Electric-electric hybrid aerocar power system and self-adaptive control method

Abstract

The invention discloses an electric-electric hybrid aerocar power system and a self-adaptive control method, and relates to the field of aerocar driving systems. The driving torque is controllable, the torque and the rotating speed are easy to measure, multiple functions of sideslip prevention, rollover prevention, differential power-assisted steering and the like can be achieved, and the handling stability and the running safety of the hovercar are greatly improved. According to the technical scheme, the aerocar power system comprises a hybrid power system, a distributed electric drive system and an aerocar control system. Different from other hovercars, a distributed electric driving system is adopted, rotors and propellers are driven by two driving motors respectively, wheels are driven by hub motors, the driving torque is controllable, the torque and the rotating speed are easy to measure, multiple functions such as sideslip prevention, rollover prevention and direct yawing torque control can be achieved, and the hovercar is suitable for being popularized and applied. And the handling stability and the driving safety of the automobile are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of flying car drive systems, and in particular to an electric-electric hybrid hub motor driven shear rotor flying car power system and an adaptive control method. Background Art

[0002] As a means of transportation that can travel both on the ground and in the air, flying cars have many unique advantages. Compared with cars, they have more freedom of use and higher efficiency, and are not restricted by prescribed roads. Compared with airplanes, their transportation costs are very low, and they are less affected by natural conditions. They are convenient and fast in many aspects. However, the safety of flying cars still needs to be improved, and their range is relatively low.

[0003] The shear rotor electric-electric hybrid flying car has greatly improved the safety performance. Even if the power system fails, the flying car can safely land by self-rotation. In terms of power system, the fuel cell-power battery hybrid-powered flying car and the previous engine-power battery hybrid-powered flying car can also solve the problem of short cruising range and are very green and environmentally friendly.

[0004] However, the ground performance of the electric-electric hybrid shear rotor flying car in the existing technology has not been fully released. The use of ordinary motor drive requires a more complex chassis design, and the transmission device will increase the weight of the entire flying car and reduce its economy. The handling stability is also not fully developed. Summary of the invention

[0005] In view of the above problems, the present invention proposes an electric-electric hybrid hub motor driven shear rotor flying car power system and an adaptive control method, the driving torque of which is controllable and the torque speed is easy to measure, and multiple functions such as anti-skid, anti-rollover, and differential power steering can be realized, which greatly improves the handling stability and driving safety of the flying car.

[0006] The technical solution of the present invention is: the flying car power system includes a hybrid power system, a distributed electric drive system and a flying car control system;

[0007] The hybrid power system includes a fuel cell system, a hydrogen storage bottle, a unidirectional DC / DC converter, a power battery and a bidirectional DC / DC, the fuel cell system is electrically connected to the unidirectional DC / DC converter, the hydrogen storage bottle and the fuel cell system are connected through a pipeline structure, and the power battery and the bidirectional DC / DC converter are electrically connected;

[0008] The distributed electric drive system includes a motor controller, four wheel hub motors and two drive motors, the four wheel hub motors drive three wheels respectively, and the two drive motors drive the shear rotor and the propulsion propeller respectively;

[0009] The motor controller is electrically connected to the bidirectional DC / DC converter and the unidirectional DC / DC converter respectively, and is also connected to the four wheel hub motors and the two drive motors, and the operation of each motor is controlled by the motor controller;

[0010] The flying car control system includes a vehicle controller and an energy management system. The vehicle controller is connected to the motor controller and the energy management system. The energy management system is also connected to the power battery and the fuel cell system to achieve vehicle mode switching, function triggering, charging and discharging, motor control, battery energy management, etc. through the control of the flying car control system.

[0011] The energy management system will arrange the hybrid power system for reasonable output according to the driver's driving and flying conditions. When the power battery power is lower than the set value, the fuel cell can supply the energy required by each motor while charging the power battery. When the power battery power is higher than the set value, the fuel cell and the power battery jointly supply energy to the motor.

