data frame of instantaneous acceleration of the intercity heavy freight vehicle and the previous data frame of instantaneous acceleration of the intercity heavy freight vehicle belong to different trips; where: Define the threshold for the journey; S24. Traverse all data frames of the frame sequence of instantaneous acceleration of intercity heavy freight vehicles, and use the formula... （4） The trip number is calculated; where: For the itinerary number; S25. Take the acquisition time, longitude, and latitude of the first data frame of the instantaneous acceleration frame sequence of the heavy freight vehicle as the start time, start position, end time, and end position of the journey. S26, Through-type （5） （6） （7） （8） （9） Calculate travel statistics; in equations (1) to (4): t i This refers to the data frame acquisition time. The time difference is in seconds. v i For speed; a i For acceleration; Define the threshold for the journey; For the trip number; in equations (5) to (9): This represents the average speed over the journey. n Number of frames in the trip data; L This refers to the mileage traveled. M n and M 1 These are the odometer readings for the last frame and the first frame of the trip, respectively. E fuel This refers to the diesel fuel consumption during the trip. f i Engine fuel flow rate; E nox NO x Emissions; u gas The ratio of exhaust component density to exhaust density; SCR i NO downstream of SCR x Sensor output value; IA i This refers to the intake air volume; Diesel density; E carbon Carbon emissions; Carbon-fuel ratio; S3. By constructing and solving the electrification replacement model for heavy-duty diesel trucks in intercity transportation, the electrification potential of target intercity heavy-duty freight vehicles is evaluated; specifically including: S31, using the spatiotemporal trajectory model and energy constraint of electric heavy trucks （10） （11） （12） Flow balance constraint, origin-end point constraint, arrival time constraint, path uniqueness constraint （13） （14） （15） （16） （17） （18） Electric heavy trucks battery swapping model at highway service areas （19） （20） （21） and objective function （22） （23） （24） （25） （26） （27） （28） Construct a model for the electrification of heavy-duty diesel trucks in intercity transportation; In equations (10) to (12): and These represent the remaining battery power of the vehicles at two adjacent time steps; x itr It indicates a vehicle i Is it in time? t Select path r The binary variable for driving, 1 indicates selection, 0 indicates no selection; L r For path r Length; EF i For vehicles i Energy consumption factor; The ratio of the work efficiency of electric motors to that of diesel engines; This refers to the volumetric calorific value of a diesel engine. This is the lower limit of the State of Charge (SOC) for electric heavy-duty trucks on highways. For electric heavy trucks i Battery capacity; y i For diesel heavy trucks i The decision variable for whether to replace with an electric heavy truck is 0, which means no replacement and 1 means replacement; M is a positive number generated by a computer representing infinity; in equations (13) to (18): For vehicles i Drive through the path r Time; n , n’ , n’’ These are different nodes in the highway network; s i and d i These are the starting point and the ending point of vehicle i, respectively; T i max Let be the latest arrival time of vehicle i; in equations (19) to (21): For vehicles i Is it in time? t The decision variable for battery swapping is 1 for swapping batteries and 0 for not swapping batteries; z n Service area nodes n The decision variable for whether or not to build a battery swapping station is 1, where 1 indicates that a battery swapping station is built and 0 indicates that a battery swapping station is not built. For the battery swapping time; in equations (22) to (28): C f Annual fuel savings revenue; C nox NO for the year x Subsidy income; C C Annual carbon trading revenue; C bss This represents the equivalent annual cost of a battery swapping station. C eht The equivalent annual cost of purchasing electric heavy-duty trucks; C T For the time cost of detours; T bss The service life of the battery swapping station; For the first t Annual operating expense ratio; r The discount rate for the project; S bss The residual value rate of the battery swapping station equipment; For the construction cost of the battery swapping station; S eht The residual value rate of electric heavy-duty trucks; T eht This refers to the service life of electric heavy-duty trucks; Cost of purchasing electric heavy-duty trucks; NO x Emissions reduction subsidies; T i For vehicles i The daily equivalent number of trips is calculated using monitoring data; For the time cost of detours; For electricity price; For oil prices; For carbon trading prices; S32. Solve the model for the electrification of heavy-duty diesel trucks in intercity transportation, and then use the formula... （29） Calculate the electrification benefits of all diesel trucks replaced by electric heavy-duty trucks; The evaluation results of step S3 are used for the electrification replacement of intercity heavy freight vehicles.

2. A heavy-duty vehicle electrification potential assessment system based on remote monitoring data, characterized in that, The method for evaluating the electrification potential of heavy-duty vehicles based on remote monitoring data as described in claim 1 includes: The data acquisition module is used to acquire engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles through a remote monitoring platform. The modeling and evaluation module is used for: Based on the acquired engine data, OBD data, and SCR data of intercity heavy-duty freight vehicles, freight OD identification is performed, and the travel data of intercity heavy-duty freight vehicles is statistically analyzed. The potential for electrification of target intercity heavy-duty freight vehicles is assessed by constructing and solving an electrification replacement model for intercity heavy-duty diesel trucks. The output module is used to visualize the assessment results of the electrification potential of the target intercity heavy freight vehicles.

3. The heavy-duty vehicle electrification potential assessment system according to claim 2, characterized in that, It also includes a heavy-duty vehicle electrification planning module, which is used to: develop an electrification plan for intercity heavy-duty freight vehicles based on the assessment results of the electrification potential of all intercity heavy-duty freight vehicles obtained.

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