A Battery Optimization Method for Unmanned Mining Trucks

Abstract

This invention provides a battery optimization method for unmanned mining trucks, relating to the field of battery optimization technology. The method includes: determining the current driving road of the unmanned mining truck based on real-time image information transmitted by a front-facing camera, and acquiring road information; analyzing the actual traction force required by the unmanned mining truck on the current driving road based on the road information and the truck's own load; obtaining an adjustment strategy for the working mode of the unmanned mining truck battery based on the principle of minimum power consumption and the corresponding actual required traction force; and adjusting the working mode of the unmanned mining truck battery according to the adjustment strategy, which allows for the selection of the most energy-efficient working mode for different driving roads, thereby optimizing the unmanned mining truck battery and achieving optimal range.

Description

Technical Field

[0001] This invention relates to the field of battery optimization technology, and in particular to a method for optimizing the battery of an unmanned mining truck. Background Technology

[0002] Currently, as electric vehicle battery technology matures, it has also been applied to the field of driverless mining trucks. However, when driverless mining trucks operate on complex underground roads, the battery output power remains relatively constant, resulting in rapid power consumption and limited range. Therefore, there is an urgent need to improve the battery's range.

[0003] Therefore, the present invention provides a method for optimizing the battery of an unmanned mining truck. Summary of the Invention

[0004] This invention provides a battery optimization method for unmanned mining trucks. By determining the road information of the current driving route of the unmanned mining truck and the actual required traction force based on the truck's own load, and adjusting the working mode according to the principle of minimizing power consumption, the method can select the most power-saving working mode for different driving routes, thereby optimizing the battery of the unmanned mining truck and obtaining the best range.

[0005] This invention provides a method for optimizing the battery of an unmanned mining truck, comprising:

[0006] Step 1: Determine the current driving route of the driverless mining truck based on the real-time image information transmitted by the front-facing camera, and obtain road information;

[0007] Step 2: Based on the road information and the load of the unmanned mining truck, analyze the actual traction force required by the unmanned mining truck on the current road.

[0008] Step 3: Based on the principle of minimizing power consumption and the corresponding actual required traction force, obtain the adjustment strategy for the working mode of the unmanned mining truck battery;

[0009] Step 4: Adjust the working mode of the unmanned mining truck battery according to the adjustment strategy.

[0010] Preferably, based on real-time image information transmitted by the front-facing camera, the current driving route of the unmanned mining truck is determined, and road information is obtained, including:

[0011] Based on the real-time image information transmitted by the front-facing camera, the road features are determined and compared with the road features in the road information database to identify the first road with similar features.

[0012] The vehicle's current driving slope is determined based on the onboard level sensor, and the first road is selected to determine the current driving route;

[0013] The system analyzes ground information based on real-time video footage and obtains data about the current road from a road information database to determine road information.

[0014] Preferably, based on real-time image information transmitted by the front-facing camera, the road features are determined and compared with road features in the road information database to determine a first road with similar features, including:

[0015] The system segments and intelligently recognizes real-time images transmitted from the front-facing camera to determine the text features of the road, as well as the road's height and width information.

[0016] Based on the text features and the road's height and width information, a comparison is made with the road features in the road information database to determine the first road with similar features.

[0017] Preferably, based on the road information and the unmanned mining truck's own load, the actual traction force required by the unmanned mining truck on the current road is analyzed, including:

[0018] The slope of the current road is obtained based on the road information, and the driving status of the unmanned mining truck is determined in combination with the vehicle's driving direction. The driving status includes: uphill, downhill, and flat road.

[0019] Based on the road information, obtain the road obstacle influence factor, and combine it with the driving status and the load of the unmanned mining truck to analyze the theoretical traction force required by the unmanned mining truck on the current driving road.

[0020] Analyze the road travel losses of unmanned mining trucks based on road information;

[0021] The wear and tear of the unmanned mining truck is determined based on its factory information, and the theoretically required traction force is corrected based on the road travel wear and tear to determine the actual required traction force.

