Vehicle air outlet control method and device, medium and program product
By using the particle swarm optimization algorithm and the online optimization controller of the dynamic prediction model in the control of the electric air outlet of the automobile air conditioner, the problem of insufficient control accuracy of the traditional PID algorithm under nonlinearity and disturbance is solved, and fast and precise adjustment and high-performance control of the vehicle air outlet are achieved.
Patent Information
- Application Number
- CN202510861314.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-25
- Publication Date
- 2025-09-16
AI Technical Summary
Traditional PID algorithms are difficult to meet high-performance control requirements in the control of electric air outlets of automotive air conditioners. In particular, it is difficult to achieve precise adjustment under nonlinearity and disturbances. In addition, the parameter adaptive adjustment capability is insufficient and cannot adapt to complex and changeable driving conditions.
An online optimization controller based on the particle swarm optimization algorithm is adopted, combined with a dynamic prediction model and the particle swarm optimization algorithm. By calculating the predicted opening sequence and actual opening error of the vehicle air outlet, precise control of the vehicle air outlet is achieved.
It achieves fast and precise control of vehicle air outlets under complex driving conditions, improves control accuracy and response speed, adapts to changes in system parameters, and meets high-performance control requirements.
Smart Images
Figure CN120645641A_ABST
Abstract
Claims
1. A vehicle air outlet control method, characterized in that: The method comprises: Calculating an actual opening degree of the vehicle air outlet based on a target control amount, and predicting the target control amount based on a dynamic prediction model to determine a predicted opening degree sequence of the vehicle air outlet; determining an opening error based on the predicted opening sequence and the actual opening, inputting the opening error, the predicted opening sequence, and the target opening of the vehicle air outlet into the online optimization controller, and solving the online optimization controller based on a particle swarm optimization algorithm to determine a control variable sequence for controlling the vehicle air outlet; The first control variable in the control variable sequence is used as the target control variable, and the control variable sequence is iteratively determined until an iteration condition is met. The first control variable in the control variable sequence finally obtained is actually used to control the vehicle air outlet.
2. The method according to claim 1, characterized in that The opening error, the predicted opening sequence, and the target opening of the vehicle air outlet are input into the online optimization controller, and the online optimization controller is solved based on a particle swarm optimization algorithm to determine a control variable sequence for controlling the vehicle air outlet, including: Inputting the opening error, the predicted opening sequence, and the target opening of the vehicle air outlet into the online optimization controller, and constructing constraint conditions based on the maximum operating voltage, the minimum operating voltage, and the movement angle of the air outlet fan blade; The online optimization controller is solved under constraint conditions based on a particle swarm optimization algorithm to determine a control quantity sequence for controlling the vehicle air outlet.
3. The method according to claim 1 or 2, characterized in that The opening error, the predicted opening sequence, and the target opening of the vehicle air outlet are input into the online optimization controller, and the online optimization controller is solved based on a particle swarm optimization algorithm to determine a control variable sequence for controlling the vehicle air outlet, including: Inputting the opening error, the predicted opening sequence, and the target opening of the vehicle air outlet into the online optimization controller to obtain a plurality of candidate control variable sequences; Based on the particle swarm optimization algorithm, multiple candidate control quantity sequences are optimized and the optimal control quantity sequence is obtained as the control quantity sequence for controlling the vehicle air outlet.
4. The method according to claim 1, wherein Determining an opening error according to the predicted opening sequence and the actual opening includes: Obtaining the first opening in the predicted opening sequence; The difference between the first opening and the actual opening is calculated as the opening error.
5. The method according to claim 1, wherein In the initial case, the process of determining the target control amount includes: Inputting the target opening and current opening required during the operation of the vehicle air outlet into the online optimization controller; The online optimization controller is solved based on the particle swarm optimization algorithm to obtain the initialized control quantity sequence; The first control variable in the initialized control variable sequence is used as the target control variable.
6. The method according to claim 1, characterized in that The construction process of the dynamic prediction model includes: Based on the relationship between input voltage and electronic rotor motion, the voltage balance equation of the drive motor of the vehicle air outlet is established; The kinematic differential equation of the vehicle air outlet is established based on the motion principle of the vehicle air outlet; Establishing a mathematical model of the vehicle air outlet based on the characteristic parameters of the vehicle air outlet; The state space equation of the vehicle air outlet is constructed based on the voltage balance equation, the kinematic differential equation and the mathematical model, and the state space equation of the vehicle air outlet is discretized to obtain a dynamic prediction model of the vehicle air outlet opening.
7. The method according to claim 1, characterized in that Continue iterating to determine the control quantity sequence until the iteration conditions are met, including: The opening error is compared with a preset error value. If the opening error is less than the preset error value, it is determined that the iteration condition is met; wherein the difference between the preset error value and zero is less than the preset difference value.
8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a vehicle air outlet control method according to any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a vehicle air outlet control method according to any one of claims 1 to 7 when executed.
10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements a vehicle air outlet control method according to any one of claims 1 to 7.