A torque control method and device for an electric vehicle, the electric vehicle and a storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGDONG GOBAO INTELLIGENT TECHNOLOGY CO LTD
- Filing Date
- 2023-07-17
- Publication Date
- 2026-06-23
Smart Images
Figure CN116691368B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electric vehicle control technology, and more particularly to an electric vehicle torque control method, device, electric vehicle, and storage medium. Background Technology
[0002] Two-wheeled electric vehicles, also known as "electric vehicles," are purely electric motor vehicles powered by batteries and driven by electric motors. Two-wheeled electric vehicles are popular due to their compact size, speed, and low pollution, and have become an essential mode of transportation. However, due to the frequent accidents involving two-wheeled electric vehicles, their driving safety is receiving increasing attention, and improving driving safety has become a key focus of research in this field.
[0003] Currently, the application of electric vehicle safety torque control technology in the two-wheeled electric vehicle field is limited. Existing methods for controlling the safety torque of two-wheeled electric vehicles adjust the motor's output torque based on the user's acceleration habits and speed requirements. However, in practical applications, the user's speed while using the electric vehicle constantly changes according to actual conditions. This method cannot adjust the motor's output torque in real time according to the actual usage of the electric vehicle, which may lead to wheel slippage, causing the electric vehicle to skid or overturn, thus reducing the safety of two-wheeled electric vehicle operation. Summary of the Invention
[0004] This invention provides a method, device, electric vehicle, and storage medium for controlling the torque of an electric vehicle. The method can adjust the output torque of the motor in real time according to the motor acceleration and the adhesion coefficient between the tire and the road surface, thereby preventing the electric vehicle from skidding or overturning and improving the safety of two-wheeled electric vehicles.
[0005] In a first aspect, embodiments of the present invention provide a torque control method for an electric vehicle, comprising:
[0006] Obtain the encoder signal of the motor encoder in the current cycle;
[0007] The vehicle speed and motor acceleration in the current cycle are calculated based on the encoder signal.
[0008] The tire-road adhesion coefficient in the current cycle is calculated based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle when it is driving, and drag constant.
[0009] The target torque of the motor in the current cycle is determined based on the adhesion coefficient between the tire and the road surface and the motor acceleration, and the torque output by the motor in the current cycle is controlled according to the target torque.
[0010] Secondly, embodiments of the present invention provide an electric vehicle torque control device, the device comprising:
[0011] The signal acquisition module is used to acquire the encoder signal of the motor encoder in the current cycle;
[0012] An acceleration calculation module is used to calculate the vehicle speed and motor acceleration in the current cycle based on the encoder signal;
[0013] The coefficient determination module is used to calculate the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle when driving, and drag constant;
[0014] The torque control module is used to determine the target torque of the motor in the current cycle based on the adhesion coefficient between the tire and the road surface and the motor acceleration, and to control the torque output by the motor in the current cycle according to the target torque.
[0015] Thirdly, embodiments of the present invention also provide an electric vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the electric vehicle torque control method as described in any of the embodiments of the present invention.
[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the electric vehicle torque control method as described in any of the embodiments of the present invention.
[0017] In this embodiment of the invention, the encoder signal of the motor encoder in the current cycle is acquired; the vehicle speed and motor acceleration in the current cycle are calculated based on the encoder signal; the tire-road adhesion coefficient in the current cycle is calculated based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle during driving, and drag constant; the target torque of the motor in the current cycle is determined based on the tire-road adhesion coefficient and motor acceleration, and the torque output of the motor in the current cycle is controlled according to the target torque. That is, in this embodiment of the invention, the target torque output of the motor can be determined based on the motor acceleration and the tire-road adhesion coefficient, and the actual torque output of the motor can be adjusted in real time according to the target torque to prevent the electric vehicle from skidding or overturning, thus improving the safety of two-wheeled electric vehicles. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 A flowchart of an electric vehicle torque control method provided in an embodiment of the present invention;
[0020] Figure 2 Another flowchart of the electric vehicle torque control method provided in an embodiment of the present invention;
[0021] Figure 3 A flowchart for determining the target torque provided in an embodiment of the present invention;
[0022] Figure 4 This is a schematic diagram of the electric vehicle torque control device provided in an embodiment of the present invention;
[0023] Figure 5 This is a schematic diagram of the structure of an electric vehicle provided in an embodiment of the present invention. Detailed Implementation
[0024] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.