[0022] Preferably, the theoretical traction force required for the unmanned mining truck on the current road is analyzed, including:

[0023] When the unmanned mining truck is going uphill, the resistance when going uphill is determined based on the road obstacle factor, the slope and the load of the unmanned mining truck itself, and the theoretical traction force required when going uphill is determined.

[0024] When the unmanned mining truck is going downhill, the driving force and resistance are determined based on the road obstacle factor, the slope, and the truck's own load. If the driving force is greater than the resistance, the theoretical traction force required for going downhill is determined to be zero; if the driving force is less than the resistance, the theoretical traction force required for going downhill is determined based on the difference between the driving force and the resistance.

[0025] When the unmanned mining truck is on a flat road, the resistance on the flat road is determined based on the road obstacle factor and the load of the unmanned mining truck itself, and the theoretical traction force required on the flat road is determined.

[0026] Preferably, the analysis of road travel losses of unmanned mining trucks based on road information also includes:

[0027] Determine the non-smoothness factor of the current driving road based on road information;

[0028] Based on the factor-state-loss mapping table, the first loss coefficient S1 of the non-smooth factor of the current driving road for the unmanned mining truck under different driving states is extracted.

[0029] Based on the driving route of the unmanned mining truck, determine the third number n3 of the unavoidable road depressions and the fourth number n4 of the protrusions;

[0030] Based on the road defect-state-impact mapping table, the first impact value of road depressions of different diameters on unmanned mining trucks under different driving conditions and the second impact value of road protrusions of different diameters on unmanned mining trucks under different driving conditions are extracted.

[0031] Based on all the first influence values, the second influence values, and the third number n3 and the fourth number n4, determine the second loss coefficient for the unmanned mining truck;

[0032] Where S2 represents the second loss coefficient; L represents the length of the travel route; It means traveling at the same speed along the route. The maximum distance traveled; This represents the first impact value of the i1th road depression; This represents the maximum impact value among n3 road depressions; This represents the second impact value of the i2th road protrusion; This represents the maximum impact value among the n4 road protrusions;

[0033] The road travel loss of the unmanned mining truck is determined based on the first loss coefficient and the second loss coefficient.

[0034] Where S represents road travel loss; Y1 represents the loss standard for the first loss coefficient; 2 indicates the loss standard for the second loss coefficient.

[0035] Preferably, based on the principle of minimizing power consumption and the corresponding actual required traction force, the adjustment strategy for the working mode of the unmanned mining truck battery is obtained, including:

[0036] When the actual required traction force is zero, the brake pedal opening of the unmanned mining truck is determined based on the relationship between power and brake pedal opening and resistance, so that the unmanned mining truck can travel within the preset speed range.

[0037] When the actual required traction force is not zero, obtain multiple working modes of the battery of the unmanned mining truck;

[0038] Determine the maximum output power and maximum traction force of the battery in the same operating mode;

[0039] Multiple first operating modes were determined, where the maximum traction force was greater than the actual required traction force.

[0040] Based on the actual required traction force and a preset speed range, determine multiple available output powers for the same first working mode;

[0041] Based on the same available output power of the same first working mode and the actual required traction force, determine the corresponding driving speed of the unmanned mining truck and the corresponding driving time through the current road.

[0042] Determine the fan power required for cooling the battery under the same available output power in the same first operating mode;

[0043] Based on the same available output power of the same first working mode, the corresponding fan power and running time, the corresponding energy consumption is predicted.

[0044] Where E represents the predicted energy consumption; This indicates the corresponding available output power; The value represents the corresponding fan power; t represents the travel time; T represents the temperature of the long-term working environment of the unmanned mining truck; and RH represents the humidity of the long-term working environment of the unmanned mining truck. Indicates the current moment The influence factor of battery aging trend function on consumption;

[0045] Based on the mode-efficiency table, the conversion efficiency of the corresponding battery is determined by the available output power of the same first working mode, and the corresponding predicted power consumption is determined based on the corresponding predicted energy consumption.

