Hybrid mine truck drive control system

Abstract

The application relates to the technical field of mine transport vehicles, in particular to a mine vehicle driving control system based on hybrid power, which comprises a vehicle-mounted control terminal and a cloud digital twin platform, the cloud digital twin platform realizes data interaction with the vehicle-mounted control terminal through a wireless communication network; through the multi-unit collaborative design of an information perception module, the problems of incomplete and inaccurate information perception of a traditional system are solved. A running state perception unit collects key parameters of a battery, an engine and an electric machine, a load perception unit estimates the load in real time based on a pressure sensor, and an environment perception unit accurately calculates the slope in combination with an IMU; the three units jointly provide comprehensive and accurate data support for subsequent control, so that the mine vehicle can master the self state and environment information in real time, and power distribution disconnection or brake overload is avoided.

Description

Technical Field

[0001] This application relates to the field of mining transport vehicle technology, and in particular to a hybrid power-based mining truck drive control system. Background Technology

[0002] In the field of mining transportation, hybrid mining trucks are gradually replacing traditional diesel mining trucks due to their advantages in energy saving and power. However, the existing drive control system still has many shortcomings under the special working conditions of mines.

[0003] Among them, the traditional control system has insufficient information perception accuracy, relies on a single sensor to collect data, cannot monitor the load of the mine car in real time, and it is difficult to obtain load data during driving, resulting in a disconnect between power distribution and actual needs; the slope measurement is easily affected by bumps and has a large error, often causing insufficient power uphill and overload braking downhill.

[0004] Furthermore, there is a lack of optimization and foresight in energy management: fixed logic is adopted, such as low-speed electric use and high-speed fuel use, without adjusting strategies in advance based on information about the slope and curves of the fixed paths in the mining area. When going uphill for a long time, the motor is prone to running out of power prematurely, thus relying on the high-fuel-consumption internal combustion engine for drive; when going downhill for a long time, the kinetic energy recovery rate is low, and the energy management strategy does not coordinate the optimization of battery life and engine wear. Overcharging and discharging will shorten battery life, and the engine will be in an inefficient operating condition for a long time, which will also increase maintenance costs.

[0005] Secondly, the braking system lacks predictive collaborative control. The mining area has a high proportion of long downhill sections, and the existing system only activates braking when it detects that the vehicle speed exceeds the limit. It does not plan the distribution of braking force in advance based on the slope ahead. Frequent use of mechanical braking leads to a shortened replacement cycle of brake pads and makes them prone to brake failure due to heat fade. The coordination between electric motor braking and mechanical braking is poor, and it is impossible to dynamically adjust the braking force ratio according to the battery SOC state, which may waste energy recovery opportunities or cause energy storage problems.

[0006] In addition, although some systems have introduced digital twin technology, the virtual model parameters are fixed and have not been corrected based on the real-time operating data of the physical mining trucks, resulting in a large deviation between the virtual simulation results and the actual working conditions. At the same time, the system lacks self-learning ability and cannot iteratively optimize the control algorithm based on the fleet's historical operating data, and the driving efficiency of the mining trucks will gradually decrease during use.

[0007] Therefore, this application provides a mining truck drive control system based on hybrid power. Summary of the Invention

[0008] The purpose of this application is to solve at least one technical problem raised in the background art.

[0009] This application provides a mining truck drive control system based on hybrid power, the mining truck drive control system includes an on-board control terminal and a cloud-based digital twin platform, the cloud-based digital twin platform interacts with the on-board control terminal through a wireless communication network;

[0010] The vehicle control terminal includes an information perception module, a positioning and path prediction module, a vehicle decision-making module, and an actuator drive module;

[0011] The information sensing module is used to collect the operating status parameters, load parameters and environmental parameters of the mining truck in real time.

[0012] The positioning and path prediction module is used to obtain the real-time high-precision location information of the mining truck and query the path information within a predetermined distance ahead based on the pre-stored high-precision map of the mining area. The path information includes slope changes and road conditions.

[0013] The vehicle-mounted decision-making module is connected to the information perception module and the positioning and path prediction module, and is used to generate control commands for the engine, drive motor and braking system based on real-time and predictive path information.

[0014] The actuator drive module is connected to the vehicle decision module and is used to receive control commands and drive the corresponding power components and braking components to move.