[0025] Figure 1 This is a flowchart illustrating an electric vehicle torque control method provided in an embodiment of the present invention. The method can adjust the motor output torque in real time to prevent the electric vehicle from skidding or overturning, thus improving the safety of two-wheeled electric vehicles. This method can be executed by the electric vehicle torque control device provided in this embodiment, which can be implemented in software and / or hardware. The following embodiments will illustrate this using the integration of this device into an electric vehicle as an example. Figure 1 The method may specifically include the following steps:
[0026] Step 101: Obtain the encoder signal of the motor encoder in the current cycle.
[0027] A motor encoder is a device used on motors to encode and convert signals or data into a signal format suitable for communication, transmission, and storage. In this solution, the motor encoder can be used to measure the speed of an electric vehicle motor. Motor encoders include photoelectric encoders, Hall sensors, Tunnel Magneto Resistance (TMR) sensors, and resolvers, etc.
[0028] Specifically, the controller can pre-set the cycle length based on the processing capability of its internal chip and acquire the encoder signal from the motor encoder according to the preset cycle. For example, in this solution, based on the processing capability of the controller chip in a typical two-wheeled electric vehicle, the cycle length can be determined to be 62.5 microseconds. That is, the controller can acquire the encoder signal once every 62.5 microseconds.
[0029] Step 102: Calculate the vehicle speed and motor acceleration in the current cycle based on the encoder signal.
[0030] Here, the current cycle refers to the cycle in which the current moment occurs. In one optional implementation, the controller can calculate the wheel speed of the electric vehicle in the current cycle based on the encoder signal, and determine the overall vehicle speed of the electric vehicle in the current cycle based on the wheel speed. Furthermore, the controller can determine whether the wheel speed of the current cycle needs to be updated based on whether there is a sudden change in the motor acceleration in the current cycle. For example, when the motor acceleration exceeds a preset acceleration range, it can be determined that the wheel speed of the electric vehicle in the current cycle is abnormal, and further, it can be determined that the wheel speed of the electric vehicle in the current cycle does not need to be updated. In this case, the wheel speed of the electric vehicle in the previous cycle can be determined as the wheel speed of the electric vehicle in the current cycle.
[0031] In this scheme, the motor acceleration in the current cycle is calculated based on the encoder signal, including the following steps A1-A3:
[0032] Step A1: Calculate the candidate motor speed in the current cycle based on the encoder signal.
[0033] Motor encoders include photoelectric encoders, Hall sensors, TMR sensors, and rotary transformers. Among them, Hall sensors have advantages such as simple installation, good linearity, and small size. The principle of Hall sensors in detecting motor speed and position is to install three Hall elements at 120° intervals on the motor stator, while the permanent magnet is located on the rotor. When the rotor rotates, influenced by the permanent magnet, the three-phase Hall elements output square wave signals with a 120° phase difference and a 50% duty cycle. Alternatively, based on the angle between the three-phase Hall elements and the rotor, the motor position information can be divided into Hall intervals of 60° intervals. The control algorithm can then linearly fit the discrete Hall positions to obtain the accurate rotor position.
[0034] For example, using a Hall sensor installed on the motor, the traditional M-method for speed measurement is employed to measure the motor speed: a fixed sampling (collecting the three-phase Hall signals sent by the Hall sensor) period T is set, and the number of times the rising and falling edges of the three-phase Hall signals transition, m, is recorded within the fixed sampling time T. The interval between each rise and fall edge transition is every 60° electrical angle. Based on the electrical angle rotated and the elapsed time, the candidate motor speed can be calculated as follows:
[0035]
[0036] Using Hall effect sensors mounted on the motor, the T-method, a traditional speed measurement method, is employed to measure motor speed: This involves detecting the time t taken for the motor rotor to rotate through N fixed Hall effect sectors. The electrical angle between each sector of the three-phase Hall effect signal is a fixed 60° electrical angle. Based on the fixed angle rotated by the motor and the known time, the candidate motor speed can be calculated as follows:
[0037]
[0038] Step A2: When the candidate motor speed is within the preset speed range, filter the candidate motor speed to obtain the target motor speed.
[0039] Filtering is a process that uses the state equations of a linear system to optimally estimate the system state based on system input and output observation data. The controller can determine a preset speed range based on domain big data, which is used to determine if the candidate motor speed is abnormal. When the candidate motor speed is within the preset range, it indicates that the candidate motor speed is normal and can be used for subsequent calculations of motor acceleration. If the candidate motor speed is outside the preset range, it indicates that the candidate motor speed is abnormal and cannot be used to calculate motor acceleration.
