Heavy duty vehicle electric potential evaluation method and system based on remote monitoring data

Abstract

The invention provides a remote monitoring data-based heavy-duty vehicle electric potential evaluation method and system. The method comprises the following steps of: carrying out travel segmentation by utilizing a track time interval, speed change and a stop behavior; oil consumption, average speed and NOx emission are calculated based on data collected by a high-precision sensor and are used as important parameters for electric potential evaluation, and the effect of keeping the cargo weights, driving behaviors and path selection heterogeneity of different freight tasks in an algorithm is achieved; a method for uniformly bringing various economic factors such as purchase cost, power conversion infrastructure construction and operation expenditure, fuel saving benefit, carbon transaction benefit, nitrogen oxide emission reduction subsidy and detouring time cost into a measurement system is provided; a vehicle-time-path-electric quantity four-dimensional state space is established, and time window constraints are met while the optimization potential brought by battery swap bypassing is maximized by relying on a highway network geographic information system. The model is converted into mixed integer programming solution through binary path selection variables and integer variables, and powerful support is provided for electric replacement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, and particularly relates to a heavy vehicle electrification potential evaluation method and system based on remote monitoring data. BACKGROUND

[0002] Heavy diesel trucks are the backbone of intercity freight transport, and are also an important source of oil consumption, carbon emissions and pollutant emissions. Accelerating truck electrification in the field of intercity freight transport is of great significance to national energy security, climate change response and air pollution-related health problems. The high cost of vehicle purchase and supporting energy supplement facility construction, as well as operational technical problems, have caused problems in the electrification replacement of this type of vehicle. There is an urgent need for a diesel truck electrification potential evaluation method to support the development of electrification schemes and the planning of supporting energy supplement facilities.

[0003] Even if the charging infrastructure has almost covered all highway service areas, it is still necessary to build battery swap facilities in some service areas to support heavy truck electrification. Intercity heavy truck transportation has the characteristics of high energy consumption and strong time constraints. The current range of electric heavy trucks is difficult to support the entire intercity freight journey. The current charging facilities, even the super-charging technology, cannot complete charging within an hour. Charging during highway travel, especially for cold-chain vehicles, will have a high probability of causing logistics delays. On the one hand, battery swap stations have high construction costs and battery storage costs. Charging stations should be built in service areas with high potential demand for battery swapping to avoid resource mismatch and economic loss. On the other hand, due to differences in transportation frequency, cargo weight, route, traffic conditions and road slope, there is significant heterogeneity in energy consumption characteristics among different vehicles, resulting in significant differences in the spatial and temporal distribution of battery swap demand and economic and environmental benefits after electrification. The electric heavy truck freight path determines the distribution of service area battery swap demand, and whether the service area has a battery swap station also affects the electric heavy truck path planning. Under a specific electrification target and investment strategy, a diesel truck electrification potential evaluation method that considers path planning, economic benefits and environmental benefits can optimize resource allocation schemes and improve system operation efficiency.

[0004] Currently, there is a lack of research on electric replacement solutions for fuel trucks considering the demand of logistics system and the economic and environmental benefits of the whole life cycle. From the perspective of the enterprise subject, the high purchase cost of electric trucks, limited range, and imperfect charging facilities significantly increase the operational uncertainty. Due to the lack of scientific and systematic electric replacement solutions, enterprises are difficult to accurately assess the economic feasibility of electric trucks based on existing logistics demand, leading to insufficient replacement power. From the perspective of the government subject, existing policy making relies more on pilot experience, lacks systematic analysis of the fine features of freight demand, energy supply network layout, and economic and environmental benefits of the whole life cycle, and is difficult to formulate precise subsidy policies and mandatory measures. It is urgent to build a scientific and systematic electric replacement solution for freight vehicles to provide economic decision support for enterprises and provide a theoretical basis for the government to formulate precise policy tools.

[0005] The prior art solution is usually based on road section flow or speed, and makes rough estimates of energy consumption, emissions, and freight demand distribution. This will lead to inaccurate economic and environmental benefit evaluation and limited optimization potential, causing resource mismatch. Complex operating conditions such as heavy load, uphill, and congestion have a great impact on fuel consumption. The daily average carbon dioxide emissions of a truck can reach more than 200 kg, and the dominant position of pollutant emissions is even more significant. In the "Technical Specification for Remote Monitoring of Heavy Vehicle Emissions Part 3 Communication Protocol and Data Format" standard (hereinafter referred to as "national six monitoring standard"), it is clearly stipulated that heavy diesel vehicles must have the ability to collect and transmit on-board diagnostic system (OBD) data, engine data, and data of exhaust aftertreatment devices dominated by selective catalytic reduction technology. SUMMARY

[0006] Embodiments of the present application provide a heavy vehicle electric potential evaluation method and system based on remote monitoring data to solve the technical problems existing in the prior art.