[0015] The cloud-based digital twin platform includes a digital twin model library, a predictive braking simulation module, and a strategy optimization learning module.

[0016] The digital twin model library is used to store high-fidelity virtual mining truck models corresponding to physical mining trucks;

[0017] The predictive braking simulation module is used to simulate and calculate the optimal distribution curve of electric motor power and mechanical braking force based on a virtual mine car model and the path information ahead before the mine car enters a long downhill section.

[0018] The strategy optimization learning module is used to continuously optimize the energy management strategy based on the fleet's historical operation data using reinforcement learning algorithms, and then sends the optimized strategy parameters to the on-board decision module.

[0019] By adopting the above technical solutions, the problems of low braking energy recovery efficiency, lack of dynamic optimization of energy management strategies, and severe wear of braking systems in traditional mining trucks on long downhill slopes have been solved, reducing safety accidents caused by braking problems and bringing significant economic and social benefits.

[0020] Preferably, the information sensing module includes an operation status sensing unit, a load sensing unit, and an environment sensing unit;

[0021] The operating status sensing unit is used to collect parameters including the battery's state of charge (SOC), state of health (SOH), engine speed, engine torque, and drive motor speed / torque.

[0022] The load sensing unit is used to estimate the load of the mining truck in real time through the suspension pressure sensor.

[0023] The environmental sensing unit is used to collect the current pitch angle of the mine car through the inertial measurement unit (IMU) to calculate the real-time slope.

[0024] By adopting the above technical solution, the problem of incomplete and inaccurate information perception of traditional mining trucks has been solved, enabling mining trucks to grasp their own operating status, load conditions, and slope information of the environment in real time and with precision.

[0025] Preferably, the on-board decision module includes a multi-objective real-time energy management unit and a braking coordination control unit;

[0026] The multi-objective real-time energy management unit adopts the model predictive control (MPC) algorithm, with the minimum total fuel consumption for a future route as the main optimization objective, and constrains the battery SOC to be maintained within the high-efficiency window, and solves the optimal power allocation command for the engine and motor in real time.

[0027] The braking coordination control unit is used to receive predictive braking distribution curves from the cloud-based digital twin platform and accordingly perform real-time coordinated control of motor braking and mechanical hydraulic braking.

[0028] By adopting the above technical solutions, the problems of lack of dynamic optimization in energy management and insufficient coordinated control of braking system in traditional mining trucks have been solved.

[0029] Preferably, the strategy optimization learning module includes an algorithm optimization unit and a parameter distribution unit;

[0030] The algorithm optimization unit adopts a reinforcement learning algorithm, and its reward function integrates fuel economy reward, battery life degradation penalty, and engine wear penalty.

[0031] The parameter distribution unit is used to encrypt and package the optimized energy management strategy parameters, distribute them to the vehicle decision module through the wireless communication network, and simultaneously record the parameter update log.

[0032] By adopting the above technical solutions, the problems of traditional mining truck energy management strategies, such as lack of adaptive optimization, untimely parameter updates, and safety issues, have been solved.

[0033] Preferably, the predictive braking simulation module includes a path analysis unit and a braking curve calculation unit;

[0034] The path analysis unit is used to quantitatively analyze the slope changes, curve radii and lengths of the path ahead, and determine the starting point, ending point and slope gradient of long downhill sections.

[0035] The braking curve calculation unit is used to calculate the braking distribution curve that maximizes the proportion of motor braking energy recovery, based on the constraint that the mine car is limited to a safe speed range throughout the long downhill journey, and in combination with the power parameters of the virtual mine car model.

[0036] By adopting the above technical solution, the problems of traditional mine car braking systems lacking the ability to predict the path ahead and low efficiency of motor braking energy recovery are solved.

[0037] Preferably, the positioning and path prediction module includes a high-precision positioning unit and a path query unit;

[0038] The high-precision positioning unit uses Beidou + GPS dual-mode positioning technology to obtain the real-time location information of the mining truck;

[0039] The path query unit is used to query path information within 1 to 3 kilometers ahead of the mining truck based on a pre-stored high-precision map of the mining area, and to classify and mark slope changes and road condition levels.

[0040] By adopting the above technical solution, the problems of low positioning accuracy and inability to predict detailed information about the path ahead in traditional mining trucks have been solved.