[0040] Specifically, even if the candidate motor speeds meet the preset speed range, the candidate motor speeds obtained using the M-method and T-method may still contain errors. Filtering the candidate motor speeds within the preset speed range yields a more accurate motor speed, i.e., the target motor speed. In this scheme, the Kalman filter algorithm can be used to filter the calculated candidate motor speeds. The Kalman filter algorithm estimates the motor's state variables based on the candidate motor speeds according to the motor's state equations. The estimated state variables and zero are used as feedback signals to control the correct commutation of the motor and achieve closed-loop control, thereby obtaining the target motor speed.
[0041] A3: Calculate the motor acceleration in the current cycle based on the target motor speed.
[0042] Specifically, after calculating the target motor speed, the motor acceleration can be calculated using the formula: Motor Acceleration = (Target Motor Speed 2 - Target Motor Speed 1) / Time. Here, Target Motor Speed 2 is the target motor speed for the current cycle, and Target Motor Speed 1 is the target motor speed for the previous cycle. Time is the time difference between Target Motor Speed 2 and Target Motor Speed 1, i.e., the preset cycle. Of course, after obtaining the motor acceleration, it can be filtered to improve its accuracy.
[0043] Based on the above steps, the controller can accurately calculate the motor speed in the current cycle, thereby improving the calculation results of motor acceleration and laying the foundation for subsequent accurate adjustment of the motor output torque.
[0044] Step 103: Calculate the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters.
[0045] Road surface adhesion parameters are a collective term for one or more parameters used to calculate the coefficient of friction between the tire and the road surface. These parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle, and drag constant. The drag coefficient is a mathematical parameter determined through wind tunnel and glide slope experiments; it calculates the air resistance of the vehicle during operation, and its magnitude depends on the vehicle's shape. The drag constant is a constant preset by the controller based on large-scale data from the domain, used to calculate the wind resistance of the electric vehicle in the current cycle. The frontal area of the electric vehicle is its projected area in the direction of travel. The coefficient of friction between the tire and the road surface is the ratio of the tire's adhesion force to the wheel's normal (perpendicular to the road surface) pressure. The adhesion force is the mutual attraction between the tire and the road surface at their contact points. The coefficient of friction reflects the static friction coefficient between the tire and the road surface. A higher coefficient of friction results in greater available adhesion, making the electric vehicle less prone to slippage.
[0046] In one optional implementation, after obtaining the vehicle speed and road adhesion parameters for the current cycle, the controller can multiply the drag coefficient, the frontal area of the electric vehicle, and the vehicle speed to obtain a first product; divide the first product by the drag constant to obtain the wind resistance of the electric vehicle in the current cycle; calculate the adhesion force of the electric vehicle in the current cycle based on the predetermined difference between the motor driving torque and the wind resistance and the pre-acquired wheel moment of inertia; divide the adhesion force by the product of the vehicle weight and the rear wheel radius to obtain the tire-road adhesion coefficient of the electric vehicle in the current cycle.
[0047] Step 104: Determine the target torque of the motor in the current cycle based on the adhesion coefficient between the tire and the road surface and the motor acceleration, and control the output torque of the motor in the current cycle according to the target torque.
[0048] The target torque is the optimal torque that the motor should output to the rear wheel of the electric vehicle, as determined by the controller. The torque output by the motor in the current cycle is the actual torque output to the rear wheel.
[0049] In one optional implementation, after the controller determines the tire-road adhesion coefficient and the motor acceleration, it inputs the motor acceleration to a proportional-integral-derivative (PID) controller to obtain the motor regulating torque corresponding to the motor acceleration; it also acquires the throttle opening of the electric vehicle in the current cycle; when the tire-road adhesion coefficient is less than a preset adhesion coefficient and the motor regulating torque is less than 0, if the throttle opening in the current cycle is greater than the throttle opening in the previous cycle, the pre-acquired given torque and regulating torque from the previous cycle are added together to obtain the target torque; if the throttle opening in the current cycle is not greater than the throttle opening in the previous cycle, the given torque for the current cycle is determined based on the throttle opening in the current cycle; the given torque and regulating torque for the current cycle are then added together to obtain the target torque. When the tire-road adhesion coefficient is not less than a preset adhesion coefficient, or when the tire-road adhesion coefficient is less than a preset adhesion coefficient and the motor regulating torque is not less than 0, the given torque for the current cycle is determined as the target torque. Furthermore, the controller can adjust the motor's output torque in the current cycle in real time according to the target torque, so that the torque output by the motor to the rear wheel is the optimal torque.
