New energy tractor control method

Abstract

The invention relates to the technical field of new energy vehicle control, in particular to a new energy tractor control method which can automatically switch driving modes according to different road conditions and effectively control the rotating speed, torque, power, energy and the like according to the driving modes. By optimizing the working mode of the motor and controlling the charging and discharging process of the battery, the load of the motor and the battery can be reduced, the aging speed is reduced, the service life of the motor and the battery is prolonged, and the maintenance cost of the vehicle is effectively reduced. The vehicle running stability and safety can be enhanced, power output of the vehicle can be more stable through accurate control, vehicle out-of-control or safety accidents caused by sudden power change are avoided, the running states such as the speed, the battery electric quantity and the motor temperature of the vehicle can be monitored in real time, corresponding measures can be taken conveniently when abnormal conditions occur, and the safety of the vehicle is improved. And safe operation of the vehicle is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle control, and particularly to a control method for a new energy tractor. Background Art

[0002] The continuous progress of electric vehicle technology has gradually popularized new energy vehicles, which have achieved remarkable results in reducing exhaust emissions and dependence on fossil fuels. Applying new energy technology to the tractor field can not only achieve the goal of green environmental protection, but also combine the advantages of railway and road transportation to improve transportation efficiency and flexibility. At present, some products that combine new energy and tractors have emerged on the market, but most of these products have problems such as single function and limited application range. For example, some electric tractors can only drive on the road and cannot meet the needs of railway transportation; while some dual-purpose tractors have problems such as inconvenient switching between railway and road modes, unstable performance, and inaccurate control during actual use. Therefore, an efficient and intelligent control method is needed to optimize the performance of new energy tractors. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and propose a control method for a new energy tractor, which has efficient power control, stable performance, convenient road switching, and good maneuverability, and can adapt to different transportation scenarios and requirements.

[0004] The technical solution adopted by the present invention to solve its technical problems is as follows: A control method for a new energy tractor, comprising the following steps: S1. Obtain the current state information of the vehicle, where the state information includes the position, speed, battery power, track state, vehicle load, and road surface condition of the vehicle, etc.; the track state includes the presence or absence of a track, whether the track is intact, etc., and the road surface condition includes road surface roughness, wetness, slope, flatness, etc.; S2. Judge the current operating mode of the vehicle according to the current state information of the vehicle, where the operating mode includes a track mode and a road mode; S3. Select a control strategy according to the current operating mode of the vehicle; in the track mode, control the output of the motor according to the first control strategy, and the first control strategy adjusts the rotation speed, torque, and power output of the motor according to the track characteristics and vehicle speed requirements to ensure that the vehicle travels smoothly on the track; in the road mode, control the output of the motor according to the second control strategy, and the second control strategy adjusts the output of the motor according to the road surface condition and vehicle load to ensure the driving performance and safety of the vehicle on the road; S4. Monitor and control the operating state of the vehicle in real time according to the current operating mode of the vehicle. Monitor the motor parameters in real time through sensors. The motor parameters include information such as temperature, speed, torque, and battery power. When the motor temperature exceeds the preset threshold, adjust the output power of the motor. When the battery power is lower than the preset threshold, switch to the energy recovery mode. When the battery power is insufficient, convert the kinetic energy of the vehicle into electrical energy by reversing the motor to increase the battery power.

[0005] S5. Integrate the real-time operating state of the vehicle and the external environment information, automatically adjust the parameters in the control strategy, optimize the control effect, and improve the control accuracy. The external environment information includes wind speed, ambient temperature, ambient humidity, etc., and automatically adjust the output parameters of the motor.

[0006] Further, the step S1 includes the following steps: S11. Formulate a sample data set based on the current state information of the vehicle. Any piece of data in the sample data set includes the position, speed, battery power, track state, and road surface condition of the vehicle, etc. S12. Group the sample data set based on the track state record data to obtain multiple groups of sample data. S13. Perform a traversal operation on the multiple groups of sample data to obtain the correlation screening results of the multiple groups of sample data. S14. Extract the correlation set of each attribute according to the correlation screening results.