[0041] Preferably, the actuator drive module includes a power component drive unit and a braking component drive unit;

[0042] The power component drive unit is used to receive power allocation commands from the vehicle decision module and drive the diesel engine, drive motor and gearbox to work together. The drive motor also serves as a generator.

[0043] The braking component drive unit is used to receive braking commands from the braking coordination control unit and drive the motor brake and the mechanical hydraulic brake to operate.

[0044] By adopting the above technical solution, the problems of untimely response of traditional mine car actuator drive modules and poor coordination between power and braking components have been solved.

[0045] Preferably, the digital twin model library includes a virtual mining truck modeling unit and a model updating unit;

[0046] The virtual mining truck modeling unit is used to construct a high-fidelity model that is consistent with the physical mining truck structure and parameters, covering the power system, braking system and body structure;

[0047] The model update unit is used to periodically correct the virtual mining truck model parameters based on the real-time operating data of the physical mining trucks.

[0048] By adopting the above technical solution, the problem of inconsistency between the traditional virtual model of mining truck and the physical entity, and the inability to reflect the actual operating status of mining truck in real time, has been solved.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] 1. The hybrid power-based mining truck drive control system described in this application solves the problems of incomplete and inaccurate information perception in traditional systems through the multi-unit collaborative design of the information perception module. The operating status perception unit collects key parameters of the battery, engine, and motor; the load perception unit estimates the load in real time based on the pressure sensor; and the environmental perception unit accurately calculates the slope in conjunction with the IMU. Together, these three components provide comprehensive and accurate data support for subsequent control, ensuring that the mining truck can grasp its own status and environmental information in real time, and avoiding power distribution disconnection or braking overload.

[0051] 2. The hybrid power-based mining truck drive control system described in this application, relying on the on-board decision module and the predictive braking simulation module in the cloud-based digital twin platform, solves the problems of insufficient energy management optimization and inadequate braking coordination. The multi-objective real-time energy management unit dynamically allocates power with the goal of minimizing fuel consumption, and the braking coordination control unit coordinates motor braking and mechanical braking by combining the braking curve simulated in the cloud. This not only improves the energy recovery efficiency on long downhill slopes but also reduces mechanical brake wear, ensuring braking safety, while extending the service life of the battery and engine.

[0052] 3. The hybrid power-based mining truck drive control system described in this application solves the problems of the disconnect between the virtual model and the physical mining truck and the lack of continuous optimization capability of the system by using a digital twin model library and a strategy optimization learning module. The virtual mining truck modeling unit constructs a high-fidelity model, the model update unit corrects parameters based on real-time data, and the strategy optimization learning module uses an iterative algorithm based on historical data to ensure that the system can adapt to changes in mining conditions over a long period of time, maintain efficient drive performance, and improve the economy and stability of long-term mining truck operation. Attached Figure Description

[0053] Figure 1 This is the system architecture diagram of this application;

[0054] Figure 2 This is an architecture diagram of the information perception module of this application;

[0055] Figure 3 This is a diagram of the vehicle-mounted decision module architecture of this application;

[0056] Figure 4 This is the architecture diagram of the strategy optimization learning module in this application;

[0057] Figure 5This is a diagram of the predictive braking simulation module architecture of this application;

[0058] Figure 6 This is the architecture diagram of the positioning and path prediction module in this application;

[0059] Figure 7 This is the architecture diagram of the actuator driver module of this application;

[0060] Figure 8 This is the architecture diagram of the digital twin model library of this application. Detailed Implementation

[0061] The following is in conjunction with the appendix Figure 1 To be continued Figure 8 This application will be described in further detail below.

[0062] Example 1, please refer to Figure 1 As shown, a mining truck drive control system based on hybrid power is disclosed. The mining truck drive control system includes an on-board control terminal and a cloud-based digital twin platform. The cloud-based digital twin platform interacts with the on-board control terminal through a wireless communication network.

[0063] The vehicle control terminal includes an information perception module, a positioning and path prediction module, a vehicle decision-making module, and an actuator drive module;

[0064] The information sensing module is used to collect real-time operating status parameters, load parameters, and environmental parameters of the mining truck;

[0065] The positioning and path prediction module is used to obtain the real-time high-precision location information of the mining truck and query the path information within a predetermined distance ahead based on the pre-stored high-precision map of the mining area. The path information includes slope changes and road conditions.