[0050] The technical solution of this embodiment acquires the encoder signal of the motor encoder in the current cycle; calculates the vehicle speed and motor acceleration in the current cycle based on the encoder signal; calculates the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle during driving, and drag constant; determines the target torque of the motor in the current cycle based on the tire-road adhesion coefficient and motor acceleration, and controls the torque output of the motor in the current cycle according to the target torque. This technical solution can determine the target torque output of the motor based on the motor acceleration and the tire-road adhesion coefficient, and adjust the actual output torque of the motor in real time according to the target torque, preventing the electric vehicle from skidding or overturning, thus improving the safety of two-wheeled electric vehicles.
[0051] Figure 2 This is another flowchart of the electric vehicle torque control method provided in an embodiment of the present invention. This embodiment is a refinement based on the above embodiment. The specific method can be as follows: Figure 2 As shown, the method may include the following steps:
[0052] Step 201: Obtain the encoder signal of the motor encoder in the current cycle, and calculate the vehicle speed and motor acceleration in the current cycle based on the encoder signal.
[0053] Step 202: Multiply the predetermined drag coefficient, the predetermined frontal area of the electric vehicle, and the square of the vehicle speed to obtain the first product.
[0054] The drag coefficient is a mathematical parameter determined through wind tunnel and glide slope experiments. It is used to calculate the air resistance of a vehicle during operation, and its magnitude depends on the vehicle's shape. In this scheme, based on domain big data, the drag coefficient of the two-wheeled electric vehicle can be set to 0.65. The frontal area of an electric vehicle is its projected area in the direction of travel. This frontal area can be obtained through measurement using a three-dimensional digital model. When three-dimensional data is incomplete, the frontal area can also be measured using the overall design layout of the electric vehicle.
[0055] For example, let V represent the vehicle speed, S represent the frontal area of the electric vehicle when it is moving, and C... d Representing the drag coefficient, we can obtain the first product as: C d *S*V 2 .
[0056] Step 203: Divide the first product by the drag constant to obtain the drag in the current cycle.
[0057] The drag constant is a constant preset by the controller based on domain big data, used to calculate the wind resistance of the electric vehicle in the current cycle. In this scheme, the drag constant is set to 21.25. For example, the first product is: C d *S*V 2 The drag constant is 21.25, using F w Let F represent wind resistance. Then, the wind resistance of the electric vehicle in the current cycle can be determined as: F w =C d *S*V 2 / 21.15
[0058] Step 204: Calculate the coefficient of adhesion between the tires and the road surface in the current cycle based on wind resistance, the predetermined vehicle weight, and the rear wheel radius.
[0059] The coefficient of friction between the tire and the road surface reflects the static friction coefficient between them. This coefficient is determined by both the tire and the road surface; a higher coefficient results in greater available traction, making it less likely for the electric vehicle to slip. The controller can determine the vehicle's total weight and rear wheel radius directly from the manufacturer's specifications, or it can obtain the vehicle's current weight from the gravity sensor on the vehicle.
[0060] In this embodiment, optionally, the tire-road adhesion coefficient for the current cycle is calculated based on wind resistance, a predetermined vehicle weight, and the rear wheel radius. This includes: calculating the adhesion force for the current cycle based on a predetermined difference between the motor drive torque and wind resistance, and a pre-acquired wheel moment of inertia. The tire-road adhesion coefficient for the current cycle is obtained by dividing the adhesion force by the product of the vehicle weight and the rear wheel radius.
[0061] Here, wheel moment of inertia refers to the inertia a wheel possesses when rotating. Specifically, the controller can receive the current and voltage output by the motor in real time. Multiplying the current and voltage outputs yields the motor's output power. Multiplying the output power by the obtained motor speed gives the motor's driving torque. Furthermore, based on the difference between the motor's driving torque and wind resistance, and the pre-acquired wheel moment of inertia, the adhesion force of the electric vehicle in the current cycle is calculated.
[0062] For example, using T m The driving torque of a motor is represented by F. w The wind resistance of the electric vehicle in the current cycle is represented by J. ω Let ω represent the moment of inertia of the wheel, ω represent the rotational speed of the rear wheel, and t represent the duration of the current cycle. Then, the adhesion force of the electric vehicle in the current cycle can be expressed as:
[0063] Furthermore, after obtaining the adhesion force of the electric vehicle in the current cycle, the adhesion force is divided by the product of the total vehicle weight and the radius of the rear wheel to obtain the tire-road adhesion coefficient of the electric vehicle in the current cycle. For example, let G represent the total vehicle weight, and R... ω Let μ represent the tire-road adhesion coefficient of the electric vehicle in the current cycle, where the radius of the rear wheel is denoted as μ.
