CN-121279531-B - Expressway vehicle energy supplementing load prediction method and system based on traffic state

CN121279531BCN 121279531 BCN121279531 BCN 121279531BCN-121279531-B

Abstract

The invention provides a highway vehicle energy supplementing load prediction method and system based on traffic states, relates to the technical field of intelligent traffic and multi-energy collaborative management, and aims at solving the problems that the prior art has the limitation of a single energy model, lacks traffic state dynamics, and does not accord with actual requirements in path planning. The method comprises the steps of obtaining energy consumption data and the like of a multi-energy type vehicle, constructing a vehicle specific power model, dividing traffic states according to a public road bureau model, calibrating energy consumption factors of the multi-energy type vehicle in different traffic states, obtaining an optimal path for vehicle running through a multi-target path planning algorithm based on road network topology information of the road traffic model, simulating travel time, initial energy states and maximum energy capacity by adopting a Monte Carlo method, and predicting space-time distribution of energy supplementing loads of the multi-energy type vehicle. The method solves the problems existing in the prior art and realizes the accurate prediction of the multi-energy type vehicle under different traffic states.

Inventors

ZHU WENXING
ZHANG JIEPEI

Assignees

山东大学

Dates

Publication Date: 20260508
Application Date: 20250930

Claims (8)

1. The highway vehicle energy supplementing load prediction method based on the traffic state is characterized by comprising the following steps of: Acquiring energy consumption data, traffic road network operation data and service area energy supplementing facility information of a multi-energy type vehicle; constructing a vehicle specific power model based on energy consumption data of the multi-energy type vehicle; dividing traffic states according to a public road bureau model, and calibrating energy consumption factors of the multi-energy type vehicle under different traffic states based on specific power of the vehicle, wherein the energy consumption factor calculation formula is as follows: ; Wherein, the 、、 And The vehicle running speeds under the conditions of 0, 0.6, 0.8 and 1 are respectively represented by the saturation; indicating the energy source type as Is in a smooth traffic state and an average speed interval Under the condition of the first Personal (S) A distribution value of the interval; indicating the energy source type as Is in a slow traffic state and an average speed interval Under the condition of the first Personal (S) A distribution value of the interval; indicating the energy source type as Is in a congestion traffic state and average speed interval Under the condition of the first Personal (S) A distribution value of the interval; indicating the energy source type as In a severe traffic state and an average speed interval Under the condition of the first Personal (S) A distribution value of the interval; Is of the energy type The energy consumption power corresponding to the specific power interval is carried out on the automobile in a smooth traffic state; Is of the energy type The energy consumption power corresponding to the specific power interval is carried out on the automobile in the slow traffic state; Is of the energy type The energy consumption power corresponding to the specific power interval is carried out on the automobile in a congestion traffic state; Is of the energy type The energy consumption power corresponding to the specific power interval is higher than the energy consumption power of the automobile in the serious traffic jam state; Constructing a road traffic model containing road section distance, flow, maximum traffic capacity, time and energy consumption information according to traffic network operation data; the method comprises the steps of obtaining an optimal path for vehicle running through a multi-target path planning algorithm based on road network topology information of a road traffic model, wherein the specific process is as follows: Initializing a weight matrix and a path matrix, wherein the weight matrix comprises weighting values of road section distance, time and energy consumption; in a road network topological structure of a road traffic model, traversing all nodes through three layers of nested circulation, and updating a weight matrix and a path matrix to obtain an optimal path node set; the weight coefficients of the road section distance, the time and the energy consumption are adjusted to obtain an optimal path with the shortest road section distance, the shortest time and the lowest energy consumption; Sampling and simulating a multi-energy type vehicle starting node, a target node, a trip time, an initial energy state and a total energy capacity related event probability model by adopting a Monte Carlo method according to the optimal path of vehicle running and service area energy supplementing facility information, and predicting the space-time distribution of the multi-energy type vehicle energy supplementing load, wherein the specific process is as follows: Setting the total number of the multi-energy type vehicles and acquiring energy consumption factors of the multi-energy type vehicles in different traffic states; Constructing a multi-energy type vehicle starting node, a target node, a travel initial moment, an initial energy state and a total energy capacity related event probability model, and generating the starting node, the target node, the travel initial moment, the initial energy state and the total energy capacity of the multi-energy type vehicle by adopting a Monte Carlo sampling method; determining a travel path of the multi-energy type vehicle through a multi-target path planning algorithm; For each path, calculating the running speed, running time and energy consumption of the vehicle on the path, and updating the residual energy state of the vehicle; And judging whether to supplement energy or not based on the set energy supplementing conditions, calculating the energy supplementing load of the multi-energy type vehicle of each service area node, and generating an energy supplementing load space-time distribution prediction result.