[0066] The vehicle-mounted decision-making module is connected to the information perception module and the positioning and path prediction module, and is used to generate control commands for the engine, drive motor and braking system based on real-time and predictive path information.

[0067] The actuator drive module is connected to the vehicle decision module and is used to receive control commands and drive the corresponding power and braking components to move.

[0068] The cloud-based digital twin platform includes a digital twin model library, a predictive braking simulation module, and a strategy optimization learning module;

[0069] The digital twin model library is used to store high-fidelity virtual mining truck models corresponding to physical mining trucks;

[0070] The predictive braking simulation module is used to simulate and calculate the optimal distribution curve of electric motor power and mechanical braking force based on a virtual mine car model and the path ahead before the mine car enters a long downhill section.

[0071] The strategy optimization learning module is used to continuously optimize the energy management strategy based on the fleet's historical operating data using reinforcement learning algorithms, and then sends the optimized strategy parameters to the onboard decision module.

[0072] Example 2, please refer to Figures 2 to 3 As shown, the information sensing module includes an operation status sensing unit, a load sensing unit, and an environment sensing unit.

[0073] The operating status sensing unit is used to collect parameters including the battery's state of charge (SOC), state of health (SOH), engine speed, engine torque, and drive motor speed / torque.

[0074] The load sensing unit is used to estimate the load of the mining truck in real time through the suspension pressure sensor;

[0075] Formula for estimating the load capacity of a mine car:

[0076] ;

[0077] in, Real-time load measurement for mining trucks. The pressure-load conversion factor ranges from 0.8 to 1.2 (dimensionless, determined through on-site calibration experiments, used to compensate for systematic errors caused by all non-ideal factors), and g is the gravitational acceleration, usually taken as 9.8 m / s², ensuring the correct conversion of dimensions (from force to mass). This refers to the number of suspension pressure sensors. For the first Real-time pressure collected by a sensor. The pressure area of ​​a single sensor. This refers to the empty weight of the mine car. The product of the total pressure collected by all suspension pressure sensors and the individual pressure-bearing area;

[0078] Real-time load calculation provides accurate data for power distribution in mining trucks. Under different load conditions, the system adjusts the power output of the engine and electric motor in the hybrid power system accordingly. When the load is light, engine power is reduced and the electric motor's drive ratio is increased to achieve energy conservation and emission reduction. Simultaneously, braking strategies are optimized based on the calculation results. Braking intensity is adjusted in advance when going downhill under heavy loads to avoid potential hazards. Speed ​​is dynamically adjusted during operation, with maximum speed limited when the load is too high to ensure safe and stable operation. Furthermore, long-term monitoring and data analysis provide a basis for mining truck maintenance. When the load frequently exceeds the range, key components are inspected and maintained in advance to extend their lifespan and reduce costs. This function has significant application value in the actual operation of mining trucks, improving performance and efficiency.

[0079] The environmental sensing unit is used to collect the current pitch angle of the mine car via the inertial measurement unit (IMU) to calculate the real-time slope.

[0080] Real-time slope calculation formula:

[0081] ;

[0082] in, For real-time slope of the mining truck, The elevation angle of the minecart collected by the IMU. This represents the tangent trigonometric function.

[0083] exist The formula calculates the real-time gradient of the mining truck (ensuring the angle unit (degrees or radians) of the trigonometric calculator matches the unit of the IMU output), providing crucial environmental data support for subsequent drive control. When the mining truck is traveling on an incline, the system increases the output power of the engine and motor based on the real-time gradient calculation to meet the greater traction required for climbing. If the gradient is gentle, the system appropriately reduces the power output to avoid unnecessary energy consumption.

[0084] Meanwhile, real-time slope data can also assist in the control of the braking system. On downhill sections, the braking force can be adjusted according to the slope to prevent the mine car from losing control due to excessive speed. Based on real-time slope information, the system can also dynamically adjust the suspension system to ensure that the mine car can maintain a good driving posture on different slopes.

[0085] When the mining car goes downhill, the system can reasonably control the power generation of the motor according to the slope, convert the potential energy of the vehicle into electrical energy and store it, thereby improving the energy utilization efficiency through accurate grasp and effective utilization of the real-time slope.