[0064] Step 205: Input the motor acceleration to the PID controller to obtain the motor regulating torque corresponding to the motor acceleration.
[0065] The PID controller consists of a proportional unit (P), an integral unit (I), and a derivative unit (D). It is primarily suitable for systems with fundamentally linear characteristics and dynamic properties that do not change over time. The PID controller compares the collected acceleration with a reference value, uses this difference to calculate a new input value, and then determines the corresponding motor regulating torque based on this new input value. Thus, the controller can input the motor acceleration into the PID controller and obtain the corresponding motor regulating torque.
[0066] Step 206: Obtain the throttle opening of the electric vehicle in the current cycle.
[0067] The throttle grip mainly consists of a magnet, Hall effect sensor, return spring, sensing circuitry, and a plastic housing. There is a direct correlation between the throttle opening and the motor's torque; the larger the throttle opening, the greater the motor's torque, and the faster the electric vehicle. The controller can monitor the throttle opening in real time.
[0068] Step 207: When the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is less than 0, determine the target torque based on the throttle opening and the motor adjustment torque of the current cycle.
[0069] The preset adhesion coefficient is a value pre-determined by the controller based on big data from the field. This coefficient is used to determine how the electric vehicle calculates a given torque in the current cycle. A higher tire-road adhesion coefficient results in greater available traction, making it less likely for the electric vehicle to slip.
[0070] In this scheme, optionally, when the tire-road adhesion coefficient is less than the preset adhesion coefficient and the motor adjustment torque is less than 0, the target torque is determined based on the throttle opening and motor adjustment torque of the current cycle, including the following steps B1-B2:
[0071] Step B1: If the throttle opening in the current cycle is greater than the throttle opening in the previous cycle, add the given torque and the adjusted torque of the previous cycle obtained in advance to obtain the target torque.
[0072] Specifically, if the coefficient of friction between the tire and the road surface is less than the preset coefficient of friction and the motor's adjustable torque is less than 0, and the throttle opening in the current cycle is greater than that in the previous cycle, it indicates that the given torque in the current cycle is greater than the given torque in the previous cycle. The electric vehicle may be at risk of slipping and is currently accelerating. In this case, the given torque and the adjustable torque from the previous cycle can be added together to obtain the target torque. For example, if the coefficient of friction between the tire and the road surface is 0.7 in the current cycle, and the preset coefficient of friction is 0.75, the motor's adjustable torque is -5, and the given torque from the previous cycle is 7, then the target torque for the motor in the current cycle can be determined to be -5 + 7.
[0073] Step B2: If the throttle opening of the current cycle is not greater than the throttle opening of the previous cycle, determine the given torque of the current cycle based on the throttle opening of the current cycle; add the given torque and the adjustment torque of the current cycle to obtain the target torque.
[0074] Specifically, the controller stores the correspondence between throttle opening and given torque. The controller can determine the given torque for the current cycle corresponding to the current throttle opening based on this correspondence. If the throttle opening for the current cycle is not greater than the throttle opening for the previous cycle, it means the given torque for the current cycle is not greater than the given torque for the previous cycle. While the electric vehicle is at risk of slipping, it is about to decelerate or is currently decelerating. In this case, the given torque for the current cycle and the adjusted torque can be added together to obtain the target torque.
[0075] Step 208: When the coefficient of adhesion between the tire and the road surface is not less than the preset coefficient of adhesion, or when the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is not less than 0, determine the target torque based on the throttle opening of the current cycle.
[0076] Specifically, when the coefficient of adhesion between the tire and the road surface is not less than the preset coefficient of adhesion, or when the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is not less than 0, it indicates that the electric vehicle has a large adhesion force and the possibility of sideslip is small. The controller can determine the given torque corresponding to the throttle opening in the current cycle from the correspondence between the throttle opening and the given torque, and directly determine the given torque of the current cycle as the target torque.