[0007] Further, the step S2 includes the following steps: Step S21. Initialize the correlation set dictionary to store the correlation sets of each attribute. The key is the attribute name, and the value is a list. Step S22. For each record in the correlation set of each attribute, read the correlation of each attribute and add the correlation of each attribute to the corresponding list. Step S23. Calculate statistical information such as the average value, maximum value, and minimum value of each attribute correlation set to obtain the data feature distribution area. Step S24. Judge the preset area into which the data feature distribution area falls, and judge the current operating mode of the vehicle according to the preset area into which it falls. If the data feature distribution area falls into the track preset area, it is judged that the current mode is the track mode. If the data feature distribution area falls into the road preset area, it is judged that the current mode is the road mode.

[0008] Further, the first control strategy of the step S3 is: Obtain the track characteristic data and the current speed of the vehicle from the current state information of the vehicle. The track characteristic data includes track slope, radius of curvature, etc. Calculate the target speed and target torque of the motor by combining the track characteristic data and the current vehicle speed; Obtain the target power of the motor according to the target speed and target torque; Use a PID controller to perform closed-loop control on the actual speed, torque, and power of the motor. Take the deviation between the target value and the actual value as the input of the PID controller. The control signal adjusted by the PID controller is converted into a motor drive signal. The motor drive signal includes a voltage or current signal to drive the motor to operate.

[0009] Further, in step S3, the second control strategy is as follows: Obtain the road surface roughness, friction coefficient, vehicle load, and current vehicle speed, etc. from the current state information of the vehicle; Fuse the road surface roughness, friction coefficient, vehicle load, and current vehicle speed to calculate the target speed and target torque of the motor; Obtain the target power of the motor according to the target speed and target torque; Adopt a fuzzy controller to perform online adjustment on the actual speed, torque, and power of the motor. Take the road surface roughness, friction coefficient, vehicle load, and current vehicle speed as the input of the fuzzy controller. The fuzzy control signal adjusted by the fuzzy controller is converted into a motor drive signal. The motor drive signal includes a voltage or current signal to drive the motor to operate.

[0010] Further, in step S4, when the motor temperature exceeds the preset threshold, the specific process of adjusting the output power of the motor is as follows: Calculate the difference between the current motor temperature and the preset threshold; Adopt a fuzzy logic algorithm. Take the deviation and deviation change rate between the motor temperature and the preset threshold as the input variables of the fuzzy logic algorithm. The output variable of the fuzzy logic algorithm is the power adjustment amount to make the motor temperature gradually fall back to the safe range.

[0011] Further, in step S4, when the battery power is lower than the preset threshold, switch to the energy recovery mode; when the battery power is insufficient, convert the kinetic energy of the vehicle into electrical energy by reversing the motor. The specific process is as follows: Real-time monitor the battery power status and obtain the current battery power; Take the current battery power as the input of the energy recovery decision model, and take the reverse torque of the motor as the output variable of the energy recovery decision model; When the battery power is lower than the lowest preset threshold, in order to quickly increase the battery power, the reverse torque of the motor is increased. When the battery power is within the preset threshold range, medium-intensity energy recovery is adopted. When the battery power is higher than the highest preset threshold, in order to reduce the impact on the vehicle driving performance and avoid overcharging of the battery, the reverse torque is reduced or energy recovery is not performed.

[0012] Further, step S5 includes the following steps: Step S51: Denoise the external environment information data, and then convert data parameters with different dimensions to the same scale; Step S52: Extract the real-time operating state of the vehicle and the historical data of the external environment information, train the neural network model, and adjust the model parameters to minimize the prediction error; Step S53: Obtain the real-time operating state of the vehicle and the external environment information, input them into the trained model, and the model outputs the adjusted motor control parameters such as current, voltage, speed, etc. The motor operates according to the new control parameters to achieve the optimized control effect.

