A speed control optimization method for electric vehicles

Abstract

The invention discloses a speed control optimization method for an electric vehicle, which belongs to the technical field of PID control. The steps include: S1, constructing an electric vehicle speed control system; S2, improving an information acquisition optimization algorithm, and the improvement strategy is: D1, introducing a hyperbolic cosine adaptive weight factor ω, D2, using a growth spiral update strategy integrating reverse learning; S3, using the improved information acquisition optimization algorithm to perform parameter tuning on a PID controller module of the electric vehicle speed control system, and obtaining the optimal three parameters of Kp, Ki and Kd through algorithm optimization; S4, using the three optimal parameters of the PID controller obtained by optimizing the improved information acquisition optimization algorithm for the PID controller of the electric vehicle speed control system, thereby optimizing the control performance of the entire control system.

Description

Technical Field

[0001] The invention belongs to the field of PID control optimization, and in particular relates to a speed control optimization method for an electric vehicle. Background Art

[0002] Electric vehicle technology has made significant progress in recent years, covering multiple fields from battery technology, motor drive systems and intelligent driving assistance. Electric vehicle speed control is crucial to the performance, safety and user experience of electric vehicles. Combining PID control technology with the speed control system of electric vehicles can achieve precise adjustment of electric vehicle speed and improve the dynamic performance, comfort and safety of the vehicle. Combined with other control strategies and technologies, the speed control effect can be further optimized to meet the needs under different driving conditions.

[0003] PID control technology is a classic feedback control strategy, which is widely used in various automation systems, such as industrial process control, electric vehicle speed control, aerospace systems, etc. The core idea of ​​PID controller is to adjust the control input of the system through real-time feedback of the system output, so that the system output reaches and maintains the set value. PID controller consists of three main parts: proportional (P), integral (I) and differential (D). Each part is responsible for different control functions to achieve precise adjustment of the system. Traditional PID control technology is widely used due to its simplicity and wide applicability, but its limitations are complex parameter adjustment, difficulty in balancing stability and responsiveness, poor adaptability to nonlinearity and disturbances, etc. Intelligent optimization algorithms can overcome these shortcomings through automated parameter adjustment, adaptive control, multi-objective optimization, etc., and improve the performance and adaptability of PID controllers.

[0004] The Information Acquisition Optimization (IAO) algorithm is an intelligent optimization algorithm proposed to address the increasingly complex problems in the field of continuous nonlinear optimization. The algorithm consists of three key strategies: information collection, information filtering and evaluation, and information analysis and organization to adapt to different optimization needs. IAO has advantages such as good stability and solution accuracy, but it also has some potential disadvantages and limitations. Like other common optimization algorithms, IAO has a slow convergence speed in some complex optimization scenarios and is prone to fall into the local optimal solution in the later stage of algorithm optimization. Summary of the invention

[0005] The purpose of the present invention is to propose a speed control optimization method for an electric vehicle. The method optimizes the PID controller of the speed control system of the electric vehicle by improving the information acquisition optimization algorithm. The improved information acquisition optimization algorithm performs better in optimization performance, and can adjust the three parameters of the PID controller faster and more accurately, thereby improving the stability of the PID controller, thereby increasing the control performance and robustness of the speed control system of the entire electric vehicle.

[0006] To achieve the above object, the present invention adopts a speed control optimization method for an electric vehicle, and the specific steps of the method are as follows.

[0007] S1. Construct an electric vehicle speed control system, which includes an error calculation module, a PID controller module, an improved information acquisition optimization algorithm module, a vehicle dynamics simulation module, and a speed detection module.

[0008] S2. Improve the information acquisition optimization algorithm. The specific improvement strategy is:

[0009] S21. Introduce a hyperbolic cosine adaptive weight factor ω to improve the mathematical model of the information collection phase of the information acquisition optimization algorithm. The hyperbolic cosine adaptive weight factor ω disturbs the update of the population position through the nonlinear change of the hyperbolic cosine function, and adaptively adjusts the weight according to the number of iterations.