[0007] To achieve the above purpose, the present application adopts the following technical solutions.

[0008] The heavy vehicle electric potential evaluation method based on remote monitoring data comprises:

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

[0010] S2, based on the obtained engine data, OBD data and SCR data of intercity heavy freight vehicles, identifying freight OD and statistically analyzing the travel data of intercity heavy freight vehicles;

[0011] S3, by constructing and solving an intercity transport heavy diesel truck electric replacement model, the electric replacement potential of the target intercity heavy freight vehicle is evaluated;

[0012] The evaluation result of step S3 is used for the electrically replacing operation of the intercity heavy freight vehicle.

[0013] Preferably, step S2 specifically comprises:

[0014] S21, calculating the frame sequence of the instantaneous acceleration of the intercity heavy freight vehicle by formula

[0015] △t i = t i -t i-1 , i = 2, 3, …, N (1)

[0016]

[0017] calculating the frame sequence of the instantaneous acceleration of the intercity heavy freight vehicle; in the formula: t i is the collection time of the data frame; △t i is the time difference; v i is the speed; a i is the acceleration;

[0018] S22, removing the idle speed frame in which the speed and acceleration of the instantaneous acceleration of the intercity heavy freight vehicle are both 0, and then calculating the non-idle speed time difference by formula (1);

[0019] S23, if the non-idle speed time difference satisfies formula

[0020] △t i > τ (3) then determining that the data frame of the instantaneous acceleration of the current intercity heavy freight vehicle and the data frame of the instantaneous acceleration of the last intercity heavy freight vehicle belong to different trips; in the formula: τ is a trip division threshold;

[0021] S24, traversing all data frames of the frame sequence of the instantaneous acceleration of the intercity heavy freight vehicle, and calculating the trip number by formula

[0022]

[0023] calculating the trip number; in the formula: trip_id i is the trip number;

[0024] S25, taking the collection time, longitude and latitude of the first data frame of the frame sequence of the instantaneous acceleration of the heavy freight vehicle as the start time, start position, end time and end position of the trip;

[0025] S26, calculating the trip distance by formula

[0026]

[0027] L = M n -M1 (6)

[0028]

[0029] E carbon = a p f · E fuel (9)

[0030] Calculate trip statistics; in equations (1) to (4): t i is the acquisition time of the data frame;△t i is the time difference (s); v i is the speed; a i is the acceleration; τ is the trip division threshold; trip_id i is the trip number; in equations (5) to (9): is the average speed of the trip; n is the number of trip data frames; L is the distance traveled; M n and M1 are the odometer readings of the last and first frames of the trip, respectively; E fuel is the diesel consumption of the trip; f i is the engine fuel flow; E nox is the NO x x emission; u gas is the exhaust component density and exhaust density ratio; SCR i is the NOx sensor output value downstream of the SCR; IA i is the intake air amount; ρ f is the diesel density; E carbon is the carbon emission; and a is the carbon combustion ratio.

[0031] Preferably, step S3 specifically comprises:

[0032] S31, through the space-time trajectory model of the electric heavy truck and the energy constraint equation

[0033] e it' = e it - x itr · L r · EF i (10)

[0034]

[0035] SOC min · e i Cap - (1-y i )· M≤ e it (12)

[0036] flow balance constraint equation, start and end point constraint equation, arrival time constraint equation, path uniqueness constraint equation

[0037]

[0038] Electric heavy truck in highway service area battery swap model

[0039]

[0040] And the objective function

[0041] max C=C f +C nox +C C -C bss -C eht -C T (22)

[0042]

[0043] Build an electric heavy-duty diesel truck replacement model for intercity transportation;

[0044] In formula (10) to formula (12): e it' and e it are the remaining power of the vehicle at adjacent two time steps; x itr is a binary variable indicating whether vehicle i chooses path r to travel at time t, 1 means choosing, 0 means not choosing; L r is the length of path r; EF i is the energy consumption factor of vehicle i; η is the work efficiency ratio of motor and diesel engine; δ is the volume heat value of diesel engine; SOC min is the lower limit of SOC of electric heavy truck on highway; e i Cap is the battery capacity of electric heavy truck i; y i is the decision variable of whether diesel heavy truck i is replaced by electric heavy truck, 0 means not replaced, 1 means replaced; M is a positive number generated by computer representing infinity; In formula (13) to (18): τ ir is the time for vehicle i to travel through path r; n, n', n" are different nodes in highway network; s i and d i are the starting point and the destination of vehicle i respectively; T i max is the latest arrival time of vehicle i; In formula (19) to formula (21): v it is the decision variable of whether vehicle i performs battery swap at time t, 1 means battery swap, 0 means no battery swap; z n is the decision variable of whether service area node n is equipped with battery swap station, 1 means equipped with battery swap station, 0 means not equipped with battery swap station; δ is the battery swap time; In formula (22) to formula (28): C f is the annual fuel saving income; C nox is the annual NO x subsidy income; CC is the annual carbon trading income; C bss is the equivalent annual cost of the battery swap station; C eht is the equivalent annual cost of the purchase of the electric heavy truck; C T is the detour time cost; T bss is the service life of the battery swap station; γ t,bss is the operation cost ratio in the t-th year; r is the project discount rate; S bss is the residual value rate of the battery swap station equipment; P bss is the construction cost of the battery swap station; S eht is the residual value rate of the electric heavy truck; T eht is the service life of the electric heavy truck; p eht is the purchase cost of the electric heavy truck; ε nox is the NO x is the emission reduction subsidy; T i is the daily equivalent travel distance of the vehicle i, which is calculated by monitoring data; ε T is the detour time cost; ε e is the electricity price; ε f is the oil price; ε carbon is the carbon trading price;