[0013] The technical effects of the present invention: Compared with the prior art, in the whole driving process of a new energy tractor control method of the present invention, through the control method of the present invention, the rail-road dual-use new energy tractor can automatically switch the driving mode according to different road conditions, and effectively control aspects such as speed, torque, power, and energy, ensuring the safe, efficient, and energy-saving operation of the tractor, and being able to adapt to different transportation scenarios and requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the new energy tractor control method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the specification.

[0016] Embodiment 1: As Figure 1 shown, a new energy tractor control method involved in this embodiment includes the following steps.

[0017] S1: Obtain the current state information of the vehicle, and the state information includes the position, speed, battery power, track state, vehicle load, and road surface condition of the vehicle, etc.; the track state includes whether there is a track and whether the track is intact, etc., and the road surface condition includes road surface roughness, wetness, slope, flatness, etc.

[0018] Specifically, step S1 includes the following steps: S11. Develop a sample data set based on the current state information of the vehicle. Any piece of data in the sample data set includes the vehicle's position, speed, battery power, track status, road surface condition, etc. S12. Group the sample data set based on the track status record data to obtain multiple groups of sample data. S13. Perform a traversal operation on the multiple groups of sample data to obtain the correlation screening results of the multiple groups of sample data. S14. Extract the correlation set of each attribute according to the correlation screening results.

[0019] S2. Judge the current operating mode of the vehicle according to the current state information of the vehicle. The operating mode includes the track mode and the road mode.

[0020] Specifically, step S2 includes the following steps: Step S21. Initialize the correlation set dictionary to store the correlation sets of each attribute. The key is the attribute name, and the value is a list. Step S22. For each record in the correlation set of each attribute, read the correlation of each attribute and add the correlation of each attribute to the corresponding list. Step S23. Calculate statistical information such as the average value, maximum value, and minimum value of each attribute correlation set to obtain the data feature distribution area. Step S24. Judge the preset area into which the data feature distribution area falls, and judge the current operating mode of the vehicle according to the preset area into which it falls. If the data feature distribution area falls into the track preset area, it is judged that the current mode is the track mode. If the data feature distribution area falls into the road preset area, it is judged that the current mode is the road mode.

[0021] S3. Select a control strategy according to the current operating mode of the vehicle. In the track mode, control the motor output according to the first control strategy. The first control strategy adjusts the speed, torque, and power output of the motor according to the track characteristics and the vehicle speed requirements to ensure the smooth driving of the vehicle on the track. In the road mode, control the motor output according to the second control strategy. The second control strategy adjusts the output of the motor according to the road surface condition and the vehicle load to ensure the driving performance and safety of the vehicle on the road.

[0022] Specifically, the first control strategy is: Obtain the track characteristic data and the current vehicle speed v from the current state information of the vehicle. The track characteristic data includes the track slope φ, the radius of curvature r, etc. Combine the track characteristic data and the current vehicle speed to calculate the target speed V 转速 = F(v, φ, r), and the target torque T扭矩 =G(v, φ, r), where F and G are functions based on the vehicle dynamics model; According to the target rotational speed V 转速 and the target torque T 扭矩 Obtain the target power P of the motor 功率 =P(f, g), for example P 功率 =T 扭矩 *(2π*V 转速 / 60); Use a PID controller to perform closed-loop control on the actual rotational speed, torque, and power of the motor. Take the deviation between the target value and the actual value as the input of the PID controller. The control signal adjusted by the PID controller is converted into a motor drive signal. The motor drive signal includes a voltage or current signal to drive the motor to operate.

