CN118036987A - A low energy consumption dispatching method for hybrid mining trucks based on WPT technology - Google Patents

Abstract

The present invention provides a hybrid mining truck low-energy consumption scheduling method based on WPT technology, which relates to the field of wireless power transmission technology. The method first sets the working conditions, working mode and vehicle status of the hybrid mining truck; then constructs the objective function of the mining truck optimization scheduling model to minimize the energy consumption of the entire mining area; and sets the constraints of the mining truck optimization scheduling model to ensure the feasibility and efficiency of the open-pit mine hybrid mining truck scheduling; finally, a hybrid mining truck low-energy consumption scheduling linear programming algorithm based on WPT technology is designed to solve the mining truck optimization scheduling model and optimize the low-energy consumption scheduling path of the hybrid mining truck in the open-pit mine. The method formulates efficient transportation and charging strategies by calculating the energy consumption between each path, provides a new energy replenishment method for hybrid mining trucks, reduces dependence on traditional fuels, and further reduces energy consumption.

Description

A low energy consumption dispatching method for hybrid mining trucks based on WPT technology

Technical Field

The present invention relates to the field of wireless power transmission technology, and in particular to a low-energy consumption scheduling method for a hybrid mining truck based on WPT (Wireless Power Transmission) technology.

Background technique

Open-pit mining is an important way to develop mineral resources worldwide. How to dispatch hybrid mining trucks more reasonably and effectively to further reduce energy consumption and improve operational efficiency is an important issue in open-pit mining operations. The transportation system of open-pit mines has unique transportation needs and constraints. In actual scheduling, there are many influencing factors, including road conditions, distribution of loading and unloading points, and energy status of mining trucks. In addition, since open-pit mining areas usually have complex and changeable terrain conditions, such as uphill, downhill, and tortuous sections of varying degrees, these will affect the energy consumption and energy recovery efficiency of mining trucks. Especially in downhill sections, effective energy recovery can significantly reduce the energy consumption of mining trucks. The traditional mining truck scheduling mode is often based on fixed transportation routes and schedules, lacking response and optimization to real-time situations, resulting in energy waste and low operational efficiency. In addition, in open-pit mine operations, energy supply and management are also important factors affecting operational efficiency and cost, while traditional wired power transmission methods have problems such as difficult wiring, high maintenance costs, and affecting mining area transportation efficiency. In order to achieve the goal of reducing energy consumption and improving operational efficiency. The present invention designs a low-energy consumption scheduling method for hybrid mining trucks based on WPT technology.

Summary of the invention

The technical problem to be solved by the present invention is to provide a low-energy consumption scheduling method for hybrid mining trucks based on WPT technology in response to the deficiencies of the above-mentioned prior art. By calculating the energy consumption between each path, efficient transportation and charging strategies are formulated to provide a new energy replenishment method for hybrid mining trucks. By setting up charging piles, charging of mining trucks during operation is realized, reducing dependence on traditional fuels and further reducing energy consumption.

In order to solve the above technical problems, the technical solution adopted by the present invention is: a low-energy consumption scheduling method for hybrid mining trucks based on WPT technology, comprising the following steps:

Step 1: Set the working conditions, working mode and vehicle status of the hybrid mining truck;

Step 2: Construct the objective function of the mining truck optimization scheduling model to minimize the energy consumption of the entire mining area;

In the study of low-energy dispatching technology for hybrid mining trucks in open-pit mines, the goal is to minimize the energy consumption of the entire mining area to ensure operational efficiency and economy;

The calculation of energy consumption depends on the driving distance between loading and unloading points and the load status of the hybrid mining truck; considering the energy recovery when driving downhill, the distance between loading and unloading points is divided into uphill and flat road distances. and downhill distance/> Since the energy recovery efficiency is related to the load state of the mining truck, the energy consumption per kilometer Q and the energy recovery efficiency η of the hybrid mining truck when fully loaded and unloaded must also be considered when calculating the energy consumption; the energy consumption E _ij of the mining truck from loading point i to unloading point j and the energy consumption from unloading point j to loading point i are calculated according to the following formula:

Therefore, the total energy consumption _Emin of minimizing the transportation of mining trucks between m loading points and n unloading points during the entire operation cycle is:

Among them, i represents the loading point of the mining truck, j represents the unloading point of the mining truck, E _ij represents the total energy consumption of the mining truck from the loading point to the unloading point, and E _ji represents the total energy consumption of the mining truck from the unloading point to the loading point. Indicates the uphill and flat road distance from the loading point to the unloading point. Indicates the downhill distance from the loading point to the unloading point, /> Indicates the uphill and flat road distance from the unloading point to the loading point,/> represents the downhill distance from the unloading point to the loading point, _Qf represents the energy consumed per kilometer when the mining truck is fully loaded, _Qe represents the energy consumed per kilometer when the mining truck is unloaded, _ηf represents the energy recovery rate of the mining truck when fully loaded, and _ηe represents the energy recovery efficiency of the mining truck when unloaded;

Minimizing the energy consumption _Emin of mining trucks in transporting between loading and unloading points is used as the objective function of the mining truck optimization scheduling model, which optimizes efficiency and cost while ensuring low energy consumption.

Step 3: Set the constraints of the mining truck optimization scheduling model to ensure the feasibility and efficiency of hybrid mining truck scheduling in open-pit mines;

The mining truck optimization scheduling model includes the following constraints:

a) The total volume of transportation carried by mining trucks to each unloading point cannot be greater than the maximum capacity of the unloading point;

b) The total volume of the mining truck transported at the loading point cannot exceed the total storage capacity of the loading point;

c) The number of loadings at a loading point must be less than the number of loadings at all loading and unloading points within a shift;

d) The total output of ore from the loading point must be greater than the minimum output requirement;

e) The mining truck transportation task must be completed within the set working time T;

f) The remaining power of the mining card must meet the minimum requirement from the current location to the next destination or charging station;

Step 4: Design a low-energy consumption scheduling linear programming algorithm for hybrid mining trucks based on WPT technology, solve the mining truck optimization scheduling model, and optimize the low-energy consumption scheduling path of hybrid mining trucks in open-pit mines;

First, the key factors that affect energy consumption are identified. Then, these factors are integrated into the linear programming model to form a mining truck optimization scheduling model that includes an objective function and a series of constraints. The objective function reflects the need to minimize total energy consumption, while the constraints ensure that the model's solution can be implemented in actual operations.

Optimize the computational complexity of the algorithm, reduce the problem size through algorithm preprocessing steps, and use MATLAB to solve the model to ensure that a feasible scheduling solution can be obtained within a reasonable time;

The specific method of solving the mining truck optimization scheduling model using the hybrid mining truck low energy consumption scheduling linear programming algorithm based on WPT technology is as follows:

Step 1: Set the initial state of the mining card and calculate the energy consumption;

Set the initial power SOC of the mining truck and define all the constraints of the mining truck optimization scheduling model; use map information to calculate the energy recovery of the hybrid mining truck during downhill braking under different road conditions, and solve the energy consumption E _ij between each loading and unloading point;

Step 2: Select the destination of the mining card and calculate the remaining energy consumption;

According to the current location of the mining truck, the energy consumption of the mining truck to reach each possible destination is evaluated, and the station with the lowest energy consumption is selected as the next destination; the expected remaining power of the mining truck after reaching the destination is calculated;

Step 3: Determine the feasibility of electricity;

Evaluate whether the mining card has enough power to reach the next planned destination; if the power is sufficient, the mining card will go to the destination; if not, go to Step 5;

Step 4: Check the satisfaction of constraint conditions;

After arriving at the destination, check whether the mining truck meets the preset constraints; if the constraints are met, the scheduling task ends; otherwise, return to Step 2 to continue optimizing the path selection;

Step 5: Determine and execute charging strategy;

If the mining truck cannot reach the next destination, determine whether it has enough power to reach the nearest WPT charging station; if it can reach the destination, go to charge; if not, use diesel to provide power to go to the charging station; after charging or power generation is completed, restart the optimization process of the path and destination and return to Step 2.