[0046] Based on the predicted power consumption corresponding to the multiple available output powers of all first operating modes, determine the first available output power of the second operating mode with the lowest power consumption, and determine the adjustment strategy for the operating mode.

[0047] Preferably, the operating mode of the battery in the unmanned mining truck is adjusted according to the adjustment strategy, including:

[0048] The operating mode of the unmanned mining truck battery is adjusted according to the aforementioned adjustment strategy;

[0049] After completing the current driving route, determine the actual amount of electricity consumed by the battery;

[0050] Based on the corresponding predicted power consumption and the other power-consuming modules of the unmanned mining truck, the power consumption error is determined and recorded in the database for future correction of predicted power consumption when the unmanned mining truck travels on the same road.

[0051] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a flowchart of a battery optimization method for an unmanned mining truck according to an embodiment of the present invention. Detailed Implementation

[0055] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0056] This invention provides a battery optimization method for unmanned mining trucks, such as... Figure 1 As shown, it includes:

[0057] Step 1: Determine the current driving route of the driverless mining truck based on the real-time image information transmitted by the front-facing camera, and obtain road information;

[0058] Step 2: Based on the road information and the load of the unmanned mining truck, analyze the actual traction force required by the unmanned mining truck on the current road.

[0059] Step 3: Based on the principle of minimizing power consumption and the corresponding actual required traction force, obtain the adjustment strategy for the working mode of the unmanned mining truck battery;

[0060] Step 4: Adjust the working mode of the unmanned mining truck battery according to the adjustment strategy.

[0061] In this embodiment, real-time image information is the information of the image determined by the segmentation and intelligent recognition of real-time images. It refers to information such as the text features of the road in the image. The current driving road is determined by determining the characteristics of the road based on the real-time image information, comparing them with the road features in the road information database, and obtaining the information based on the driving slope. The road information refers to information such as the length, width, slope, depressions and protrusions of the road.

[0062] In this embodiment, the self-load condition refers to the total weight of the unmanned mining truck and its load. The actual required traction force is obtained based on the theoretical required traction force, road travel losses, and the self-loss of the unmanned mining truck.

[0063] In this embodiment, the working mode adjustment strategy is to predict the power consumption of the unmanned mining truck battery in different modes of different output power after driving the current road, and select the corresponding output power of the working mode with the least power consumption for adjustment.

[0064] The beneficial effects of the above technical solution are: by determining the road information of the current driving road of the unmanned mining truck and determining the actual required traction force based on the load of the unmanned mining truck, and adjusting the working mode according to the principle of minimizing power consumption, the most power-saving working mode can be selected for different driving roads, thereby optimizing the battery of the unmanned mining truck and obtaining the best range.

[0065] This invention provides a battery optimization method for unmanned mining trucks, which determines the current driving road of the unmanned mining truck based on real-time image information transmitted by a front-facing camera and obtains road information, including:

[0066] Based on the real-time image information transmitted by the front-facing camera, the road features are determined and compared with the road features in the road information database to identify the first road with similar features.

[0067] The vehicle's current driving slope is determined based on the onboard level sensor, and the first road is selected to determine the current driving route;

[0068] The system analyzes ground information based on real-time video footage and obtains data about the current road from a road information database to determine road information.

[0069] In this embodiment, road features refer to the textual features, height, and other features of the road. The road information database is pre-set based on the information of all driving roads. The first road is a road in the road information database with a road feature similarity of more than 90%.

[0070] In this embodiment, ground information refers to road obstruction factors, etc., and the data of the current driving road refers to information such as the length of the current driving road.

[0071] The beneficial effects of the above technical solution are: by comparing the determined road features with the road features in the road information database, roads with similar features are identified, and the current driving road is determined by filtering the roads with similar features according to the slope, thus determining the road information and laying the foundation for subsequently determining the actual required traction force.