[0012] The adaptive control method comprises the following steps:

[0013] Step 1: Build a mathematical model of the multi-motor system to obtain the basic parameters required for control;

[0014] Step 2: Design a multi-motor adaptive backstepping controller to adjust the speed error caused by disturbances in the system;

[0015] Step 3: Design a fuzzy PID controller and optimize the basic parameters of the multi-motor adaptive backstepping controller;

[0016] Step 4: Use the improved grey wolf algorithm to optimize the fuzzy PID controller;

[0017] Step 5: Obtain the optimal control solution for the electric-electric hybrid hub motor-driven shear rotor flying car power system through a multi-motor adaptive backstepping controller.

[0018] The mathematical model expression of the multi-motor system described in step 1 is:

[0019]

[0020] In the formula, i d ,i q are the d and q axis stator currents respectively; u d ,u q are the d and q axis stator voltages respectively; Ld , L q are the d-axis and q-axis stator inductances respectively; R is the stator resistance; ω is the rotor mechanical angular velocity; n p is the number of pole pairs of the motor; is the magnetic flux generated by the permanent magnet; J is the moment of inertia; B is the friction damping coefficient of the motor; φ d ,φ q and φ ω They represent the interference caused by parameter changes and external load disturbances respectively.

[0021] The design control rate of the multi-motor adaptive backstepping controller described in step 2 is:

[0022]

[0023] Where k is the speed feedback gain.

[0024] The corresponding interference adaptation rate is:

[0025]

[0026] Where e is the current following error and γ is the controller parameter.

[0027] The improved gray wolf algorithm described in step 5 defines the entropy value of the wolf pack and measures the density of the wolf pack by the size of the entropy value:

[0028]

[0029] In the formula, the entropy value σ 2 The larger the value, the more dispersed the population is, and vice versa, the more convergent the population is; N is the size of the population; F i , are the fitness value and average fitness value of the i-th wolf respectively; χ is used to limit the size of the entropy value.

[0030] The beneficial effects of the present invention are:

[0031] 1. The present invention adopts a distributed electric drive system. Different from other flying cars, the rotor and propeller are driven by two drive motors respectively, and the wheel part is driven by a hub motor. Its driving torque is controllable and the torque speed is easy to measure. It can realize multiple functions such as anti-skid, anti-rollover, direct yaw torque control, etc., which greatly improves the car's handling stability and driving safety.

[0032] 2. The present invention adopts a fuel cell-power battery hybrid power system. Different from other flying cars, the electric-electric hybrid power system equipped with an energy management system is not only more environmentally friendly, but also improves the flying car's driving range, efficiency and fuel cell battery life, and saves costs.

[0033] 3. The present invention adopts dual motors to drive the shear rotor and the propulsion propeller respectively, which reduces fuel emissions and improves the energy efficiency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the fuel cell-power battery hybrid driving mode.

[0035] Figure 2 This is the logic framework diagram of the control algorithm. DETAILED DESCRIPTION

[0036] In order to clearly illustrate the technical features of this patent, this patent is elaborated in detail below through a specific implementation method and in combination with its accompanying drawings.

[0037] like Figure 1 As shown, the flying car power system includes a hybrid power system, a distributed electric drive system, and a flying car control system.

[0038] The hybrid power system includes a fuel cell system, a hydrogen storage bottle, a unidirectional DC / DC converter, a power battery and a bidirectional DC / DC, the fuel cell system is electrically connected to the unidirectional DC / DC converter, the hydrogen storage bottle and the fuel cell system are connected through a pipeline structure, and the power battery and the bidirectional DC / DC converter are electrically connected;

[0039] The fuel cell system includes a fuel cell housing and a fuel cell itself;

[0040] The distributed electric drive system includes a motor controller, four wheel hub motors and two drive motors, the four wheel hub motors drive three wheels respectively, and the two drive motors drive the shear rotor and the propulsion propeller respectively; each wheel hub motor is connected to a wheel through a mechanical structure, and the two drive motors are mechanically connected to a rotor and a propeller respectively;

[0041] The motor controller is electrically connected to the bidirectional DC / DC converter and the unidirectional DC / DC converter respectively, and the motor controller is also connected to four wheel hub motors and two drive motors, and the operation of each motor is controlled by the motor controller; each wheel hub motor is mechanically connected to a wheel, a drive motor is mechanically connected to a shear rotor, and another drive motor is mechanically connected to a propulsion propeller; when driving, the flying car is driven by the four-wheel hub motors, and when flying, the rotors generate lift and are propelled by the propellers.