The beneficial effect of adopting the above technical solution is that the low-energy consumption scheduling method of hybrid mining trucks based on WPT technology provided by the present invention plans the transportation route of mining trucks in detail, and accurately calculates the total amount of loaded ore, the total amount of electricity and fuel consumed, and the total time and total cost of the entire transportation process. The optimization process ensures that the goal of minimizing energy consumption and cost is achieved while meeting the needs of mine operation through iterative calculations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a linear programming algorithm for low energy consumption scheduling of hybrid mining trucks based on WPT technology provided by an embodiment of the present invention;

FIG2 is a diagram showing an example of a mining truck scheduling operation according to an embodiment of the present invention;

FIG3 is a schematic diagram of an empty and fully loaded operation cycle of a hybrid mining truck provided by an embodiment of the present invention;

FIG4 is a diagram showing energy consumption changes of a hybrid mining truck based on WPT according to an embodiment of the present invention;

FIG5 is a comparison diagram of results of using different scheduling technologies provided by an embodiment of the present invention;

FIG6 is a comparison diagram of energy consumption of three mining cards provided in an embodiment of the present invention, wherein (a) is the energy consumption of THMT, (b) is the energy consumption of DMT, and (c) is the energy consumption of LPWHT.

Detailed ways

The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

In this embodiment, a low-energy consumption scheduling method for hybrid mining trucks based on WPT technology is proposed. First, the assumptions are proposed to simplify the complex factors that may be encountered in actual operation, and to provide a clear analysis framework for subsequent model construction; secondly, the energy consumption of the entire mining area is minimized as the objective function; then a series of constraints are set to ensure the feasibility and efficiency of the scheduling of hybrid mining trucks in open-pit mines; finally, a linear programming algorithm for low-energy consumption scheduling of hybrid mining trucks based on WPT technology (Linear Programming for WPT-Assisted Hybrid Trucks, LPWHT) is designed and solved. The LPWHT algorithm uses the characteristics of hybrid mining trucks that can recover energy during downhill braking, and selects as many downhill paths as possible when solving the algorithm to achieve low energy consumption. In this algorithm, the mining truck only starts diesel power when the remaining power cannot reach the charging pile, reducing dependence on traditional fuels and achieving the purpose of further reducing energy consumption.

The method specifically comprises the following steps:

Step 1: Set the working conditions, working mode and vehicle status of the hybrid mining truck;

In order to simplify the complex factors that may be encountered in actual operations and provide a clear analysis framework for subsequent model construction, this embodiment proposes the following assumptions:

(1) The output of ore from each loading point during a working period is fixed and will not change during a working period.

(2) The initial charge of the hybrid mining truck in a shift is full, and when the charge is insufficient to complete the next transportation task, it will go to the charging station for charging. If the charge is insufficient when going to the charging station, the diesel engine is used to generate electricity.

(3) The required loading time at the loading point and the required unloading time at the unloading point are fixed and are not affected by other factors.

(4) The speed of the fully loaded or empty mining truck during transportation is constant and is not affected by external factors such as traffic and weather.

(5) The maximum unloading capacity of each unloading point is fixed and will not change during a working period.

(6) The energy consumption and recovery efficiency of a mining truck are determined by its loading status and road conditions.

(7) The charging speed of the charging station is fixed and is not affected by other factors.

(8) During a working period, the locations of loading points, unloading points and charging stations are fixed and will not change.

(9) After the mining truck has completed loading, it can choose any unloading point to unload, but only one unloading point can be selected at a time.

(10) The mining card will not malfunction or otherwise require additional processing time during transportation and charging.

The above assumptions are intended to simplify the complex factors that may be encountered in actual operations and provide a clear analysis framework for subsequent model construction.