[0072] This invention provides a battery optimization method for unmanned mining trucks. Based on real-time image information transmitted by a front-facing camera, road features are determined and compared with road features in a road information database to identify a first road with similar features. The method includes:

[0073] The system segments and intelligently recognizes real-time images transmitted from the front-facing camera to determine the text features of the road, as well as the road's height and width information.

[0074] Based on the text features and the road's height and width information, a comparison is made with the road features in the road information database to determine the first road with similar features.

[0075] In this embodiment, textual features refer to whether there are textual information such as road signs beside the driving road, and height information refers to the height of the underground road.

[0076] The beneficial effects of the above technical solution are: by intelligently recognizing real-time images, determining the characteristics of the road, comparing the features, and identifying similar roads, a foundation is laid for determining the current driving road.

[0077] This invention provides a battery optimization method for unmanned mining trucks, which analyzes the actual traction force required by the unmanned mining truck on the current road based on road information and the truck's own load, including:

[0078] The slope of the current road is obtained based on the road information, and the driving status of the unmanned mining truck is determined in combination with the driving direction of the vehicle. The driving status includes: uphill, downhill, and flat road.

[0079] Based on the road information, obtain the road obstacle influence factor, and combine it with the driving status and the load of the unmanned mining truck to analyze the theoretical traction force required by the unmanned mining truck on the current driving road.

[0080] Analyze the road travel losses of unmanned mining trucks based on road information;

[0081] The wear and tear of the unmanned mining truck is determined based on its factory information, and the theoretically required traction force is corrected based on the road travel wear and tear to determine the actual required traction force.

[0082] In this embodiment, when the current road gradient is zero, it is a flat road. When the gradient is not zero, the vehicle's direction of travel is uphill from the bottom of the slope and downhill from the top of the slope.

[0083] In this embodiment, the road resistance factor refers to the dynamic friction factor. The theoretically required traction force is determined based on the rolling friction of the unmanned mining truck in different driving states and the component of gravity along the road direction.

[0084] In this embodiment, road travel loss refers to the loss caused by road unevenness, road depressions and protrusions when the unmanned mining truck travels on the road. It is determined based on the road unevenness factor and the unavoidable road depressions and protrusions during travel.

[0085] In this embodiment, the self-loss of the unmanned mining truck refers to the friction between the vehicle's mechanical parts, which is obtained based on the factory information. The actual required traction force is obtained by adding the theoretical required traction force, road travel loss, and the self-loss of the unmanned mining truck.

[0086] The beneficial effects of the above technical solution are: by determining the driving state of the unmanned mining truck, the theoretical traction force is determined based on the road obstacle factor and its own load, and the actual traction force is determined based on the road driving loss and the unmanned mining truck's own loss, laying the foundation for the subsequent selection of the working mode of the unmanned mining truck.

[0087] This invention provides a battery optimization method for unmanned mining trucks, which analyzes the theoretical traction force required by the unmanned mining truck on the current driving road, including:

[0088] When the unmanned mining truck is going uphill, the resistance when going uphill is determined based on the road obstacle factor, the slope and the load of the unmanned mining truck itself, and the theoretical traction force required when going uphill is determined.

[0089] When the unmanned mining truck is going downhill, the driving force and resistance are determined based on the road obstacle factor, the slope, and the truck's own load. If the driving force is greater than the resistance, the theoretical traction force required for going downhill is determined to be zero; if the driving force is less than the resistance, the theoretical traction force required for going downhill is determined based on the difference between the driving force and the resistance.

[0090] When the unmanned mining truck is on a flat road, the resistance on the flat road is determined based on the road obstacle factor and the load of the unmanned mining truck itself, and the theoretical traction force required on the flat road is determined.

[0091] In this embodiment, the resistance in the uphill state is obtained by adding the separation of gravity along the road direction determined by the slope and its own load, and the friction determined by the road resistance factor, slope and its own load. The theoretically required traction force when going uphill is equal to the resistance.