[0077] Figure 3 A flowchart for determining the target torque provided in an embodiment of the present invention. For example... Figure 3 As shown, after the controller obtains the motor adjustment torque based on the motor acceleration, it acquires the throttle opening of the electric vehicle in the current cycle. It determines whether the tire-road adhesion coefficient is less than a preset adhesion coefficient. If the tire-road adhesion coefficient is less than the preset adhesion coefficient, it determines whether the motor adjustment torque is less than 0. If the motor adjustment torque is less than 0, it determines whether the throttle opening in the current cycle is greater than the throttle opening in the previous cycle (whether the throttle opening is increasing). If the throttle opening in the current cycle is greater than the throttle opening in the previous cycle, the given torque and adjustment torque of the previous cycle are added together to obtain the target torque. If the throttle opening in the current cycle is not greater than the throttle opening in the previous cycle, the given torque for the current cycle is calculated based on the throttle opening in the current cycle, and the given torque and adjustment torque of the current cycle are added together to obtain the target torque. If the tire-road adhesion coefficient is less than the preset adhesion coefficient and the motor adjustment torque is not less than 0, the given torque for the current cycle is determined as the target torque. If the tire-road adhesion coefficient is not less than the preset adhesion coefficient, the given torque for the current cycle is determined as the target torque.
[0078] Through the above steps, the controller can determine the calculation method for the motor's given torque based on the adhesion coefficient between the tire and the road surface, and accurately calculate the given torque of the motor according to the corresponding calculation method. Furthermore, it can quickly and accurately determine the target torque of the motor based on the given torque, enabling the electric vehicle to output appropriate torque, thereby improving the safety of two-wheeled electric vehicles.
[0079] Step 209: Control the torque output of the motor in the current cycle according to the target torque.
[0080] In this embodiment of the invention, the encoder signal of the motor encoder in the current cycle is obtained, and the vehicle speed and motor acceleration in the current cycle are calculated based on the encoder signal; a first product is obtained by multiplying a predetermined drag coefficient, a predetermined frontal area of the electric vehicle during driving, and the square of the vehicle speed; the first product is divided by the drag constant to obtain the wind resistance in the current cycle; the tire-road adhesion coefficient in the current cycle is calculated based on the wind resistance, a predetermined vehicle weight, and the rear wheel radius; the motor acceleration is input to a PID controller to obtain the motor adjustment torque corresponding to the motor acceleration; the throttle opening of the electric vehicle in the current cycle is obtained; when the tire-road adhesion coefficient is less than a preset adhesion coefficient and the motor adjustment torque is less than 0, the target torque is determined based on the throttle opening and the motor adjustment torque in the current cycle; when the tire-road adhesion coefficient is not less than a preset adhesion coefficient, or when the tire-road adhesion coefficient is less than a preset adhesion coefficient and the motor adjustment torque is not less than 0, the target torque is determined based on the throttle opening in the current cycle. The technical solution of this embodiment can accurately calculate the coefficient of adhesion between the tires and the road surface of the electric vehicle in the current cycle. Based on the motor acceleration of the electric vehicle and the coefficient of adhesion between the tires and the road surface, the torque output of the motor is adjusted in real time to prevent the electric vehicle from skidding or overturning, thereby improving the safety of two-wheeled electric vehicles.
[0081] Figure 4 This is a schematic diagram of the structure of an electric vehicle torque control device provided in an embodiment of the present invention. This device is suitable for executing the electric vehicle torque control method provided in an embodiment of the present invention. Figure 4 As shown, the device may specifically include:
[0082] The signal acquisition module 401 is used to acquire the encoder signal of the motor encoder in the current cycle;
[0083] Acceleration calculation module 402 is used to calculate the vehicle speed and motor acceleration in the current cycle based on the encoder signal;
[0084] The coefficient determination module 403 is used to calculate the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle when it is driving, and drag constant.
[0085] The torque control module 404 is used to determine the target torque of the motor in the current cycle based on the adhesion coefficient between the tire and the road surface and the motor acceleration, and to control the torque output by the motor in the current cycle according to the target torque.
[0086] Optionally, the coefficient determination module 403 is specifically used to: multiply the drag coefficient, the frontal area of the electric vehicle when it is driving, and the square of the vehicle speed to obtain a first product;
[0087] Divide the first product by the drag constant to obtain the drag force in the current cycle;
[0088] The coefficient of adhesion between the tires and the road surface in the current cycle is calculated based on the wind resistance, the predetermined vehicle weight, and the rear wheel radius.
[0089] Optionally, the coefficient determination module 403 is also used to: calculate the adhesion force in the current cycle based on the difference between the pre-determined motor driving torque and the wind resistance, and the pre-acquired wheel rotational inertia.