Step 2: Construct the objective function of the mining truck optimization scheduling model to minimize the energy consumption of the mining area;

In this embodiment, in order to minimize the energy consumption and cost of the mining truck operation cycle, the mining truck operation cycle containing only one loading point and one unloading point and a two-way distance is taken as a mining truck unit, and the transportation time optimization is considered to ensure the operation efficiency and economy, and the objective function is constructed. The energy consumption calculation is related to the distance between the loading point and the unloading point and the load state of the hybrid mining truck; the energy recovery efficiency of downhill driving is related to the load state of the mining truck.

The energy consumption _Eij of the mining truck from loading point i to unloading point j and the energy consumption from unloading point j to loading point i are calculated according to the following formula:

The total energy consumption of the mining truck unit in transporting between loading points and unloading points during the entire mining operation cycle is:

In order to make the description of the objective function clearer, Table 1 gives the symbolic meaning of each parameter used in the objective function:

Table 1 Description of parameters in the objective function

Taking the energy consumption of the mining truck unit as the objective function, while ensuring low energy consumption, the efficiency and cost are optimized, providing decision support for hybrid mining truck scheduling.

Step 3: Set the constraints of the mining truck optimization scheduling model to ensure the feasibility and efficiency of hybrid mining truck scheduling in open-pit mines;

In this embodiment, the mining truck optimization scheduling model includes the following constraints:

a) The total volume of transportation carried by mining trucks to each unloading point cannot be greater than the maximum capacity of the unloading point:

b) The total amount of the mining truck transported at loading point i cannot exceed the total storage capacity of loading point i:

c) The number of loadings at loading point i must be less than the number of loadings at all loading points within a shift:

d) The total output of the loading point must be greater than the minimum output requirement:

e) The working time of the mining truck is fixed at eight hours, and all transportation tasks must be completed within this time:

f) The remaining power of the mining card must meet the minimum requirement from the current location to the next destination or charging station:

E _current -E _required ≥ 0 (9)

In this embodiment, Table 2 gives the symbolic meanings of various parameters used in the constraint conditions in the mining truck optimization scheduling model.

Table 2 Description of parameters in constraint conditions

Step 4: Design a low-energy consumption scheduling algorithm for hybrid mining trucks based on WPT technology, solve the mining truck optimization scheduling model, and optimize the low-energy consumption scheduling path of hybrid mining trucks in open-pit mines;

The present invention proposes an algorithm based on linear programming theory to optimize the low-energy consumption scheduling path of hybrid mining trucks in open-pit mines, while minimizing the energy consumption of mining trucks, and meeting actual operational needs such as transportation time, power restrictions, and capacity constraints at loading and unloading points. In the design of the algorithm, the key factors affecting energy consumption are first determined, such as the heavy-load and empty-load driving paths of mining trucks, the difference in energy consumption between uphill and downhill driving, and the charging efficiency of WPT technology. Subsequently, these factors are incorporated into the linear programming model to form a mathematical model containing objective functions and constraints. In order to ensure that the linear programming algorithm can be executed efficiently, the computational complexity of the algorithm is also optimized to reduce the scale of the problem being processed.

In this embodiment, the process of the low energy consumption scheduling algorithm of the hybrid mining truck based on WPT technology is shown in Figure 1. The flowchart is the core of the algorithm, which clearly describes the logical path from the initial state to the target state.

The algorithm can achieve minimum energy consumption and maximum transportation efficiency by calculating the remaining power of hybrid mining trucks and optimizing the route. The implementation process of this embodiment ensures the clarity and effectiveness of the scheduling algorithm, and provides a feasible solution for low-energy scheduling of hybrid mining trucks in open-pit mines.

In this embodiment, the specific steps of using the LPWHT algorithm to solve the mining truck optimization scheduling model are as follows:

Step S1: Parameter initialization: Set the initial power state of the mining truck, and mark the locations of loading points, unloading points and charging stations on the mining area map, and then initialize the demand at these points and various parameters of the mining truck as the basis for scheduling.

