CN-120974752-B - Method and system for evaluating motorized potential of heavy truck based on remote monitoring data

Abstract

The invention provides a heavy truck motorization potential evaluation method and system based on remote monitoring data, which utilize track time intervals, speed changes and stopping behaviors to carry out travel segmentation, calculate oil consumption, average speed and NO x emission based on high-precision sensor acquisition data as important parameters for motorization potential evaluation, achieve the effect of reserving heterogeneity of cargo weights, driving behaviors and path selection of different freight tasks in an algorithm, provide a method for uniformly incorporating multiple economic factors such as purchase cost, electricity changing infrastructure construction and operation expenditure, fuel saving benefit, carbon transaction income, nitrogen oxide emission reduction patch, detour time cost and the like into a measurement system, establish a vehicle-time-path-electric quantity four-dimensional state space, rely on a highway network geographic information system, maximize optimization potential brought by electricity changing detour and simultaneously meet time window constraint. The model is converted into the mixed integer programming solution through binary path selection variables and integer variables, and powerful support is provided for motorized replacement.

Inventors

• LIU RUI
• SONG GUOHUA
• YANG YI
• LIU NIAN
• Wei Xiuhuan
• ZHANG SITING
• CHEN YUHANG
• LI GEN
• FANG WEI
• WANG YUESONG

Assignees

• 北京市首发工贸有限责任公司
• 北京市首都公路发展集团有限公司

Dates

Publication Date

20260505

Application Date

20250813

Claims (3)

1. 1. The method for evaluating the motorization potential of the heavy vehicle based on the remote monitoring data is characterized by comprising the following steps of: S1, acquiring engine data, OBD data and SCR data of an inter-city heavy freight vehicle through a remote monitoring platform; S2, carrying out freight OD identification and statistics on journey data of the inter-city heavy freight vehicles based on the acquired engine data, OBD data and SCR data of the inter-city heavy freight vehicles, wherein the method specifically comprises the following steps: s21, through type (1) (2) Calculating a frame sequence of the instantaneous acceleration of the inter-city heavy freight vehicle, wherein t i is the acquisition time of a data frame; V i is velocity, a i is acceleration; s22, removing idle frames with the speed and the acceleration of 0 in a frame sequence of the instantaneous acceleration of the inter-city heavy freight vehicle, and calculating through a formula (1) to obtain a non-idle time difference; S23, if the non-idle time difference meets the requirement (3) Judging that the data frame of the instant acceleration of the current intercity heavy freight vehicle and the data frame of the instant acceleration of the last intercity heavy freight vehicle belong to different strokes, wherein: Dividing a threshold value for a journey; s24, traversing all data frames of a frame sequence of the instantaneous acceleration of the inter-city heavy freight vehicle, and passing through (4) Calculating to obtain a travel number, wherein: Numbering the strokes; s25, taking the acquisition time, longitude and latitude of a first data frame of a frame sequence of the instantaneous acceleration of the heavy freight vehicle as the starting time, the starting position, the ending time and the ending position of the journey; s26, through type (5) (6) (7) (8) (9) In the formulas (1) to (4), t i is the acquisition time of the data frame; Time difference(s), v i is speed, a i is acceleration; Dividing a threshold value for a journey; are travel numbers, and in the formulas (5) to (9): The average speed of the stroke, n is the number of frames of the stroke data, L is the driving mileage, M n and M 1 are the readings of an odometer of the last frame and the first frame of the stroke respectively, E fuel is the consumption of the diesel of the stroke, f i is the fuel flow of the engine, E nox is the emission of NO x , u gas is the density of the exhaust components and the density ratio of the exhaust, SCR i is the output value of a NO x sensor at the downstream of the SCR, and IA i is the air inflow; e carbon is the carbon emission; is a carbon-fuel ratio; S3, evaluating the electric replacement potential of the target inter-city heavy freight vehicle by constructing and solving an electric replacement model of the inter-city transport heavy diesel truck, wherein the method specifically comprises the following steps: s31, space-time track model and energy constraint type through electric heavy truck (10) (11) (12) Flow balance constraint, start-end constraint, arrival time constraint, and path uniqueness constraint (13) (14) (15) (16) (17) (18) Electric heavy truck power conversion model in expressway service area (19) (20) (21) Target function (22) (23) (24) (25) (26) (27) (28) Constructing an electric substitution model of the inter-city transportation heavy diesel truck; In the formulae (10) to (12): And Respectively, the residual electric quantity of two adjacent time step vehicles, wherein x itr is a binary variable which indicates whether the vehicle i runs along a selected path r at time t, 1 indicates selection, 0 indicates non-selection, L r is the length of the path r, and EF i is the energy consumption factor of the vehicle i; The working efficiency ratio of the motor and the diesel engine is set; volumetric heating value for diesel engine; The lower limit of the SOC of the electric heavy truck on the expressway; The method comprises the steps of (1) determining whether an electric heavy truck is used as a diesel heavy truck, wherein y i is a decision variable for determining whether the electric heavy truck is used as a diesel heavy truck, 0 represents no substitution, 1 represents substitution, M is a positive number representing infinity generated by a computer, and the following formulas (13) to (18): Time when vehicle i is driving through path r, n', n″ are different nodes in the expressway 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, and equations (19) to (21): The decision variable of whether the vehicle i is subjected to power change at time t is that 1 is subjected to power change, 0 is not subjected to power change, z n is that whether a service area node n is provided with a power change station or not, 1 is that the power change station is provided, and 0 is that the power change station is not provided; The method is characterized in that the method is power exchange time, in the formulas (22) to (28), C f is annual fuel saving income, C nox is annual NO x subsidy income, C C is annual carbon transaction income, C bss is equivalent annual cost of a power exchange station, C eht is equivalent annual cost of purchasing an electric heavy truck, C T is bypass time cost, and T bss is service life of the power exchange station; The operation cost ratio of the t year is represented by r, the engineering discount rate and S bss , the residual value rate of the power exchange station equipment; The method is characterized by comprising the steps of building a power exchange station, S eht is the residual value rate of the electric heavy truck, and T eht is the service life of the electric heavy truck; Purchase cost for the electric heavy truck; T i is the converted number of strokes of the vehicle i in one day, and is obtained by calculation through monitoring data; Is the detour time cost; Is electricity price; Is the oil price; A trade price for carbon; S32, solving an electric substitution model of the inter-city transportation heavy diesel truck, and then passing through (29) Calculating to obtain the electric benefits of all diesel trucks replaced by the electric heavy truck; the evaluation result of step S3 is used for the motorized replacement work of the inter-city heavy freight vehicle.
2. 2. A heavy vehicle motorization potential evaluation system based on remote monitoring data, for performing the heavy vehicle motorization potential evaluation method based on remote monitoring data of claim 1, comprising: the data acquisition module is used for acquiring engine data, OBD data and SCR data of the inter-city heavy freight vehicle through the remote monitoring platform; a modeling evaluation module for: Carrying out freight OD identification and statistics on journey data of the inter-city heavy freight vehicles based on the acquired engine data, OBD data and SCR data of the inter-city heavy freight vehicles; The method comprises the steps of evaluating the electric replacement potential of a target inter-city heavy freight vehicle by constructing and solving an electric replacement model of the inter-city transport heavy diesel truck; And the output module is used for visually outputting the obtained evaluation result of the electric reloading potential of the target inter-city heavy freight vehicle.
3. 3. The system for evaluating the motorized potential of a heavy truck according to claim 2, further comprising a heavy truck motorized plan preparation module for preparing an motorized replacement plan for the intercity heavy truck based on the obtained evaluation results of motorized replacement potentials of all intercity heavy trucks.