[0090] Divide the adhesion force by the product of the vehicle weight and the rear wheel radius to obtain the tire-road adhesion coefficient in the current cycle.
[0091] Optionally, the torque control module 404 is specifically used to: input the motor acceleration to a proportional-integral-derivative PID controller to obtain the motor regulating torque corresponding to the motor acceleration;
[0092] Obtain the throttle opening of the electric vehicle in the current cycle;
[0093] When the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is less than 0, the target torque is determined based on the throttle opening and the motor adjustment torque of the current cycle.
[0094] Optionally, the torque control module 404 is further configured to: if the throttle opening of the current cycle is greater than the throttle opening of the previous cycle, add the pre-obtained given torque of the previous cycle to the adjustment torque to obtain the target torque;
[0095] If the throttle opening of the current cycle is not greater than the throttle opening of the previous cycle, the given torque of the current cycle is determined based on the throttle opening of the current cycle.
[0096] The target torque is obtained by adding the given torque of the current cycle and the adjusted torque.
[0097] Optionally, the torque control module 404 is further configured to: determine the target torque based on the throttle opening of the current cycle when the coefficient of adhesion between the tire and the road surface is not less than the preset coefficient of adhesion, or when the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is not less than 0.
[0098] Optionally, the acceleration calculation module 402 is specifically used to: calculate the candidate motor speed in the current cycle based on the encoder signal;
[0099] When the candidate motor speed is within a preset speed range, the candidate motor speed is filtered to obtain the target motor speed;
[0100] The motor acceleration for the current cycle is calculated based on the target motor speed.
[0101] The electric vehicle torque control device provided in this embodiment of the invention can execute the electric vehicle torque control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution. Contents not described in detail in this embodiment can be referred to the descriptions in any method embodiment of the invention.
[0102] Figure 5 This is a schematic diagram of the structure of an electric vehicle provided in an embodiment of the present invention, with reference to... Figure 5 It shows a schematic diagram of the structure of a computer system 12 suitable for implementing an electric vehicle according to an embodiment of the present invention. Figure 5 The electric vehicle shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. Components of the electric vehicle 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).
[0103] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.
[0104] The electric vehicle 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electric vehicle 12, including volatile and non-volatile media, and removable and non-removable media.
[0105] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electric vehicle 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.
[0106] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.
[0107] The electric vehicle 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that allow a user to interact with the electric vehicle 12, and / or with any device that allows the electric vehicle 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via the input / output (I / O) interface 22. Furthermore, in this embodiment, the display 24 is not a separate entity but is embedded in a mirror, so that when the display surface of the display 24 is not displayed, it visually blends seamlessly with the mirror surface. Additionally, the electric vehicle 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via the network adapter 20. Figure 5 As shown, network adapter 20 communicates with other modules of electric vehicle 12 via bus 18. It should be understood that, although... Figure 5As not shown, other hardware and / or software modules can be used in conjunction with the electric vehicle 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0108] The processing unit 16 executes various functional applications and electric vehicle torque control by running programs stored in the system memory 28. For example, it implements an electric vehicle torque control method provided in this embodiment of the invention: acquiring the encoder signal of the motor encoder in the current cycle; calculating the vehicle speed and motor acceleration in the current cycle based on the encoder signal; calculating the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters; wherein the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle when driving, and drag constant; determining the target torque of the motor in the current cycle based on the tire-road adhesion coefficient and the motor acceleration, and controlling the torque output by the motor in the current cycle according to the target torque.
[0109] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements an electric vehicle torque control method as provided in all embodiments of this invention: acquiring the encoder signal of the motor encoder in the current cycle; calculating the vehicle speed and motor acceleration in the current cycle based on the encoder signal; calculating the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters; wherein the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle during operation, and drag constant; determining the target torque of the motor in the current cycle based on the tire-road adhesion coefficient and the motor acceleration, and controlling the torque output by the motor in the current cycle according to the target torque. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.
[0110] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.
[0111] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0112] Computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0113] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.