[0092] In this embodiment, the driving force in the downhill state is the component of gravity along the road direction, and the resistance is friction. The theoretically required traction force when going downhill is equal to the difference between the driving force and the friction.

[0093] In this embodiment, the resistance in the flat road state is friction, and the theoretical traction force in the flat road state is equal to the friction force.

[0094] The beneficial effect of the above technical solution is that by determining the theoretical traction force of the unmanned mining truck in uphill, downhill and flat road conditions, a foundation is laid for the subsequent determination of the actual traction force.

[0095] This invention provides a battery optimization method for unmanned mining trucks, which analyzes the road travel losses of unmanned mining trucks based on road information, and further includes:

[0096] Determine the non-smoothness factor of the current driving road based on road information;

[0097] Based on the factor-state-loss mapping table, the first loss coefficient S1 of the non-smooth factor of the current driving road for the unmanned mining truck under different driving states is extracted.

[0098] Based on the driving route of the unmanned mining truck, determine the third number n3 of the unavoidable road depressions and the fourth number n4 of the protrusions;

[0099] Based on the road defect-state-impact mapping table, the first impact value of road depressions of different diameters on unmanned mining trucks under different driving conditions and the second impact value of road protrusions of different diameters on unmanned mining trucks under different driving conditions are extracted.

[0100] Based on all the first influence values, the second influence values, and the third number n3 and the fourth number n4, determine the second loss coefficient for the unmanned mining truck;

[0101] Where S2 represents the second loss coefficient; L represents the length of the travel route; It means traveling at the same speed along the route. The maximum distance traveled; This represents the first impact value of the i1th road depression; This represents the maximum impact value among n3 road depressions; This represents the second impact value of the i2th road protrusion; This represents the maximum impact value among the n4 road protrusions;

[0102] The road travel loss of the unmanned mining truck is determined based on the first loss coefficient and the second loss coefficient.

[0103] Where S represents road travel loss; Y1 represents the loss standard for the first loss coefficient; 2 indicates the loss standard for the second loss coefficient.

[0104] In this embodiment, the non-smoothness factor represents the smoothness of the road. For example, if the road is a straight line, then the non-smoothness factor is zero.

[0105] In this embodiment, the factor-state-loss mapping table is pre-set based on the non-smooth factor, driving state, and loss.

[0106] In this embodiment, the driving route is determined based on the width of the road and the width of the driverless mining truck, with the least road obstruction. For example, if the road width is 4 meters and the width of the driverless mining truck is 2 meters, and there is a 0.5-meter depression on the right front of the driverless mining truck, it can avoid the depression and drive through it. The third number refers to the number of road depressions on the driving route, and the fourth number refers to the number of road protrusions on the driving route.

[0107] In this embodiment, the road defect-state-impact mapping table is pre-set according to the impact of the size of the road defect on different driving states.

[0108] In this embodiment, Y1+Y2=1.

[0109] The beneficial effects of the above technical solution are as follows: the first loss coefficient of the unmanned mining truck is determined by the non-smooth factor and the factor-state-loss mapping table; the impact value of road depressions and protrusions on the unmanned mining truck is determined by the road defect-state-influence mapping table; the second loss coefficient is determined by the impact value and the number of depressions and protrusions; and the road driving loss is determined based on the first and second loss coefficients, which lays the foundation for determining the actual required traction force and indirectly improves the accuracy of battery working mode selection.

[0110] This invention provides a battery optimization method for unmanned mining trucks. Based on the principle of minimizing power consumption and the corresponding actual required traction force, an adjustment strategy for the working mode of the unmanned mining truck battery is obtained, including:

[0111] When the actual required traction force is zero, the brake pedal opening of the unmanned mining truck is determined based on the relationship between power and brake pedal opening and resistance, so that the unmanned mining truck can travel within the preset speed range.