2. The traffic state-based highway vehicle energy load prediction method according to claim 1, wherein the energy consumption data of the multi-energy type vehicle includes position data of a vehicle driving one second by one second, speed data, acceleration data, instantaneous fuel consumption rate, engine speed, battery voltage, battery current, hydrogen fuel cell voltage, hydrogen fuel cell current; the traffic network operation data comprise static road network attribute data and dynamic road network attribute data, wherein the static road network attribute data comprise road section length, lane number, road type and maximum traffic capacity; The service area energy supplementing facility information mainly comprises the number of refueling piles, charging piles and hydrogenation piles contained in the service area, the charging power of the electric automobile, the use number of the refueling piles, the charging piles and the charging piles in real time and the queuing number of multi-energy type vehicles in the service area.
3. The traffic state-based highway vehicle energy supplementing load prediction method according to claim 1, wherein the calculation formula of the vehicle specific power model is as follows: ; Wherein, the For the running speed of the vehicle, As the total mass of the vehicle, Is the included angle of the gradient of the road, In order to roll the quality coefficient of the product, In order to be a rolling damping coefficient, In order to achieve an air density of the air, Is the wind resistance coefficient of the steel plate, For the vehicle's wind-blocking area, For the windward speed of the vehicle, In order for the acceleration to be a function of the acceleration, Gravitational acceleration.
4. The method for predicting the energy supplementing load of the expressway vehicle based on the traffic state as set forth in claim 1, wherein the traffic state is divided according to a public road bureau model, and energy consumption factors of the multi-energy type vehicle in different traffic states are calibrated, and the method comprises the following specific steps: dividing speed intervals according to four traffic states divided by the public road bureau model; according to the energy consumption data of the multi-energy type vehicle, building the vehicle specific power interval distribution of each speed interval of the multi-energy type vehicle under different traffic states; Based on the distribution of the specific power intervals of the vehicles, respectively calculating the average energy consumption power of each specific power interval of the vehicles, and fitting the average energy consumption power of each specific power interval to obtain the mapping relation between the energy consumption power and the specific power; And calculating energy consumption factors corresponding to the speed intervals of the multi-energy type vehicle under different traffic states based on the specific power distribution of the speed intervals and combining the mapping relation between the energy consumption power and the specific power intervals.
5. The traffic state-based highway vehicle energy supplementing load prediction method according to claim 4, wherein the formula of the mapping relation between the energy consumption power and the specific power is: ; Wherein, the Is of the energy type of A functional mapping of the energy consumed by the vehicle with the vehicle specific power interval, In order to be in a state of being clear, In the state of being in a slow-moving state, In the event of a congestion state, Is a severely congested state.
6. The traffic state-based highway vehicle energy supplementing load prediction method according to claim 1, wherein the road traffic model adopts graph theory modeling, and the road network topology structure is represented as: ; Wherein, the Representing a set of all road segment endpoints in the highway network, Representing a collection of road segments in a highway network, Representing a length matrix of each road segment in the highway network, Representing a traffic matrix for each road segment in the highway network, Representing the maximum traffic capacity moment array of each road section in the highway network, A time matrix for the vehicle to travel on each road segment in the highway network, And the energy consumption matrix is used for predicting the driving of each road section in the highway network.
7. The traffic state-based expressway vehicle energy-supplementing load prediction system, characterized in that it realizes the traffic state-based expressway vehicle energy-supplementing load prediction method according to any one of claims 1-6, comprising: The information acquisition module is used for acquiring energy consumption data, traffic road network operation data and service area energy supplementing facility information of the multi-energy type vehicle; The vehicle specific power construction module is used for constructing a vehicle specific power model according to the energy consumption data of the multi-energy type vehicle; the energy consumption factor calibration module is used for dividing traffic states according to the public road bureau model and calibrating energy consumption factors of the multi-energy type vehicle under different traffic states based on the specific power of the vehicle; The road network traffic module is used for constructing a road traffic model containing road section distance, flow, maximum traffic capacity, time and energy consumption information according to traffic network operation data; The path planning module is used for obtaining an optimal path for vehicle running through a multi-target path planning algorithm based on road network topology information of the road traffic model; the multi-energy type vehicle energy supplementing load prediction module is used for sampling and simulating a multi-energy type vehicle starting node, a target node, a trip moment, an initial energy state and a total energy capacity related event probability model by adopting a Monte Carlo method according to the optimal path of vehicle driving and the energy supplementing facility information of a service area, and predicting the space-time distribution of the multi-energy type vehicle energy supplementing load.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1-6 when the program is executed.