[0042] The flying car control system includes a vehicle controller and an energy management system, which are equivalent to the brain and trunk of the vehicle. The vehicle controller is connected to the motor controller and the energy management system. The energy management system is also connected to the power battery and the fuel cell system. Through the control of the flying car control system, the vehicle mode switching, function triggering, charging and discharging, motor control, battery energy management, etc. are realized. The connection of each module in the control system is specifically connected through the CAN bus communication connection.

[0043] The energy management system will arrange the hybrid power system for reasonable output according to the driver's driving and flying conditions. When the power battery power is lower than the set value, the fuel cell can supply the energy required by each motor while charging the power battery. When the power battery power is higher than the set value, the fuel cell and the power battery jointly supply energy to the motor. The specific power distribution is designed by the energy management system.

[0044] The allocation logic is as follows:

[0045]

[0046] Among them, SOC is the power battery capacity, SOC min and SOC max They are the low level critical value and high level critical value of the power battery respectively; P represents the current required power, P max and P min Represent high demand power and low demand power respectively.

[0047] a: The power demand is small, the battery SOC is at a low level, and the fuel cell is turned on;

[0048] b: Keep the fuel cell switch state at the last moment;

[0049] c: Keep the fuel cell switch state at the last moment;

[0050] d: fuel cell turned on;

[0051] e: Keep the fuel cell switch state at the last moment;

[0052] f: Keep the fuel cell switch state at the last moment;

[0053] g~i: Fuel cell is turned on.

[0054] The specific parameter values ​​must be obtained through actual vehicle calibration.

[0055] The distributed drive system greatly reduces the difficulty of chassis design, improves energy efficiency, and can achieve multiple functions such as anti-skid, anti-rollover, and direct yaw moment control, greatly improving the vehicle's handling stability and driving safety. The existence of the rotor greatly improves safety performance and provides protection for human life.

[0056] The adaptive control method comprises the following steps: the target control parameter is the motor current, and each motor, specifically a permanent magnet synchronous motor, is controlled;

[0057] Step 1: Build a mathematical model of the multi-motor system to obtain the basic parameters required for control; the mathematical model expression of the multi-motor system is:

[0058]

[0059] In the formula, i d ,i q are the d and q axis stator currents respectively; u d ,u q are the d and q axis stator voltages respectively; L d , L q are the d-axis and q-axis stator inductances respectively; R is the stator resistance; ω is the rotor mechanical angular velocity; n p is the number of pole pairs of the motor; is the magnetic flux generated by the permanent magnet; J is the moment of inertia; B is the friction damping coefficient of the motor; φ d ,φ q and φ ω They represent the interference caused by parameter changes and external load disturbances respectively.

[0060] Step 2: Design a multi-motor adaptive backstepping controller to adjust the speed error of the system caused by disturbance; the design control rate of the multi-motor adaptive backstepping controller is:

[0061]

[0062] Where k is the speed feedback gain.

[0063] The corresponding interference adaptation rate is:

[0064]

[0065] Where e is the current following error and γ is the controller parameter.

[0066] Step 3: Design a fuzzy PID controller and optimize the basic parameters of the multi-motor adaptive backstepping controller;

[0067] Step 4: Use the improved grey wolf algorithm to optimize the fuzzy PID controller;

[0068] Step 5: Obtain the optimal control solution of the electric-electric hybrid wheel motor driven shear rotor flying car power system through a multi-motor adaptive backstepping controller. The improved gray wolf algorithm defines the wolf pack entropy value and measures the density of the wolf pack by the size of the entropy value:

[0069]

[0070] In the formula, the entropy value σ 2 The larger the value, the more dispersed the population is, and vice versa, the more convergent the population is; N is the size of the population; F i , are the fitness value and average fitness value of the i-th wolf respectively; χ is used to limit the size of the entropy value.