[0045] S32, solve the intercity transport heavy diesel truck electrification replacement model, and then calculate the electrification benefit of all diesel trucks replaced by electric heavy trucks by formula

[0046]

[0047]

[0048] In a second aspect, the present application provides a heavy vehicle electrification potential evaluation system based on remote monitoring data, characterized in that it comprises:

[0049] A data acquisition module is configured to acquire engine data, OBD data and SCR data of intercity heavy freight vehicles through a remote monitoring platform.

[0050] A modeling and evaluation module is configured to:

[0051] Based on the acquired engine data, OBD data and SCR data of intercity heavy freight vehicles, freight OD identification is performed, and the travel data of intercity heavy freight vehicles is counted.

[0052] The electrification replacement potential of the target intercity heavy freight vehicle is evaluated by constructing and solving an intercity transport heavy diesel truck electrification replacement model.

[0053] An output module is configured to visually output the evaluation result of the electrification replacement potential of the target intercity heavy freight vehicle.

[0054] ​Preferably, the heavy vehicle electrification planning module is further included for formulating an electrification replacement plan for the intercity heavy freight vehicle according to the evaluation results of the obtained electrification replacement potential of all intercity heavy freight vehicles.

[0055] As can be seen from the technical solutions provided by the above-mentioned embodiments of the present application, the present application provides a heavy vehicle electrification potential evaluation method and system based on remote monitoring data, which utilizes trajectory time interval, speed change and stop behavior to divide the trip; calculates fuel consumption, average speed and NO x emission based on high-precision sensor collected data as important parameters for electrification potential evaluation, achieving the effect of preserving the heterogeneity of freight weight, driving behavior and path selection of different freight tasks in the algorithm. A method is proposed to unify multiple economic factors such as purchase cost, replacement infrastructure construction and operation expenditure, fuel saving benefit, carbon trading income, nitrogen oxide emission reduction subsidy, detour time cost, etc. into the measurement system, and to establish a measurement framework through equivalent annual cost conversion mechanism to optimize the scheme. A four-dimensional state space of vehicle-time-path-electricity is established, relying on the highway network geographic information system, considering path selection, replacement station deployment, replacement behavior, energy consumption heterogeneity and task time limit, realizing the joint optimization of replacement station service area site selection and freight vehicle electrification scheme, maximizing the optimization potential brought by replacement detour while meeting the time window constraint. The model is converted into a mixed integer programming through binary path selection variable and integer variable. The method and system provided by the present application have the following beneficial effects:

[0056] (1) Comprehensive consideration of electrification potential evaluation factors

[0057] The present application comprehensively considers the purchase cost, replacement station construction investment, fuel expenditure difference, transportation task time cost, carbon income under the carbon trading mechanism, and NOx emission reduction subsidy. Detailed quantitative analysis of the electrification benefits in different aspects can more effectively support the formulation and implementation of electrification schemes by different subjects.

[0058] (2) Accurate quantification of economic and environmental benefits

[0059] Compared with the calculation method based on constant parameters and traffic survey data, the calculation method based on remote monitoring data can reflect the index changes brought by the heterogeneity of freight tasks. The evaluation of vehicle electrification potential is more accurate; the effectiveness of the system electrification scheme is stronger.

[0060] (3) Maximize replacement detour optimization potential while ensuring logistics effectiveness

[0061] The application can maximize the system optimization potential brought by the battery swap detour, and reduce the construction cost of the battery swap facility, relying on the geographic information system and the space-time graph modeling framework of the high-speed network. Based on the identified logistics time window constraint, timely arrival without delay can be ensured. The above two points overcome the limitations brought by the assumption that the trajectory remains unchanged before and after electrification in the prior art.

[0062] (4) The system of the application uses the above-mentioned evaluation method, which can effectively save system resources and improve evaluation accuracy.