[0112] When the actual required traction force is not zero, obtain multiple working modes of the battery of the unmanned mining truck;

[0113] Determine the maximum output power and maximum traction force of the battery in the same operating mode;

[0114] Multiple first operating modes were determined, where the maximum traction force was greater than the actual required traction force.

[0115] Based on the actual required traction force and a preset speed range, determine multiple available output powers for the same first working mode;

[0116] Based on the same available output power of the same first working mode and the actual required traction force, determine the corresponding driving speed of the unmanned mining truck and the corresponding driving time through the current road.

[0117] Determine the fan power required for cooling the battery under the same available output power in the same first operating mode;

[0118] Based on the same available output power of the same first working mode, the corresponding fan power and running time, the corresponding energy consumption is predicted.

[0119] Where E represents the predicted energy consumption; This indicates the corresponding available output power; The value represents the corresponding fan power; t represents the travel time; T represents the temperature of the long-term working environment of the unmanned mining truck; and RH represents the humidity of the long-term working environment of the unmanned mining truck. Indicates the current moment The influence factor of battery aging trend function on consumption;

[0120] Based on the mode-efficiency table, the conversion efficiency of the corresponding battery is determined by the available output power of the same first working mode, and the corresponding predicted power consumption is determined based on the corresponding predicted energy consumption.

[0121] Based on the predicted power consumption corresponding to the multiple available output powers of all first operating modes, determine the first available output power of the second operating mode with the lowest power consumption, and determine the adjustment strategy for the operating mode.

[0122] In this embodiment, the relationship between the brake pedal opening and the resistance is obtained based on the factory information of the unmanned mining truck. The brake pedal opening is determined based on the brake pedal opening corresponding to the resistance when the resistance is equal to the power. The preset speed range is set in advance.

[0123] In this embodiment, the operating mode is determined based on the battery voltage and maximum output power; different operating modes have different voltages and maximum output power.

[0124] In this embodiment, the maximum traction force is determined based on a power-maximum traction force table, which is pre-set.

[0125] In this embodiment, the available output power is the output power that does not exceed the maximum output power, obtained by multiplying the actual required traction force by the speed within the preset speed range.

[0126] In this embodiment, the driving speed is determined based on the ratio of the available output power to the actual required traction force, and the driving time is determined based on the ratio of the length of the current driving road to the driving speed.

[0127] In this embodiment, the fan power is determined based on the output power-fan power table, which is pre-set.

[0128] In this embodiment, the mode-efficiency table is pre-set according to the corresponding conversion efficiency of multiple output powers of multiple operating modes of the battery, and the predicted power consumption is determined based on the ratio of predicted power consumption to conversion efficiency.

[0129] In this embodiment, the adjustment strategy refers to adjusting the output efficiency of the current operating mode to the first available output power of the second operating mode.

[0130] The beneficial effects of the above technical solution are as follows: by determining the pedal opening when the actual traction force is zero, and determining multiple battery operating modes when it is not zero, the first operating mode with the maximum traction force greater than the actual traction force is selected, the predicted power consumption of the unmanned mining truck to travel the current road under multiple available output efficiencies in the first operating mode is determined, the output power of the operating mode with the least predicted power consumption is determined, and the adjustment strategy of the operating mode is determined. The optimal operating mode can be determined for different driving roads, thus saving battery power.

[0131] This invention provides a method for optimizing the battery of an unmanned mining truck, which involves adjusting the operating mode of the battery according to an adjustment strategy, including:

[0132] The operating mode of the unmanned mining truck battery is adjusted according to the aforementioned adjustment strategy;

[0133] After completing the current driving route, determine the actual amount of electricity consumed by the battery;

[0134] Based on the corresponding predicted power consumption and the other power-consuming modules of the unmanned mining truck, the power consumption error is determined and recorded in the database for future correction of predicted power consumption when the unmanned mining truck travels on the same road.