Linear programming is used to quantitatively calculate the optimal transportation route and scheduling strategy of hybrid mining trucks. By precisely adjusting the operating route and transportation tasks of each mining truck, it ensures that energy consumption is as low as possible without violating any constraints.

In this embodiment, an example of mining truck scheduling operation is shown in FIG2 .

In the initial stage of building a low-energy consumption scheduling algorithm for hybrid mining trucks in open-pit mines, this embodiment first establishes the basic operation architecture in the mining area, involving six loading points A, B, C, D, E, and F and three unloading points a, b, and c, as well as a centralized charging station. In one shift, ore is transported from the loading point to the unloading point, and the ore is transported back and forth between the loading and unloading points.

In the charging strategy section, the charging rate parameters of the charging piles are set to effectively charge the mining trucks during operation, ensuring that the mining trucks can continue to operate and maximize their operating efficiency. The output of each loading point and the maximum processing capacity of the unloading point are also clearly defined. These data are crucial for subsequent transportation activities to comply with the capacity constraints of the mining area.

In this embodiment, the information of the loading and unloading points and charging piles in the open-pit mine is shown in Table 3.

Table 3 Open-pit mine basic data

name describe Mounting point A, B, C, D, E, F Uninstall point a, b, c charging station Charging station Minimum transportation volume within one working period 9000t Ore output from loading point A 2000t Ore output from loading point B 1000t Ore output from loading point C 2200t Ore output from loading point D 1400t Ore output from loading point E 1200t Ore output from loading point F 1200t Maximum unloading capacity at unloading point a 3000 tons Maximum unloading capacity at unloading point b 3000 tons Maximum unloading capacity of unloading point c 3000 tons Loading time, unloading time 5 minutes, 3 minutes Charging speed of charging pile 5kWh/min

In this embodiment, a simplified diagram of the mining truck empty and full-load operation cycle is shown in FIG3 .

The parameters of the hybrid mining truck used in this embodiment include vehicle size, gross weight, cargo box volume, and battery capacity as shown in Table 4. In particular, the energy consumption characteristics of the vehicle under different loading conditions, such as the energy recovery efficiency when going downhill with empty or full load, are also accurately calculated and incorporated into the algorithm to optimize the operation path selection of the mining truck.

Table 4 Basic parameters of hybrid mining trucks

In addition, the distance from the loading point to the unloading point and its corresponding energy consumption calculation are also fully considered in the algorithm, including the distance of uphill, downhill and flat roads. The accuracy of these data directly affects the reliability of the energy consumption calculation, which has a significant impact on the scheduling efficiency. In this embodiment, the distance between the loading and unloading points and the charging piles is shown in Tables 5 and 6 below.

Table 5 Uphill and downhill flat road distances between loading points A, B, C and unloading points and charging stations (km)

Table 6 Distances on flat roads up and downhill between loading points D, E, F and unloading points and charging stations (km)

Step S2: Calculate energy consumption and select paths: Calculate energy consumption for all possible paths in the mining area, select the next destination based on the energy consumption calculation results, and give priority to the path with the lowest energy consumption. The power required between each point calculated in this embodiment is shown in Table 7 and Table 8.

Table 7 Power required from loading point to unloading point and charging pile (kWh)

a b c Charging A 12.6092 33.15685 19.37055 12.5483 B 21.42875 38.56275 45.4725 15.8115 C 25.089075 31.601625 35.022675 18.9393 D 37.8923 37.0273 31.601625 23.2569 E 36.94175 39.51975 32.78105 24.5835 F 42.70835 20.358475 47.7386 29.6504

Table 8 Power required from unloading point to loading point and charging pile (kWh)

A B C D E F Charging a 10.5096 23.6684 40.2318 20.307 4.1944 18.2156 29.65 b -7.9132 18.3744 -6.063 26.639 23.937 24.1482 3.026 c 5.1484 29.7228 14.0778 11.75 2.1616 16.8848 31.45

Step S3: power check and charging decision;

Before heading to the next destination, check whether the current power of the mining truck is sufficient to support the destination. If the power is insufficient, the algorithm will automatically decide to go to the charging station to replenish the power. If the power is insufficient to go to the charging station, the diesel engine will be used to generate electricity to ensure the smooth progress of the transportation task.