[0063] Additional aspects and advantages of the application will be set forth in part in the description which follows, and will be apparent from the description, or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0065] Figure 1 The processing flow chart of the heavy vehicle electrification potential evaluation method based on remote monitoring data provided by the application;

[0066] Figure 2 The process schematic diagram of a preferred embodiment of the heavy vehicle electrification potential evaluation method based on remote monitoring data provided by the application;

[0067] Figure 3 The logic block diagram of the heavy vehicle electrification potential evaluation system based on remote monitoring data provided by the application. DETAILED DESCRIPTION

[0068] The embodiments of the application will be described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the application, and cannot be interpreted as a limitation on the application.

[0069] As those skilled in the art will appreciate, unless otherwise indicated herein, the singular forms "a", "an" and "the" include plural referents. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "connected" or "coupled" can include both direct connections and indirect connections (i.e., via one or more other elements). In addition, the term "connected" or "coupled" can include both wireless connections and / or wired connections. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0070] As those skilled in the art will appreciate, unless otherwise indicated herein, the singular forms "a", "an" and "the" include plural referents. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "connected" or "coupled" can include both direct connections and indirect connections (i.e., via one or more other elements). In addition, the term "connected" or "coupled" can include both wireless connections and / or wired connections. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0071] For the purpose of clarity, the present application will be further described with reference to the accompanying drawings, in which several embodiments of the application will be illustrated, and the various embodiments are not intended to limit the scope of the application.

[0072] The present application provides a heavy vehicle electrification potential evaluation method and system based on remote monitoring data to solve the following technical problems existing in the prior art:

[0073] (1) Single dimension of electrification potential evaluation

[0074] Current heavy diesel truck electrification potential evaluation usually only considers the purchase cost and the price difference between electricity and diesel, ignoring other key economic factors. For example, the matching relationship between the construction cost of battery swap infrastructure and its service capacity, the impact of transportation timeliness on operating income, carbon emission trading policy and local government NOx emission reduction subsidies, etc. These factors have a significant impact on project payback period and alternative strategies. The evaluation results are one-sided and cannot reflect the true economic feasibility under the coexistence of various costs and benefits, which may underestimate the electrification potential or mislead investment deployment, affecting policy implementation and enterprise decision-making.

[0075] (2) Inaccurate prediction of energy supplement demand

[0076] Traditional methods evaluate the heavy truck battery swap demand in service area based on cross-section flow or vehicle speed data, which cannot accurately reflect the energy consumption changes caused by load, working condition difference, traffic congestion and other factors in actual operation, leading to incorrect estimation of battery swap demand in electric vehicle potential evaluation. At the same time, the energy supplement point position is often based on a static layout model, which does not have the ability to identify dynamic energy supplement demand. There is a large error in the design of battery swap path, which leads to the interruption of part of the task in the middle of the endurance or the redundancy of the layout, reducing the system operation reliability and resource utilization.

[0077] (3) unable to consider freight time constraints and detours

[0078] Existing models are mainly based on two assumptions: 1) the transportation route does not change before and after the vehicle is electrified. Unable to consider the path induction caused by the deployment of battery swap stations, which may cause incorrect evaluation of electric vehicle potential (unable to electrify due to lack of battery swap stations on the transportation route) or mismatch of battery swap facility resources (redundant configuration / incorrect location). 2) route planning does not affect transportation efficiency. Ignored the strict requirements of heavy freight tasks on timeliness and the problems of path delay and scheduling conflict caused by battery swap detour. Even if the battery swap path is sufficient in theory, if it needs to detour a long distance to the battery swap station or the battery swap takes too long, it will violate the transportation contract and miss the loading and unloading time window, making it difficult to implement the electric vehicle strategy.

[0079] Therefore, the purpose of the present application is to:

[0080] (1) Build a multi-dimensional economic evaluation model that considers the purchase cost of the vehicle, the construction investment of the battery swap station, the difference in fuel expenditure, the time cost of transportation tasks, the carbon income under the carbon trading mechanism, and the NOx emission reduction subsidy, to improve the full-cycle and full-factor judgment ability of heavy diesel truck electrification path;

[0081] (2) Relying on remote monitoring trajectory and energy consumption data, build a dynamic energy consumption calculation and battery swap demand calculation mechanism based on task path, support high-precision matching and feasibility verification of the relationship between transportation path and battery swap station coverage under the condition of limited battery swap station resources;

[0082] (3) Explicitly introduce the arrival time limit constraint and detour cost term of freight tasks in the electric heavy truck path planning model, to ensure that the generated alternative path is not only energy feasible and economically reasonable, but also has practical operational feasibility.

[0083] Reference Figure 1 The present application provides a heavy vehicle electrification potential evaluation method based on remote monitoring data, comprising the following steps:

[0084] S1, through the remote monitoring platform, obtain engine data, OBD data and SCR data of intercity heavy freight vehicles;

[0085] S2, based on the obtained engine data, OBD data and SCR data of the intercity heavy freight vehicle, performing freight OD identification, and counting the trip data of the intercity heavy freight vehicle;

[0086] S3, by constructing and solving an intercity transport heavy diesel freight vehicle electricization replacement model, evaluating the electricization replacement potential of the target intercity heavy freight vehicle.