[0135] In this embodiment, the actual power consumption is determined based on the battery percentage before and after driving on the current road. For example, if the battery percentage before driving is A and the battery percentage after driving is B, and the current battery capacity is X, the actual power consumption is (AB)*X.

[0136] In this embodiment, the remaining power-consuming modules refer to power-consuming modules such as camera shooting and analysis. The power consumption error is determined based on the difference between the actual power consumption, the predicted power consumption, and the power consumption of the remaining power-consuming modules. The correction is based on adding or subtracting the power consumption error from the subsequent predicted power consumption.

[0137] The beneficial effects of the above technical solution are: by adjusting the working mode of the unmanned mining truck through adjustment strategy, the actual power consumption, the predicted power consumption, and the power consumption error of other power-consuming modules are determined, which corrects the predicted power consumption in the future, making the prediction of power consumption more accurate, selecting a more energy-saving working mode, and achieving the best range.

[0138] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method of optimizing battery usage for an unmanned mine vehicle, the method comprising: include: Step 1: Determine the current driving route of the driverless mining truck based on the real-time image information transmitted by the front-facing camera, and obtain road information; Step 2: Based on the road information and the load of the unmanned mining truck, analyze the actual traction force required by the unmanned mining truck on the current road. Step 3: Based on the principle of minimizing power consumption and the corresponding actual required traction force, obtain the adjustment strategy for the working mode of the unmanned mining truck battery; Step 4: Adjust the working mode of the unmanned mining truck battery according to the adjustment strategy; Based on the principle of minimizing power consumption and the corresponding actual required traction force, the adjustment strategy for the working mode of the unmanned mining truck battery is derived, including: When the actual required traction force is zero, the brake pedal opening of the unmanned mining truck is determined based on the relationship between power and brake pedal opening and resistance, so that the unmanned mining truck can travel within the preset speed range. When the actual required traction force is not zero, obtain multiple working modes of the battery of the unmanned mining truck; Determine the maximum output power and maximum traction force of the battery in the same operating mode; Multiple first operating modes were determined, where the maximum traction force was greater than the actual required traction force. Based on the actual required traction force and a preset speed range, determine multiple available output powers for the same first working mode; Based on the same available output power of the same first working mode and the actual required traction force, determine the corresponding driving speed of the unmanned mining truck and the corresponding driving time through the current road. Determine the fan power required for cooling the battery under the same available output power in the same first operating mode; Based on the same available output power of the same first working mode, the corresponding fan power and running time, the corresponding energy consumption is predicted. Where E represents the predicted energy consumption; This indicates the corresponding available output power; The value represents the corresponding fan power; t represents the travel time; T represents the temperature of the long-term working environment of the unmanned mining truck; and RH represents the humidity of the long-term working environment of the unmanned mining truck. Indicates the current moment The influence factor of battery aging trend function on consumption; Based on the mode-efficiency table, the conversion efficiency of the corresponding battery is determined by the available output power of the same first working mode, and the corresponding predicted power consumption is determined based on the corresponding predicted energy consumption. Based on the predicted power consumption corresponding to the multiple available output powers of all first operating modes, determine the first available output power of the second operating mode with the lowest power consumption, and determine the adjustment strategy for the operating mode.

2. The battery optimization method for unmanned mining trucks as described in claim 1, characterized in that, Based on real-time image information transmitted from the front-facing camera, the current driving route of the unmanned mining truck is determined, and road information is obtained, including: Based on the real-time image information transmitted by the front-facing camera, the road features are determined and compared with the road features in the road information database to identify the first road with similar features. The vehicle's current driving slope is determined based on the onboard level sensor, and the first road is selected to determine the current driving route; The system analyzes ground information based on real-time video footage and obtains data about the current road from a road information database to determine road information.