Step S4: Execute transport and update status:

The mining trucks transport according to the selected route and update their position and power status in real time. After completing the loading and unloading operations, the remaining demand at the loading and unloading point is updated synchronously to prepare for the next round of transportation tasks.

Step S5: Constraint verification and transportation adjustment:

During transportation, the current status of the mining truck is continuously checked to see if it meets the transportation requirements and other key constraints. If necessary, the route selection or charging strategy is adjusted in time to optimize transportation efficiency.

Step S6: Iterative optimization:

Repeat steps S2 to S5 until the optimal transportation plan that satisfies all constraints is found. Perform cost and efficiency evaluation and continue iterating until the predetermined optimization goal is met.

The algorithm can minimize energy consumption by precisely controlling the power of hybrid mining trucks and optimizing route selection. The algorithm process ensures the clarity of the scheduling algorithm and the effectiveness of its execution, providing a feasible solution for low-energy scheduling of hybrid mining trucks in open-pit mines.

The algorithm proposed in this paper plans the transportation route of the mining truck in detail and accurately calculates the total amount of ore loaded, the total amount of electricity and fuel consumed, and the total time and total cost of the entire transportation process. The optimization process ensures that the goal of minimizing energy consumption and cost is achieved while meeting the needs of mine operation through iterative calculations. The pseudo code of this algorithm is shown in Table 9:

Table 9 Pseudo code of low energy consumption scheduling algorithm for hybrid mining trucks based on WPT technology

With the detailed display of the solution steps, the algorithm design of this embodiment is fully presented. Each step is to ensure the accuracy and efficiency of the algorithm, while taking into account the dual needs of energy consumption optimization and operation continuity. The initialization of the parameter settings provides a solid foundation for transportation scheduling, while the path selection and power management strategies ensure the smoothness and economy of the transportation process. Through real-time monitoring and adjustment of loading and unloading points, the algorithm can flexibly respond to variables that may occur during transportation. The steps described are not only the key to achieving low energy consumption in mining transportation, but also provide a solid starting point for subsequent invention research, especially in the further optimization of open-pit mine scheduling.

In this embodiment, the energy consumption change of the hybrid mining truck based on WPT is shown in Figure 4. It can be observed from Figure 4 that the total energy consumption of the mining truck, the remaining power during the operation of the mining truck, that is, the current energy in Figure 4. Figure 4 particularly highlights the time period before reaching the charging pile. At this time, the remaining power of the mining truck is not enough to support it to reach the charging station, so the diesel engine is started to generate electricity, that is, the range extender. Since the rated operating fuel consumption rate of the hybrid mining truck's range extender is 4.11kWh/L, it can be clearly seen that the total energy consumption of the mining truck has a significant increase during this period.

In addition, the ups and downs of the two curves reflect the energy recovery process of the mining truck on the downhill section. In some cases, the energy recovery on the downhill section even exceeds the total energy consumption of this section, resulting in a reduction in total energy consumption and an increase in the remaining power. When the mining truck arrives at the charging station and starts charging, the total energy consumption is presented as a horizontal straight line, indicating that no energy consumption is generated during charging; at the same time, the power steadily increases in an oblique line until it returns to the initial 422kWh. After completing charging, the mining truck continues to operate until the end of the working cycle.