[0086] The onboard decision-making module includes a multi-objective real-time energy management unit and a braking coordination control unit;

[0087] The multi-objective real-time energy management unit adopts the model predictive control (MPC) algorithm, with the minimum total fuel consumption for a future route as the main optimization objective, and constrains the battery SOC to be maintained within the high-efficiency window, and solves the optimal power distribution command for the engine and motor in real time.

[0088] The brake coordination control unit receives predictive brake distribution curves from a cloud-based digital twin platform and uses these curves to perform real-time coordinated control of the electric motor brake and the mechanical hydraulic brake.

[0089] The multi-objective real-time energy management unit uses a model predictive control algorithm to solve for the optimal power allocation between the engine and the electric motor, aiming to minimize total fuel consumption while constraining the battery SOC within the high-efficiency window, as shown in the following formula:

[0090] ;

[0091] ;

[0092] ;

[0093] in, To optimize the objective function, For MPC prediction in the time domain, typically 5–10 seconds are elapsed. This is a function of engine fuel consumption rate, expressed in g / kWh, fitted based on engine bench test data. This refers to the engine's output power. The SOC deviation penalty coefficient ranges from 5 to 10, balancing fuel consumption and battery protection. This refers to the real-time state of charge of the battery. This is the midpoint of the optimal SOC window for the battery (usually taken as 0.6). The real-time power requirement for the mining truck (calculated based on vehicle speed, load, and gradient). Power loss of the transmission system (unit: kW, pre-calibrated). , These are the minimum and maximum limits for battery SOC (usually 0.3 and 0.9).

[0094] The multi-objective real-time energy management unit calculates the optimal power distribution ratio between the engine and the motor using a formula. When the mining truck is running, the output power of the engine and motor is adjusted according to the real-time power demand changes. When climbing or under heavy load, the system increases the output power of the engine and the motor works in coordination. When on flat roads or under light load, the motor undertakes part of the power, enabling the engine to operate efficiently and reducing fuel consumption. The multi-objective real-time energy management unit controls the battery SOC within the high-efficiency window, extending battery life and reducing losses. When the real-time state of charge of the battery is close to the minimum limit, the system prioritizes charging the engine and reduces the power output of the motor. When it is close to the maximum limit, the system reduces the charging power of the engine to avoid overcharging.

[0095] Example 3, please refer to Figures 4 to 6 As shown, the strategy optimization learning module includes an algorithm optimization unit and a parameter distribution unit;

[0096] The algorithm optimization unit adopts a reinforcement learning algorithm, and its reward function integrates fuel economy reward, battery life degradation penalty, and engine wear penalty.

[0097] The parameter distribution unit is used to encrypt and package the optimized energy management strategy parameters, distribute them to the vehicle decision module through the wireless communication network, and record the parameter update log simultaneously.

[0098] The predictive braking simulation module includes a path analysis unit and a braking curve calculation unit;

[0099] The path analysis unit is used to quantitatively analyze the slope changes, curve radii and lengths of the path ahead, and to determine the starting point, ending point and slope gradient of long downhill sections.

[0100] The braking curve calculation unit is used to calculate the braking distribution curve that maximizes the proportion of motor braking energy recovery, based on the constraint that the mine car is limited to a safe speed range throughout the long downhill journey, combined with the power parameters of the virtual mine car model.

[0101] The positioning and path prediction module includes a high-precision positioning unit and a path query unit;

[0102] The high-precision positioning unit uses BeiDou + GPS dual-mode positioning technology to obtain the real-time location information of the mining truck;

[0103] The path query unit is used to query path information within 1 to 3 kilometers ahead of the mining truck based on a pre-stored high-precision map of the mining area, and to classify and mark slope changes and road condition levels.

[0104] Example 4, please refer to Figures 7 to 8 As shown, the actuator drive module includes a power component drive unit and a braking component drive unit;

[0105] The power unit drive unit is used to receive power distribution commands from the on-board decision module and drive the diesel engine, drive motor and gearbox to work together. The drive motor also serves as a generator.