[0087] The evaluation result of step S3 is used for the electricization replacement operation of the intercity heavy freight vehicle, for example, providing data support for formulating the electricization replacement scheme of the intercity heavy freight vehicle.

[0088] In the preferred embodiments provided by the present application, the specific execution process of steps S1 and S2 includes:

[0089] (1) Freight OD identification

[0090] According to the national six platform national six monitoring standard heavy diesel freight vehicle needs to collect and transmit vehicle speed, collection time, engine fuel flow, selective catalytic reduction system (SCR) upstream / downstream NOx sensor output value and other data with 1HZ frequency. The present application provides a kind of trip division method based on frame monitoring data. According to formula (1) and (2), the instantaneous acceleration of vehicle is calculated, and the idle speed frame in which the speed and acceleration are both 0 is removed. According to formula (1), the non-idle time difference is calculated again, and when formula (3) is satisfied, the current data frame and the last data frame belong to different trips. The data frame is traversed, and the trip number is calculated according to formula (4).

[0091] For example, in some feasible embodiments, the following sub-steps are performed:

[0092] S21, by formula

[0093] △t i =t i -t i-1 ,i=2,3,…,N (1)

[0094]

[0095] The frame sequence of the instantaneous acceleration of the intercity heavy freight vehicle is calculated; in the formula: t i is the collection time of data frame; △t i is the time difference; v i is the speed; a i is the acceleration;

[0096] S22, removing the idle speed frame in which the speed and acceleration of the instantaneous acceleration frame sequence of the intercity heavy freight vehicle are both 0, and then calculating the non-idle time difference by formula (1);

[0097] S23, if the non-idle time difference satisfies formula

[0098] △t i >τ (3) then determine the current intercity heavy freight vehicle instantaneous acceleration data frame and the last intercity heavy freight vehicle instantaneous acceleration data frame belongs to different trips; In the formula: τ is the trip division threshold;

[0099] S24, all data frames of the frame sequence of the intercity heavy freight vehicle instantaneous acceleration are traversed, and the formula

[0100]

[0101] The trip number is obtained by calculation; In the formula: trip_id i is the trip number.

[0102] In the formula: t i is the collection time of the data frame;△t i is the time difference (s); v i is the speed (km / h); a i is the acceleration (m / s 2 ); τ is the trip division threshold (s); trip_id i is the trip number.

[0103] The collection 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. The trip statistical data is calculated for electricization potential evaluation, such as formulas (5) to (9).

[0104] S26, by formula

[0105]

[0106] L=M n -M1 (6)

[0107]

[0108] E carbon =α·ρ f ·E fuel (9)

[0109] The trip statistical data is calculated.

[0110] In the formula: is the average speed of the trip (km / h); n is the number of trip data frames; L is the driving mileage (km); M n and M1 are the mileage table readings (km) of the last frame and the first frame of the trip respectively; E fuel is the diesel consumption of the trip (L); f i is the engine fuel flow (L / h) E noxNO x Emission (g); u gas is the exhaust component density and exhaust density ratio;

[0111] SCR i NOx sensor output value (ppm) downstream of SCR; IA i Intake air amount (kg / h); p f Diesel density (kg / L); E carbon Carbon emission (kg); a is the carbon combustion ratio.

[0112] The specific process of step S3 includes:

[0113] (2) Intercity transport heavy diesel truck electrification replacement model

[0114] The present application proposes an electrification replacement and infrastructure deployment joint optimization model considering vehicle purchase cost, battery swap infrastructure investment, spare battery configuration cost, energy supplement cost change, freight path detour time cost, carbon emission and NO x x reduction subsidy income. The model takes the minimum total system cost as the optimization objective, and the decision variables include battery swap station site selection scheme, electric truck replacement ratio and task path selection, etc. By constructing a mixed integer programming model, the replacement strategy in different scenarios is comprehensively evaluated, and the global optimal solution or approximate optimal solution is obtained based on a commercial optimization solver, and the operability of the electrification replacement and battery swap network construction scheme is output. Formulas (10) to (12) are the space-time trajectory model and energy constraint of electric heavy truck. In order to consider the influence 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 heat value of the diesel engine and the work efficiency ratio of the motor and the diesel engine. By introducing the electrification replacement variable and the penalty term M, the control of the energy structure of the vehicle fleet and the effectiveness of the constraint is realized.