[0023] The second control strategy is: Obtain the road surface roughness σ, friction coefficient μ, vehicle load m, and vehicle current speed v, etc. from the current state information of the vehicle; Fuse the road surface roughness σ, friction coefficient μ, vehicle load m, and vehicle current speed v to calculate the target rotational speed V of the motor 道路转速 =f(v, σ, μ, m), and the target torque T 道路扭矩 =g(v, σ, μ, m), where f and g are functions based on the vehicle dynamics model; According to the target rotational speed V 道路转速 and the target torque T 道路扭矩 Obtain the target power p = P(f, g) of the motor. For example, p = t 扭矩 *(2π*V 转速 / 60); Use a fuzzy controller to perform online adjustment on the actual rotational speed, torque, and power of the motor. Take the road surface roughness σ, friction coefficient μ, vehicle load m, and vehicle current speed v as the input of the fuzzy controller. The fuzzy control signal adjusted by the fuzzy controller is converted into a motor drive signal. The motor drive signal includes a voltage or current signal to drive the motor to operate.

[0024] S4. According to the current operating mode of the vehicle, monitor and control the operating state of the vehicle in real time. Real-time monitor the motor parameters through sensors. The motor parameters include information such as temperature, rotational speed, torque, and battery power, etc.; When the motor temperature exceeds the preset threshold, adjust the output power of the motor. If the motor temperature is too high, the temperature can be reduced by reducing the output power of the motor, increasing the rotational speed of the cooling fan, etc.; When the battery power is lower than the preset threshold, switch to the energy recovery mode; When the battery power is insufficient, convert the kinetic energy of the vehicle into electrical energy by reversing the motor to increase the battery power.

[0025] Specifically, when the motor temperature exceeds the preset threshold, the specific process of adjusting the output power of the motor is as follows: Calculate the difference between the current motor temperature and the preset threshold. Adopt a fuzzy logic algorithm, taking the deviation between the motor temperature and the preset threshold and the rate of change of the deviation as the input variables of the fuzzy logic algorithm. The output variable of the fuzzy logic algorithm is the power adjustment amount, so that the motor temperature gradually drops back to the safe range. For example, when the deviation is positive and large, and the rate of change of the deviation is positive and small, it indicates that the motor temperature is high and has an upward trend. At this time, a large power reduction amount should be given to quickly suppress the temperature rise.

[0026] When the battery power is lower than the preset threshold, switch to the energy recovery mode; when the battery power is insufficient, convert the kinetic energy of the vehicle into electrical energy by reversing the motor. The specific process is as follows: Real-time monitor the battery power status and obtain the current battery power. Take the current battery power as the input of the energy recovery decision model, and take the reverse torque of the motor as the output variable of the energy recovery decision model. When the battery power is lower than the lowest preset threshold, in order to quickly increase the battery power, increase the reverse torque of the motor. When the battery power is within the preset threshold range, adopt medium-intensity energy recovery. When the battery power is higher than the highest preset threshold, in order to reduce the impact on the vehicle driving performance and avoid overcharging the battery, adopt a smaller reverse torque or do not perform energy recovery; among them, the energy recovery decision model adopts an energy recovery decision model based on a linear function or an energy recovery decision model based on the battery state and vehicle speed.

[0027] S5. Integrate the real-time operating state of the vehicle and external environment information, automatically adjust the parameters in the control strategy, optimize the control effect, and improve the control accuracy; the external environment information includes wind speed, ambient temperature, ambient humidity, etc., and automatically adjust the output parameters of the motor.

[0028] Specifically, step S5 includes the following steps: Step S51. Denoise the external environment information data, and then convert data parameters with different dimensions to the same scale. Step S52. Extract the historical data of the real-time operating state of the vehicle and external environment information, train the neural network model, and adjust the model parameters to minimize the prediction error. Step S53. Obtain the real-time operating state of the vehicle and external environment information, input it into the trained model, and the model outputs the adjusted motor control parameters, such as current, voltage, speed, etc. The motor operates according to the new control parameters to achieve an optimized control effect.

[0029] The present invention is equipped with a variety of sensors, at least including a speed sensor, an acceleration sensor, a battery power sensor, a motor temperature sensor, a road condition recognition sensor, etc., which respectively collect relevant parameters.