Description

Method and system for evaluating motorized potential of heavy truck based on remote monitoring data Technical Field The invention relates to the technical field of intelligent transportation, in particular to a heavy vehicle motorization potential evaluation method and system based on remote monitoring data. Background Heavy diesel trucks serve as backbone supports for inter-urban freight and are also an important source of crude oil consumption, carbon emissions and pollutant emissions. Accelerating truck electrodynamic performance in the field of intercity freight has important significance for national energy safety, coping with climate change and relieving health problems related to air pollution. The problems of high vehicle purchase and construction cost and operation technology of matched energy supplementing facilities cause the problem of electric substitution of the similar vehicle types. The diesel truck motorization potential evaluation method is needed to support the establishment of an motorization scheme and the planning of a matched energy supplementing facility. Even though the charging infrastructure has almost covered the entire highway service area, a power conversion facility needs to be built in a part of the service area to support heavy goods motorization. The inter-city heavy cargo transportation has the characteristics of high energy consumption and strong time constraint. The endurance mileage of the current electric heavy truck is difficult to support the complete inter-city freight travel. Current charging facilities, even with the super-charging technology, cannot complete charging within one hour. Charging during highway driving, especially for cold chain vehicles, will have a high probability of causing logistical delays. On the one hand, the power exchange station has high construction costs and battery reserve costs. Service areas with high-frequency power conversion requirement potential should be selected to build charging stations to avoid resource mismatch and economic benefit loss. On the other hand, due to the differences of factors such as transportation frequency, weight, route, traffic condition and road gradient, the energy consumption characteristics of different vehicles are remarkably different, so that the space-time distribution of the electric power conversion requirements after electric power conversion and the economic environmental benefit are remarkably different. The freight path of the electric heavy truck determines the distribution of the power change requirements of the service area, and whether the service area is provided with a power change station or not also influences the path planning of the electric heavy truck. Under the specific electromotive target and investment strategy, the diesel truck electromotive potential evaluation method comprehensively considering path planning, economic benefit and environmental benefit can optimize the resource allocation scheme and improve the system operation efficiency. There is currently no research on motorized alternatives to fuel trucks that take into account logistic system requirements and full life cycle economy and environmental benefits. From the perspective of an enterprise main body, the problems of high purchase cost, limited cruising mileage, imperfect charging facilities and the like of the electric truck obviously increase operation uncertainty. Due to the lack of motorized alternatives to scientific systems, it is difficult for enterprises to accurately evaluate the economic viability of electric trucks based on existing logistic demands, resulting in insufficient replacement power. From the perspective of government bodies, the existing policy makes multiple dependent test experience, lacks systematic analysis of freight demand refinement features, energy supply network layout and full life cycle economic and environmental benefits, and is difficult to make accurate subsidy policy and enforcement measures. There is a need to construct an electromotive replacement scheme for a freight fleet of scientific systems, which provides economic decision support for enterprises and theoretical basis for government to make accurate policy tools. Existing solutions generally make rough estimates of energy consumption, emissions, freight demand distribution based on road segment flow or speed. This will lead to economic environmental benefit assessment misalignments and limited optimization potential, resulting in resource mismatch. The oil consumption is greatly influenced by the complex running conditions such as heavy load, ascending slope, congestion and the like. The daily carbon dioxide emission of a single truck can reach more than 200kg, and the dominant position of pollutant emission is more obvious. The capability of acquiring and transmitting vehicle-mounted fault diagnosis system (On Board Diagnostics, OBD) data, engine data and tail gas aftertreatment device data mainly based on selective