[0115] e it' = e it -x itr ·L r ·EF i (10)

[0116]

[0117] SOC min ·e i Cap -(1-y i )·M≤e it (12)

[0118] In formulas (10) to (12), e it' and e it are the remaining electric quantity (kWh) of the vehicle at adjacent two time steps; xitr is a binary variable indicating whether vehicle i chooses path r at time t to travel, 1 means choosing, and 0 means not choosing; L r is the length of path r (km); EF i is the energy consumption factor of vehicle i (kWh / km); η is the work efficiency ratio of the electric motor and diesel engine; δ is the volumetric heat value of the diesel engine (kWh / L); SOC min is the lower limit of the SOC (state of charge) of the electric heavy truck on the highway; e i Cap is the battery capacity of electric heavy truck i (kWh) y i is a decision variable indicating whether diesel heavy truck i is replaced by an electric heavy truck, 0 means not replaced, and 1 means replaced; M is a positive number representing infinity generated by a computer.

[0119] The above model establishes a space-time trajectory model of trucks on the highway network by introducing path selection variables, and still needs to introduce flow balance constraints, start and end point constraints, arrival time constraints, and path uniqueness constraints to ensure the validity of the trajectory. According to formulas (13) to (15), flow balance constraints are established for each node in the highway network to ensure the validity of the space-time trajectory and the validity of the start and end points. Formula (16) ensures that the vehicle arrives on time by constraining the sum of path selections later than the arrival time to be 0, and the travel time is calculated by formula (18). Formula (17) is the path uniqueness constraint.

[0120]

[0121]

[0122] In formulas (13) to (18): τ ir is the time (h) taken by vehicle i to travel path r; n, n', n" are different nodes in the highway network; s i and d i are the start and end points of vehicle i, respectively; T i max is the latest arrival time of vehicle i.

[0123] The electric heavy truck battery replacement model at the highway service area is as follows. Formula (19) ensures that the vehicle can only replace the battery if it has passed the service area and the service area has a battery replacement station. The battery capacity after replacement is 100% SOC.

[0124]

[0125] In formulas (19) to (21): v it is a decision variable indicating whether vehicle i replaces the battery at time t, 1 means replacing the battery, and 0 means not replacing the battery; z nis the decision variable of whether the service area node n is built with a battery swap station, 1 for having a battery swap station, 0 for not having a battery swap station; δ is the battery swap time.

[0126] The application converts the battery swap station and electric truck costs into equivalent annual costs, considers fuel savings, detour time costs, NO x emission reduction subsidies and carbon trading income, establishes an electricization scheme optimization model, and evaluates the electricization potential of each diesel truck in the region. The objective function C represents the annual benefit change before and after system optimization. When C is greater than 0, it means that optimization brings profit, and when C is less than 0, it means loss, which is calculated according to formula (22). The calculation formulas of each part are shown in formulas (23) to (28). The battery swap station and electric heavy truck are converted into annual costs through the discount rate of the project, and other costs are converted into ten thousand yuan through the introduction of price coefficient and daily trip conversion coefficient, with a statistical period of one year. The detour time cost needs to consider the path change before and after the system optimization. The fuel saving income needs to consider the path change and whether to be electricized. Whether the vehicle is electricized also determines the NO x emission reduction subsidies and carbon trading income.

[0127] max C=C f +C nox +C C -C bss -C eht -C T (22)

[0128]

[0129] In formulas (22) to (28), C f is the annual fuel saving income (ten thousand yuan); C nox is the annual NO x x emission reduction subsidy income (ten thousand yuan); C C is the annual carbon trading income (ten thousand yuan); C bss is the equivalent annual cost of the battery swap station (ten thousand yuan); C eht is the equivalent annual cost of the electric heavy truck (ten thousand yuan); C T is the detour time cost (ten thousand yuan); T bss is the service life of the battery swap station; γ t,bss is the operating expense ratio in the tth year; r is the project discount rate; S bss is the residual value rate of the battery swap station; P bss is the construction cost of the battery swap station (ten thousand yuan); S eht is the residual value rate of the electric heavy truck; T eht is the service life of the electric heavy truck; p eht is the purchase cost of the electric heavy truck (ten thousand yuan); ε nox is the NO x emission reduction subsidy (ten thousand yuan / g); T iis the number of equivalent trips of vehicle i in a day, which is calculated by monitoring data; ε T is the detour time cost (ten thousand yuan / h) ; ε e is the electricity price (ten thousand yuan / kWh) ; ε f is the oil price (ten thousand yuan / L) ; ε carbon is the carbon trading price.

[0130] The parameters involved in the present application are introduced and the recommended values are shown in the following table.

[0131] Table 1 Reference values of parameters

[0132]

[0133]

[0134] (3) Model solution and electricization potential evaluation

[0135] The mathematical model established by the present application is a mixed integer linear programming model with integer variables and linear constraints. The optimization objective is to maximize the total benefit of the system, and the model constraints cover path selection flow balance, electric quantity dynamic update, battery swapping behavior logic, vehicle task time limit and battery swapping facility capacity, etc. For instances with fewer vehicles, moderate path discrete granularity and sparse battery swapping station distribution, commercial mathematical optimization software (such as Gurobi, CPLEX) can be used to globally optimize and solve the model. Considering that the present application solves a planning problem and does not need to output results in real time, a high-performance computer with no less than 32 cores, a CPU frequency greater than 3.5 GHz and more than 64 GB of memory can be used for solving. In the case of large scale, path feasible set expansion and time granularity refinement leading to variable explosion, it is recommended to use approximate optimization strategies (Lagrangian relaxation method, adaptive large neighborhood search, reinforcement learning, etc.).