[0030] The present invention can automatically switch the driving mode according to different road conditions, and effectively control aspects such as rotational speed, torque, power, and energy according to the driving mode, optimize the working mode of the motor, control the charging and discharging process of the battery, etc., which can reduce the load on the motor and the battery, reduce their aging speed, thereby extending the service life of the motor and the battery, and effectively reducing the maintenance cost of the vehicle.

[0031] The present invention optimizes the management of the drive system of a new energy tractor through precise control strategies. For example, in an electric tractor, parameters such as the rotational speed and torque of the electric motor are reasonably controlled to make the electric motor work stably and reasonably. At the same time, for a tractor adopting an energy recovery decision model, the redundant energy generated during vehicle braking or deceleration can be effectively recovered, greatly improving the energy utilization rate and extending the vehicle's cruising range. The present invention can not only enhance the running stability and safety of the vehicle, but also make the power output of the vehicle more stable through precise control, avoiding vehicle out-of-control or safety accidents caused by sudden power changes. It can also monitor the running state of the vehicle in real time, such as speed, battery power, motor temperature, etc., and facilitate taking corresponding measures when abnormal situations occur to ensure the safe operation of the vehicle.

[0032] Embodiment 2: This embodiment provides a system for implementing the new energy tractor control method described in Embodiment 1. The system at least includes: A state information acquisition module for acquiring the current state information of the vehicle; A mode judgment module for judging whether the vehicle is currently in the track mode or the road mode; An electric motor control module for controlling the output of the electric motor according to a preset control strategy in the track mode and the road mode respectively; A state monitoring module for monitoring the running state of the vehicle in real time; An energy recovery module for switching to the energy recovery mode when the battery power is low; A parameter adjustment module for automatically adjusting the parameters in the preset control strategy according to the real-time running state and external environment information.

[0033] The above specific implementation manners are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above specific implementation manners. Any appropriate changes or modifications made by any person skilled in the art who meets the claims of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. A new energy tractor control method, characterized in that: include: S1. Acquire the current status information of the vehicle, wherein the status information includes the vehicle's position, speed, battery power, track status, vehicle load, and road condition; the track status includes whether there is a track and whether the track is intact; the road condition includes road roughness, slippery, slope, and level; S2. determining the current operation mode of the vehicle according to the current state information of the vehicle, wherein the operation mode includes a track mode and a road mode; S3. Select a control strategy according to the current operation mode of the vehicle; in track mode, control the motor output according to a first control strategy, wherein the first control strategy adjusts the motor speed, torque and power output according to track characteristics and vehicle speed requirements; in road mode, control the motor output according to a second control strategy, wherein the second control strategy adjusts the motor output according to road conditions and vehicle load; S4, according to the current operation mode of the vehicle, real-time monitoring and control of the vehicle's operation state, real-time monitoring of motor parameters through sensors, the motor parameters including temperature, speed, torque and battery power; when the motor temperature exceeds a preset threshold, adjusting the output power of the motor; when the battery power is lower than the preset threshold, switching to energy recovery mode; when the battery power is insufficient, converting the vehicle's kinetic energy into electrical energy by reversing the motor; S5. Integrate the real-time operating status of the vehicle and external environmental information, automatically adjust the parameters in the control strategy, and optimize the control effect; the external environmental information includes wind speed, ambient temperature, and ambient humidity.

2. The new energy traction vehicle control method according to claim 1, characterized in that: The step S1 comprises the following steps: S11, formulating a sample data set based on the current state information of the vehicle, wherein any piece of data in the sample data set includes the vehicle's position, speed, battery power, track state, and road condition; S12, grouping the sample data sets based on the track state record data to obtain multiple groups of sample data; S13, performing a traversal operation on multiple groups of sample data to obtain correlation screening results of the multiple groups of sample data; S14. Extract the attribute association degree set according to the association degree screening result.