[0010] S22. A growth spiral update strategy that integrates reverse learning is used to improve the mathematical model of the information filtering and evaluation stages of the information acquisition optimization algorithm. The growth spiral update strategy that integrates reverse learning adjusts the position update of the population by changing the spiral radius and angle increment of each iteration, and reversely calculates the current position and the optimal position obtained by the current optimization.

[0011] S3. Use the improved information acquisition optimization algorithm to adjust the parameters of the PID controller module of the electric vehicle speed control system, and obtain the optimal three parameters Kp, Ki, and Kd through algorithm optimization.

[0012] S4. The three optimal parameters of the PID controller obtained by improving the information acquisition optimization algorithm are used in the PID controller of the electric vehicle speed control system, thereby optimizing the control performance of the entire control system.

[0013] Preferably, the electric vehicle speed control system described in S1 has the following operating process: first, the target speed is set, the target speed and the actual speed detected by the speed detection module are input into the error calculation module to obtain the real-time error e(t), the real-time error e(t) is input into the PID controller module, the PID controller module is parameterized by the information acquisition optimization algorithm, the PID controller outputs the control quantity u(t) according to the obtained optimal parameters, the control quantity u(t) is input into the simulation vehicle dynamics module, the actual operation scene is simulated to obtain the speed of the electric vehicle, and the calculation formula of the control quantity u(t) is:

[0014]

[0015] In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error.

[0016] Preferably, the simulated vehicle dynamics module in the electric vehicle speed control system described in S1 is a module that constructs a mathematical model based on the actual operating state of the electric vehicle. The specific mathematical model obtained by combining the dynamics model and the control strategy is:

[0017]

[0018] In formula (2), m represents the mass of the vehicle, V represents the speed of the vehicle, and u(t) represents the control quantity output by the PID controller. As shown in formula (1), C d represents the air resistance coefficient, ρ represents the air density, A represents the frontal area of ​​the vehicle, C r represents the rolling resistance coefficient and g represents the acceleration due to gravity.

[0019] Preferably, the hyperbolic cosine adaptive weight factor ω described in S21 has a specific mathematical model as follows:

[0020]

[0021] In formula (3), cosh() represents the hyperbolic cosine function, α represents the rate of change, and its value range is [1,2]. Iter represents the current number of iterations, and max_iter represents the maximum number of iterations.

[0022] The mathematical model of the information collection phase improved by the hyperbolic cosine adaptive weight factor is:

[0023]

[0024] In formula (4), represents the updated position of the i-th search agent, represents the position of the i-th search agent at the iter-th iteration, ω represents the cosine adaptive weight factor, and the calculation formula is shown in formula (3). and denotes the positions of two search agents randomly selected from the search population.

[0025] Preferably, as the number of iterations increases, the value of the hyperbolic cosine adaptive weight factor ω decreases smoothly. By controlling the position update amplitude in the population through ω, the algorithm can adaptively adjust between global search and local search. The algorithm can maintain good global search capabilities in the early stage, and the algorithm can maintain good local search capabilities in the later stage, which is conducive to accurately finding the optimal solution.

[0026] Preferably, the mathematical model of the information filtering and evaluation phase improved by using a growth spiral updating strategy integrating reverse learning described in S22 is:

[0027]

[0028] In formula (5), represents the updated position of the i-th search agent, represents the position of the i-th search agent at the iter-th iteration, R represents the spiral radius, R = R0·e 0.5·iter , R0 represents the initial radius, iter represents the number of iterations, v1 and v2 represent two randomly generated unit vectors, θ represents the angle increment, θ=θ0+λ·iter, θ0 represents the initial angle, λ represents the angle increment change rate, β represents the weight factor of reverse learning, which is used to control the intensity of reverse learning, X best Indicates the current optimal position.

[0029] Preferably, combining the reverse learning strategy and the growth spiral strategy can enhance the overall performance of the algorithm. The growth spiral strategy improves the global search capability of the algorithm by expanding the search range. By adjusting the parameters in the algorithm as well as the initial radius and angle, the spiral trajectory of the search can be flexibly controlled, thereby increasing the adaptability of the algorithm and reducing the situation where the algorithm falls into the local optimal solution. The optimal position is used to adjust the individual search direction and position, thereby improving the algorithm search efficiency and accuracy.