[0136] After solving the model, the electricization benefit of all diesel trucks replaced by electric heavy trucks can be calculated to evaluate the electricization potential, providing data support for vehicle owners, battery swapping station operators and government departments to evaluate the feasibility of electricization. The electricization potential method proposed by the present application is shown in formula (29), which is composed of the annual fuel saving income, NO x subsidy income, carbon trading income, equivalent annual cost of electric heavy truck purchase, detour time cost and equivalent annual cost of vehicle per battery swapping station.

[0137]

[0138] In a second aspect, the present application provides a heavy vehicle electricization potential evaluation system based on remote monitoring data, as shown in formula (30), which comprises: Figure 3 ​

[0139] The data acquisition module 301 is in communication connection with an external remote monitoring platform, and is used to acquire engine data, OBD data and SCR data of the intercity heavy freight vehicle through the remote monitoring platform;

[0140] The modeling evaluation module 302 is used for:

[0141] Based on the acquired engine data, OBD data and SCR data of the intercity heavy freight vehicle, freight OD identification is performed, and travel data of the intercity heavy freight vehicle is counted;

[0142] The electric conversion potential of the target intercity heavy freight vehicle is evaluated by constructing and solving an electric conversion substitution model of the intercity heavy diesel freight vehicle.

[0143] The output module 303 is used for visualizing and outputting the evaluation result of the electric conversion potential of the target intercity heavy freight vehicle.

[0144] In some preferred embodiments, the system further has a heavy vehicle electric conversion planning module, which is used for formulating a corresponding electric conversion plan of the intercity heavy freight vehicle according to the evaluation result of the electric conversion potential of all intercity heavy freight vehicles (in the jurisdiction).

[0145] In summary, the present application provides a heavy vehicle electric conversion potential evaluation method and system based on remote monitoring data, which uses trajectory time interval, speed change and stop behavior to divide the trip; based on high-precision sensor data, fuel consumption, average speed and NO x emissions are calculated as important parameters for electric conversion potential evaluation, achieving the effect of preserving the heterogeneity of freight weight, driving behavior and path selection of different freight tasks in the algorithm. A method is proposed to unify multiple economic factors such as purchase cost, charging infrastructure construction and operation expenditure, fuel saving benefit, carbon trading income, nitrogen oxide emission reduction subsidy, and detour time cost into a measurement system, and to establish a measurement framework through equivalent annual cost conversion mechanism for scheme optimization. A four-dimensional state space of vehicle-time-path-electricity is established, relying on the highway network geographic information system, considering path selection, charging station deployment, charging behavior, energy consumption heterogeneity and task time limit, realizing the joint optimization of charging station service area site selection and electric conversion scheme of the heavy vehicle, maximizing the optimization potential brought by charging detour while meeting the time window constraint. The model is converted into a mixed integer programming by binary path selection variable and integer variable. The method and system provided by the present application have the following beneficial effects:

[0146] (1) Comprehensive consideration of electric conversion potential evaluation factors

[0147] The application comprehensively considers the purchase cost, construction investment of battery swap station, fuel expenditure difference, transportation task time cost, carbon income under the carbon trading mechanism, and NOx emission reduction subsidy. Detailed quantitative analysis of the electricization benefits in different aspects can more effectively support the formulation and implementation of electricization schemes of different subjects.

[0148] (2) Economic and environmental benefits are quantified accurately

[0149] Compared with the calculation method based on constant parameters and traffic survey data, the calculation method based on remote monitoring data can reflect the index changes brought by the heterogeneity of freight tasks. The evaluation of the electricization potential of vehicles is more accurate, and the effectiveness of the system electricization scheme is stronger.

[0150] (3) Maximize the optimization potential of battery swap detour while ensuring the effectiveness of logistics

[0151] The application relies on the geographic information system and space-time graph modeling framework of the expressway network, which can maximize the system optimization potential brought by battery swap detour and reduce the construction cost of battery swap facilities. Based on the identified logistics time window constraints, timely arrival without delay can be ensured. The above two points overcome the limitations brought by the assumption that the trajectory does not change before and after electricization in the prior art.

[0152] (4) The system of the application uses the above evaluation method, which can effectively save system resources and improve evaluation accuracy.

[0153] Those skilled in the art can understand that the drawings are only schematic diagrams of an embodiment, and the modules or processes in the drawings are not necessarily necessary for implementing the application.

[0154] From the above description of the embodiments, those skilled in the art can clearly understand that the application can be implemented by means of software and the necessary general hardware platform. Based on this understanding, the technical solutions of the application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for making a computer device (which can be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

[0155] 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.