In this embodiment, under the same working conditions, whether it is the working time or the completed transportation volume, the energy consumption and cost differences between three different types of mining trucks (WPT-based hybrid mining truck, traditional hybrid mining truck, and diesel mining truck) are shown in Figure 5. By comparison, it can be clearly seen that the LPWHT proposed in this study performs well in effectively reducing the operating costs and energy consumption of open-pit mines. Specifically, under the same transportation volume, the operating cost of LPWHT is 511.26 yuan. In contrast, the cost of the traditional hybrid mining truck (THMT) is 714.98 yuan, while the diesel mining truck (DMT) is as high as 2696.96 yuan. Similarly, within the same working hours, the cost of LPWHT is 511.26 yuan, which is significantly lower than THMT's 1052.98 yuan and DMT's 3577.98 yuan.

In addition, since LPWHT includes a period of charging time in the scheduling process, this leads to cost differences under the same working hours and the same transportation volume. In terms of energy consumption, during the same working hours, the energy consumption of LPWHT is 748.88kWh, that of THMT is 913.61kWh, and that of DMT is 2065.68kWh. This data reflects the significant impact of WPT technology and hybrid mining truck energy recovery technology in mining truck scheduling. The scheduling strategy proposed in this embodiment, by implementing WPT technology, not only optimizes energy management, but also achieves a significant reduction in energy consumption, thereby achieving positive results in both economic benefits and environmental impact.

In this embodiment, the energy consumption changes of the three types of mining trucks in the same working cycle are compared as shown in FIG6 . By placing these three energy consumption curves in the same view, it can be clearly seen that the LPWHT and THMT equipped with energy recovery technology both show obvious fluctuations in energy consumption changes. This phenomenon is attributed to the role of the energy recovery system and its use of electricity as the main energy source, which makes the energy consumption of these two hybrid mining trucks significantly lower than that of diesel mining trucks.

Specifically, the difference between LPWHT and THMT is mainly reflected in the fact that the WPT technology charging pile is used for charging in the scheduling scheme of LPWHT, while THMT uses diesel engines to generate electricity after the power is exhausted. This strategy results in the total energy consumption of THMT being significantly higher than that of LPWHT. Through this comparison, we can clearly see the obvious advantages of WPT technology and hybrid mining truck energy recovery technology in reducing energy consumption. The application of WPT technology not only optimizes the energy utilization efficiency of mining trucks, but also reduces dependence on fuel through the setting of charging piles, further reducing energy consumption. The application of this technology demonstrates its great potential to promote sustainable development and efficiency improvement in open-pit mining environments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims (5)

1. A mixed mining card low-energy-consumption scheduling method based on WPT technology is characterized by comprising the following steps: the method comprises the following steps:

Step 1: setting working conditions, working modes and vehicle states of the hybrid power mine card;

Step 2: constructing an objective function of an ore card optimization scheduling model to minimize the energy consumption of the whole mining area;

step 3: setting constraint conditions of an ore card optimizing and scheduling model, and ensuring feasibility and efficiency of mixed ore card scheduling of the strip mine;

step 4: and designing a mixed mining card low-energy-consumption scheduling linear programming algorithm based on the WPT technology, solving a mining card optimal scheduling model, and optimizing a low-energy-consumption scheduling path of the mixed mining card in the strip mine.

2. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 1, characterized by comprising the following steps: the specific method for constructing the objective function of the ore card optimal scheduling model in the step 2 is as follows:

The calculation of the energy consumption of the mining area depends on the running distance between loading and unloading points and the loading state of the mixed mining card; considering energy recovery during downhill driving, the distance between loading and unloading points is divided into the distance between an ascending slope and a flat road And distance to downhill/>Because the energy recovery efficiency is related to the loading state of the mining card, the energy consumption is calculated by taking the energy consumption Q and the energy recovery efficiency eta of each kilometer of the mixed mining card when the mining card is fully loaded and unloaded into consideration; the energy consumption E _ij of the ore card from the loading point i to the unloading point j and the energy consumption from the unloading point j to the loading point i are calculated according to the following formula:

thus, the total energy consumption E _min to minimize transport of the mining cards between the m loading points and the n unloading points throughout the entire operating cycle is:

Where i denotes a loading point of the ore card, j denotes an unloading point of the ore card, E _ij denotes the total energy consumption of the ore card from the loading point to the unloading point, E _ji denotes the total energy consumption of the ore card from the unloading point to the loading point, Representing the distance of the loading point to the unloading point on the uphill slope and level road,/>Representing the downhill distance from the loading point to the unloading point,/>Representing the distance of the load from the unloading point to the loading point on the uphill slope,/>Representing the downhill distance from the unloading point to the loading point, wherein Q _f represents the energy consumed per kilometer when the ore card is fully loaded, Q _e represents the energy consumed per kilometer when the ore card is empty, eta _f represents the full-load energy recovery rate of the ore card, and eta _e represents the empty-load energy recovery efficiency of the ore card;

The energy consumption E _min for transporting the ore cards between loading and unloading points is minimized as an objective function of an ore card optimizing and dispatching model, and the optimization of efficiency and cost is realized while the low energy consumption is ensured.

3. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 2, characterized by comprising the following steps: the ore card optimization scheduling model in the step 3 comprises the following constraint conditions:

a) The total transportation amount of the ore cards to each unloading point cannot be larger than the maximum capacity of the unloading point;

b) The total amount of the ore cards transported at the loading point cannot exceed the total storage amount of the loading point;

c) The loading times of the loading points are smaller than the loading times of all loading and unloading points in one shift;

d) The total yield of the loading point is greater than the minimum yield requirement;

e) The transportation task of the mine truck is completed within a set working time T;

f) The remaining power of the mine card must meet minimum requirements from the current location to the next target point or charging station.

4. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 3, wherein the method is characterized by comprising the following steps: step 4, firstly determining key factors influencing energy consumption; then, the factors are integrated into a linear programming model, and a mine card optimization scheduling model comprising an objective function and a series of constraint conditions is formed; the objective function reflects the need to minimize the total energy consumption, while the constraints ensure that the solution of the model can be implemented in actual operation;

Optimizing the calculation complexity of the algorithm, reducing the scale of the problem through an algorithm preprocessing step, and solving a model by utilizing MATLAB, so that a feasible scheduling scheme can be obtained in reasonable time.

5. The WPT technology-based hybrid mining card low-energy consumption scheduling method of claim 3, wherein the method is characterized by comprising the following steps: the specific method for solving the ore card optimizing and scheduling model by adopting the mixed-motion ore card low-energy-consumption scheduling linear programming algorithm based on the WPT technology in the step 3 is as follows:

step1, setting an initial state of a mine card and calculating energy consumption;

Setting an initial electric quantity SOC of the mine card and defining all constraint conditions of an optimal dispatching model of the mine card; calculating the energy recovery condition of the mixed mining truck during downhill braking under different road conditions by using map information, and solving the energy consumption E _ij among loading and unloading points;

Step2, selecting a mine card destination, and calculating residual energy consumption;

according to the current position of the ore card, evaluating the energy consumption of the ore card reaching each possible destination, and selecting a station with the lowest energy consumption as a next destination station; calculating the expected residual capacity of the ore card after reaching the destination;

Step3, judging the feasibility of the electric quantity;

Evaluating whether the mine card has enough power to reach the next planned destination; if the charge is sufficient, the mine card will go to the destination; if not, turning to Step5;

Step4, checking the satisfaction of constraint conditions;

after reaching the destination, checking whether the mine card meets preset constraint conditions; if the constraint condition is satisfied, ending the scheduling task; otherwise, returning to Step2 to continue optimizing the path selection;

step5, judging and executing a charging strategy;

if the mine card cannot reach the next destination, judging whether the mine card has enough electric quantity to reach the nearest WPT charging pile; if the charging is available, the charging is carried out; if not, diesel is adopted to provide power to go to the charging pile; after the charging or power generation is completed, the path and destination optimization process is restarted, and the process returns to Step2.