[0071] Defining chaotic perturbation: Introducing a perturbation helps the algorithm to jump out of the local optimal solution, so as to better explore the global optimal solution. The perturbation can be introduced by random numbers or other methods, so that the bats have a certain degree of randomness in the search process, thereby jumping out of the local optimal solution and improving the accuracy and performance of the algorithm. Therefore, once any bat in the population is detected to be trapped in a fixed point, the algorithm uses Chebyshev chaotic mapping to randomly perturb the bat to free it from the fixed point constraint and re-enter the chaotic state.

[0072] There are many specific implementation ways of the present invention. The above is only the preferred implementation mode of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements can be made without departing from the principle of the present invention. These improvements should also be regarded as the protection scope of the present invention.

Claims

1. An electric-electric hybrid flying car power system, characterized in that: The flying car power system includes a hybrid power system, a distributed electric drive system and a flying car control system; The hybrid power system includes a fuel cell system, a hydrogen storage bottle, a unidirectional DC / DC converter, a power battery and a bidirectional DC / DC, the fuel cell system is electrically connected to the unidirectional DC / DC converter, the hydrogen storage bottle and the fuel cell system are connected through a pipeline structure, and the power battery and the bidirectional DC / DC converter are electrically connected; The distributed electric drive system includes a motor controller, four wheel hub motors and two drive motors, the four wheel hub motors drive three wheels respectively, and the two drive motors drive the shear rotor and the propulsion propeller respectively; The motor controller is electrically connected to the bidirectional DC / DC converter and the unidirectional DC / DC converter respectively, and is also connected to the four wheel hub motors and the two drive motors, and the operation of each motor is controlled by the motor controller; The flying car control system includes a vehicle controller and an energy management system. The vehicle controller is connected to the motor controller and the energy management system. The energy management system is also connected to the power battery and the fuel cell system to achieve vehicle mode switching, function triggering, charging and discharging, motor control, battery energy management, etc. through the control of the flying car control system.

2. The electric-electric hybrid flying car power system according to claim 1, characterized in that: The energy management system will arrange the hybrid power system for reasonable output according to the driver's driving and flying conditions. When the power battery power is lower than the set value, the fuel cell can supply the energy required by each motor while charging the power battery. When the power battery power is higher than the set value, the fuel cell and the power battery jointly supply energy to the motor.

3. An adaptive control method for the electric-electric hybrid flying car power system according to claim 1, characterized in that: The following steps are involved: Step 1: Build a mathematical model of the multi-motor system to obtain the basic parameters required for control; Step 2: Design a multi-motor adaptive backstepping controller to adjust the speed error caused by disturbances in the system; Step 3: Design a fuzzy PID controller and optimize the basic parameters of the multi-motor adaptive backstepping controller; Step 4: Use the improved grey wolf algorithm to optimize the fuzzy PID controller; Step 5: Obtain the optimal control solution for the electric-electric hybrid hub motor-driven shear rotor flying car power system through a multi-motor adaptive backstepping controller.

4. The adaptive control method of the electric-electric hybrid flying car power system according to claim 3, characterized in that: The mathematical model expression of the multi-motor system described in step 1 is: In the formula, i d ,i q are the d and q axis stator currents respectively; u d ,u q are the d and q axis stator voltages respectively; L d , L q are the d-axis and q-axis stator inductances respectively; R is the stator resistance; ω is the rotor mechanical angular velocity; n p is the number of pole pairs of the motor; is the magnetic flux generated by the permanent magnet; J is the moment of inertia; B is the friction damping coefficient of the motor; φ d ,φ q and φ ω They represent the interference caused by parameter changes and external load disturbances respectively.

5. The adaptive control method of the electric-electric hybrid flying car power system according to claim 3, characterized in that: The design control rate of the multi-motor adaptive backstepping controller described in step 2 is: Where k is the speed feedback gain. The corresponding interference adaptation rate is: Where e is the current following error and γ is the controller parameter.

6. The adaptive control method of the electric-electric hybrid flying car power system according to claim 3, characterized in that: The improved gray wolf algorithm described in step 5 defines the entropy value of the wolf pack and measures the density of the wolf pack by the size of the entropy value: In the formula, the entropy value σ 2 The larger the value, the more dispersed the population is, and vice versa, the more convergent the population is; N is the size of the population; F i , are the fitness value and average fitness value of the i-th wolf respectively; χ is used to limit the size of the entropy value.

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