[0156] 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 travel data of intercity heavy-duty freight vehicles. 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. The evaluation results of step S3 are used for the electrification replacement of intercity heavy freight vehicles.

2. The method for evaluating the electrification potential of heavy-duty vehicles according to claim 1, characterized in that, Step S2 specifically includes: S21, Through-type △t i =t i -t i-1 ,i=2,3,…,N (1) Frame sequence for calculating the instantaneous acceleration of intercity heavy freight vehicles; where: t i Δt represents the data frame acquisition time; i 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 △t i If τ (3) is used, it is determined that the current data frame of the instantaneous acceleration of the intercity heavy freight vehicle and the data frame of the instantaneous acceleration of the previous intercity heavy freight vehicle belong to different trips; where: τ is the trip division threshold; S24. Traverse all data frames of the frame sequence of instantaneous acceleration of intercity heavy freight vehicles, and use the formula... Calculate the trip number; where: trip_id i 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 L=M n -M1 (6) E carbon =a·r f ·E fuel (9) Calculate travel statistics; in equations (1) to (4): t i Δt represents the data frame acquisition time; i v represents the time difference (s); i For speed; a i τ is acceleration; τ is the trip segmentation threshold; trip_id i For the trip number; in equations (5) to (9): The average speed during the trip; n is the number of trip data frames; L is the distance traveled; M n M1 and E are the odometer readings for the last and first frames of the trip, respectively; fuel This refers to the diesel fuel consumption during the trip; f i E represents engine fuel flow rate. nox NO x Emissions; u gas The exhaust component density and exhaust density ratio; SCR i The output value of the NOx sensor downstream of the SCR; IA i ρ is the intake air volume. f Diesel density; E carbon α represents carbon emissions; α represents the carbon-fuel ratio.

3. The method for evaluating the electrification potential of heavy-duty vehicles according to claim 2, characterized in that, Step S3 specifically includes: S31, using the spatiotemporal trajectory model and energy constraint of electric heavy trucks And it' =and it -x itr ·THE r ·EF i (10) SOC min ·in i Cap -(1-y i )·M≤e it (12) Flow balance constraint, origin-end point constraint, arrival time constraint, path uniqueness constraint Electric heavy trucks battery swapping model at highway service areas and objective function max C=C f +C nox +C C -C bss -C eht -C T (22) Construct a model for the electrification of heavy-duty diesel trucks in intercity transportation; In equations (10) to (12): e it' and e it These represent the remaining battery power of the vehicle at two adjacent time steps; x itr This is a binary variable representing whether vehicle i chooses path r at time t, where 1 indicates selection and 0 indicates no selection; L r EF is the length of path r; i Let be the energy consumption factor of vehicle i; η be the ratio of the work efficiency of the electric motor and the diesel engine; δ be the volumetric calorific value of the diesel engine; and SOC be the energy consumption factor of vehicle i. min This is the lower limit of the State of Charge (SOC) for electric heavy-duty trucks on highways; e i Cap The battery capacity of electric heavy truck i; y i Let τ be the decision variable for whether diesel heavy-duty truck i is replaced by electric heavy-duty truck, where 0 indicates no replacement and 1 indicates replacement; M is a positive number generated by a computer representing infinity; in equations (13) to (18): τ ir Let be the time it takes for vehicle i to travel along path r; n, n', n" represent different nodes in the highway network; s i and d i T represents the starting point and ending point of vehicle i, respectively; i max Let v be the latest arrival time of vehicle i; in equations (19) to (21): it Let z be the decision variable for whether vehicle i should swap batteries at time t, where 1 indicates swapping batteries and 0 indicates not swapping batteries; n Let C be the decision variable for whether a service area node n has a battery swapping station, where 1 represents a battery swapping station and 0 represents no battery swapping station; δ represents 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 C is the equivalent annual cost of a battery swapping station. eht The equivalent annual cost of purchasing electric heavy-duty trucks; C T For detour time cost; T bss The service life of the battery swapping station; γ t,bss S is the operating expense ratio in year t; r is the project discount rate; S bss P represents the residual value rate of the battery swapping station equipment. bss 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; p eht Cost of purchasing electric heavy-duty trucks; ε nox NO x Emissions reduction subsidies; T i ε is the equivalent number of trips per day for vehicle i, calculated from monitoring data; T For detour time cost; ε e For electricity price; ε f For oil prices; ε carbon For carbon trading prices; S32. Solve the model for the electrification of heavy-duty diesel trucks in intercity transportation, and then use the formula... Calculate the electrification benefits of all diesel trucks replaced by electric heavy-duty trucks.

4. A heavy-duty vehicle electrification potential assessment system based on remote monitoring data, characterized in that: include: 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.

5. The heavy-duty vehicle electrification potential assessment system according to claim 4, 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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