3. The battery optimization method for unmanned mining trucks as described in claim 2, characterized in that, Based on real-time image information transmitted by the front-facing camera, the road features are determined and compared with road features in the road information database to identify the first road with similar features, including: The system segments and intelligently recognizes real-time images transmitted from the front-facing camera to determine the text features of the road, as well as the road's height and width information. Based on the text features and the road's height and width information, a comparison is made with the road features in the road information database to determine the first road with similar features.

4. The battery optimization method for unmanned mining trucks as described in claim 1, characterized in that, Based on the road information and the unmanned mining truck's own load, analyze the actual traction force required by the unmanned mining truck on the current road, including: The slope of the current road is obtained based on the road information, and the driving status of the unmanned mining truck is determined in combination with the vehicle's driving direction. The driving status includes: uphill, downhill, and flat road. Based on the road information, obtain the road obstacle influence factor, and combine it with the driving status and the load of the unmanned mining truck to analyze the theoretical traction force required by the unmanned mining truck on the current driving road. Analyze the road travel losses of unmanned mining trucks based on road information; The wear and tear of the unmanned mining truck is determined based on its factory information, and the theoretically required traction force is corrected based on the road travel wear and tear to determine the actual required traction force.

5. The battery optimization method for unmanned mining trucks as described in claim 4, characterized in that, The analysis includes the theoretical traction force required for the driverless mining truck to travel on the current road, including: When the unmanned mining truck is going uphill, the resistance when going uphill is determined based on the road obstacle factor, the slope and the load of the unmanned mining truck itself, and the theoretical traction force required when going uphill is determined. When the unmanned mining truck is going downhill, the driving force and resistance are determined based on the road obstacle factor, the slope, and the truck's own load. If the driving force is greater than the resistance, the theoretical traction force required for going downhill is determined to be zero; if the driving force is less than the resistance, the theoretical traction force required for going downhill is determined based on the difference between the driving force and the resistance. When the unmanned mining truck is on a flat road, the resistance on the flat road is determined based on the road obstacle factor and the load of the unmanned mining truck itself, and the theoretical traction force required on the flat road is determined.

6. The battery optimization method for an unmanned mining truck as described in claim 4, characterized in that, The analysis of road travel losses of unmanned mining trucks based on road information also includes: Determine the non-smoothness factor of the current driving road based on road information; Based on the factor-state-loss mapping table, the first loss coefficient S1 of the non-smooth factor of the current driving road for the unmanned mining truck under different driving states is extracted. Based on the driving route of the unmanned mining truck, determine the third number n3 of the unavoidable road depressions and the fourth number n4 of the protrusions; Based on the road defect-state-impact mapping table, the first impact value of road depressions of different diameters on unmanned mining trucks under different driving conditions and the second impact value of road protrusions of different diameters on unmanned mining trucks under different driving conditions are extracted. Based on all the first influence values, the second influence values, and the third number n3 and the fourth number n4, determine the second loss coefficient for the unmanned mining truck; Where S2 represents the second loss coefficient; L represents the length of the travel route; It means traveling at the same speed along the route. The maximum distance traveled; This represents the first impact value of the i1th road depression; This represents the maximum impact value among n3 road depressions; This represents the second impact value of the i2th road protrusion; This represents the maximum impact value among the n4 road protrusions; The road travel loss of the unmanned mining truck is determined based on the first loss coefficient and the second loss coefficient. Where S represents road travel loss; Y1 represents the loss standard for the first loss coefficient; 2 indicates the loss standard for the second loss coefficient.

7. The battery optimization method for unmanned mining trucks as described in claim 1, characterized in that, The operating mode of the battery in the unmanned mining truck was adjusted according to the adjustment strategy, including: The operating mode of the unmanned mining truck battery is adjusted according to the aforementioned adjustment strategy; After completing the current driving route, determine the actual amount of electricity consumed by the battery; Based on the corresponding predicted power consumption and the other power-consuming modules of the unmanned mining truck, the power consumption error is determined and recorded in the database for future correction of predicted power consumption when the unmanned mining truck travels on the same road.

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