[0106] Drive motor power command correction formula;

[0107] ;

[0108] in, To control the current of the drive motor in real time, The target power of the motor output by the onboard decision module. The motor efficiency is defined as a value ranging from 0.85 to 0.95. This refers to the real-time terminal voltage of the battery. The motor power factor;

[0109] The braking component drive unit is used to receive braking commands from the braking coordination control unit and drive the motor brake and mechanical hydraulic brake to operate.

[0110] Formula for calculating mechanical hydraulic braking pressure:

[0111] ;

[0112] in, The target pressure for the hydraulic braking coefficient. This is a mechanical braking command (here, it represents a dimensionless control input signal). For the number of brake calipers, The piston area of ​​a single brake caliper. This is the efficiency coefficient of the hydraulic system, with a value ranging from 0.9 to 0.95.

[0113] The digital twin model library includes virtual mining truck modeling units and model updating units;

[0114] The virtual mining truck modeling unit is used to build a high-fidelity model that is consistent with the structure and parameters of the physical mining truck, covering the power system, braking system and body structure;

[0115] The model update unit is used to periodically correct the virtual mining truck model parameters based on the real-time operating data of the physical mining trucks;

[0116] Model update unit parameter correction formula:

[0117] ;

[0118] in, For the updated virtual model parameters, The parameters of the virtual model before the update. The parameter update step size ranges from 0.01 to 0.05. For physical minecarts Key parameters measured at any given time. For physical minecarts Measured parameters at any given time.

[0119] In the formula The virtual model parameters can be dynamically adjusted in real time based on the measured parameters of the physical mine car, closely following the actual operating status of the physical mine car, improving the simulation accuracy, and achieving accurate mapping as the parameters are corrected.

[0120] During operation, real-time measured parameters of the physical mining truck are collected and substituted into the formula for calculation. The parameter update step size has a reasonable range. If it is too large, it will easily lead to excessive adjustment and large fluctuations of the model parameters. If it is too small, the update speed will be slow. Continuous real-time calculation with the formula can optimize the virtual model parameters. After a period of operation and multiple updates, the virtual model can highly restore the operating characteristics of the physical mining truck, which helps to accurately evaluate its performance and provide a basis for optimizing control.

[0121] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A hybrid power-based mining truck drive control system, characterized in that, The mining truck drive control system includes an on-board control terminal and a cloud-based digital twin platform. The cloud-based digital twin platform interacts with the on-board control terminal via a wireless communication network. The vehicle control terminal includes an information perception module, a positioning and path prediction module, a vehicle decision-making module, and an actuator drive module; The information sensing module is used to collect the operating status parameters, load parameters and environmental parameters of the mining truck in real time. The positioning and path prediction module is used to obtain the real-time high-precision location information of the mining truck and query the path information within a predetermined distance ahead based on the pre-stored high-precision map of the mining area. The path information includes slope changes and road conditions. The vehicle-mounted decision-making module is connected to the information perception module and the positioning and path prediction module, and is used to generate control commands for the engine, drive motor and braking system based on real-time and predictive path information. The actuator drive module is connected to the vehicle decision module and is used to receive control commands and drive the corresponding power components and braking components to move. The cloud-based digital twin platform includes a digital twin model library, a predictive braking simulation module, and a strategy optimization learning module. The digital twin model library is used to store high-fidelity virtual mining truck models corresponding to physical mining trucks; The predictive braking simulation module is used to simulate and calculate the optimal distribution curve of electric motor power and mechanical braking force based on a virtual mine car model and the path information ahead before the mine car enters a long downhill section. The strategy optimization learning module is used to continuously optimize the energy management strategy based on the fleet's historical operation data using reinforcement learning algorithms, and then sends the optimized strategy parameters to the on-board decision module.

2. The mining truck drive control system based on hybrid power according to claim 1, characterized in that, The information sensing module includes an operation status sensing unit, a load sensing unit, and an environment sensing unit. The operating status sensing unit is used to collect parameters including the battery's state of charge (SOC), state of health (SOH), engine speed, engine torque, and drive motor speed / torque. The load sensing unit is used to estimate the load of the mining truck in real time through the suspension pressure sensor. The formula for estimating the load capacity of the mining car is as follows: ; in, Real-time load measurement for mining trucks. This is the pressure-to-load conversion factor, with a value ranging from 0.8 to 1.