Claims
1. A torque control method for an electric vehicle, the method being applied to a two-wheeled electric vehicle, characterized in that, include: Obtain the encoder signal of the motor encoder in the current cycle; The vehicle speed and motor acceleration in the current cycle are calculated based on the encoder signal. The tire-road adhesion coefficient in the current cycle is calculated based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle when it is driving, and drag constant. The target torque of the motor in the current cycle is determined based on the adhesion coefficient between the tire and the road surface and the motor acceleration, and the torque output by the motor in the current cycle is controlled according to the target torque. The step of determining the target torque of the motor in the current cycle based on the tire-road adhesion coefficient and the motor acceleration includes: The motor acceleration is input to a proportional-integral-derivative (PID) controller to obtain the motor regulating torque corresponding to the motor acceleration. Obtain the throttle opening of the electric vehicle in the current cycle; When the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is less than 0, the target torque is determined based on the throttle opening and the motor adjustment torque of the current cycle. The step of determining the target torque based on the throttle opening and the motor adjustment torque of the current cycle includes: If the throttle opening in the current cycle is greater than the throttle opening in the previous cycle, the given torque of the previous cycle obtained in advance and the adjustment torque are added together to obtain the target torque; If the throttle opening of the current cycle is not greater than the throttle opening of the previous cycle, the given torque of the current cycle is determined based on the throttle opening of the current cycle; the given torque of the current cycle and the adjustment torque are added together to obtain the target torque.
2. The method according to claim 1, characterized in that, The coefficient of friction between the tires and the road surface in the current cycle is calculated based on the vehicle speed and predetermined road surface adhesion parameters, including: Multiply the drag coefficient, the frontal area of the electric vehicle when it is moving, and the square of the vehicle speed to obtain the first product; Divide the first product by the drag constant to obtain the drag force in the current cycle; The coefficient of adhesion between the tires and the road surface in the current cycle is calculated based on the wind resistance, the predetermined vehicle weight, and the rear wheel radius.
3. The method according to claim 2, characterized in that, The coefficient of friction between the tires and the road surface in the current cycle is calculated based on the wind resistance, the predetermined vehicle weight, and the rear wheel radius, including: The adhesion force in the current cycle is calculated based on the difference between the predetermined motor driving torque and the wind resistance, and the pre-acquired wheel rotational inertia. Divide the adhesion force by the product of the vehicle weight and the rear wheel radius to obtain the tire-road adhesion coefficient in the current cycle.
4. The method according to claim 1, characterized in that, The method further includes: When the coefficient of adhesion between the tire and the road surface is not less than the preset coefficient of adhesion, or when the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is not less than 0, the target torque is determined based on the throttle opening of the current cycle.
5. The method according to claim 1, characterized in that, Calculating the motor acceleration in the current cycle based on the encoder signal includes: The candidate motor speed in the current cycle is calculated based on the encoder signal; When the candidate motor speed is within a preset speed range, the candidate motor speed is filtered to obtain the target motor speed; The motor acceleration for the current cycle is calculated based on the target motor speed.
6. A torque control device for an electric vehicle, the device being deployed on a two-wheeled electric vehicle, characterized in that, include: The signal acquisition module is used to acquire the encoder signal of the motor encoder in the current cycle; An acceleration calculation module is used to calculate the vehicle speed and motor acceleration in the current cycle based on the encoder signal; The coefficient determination module is used to calculate the tire-road adhesion coefficient in the current cycle based on the vehicle speed and predetermined road adhesion parameters; wherein, the road adhesion parameters include one or more of the following: drag coefficient, frontal area of the electric vehicle when driving, and drag constant; The torque control module is used to determine the target torque of the motor in the current cycle based on the adhesion coefficient between the tire and the road surface and the motor acceleration, and to control the torque output by the motor in the current cycle according to the target torque. Determining the target torque of the motor in the current cycle based on the tire-road adhesion coefficient and the motor acceleration includes: The motor acceleration is input to a proportional-integral-derivative (PID) controller to obtain the motor regulating torque corresponding to the motor acceleration. Obtain the throttle opening of the electric vehicle in the current cycle; When the coefficient of adhesion between the tire and the road surface is less than the preset coefficient of adhesion and the motor adjustment torque is less than 0, the target torque is determined based on the throttle opening and the motor adjustment torque of the current cycle. The step of determining the target torque based on the throttle opening and the motor adjustment torque of the current cycle includes: If the throttle opening in the current cycle is greater than the throttle opening in the previous cycle, the given torque of the previous cycle obtained in advance and the adjustment torque are added together to obtain the target torque; If the throttle opening of the current cycle is not greater than the throttle opening of the previous cycle, the given torque of the current cycle is determined based on the throttle opening of the current cycle; the given torque of the current cycle and the adjustment torque are added together to obtain the target torque.
7. An electric vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the electric vehicle torque control method as described in any one of claims 1 to 5.
8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the electric vehicle torque control method as described in any one of claims 1 to 5.