Description

Expressway vehicle energy supplementing load prediction method and system based on traffic state Technical Field The invention belongs to the technical field of intelligent traffic and multi-energy collaborative management, and particularly relates to a highway vehicle energy supplementing load prediction method and system based on traffic states. Background The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. With the rapid development of green low-carbon traffic and energy structure transformation, new energy automobiles represented by electric automobiles (EVs) and hydrogen fuel cell automobiles (FCEVs) are continuously expanding in scale, and meanwhile, the transformation of energy supply systems of road traffic systems is also promoted. Therefore, the method comprehensively fuses the information of the multi-energy type vehicles and the road, realizes the space-time distribution accurate prediction of the energy supplementing load of the multi-energy type vehicles such as the fuel automobile, the electric automobile, the hydrogen fuel cell automobile and the like in the expressway scene, and becomes a key requirement in the field of intelligent traffic and energy collaborative management. However, the existing energy load prediction methods are mostly aimed at a single energy type, and do not consider the energy consumption interaction effect of vehicles of multiple energy types (fuel automobiles, electric automobiles, hydrogen fuel cell automobiles and the like), so that the prediction deviation is obvious. In addition, the traditional prediction model adopts average speed or predicts under ideal traffic flow state, and is not considered to be smooth, creep, congestion and the nonlinear influence of serious congestion traffic state on energy consumption, especially under the congestion state, the idle oil consumption of the fuel automobile, the energy loss caused by frequent start and stop of the electric automobile and the low-efficiency operation of the fuel cell of the hydrogen energy automobile are seriously underestimated, and the prediction of the multi-scene energy load of the expressway is difficult to accurately predict. Meanwhile, the existing path planning algorithm only takes time or distance as a single optimization target, energy constraint is not carried out, so that a vehicle can select a path with high energy consumption and short time, the path is forced to bypass due to energy consumption in the middle, and load fluctuation of a service area is aggravated. Disclosure of Invention In order to overcome the defects of the prior art, the invention provides a highway vehicle energy supplementing load prediction method and system based on traffic states, which are used for realizing accurate prediction of fuel consumption, electric energy demand and hydrogen energy demand of nodes of a highway service area by fusing real-time traffic state sensing, vehicle specific power energy consumption modeling, multi-objective path planning and Monte Carlo simulation technologies. To achieve the above object, one or more embodiments of the present invention provide the following technical solutions: The first aspect of the invention provides a traffic state-based highway vehicle energy supplementing load prediction method, comprising the following steps: Acquiring energy consumption data, traffic road network operation data and service area energy supplementing facility information of a multi-energy type vehicle; constructing a vehicle specific power model based on energy consumption data of the multi-energy type vehicle; dividing traffic states according to the public road bureau model, and calibrating energy consumption factors of the multi-energy type vehicle under different traffic states based on specific power of the vehicle; Constructing a road traffic model containing road section distance, flow, maximum traffic capacity, time and energy consumption information according to traffic network operation data; obtaining an optimal path for vehicle running through a multi-target path planning algorithm based on road network topology information of a road traffic model; According to the optimal path of vehicle running and the energy supplementing facility information of the service area, sampling and simulating a multi-energy type vehicle starting node, a target node, a trip time, an initial energy state and a total energy capacity related event probability model by adopting a Monte Carlo method, and predicting the space-time distribution of the energy supplementing load of the multi-energy type vehicle. As one embodiment, the energy consumption data of the multi-energy type vehicle includes position data, speed data, acceleration data, instantaneous fuel consumption rate, engine speed, battery voltage, battery current, hydrogen fuel cell voltage, hydrogen fuel cell current of the vehicle traveling every