2. This refers to the number of suspension pressure sensors. For the first Real-time pressure collected by a sensor. The pressure area of ​​a single sensor. This refers to the empty weight of the mine car. The product of the total pressure collected by all suspension pressure sensors and the individual pressure-bearing area; The environmental sensing unit is used to collect the current pitch angle of the mine car through the inertial measurement unit (IMU) to calculate the real-time slope. The real-time slope calculation formula is as follows: ； in, For real-time slope of the mining truck, The elevation angle of the minecart, collected by the IMU.

3. The mining truck drive control system based on hybrid power according to claim 1, characterized in that, The on-board decision-making module includes a multi-objective real-time energy management unit and a braking coordination control unit; The multi-objective real-time energy management unit adopts the model predictive control (MPC) algorithm, with the minimum total fuel consumption for a future path as the main optimization objective, and constrains the battery SOC to be maintained within the high-efficiency window, and solves the optimal power allocation command for the engine and motor in real time. The braking coordination control unit is used to receive predictive braking distribution curves from the cloud-based digital twin platform and accordingly perform real-time coordinated control of motor braking and mechanical hydraulic braking.

4. A mining truck drive control system based on hybrid power according to claim 1, characterized in that, The strategy optimization learning module includes an algorithm optimization unit and a parameter distribution unit; The algorithm optimization unit adopts a reinforcement learning algorithm, and its reward function integrates fuel economy reward, battery life degradation penalty, and engine wear penalty. The parameter distribution unit is used to encrypt and package the optimized energy management strategy parameters, distribute them to the vehicle decision module through the wireless communication network, and simultaneously record the parameter update log.

5. A hybrid power-based mining truck drive control system according to claim 1, characterized in that, The predictive braking simulation module includes a path analysis unit and a braking curve calculation unit; The path analysis unit is used to quantitatively analyze the slope changes, curve radii and lengths of the path ahead, and determine the starting point, ending point and slope gradient of long downhill sections. The braking curve calculation unit is used to calculate the braking distribution curve that maximizes the proportion of motor braking energy recovery, based on the constraint that the mine car is limited to a safe speed range throughout the long downhill journey, and in combination with the power parameters of the virtual mine car model.

6. A mining truck drive control system based on hybrid power according to claim 1, characterized in that, The positioning and path prediction module includes a high-precision positioning unit and a path query unit; The high-precision positioning unit uses Beidou + GPS dual-mode positioning technology to obtain the real-time location information of the mining truck; The path query unit is used to query path information within 1 to 3 kilometers ahead of the mining truck based on a pre-stored high-precision map of the mining area, and to classify and mark slope changes and road condition levels.

7. A mining truck drive control system based on hybrid power according to claim 1, characterized in that, The actuator drive module includes a power component drive unit and a braking component drive unit; The power component drive unit is used to receive power allocation commands from the vehicle decision module and drive the diesel engine, drive motor and gearbox to work together. The drive motor also serves as a generator. The power command correction formula for the drive motor; ； in, To control the current of the drive motor in real time, The target power of the motor output by the onboard decision module. The motor efficiency is defined as a value ranging from 0.85 to 0.

95. This refers to the real-time terminal voltage of the battery. The motor power factor; The braking component drive unit is used to receive braking commands from the braking coordination control unit and drive the motor brake and the mechanical hydraulic brake to operate. The formula for calculating mechanical hydraulic braking pressure is as follows: ； in, The target pressure for the hydraulic braking coefficient. This is a mechanical braking command. For the number of brake calipers, The piston area of ​​a single brake caliper. The range of hydraulic coefficients is 0.9-0.

95.

8. A mining truck drive control system based on hybrid power according to claim 1, characterized in that, The digital twin model library includes a virtual mining truck modeling unit and a model updating unit; The virtual mining truck modeling unit is used to construct a high-fidelity model that is consistent with the physical mining truck structure and parameters, covering the power system, braking system and body structure; The model update unit is used to periodically correct the virtual mining truck model parameters based on the real-time operating data of the physical mining trucks. The formula for correcting the parameters of the model update unit is as follows: ; in, For the updated virtual model parameters, The parameters of the virtual model before the update. The parameter update step size ranges from 0.01 to 0.

05. For physical minecarts Key parameters measured at any given time. For physical minecarts Measured parameters at any given time.

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