3. The new energy traction vehicle control method according to claim 2, characterized in that: The step S2 comprises the following steps: Step S21, initialize the association degree set dictionary to store each attribute association degree set, the key is the attribute name, and the value is a list; Step S22: for each record in each attribute association degree set, read the association degree of each attribute, and add the association degree of each attribute to the corresponding list; Step S23, calculating the average value, maximum value, and minimum value of each attribute association degree set to obtain the data feature distribution area; Step S24, determine the preset area where the data feature distribution area falls, and determine the current operating mode of the vehicle based on the preset area; if the data feature distribution area falls into the track preset area, determine the current mode is the track mode; if the data feature distribution area falls into the road preset area, determine the current mode is the road mode.

4. The new energy traction vehicle control method according to claim 1, characterized in that: In step S3, the first control strategy is: Acquiring track characteristic data and the current speed of the vehicle from the current state information of the vehicle, the track characteristic data including track slope and curvature radius; Calculate the target speed and torque of the motor by combining the track characteristic data and the current speed of the vehicle; Obtaining a target power of the motor according to the target speed and the target torque; The PID controller is used to perform closed-loop control on the actual speed, torque and power of the motor. The deviation between the target value and the actual value is used as the input of the PID controller. The control signal adjusted by the PID controller is converted into a motor drive signal. The motor drive signal includes a voltage or current signal to drive the motor to operate.

5. The new energy traction vehicle control method according to claim 1, characterized in that: In step S3, the second control strategy is: Acquire road surface roughness, friction coefficient, vehicle load and current vehicle speed from the current state information of the vehicle; Integrate road roughness, friction coefficient, vehicle load and current vehicle speed to calculate the motor target speed and target torque; Obtaining a target power of the motor according to the target speed and the target torque; A fuzzy controller is used to adjust the actual speed, torque and power of the motor online. The road roughness, friction coefficient, vehicle load and current speed of the vehicle are used as inputs of the fuzzy controller. The fuzzy control signal adjusted by the fuzzy controller is converted into a motor drive signal. The motor drive signal includes a voltage or current signal to drive the motor to operate.

6. The new energy traction vehicle control method according to claim 1, characterized in that: In step S4, when the temperature of the motor exceeds a preset threshold, the specific process of adjusting the output power of the motor is as follows: Calculate the difference between the current motor temperature and the preset threshold, A fuzzy logic algorithm is used, and the deviation between the motor temperature and the preset threshold and the deviation change rate are used as input variables of the fuzzy logic algorithm. The output variable of the fuzzy logic algorithm is the power adjustment amount, so that the motor temperature gradually returns to a safe range.

7. The new energy traction vehicle control method according to claim 1, characterized in that: In step S4, when the battery power is lower than a preset threshold, the vehicle switches to the energy recovery mode; when the battery power is insufficient, the vehicle's kinetic energy is converted into electrical energy by reversing the motor. The specific process is as follows: Monitor the battery power status in real time and obtain the current battery power; The current battery power is used as the input of the energy recovery decision model, and the motor reverse torque is used as the output variable of the energy recovery decision model; When the battery power is lower than the lowest preset threshold, the motor reverse torque is increased. When the battery power is within the preset threshold range, medium-intensity energy recovery is used. When the battery power is higher than the highest preset threshold, the reverse torque is reduced or energy recovery is not performed.

8. The new energy traction vehicle control method according to claim 1, characterized in that: The step S5 comprises the following steps: Step S51, denoising the external environment information data, and then converting data parameters of different dimensions to the same scale; Step S52: extracting the real-time running status of the vehicle and historical data of external environment information, training the neural network model, and adjusting the model parameters to minimize the prediction error; Step S53: Acquire the real-time operating status and external environment information of the vehicle, input them into the trained model, and the model outputs the adjusted motor control parameters. The motor operates according to the new control parameters to achieve an optimized control effect.

Images