[0030] Preferably, the method of using the improved information acquisition optimization algorithm to perform parameter tuning on the PID controller module of the electric vehicle speed control system in S3 comprises the following specific steps:

[0031] S31, setting initial parameters of the improved information acquisition optimization algorithm, including population size N, problem dimension dim, maximum number of iterations max_iter, search space upper limit ub, lower limit lb, and initializing the population position through the initial parameters;

[0032] S32, encoding the three parameters Kp, Ki, Kd of the PID controller into the population position vector of the algorithm;

[0033] S33, setting the fitness value function of the improved information acquisition optimization algorithm. The fitness value is the evaluation standard of the algorithm in the optimization process. The time-weighted integrated absolute error ITAE is used as the fitness value function. The calculation formula of ITAE is:

[0034]

[0035] In formula (6), e(t) represents the real-time error value, and T represents the total system operation time;

[0036] S34, calculate the fitness value of each position in the population according to the fitness value function in S33, and select the individual with the best fitness value as the position X of the best individual best ;

[0037] S35, by improving the mathematical model of the information acquisition optimization algorithm, the position of the search population is updated, and the position X of the optimal individual in the population is updated. best ;

[0038] S36, determine whether the number of iterations has reached the maximum number, if so, stop the optimization and output the optimal individual position in the population as the optimal solution, if not, continue to execute S35 to perform optimization.

[0039] Preferably, the step of updating the position of the search population by improving the mathematical model of the information acquisition optimization algorithm in S35 comprises the following specific steps:

[0040] Step 1: The algorithm enters the information collection phase to update the position. The mathematical model of this phase introduces a hyperbolic cosine adaptive weight factor to improve the information collection mathematical model, as shown in formula (4). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position.

[0041] Step 2: The algorithm enters the information filtering and evaluation stage to update the position. The mathematical model of this stage uses a growth spiral update strategy that integrates reverse learning to improve the mathematical model of the information filtering and evaluation stage, as shown in formula (5). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position.

[0042] Step 3: The algorithm enters the information analysis and organization stage to update the position. The mathematical model of this stage is shown in formula (7). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position.

[0043]

[0044] In formula (7), represents the updated position of the i-th search agent, X best represents the position of the current optimal individual, η represents the control factor value for analyzing and organizing information, rand1, rand2, rand3, and rand4 represent random numbers in the range of [0,1], represents the average value of the best individual positions generated in the previous iteration, Represents the information quality factor, which is determined by The value of is used to switch different update formulas for position update. The calculation formula is shown in formula (8);

[0045]

[0046] In formula (8), σ represents a random number in the range [0, 1], iter represents the current number of iterations, and max_iter represents the maximum number of iterations.

[0047] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: by introducing a hyperbolic cosine adaptive weight factor to improve the mathematical model of the information collection phase of the information acquisition optimization algorithm, the global and local search capabilities of the algorithm are better balanced, and the randomness of the population position is increased while ensuring the quality of the population position. By using a growth spiral update strategy that integrates reverse learning, the mathematical model of the information filtering and evaluation phase of the information acquisition optimization algorithm is improved, and the convergence speed of the algorithm is improved. When the algorithm falls into a local optimal solution, it can quickly jump out of the local optimal solution and continue to optimize. The improved information acquisition optimization algorithm is used for parameter optimization of the PID controller in the electric vehicle speed control system, which increases the stability and accuracy of the electric vehicle speed control, and reduces the complexity of manual adjustment through the adaptive adjustment of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention is a flow chart of a speed control optimization method for electric vehicles.

[0049] Figure 2 Comparison chart of the change in fitness value during the tuning process of the improved information acquisition optimization algorithm and the standard information acquisition optimization algorithm.

[0050] Figure 3 The diagram of the changes in parameter values ​​Kp, Ki, and Kd during the tuning process of the standard information acquisition optimization algorithm.

[0051] Figure 4 To improve the information acquisition optimization algorithm tuning process, the parameter values ​​Kp, Ki, and Kd are varied.

[0052] Figure 5 Comparison chart of the effects of optimizing PID controller using improved information acquisition optimization algorithm and standard information acquisition optimization algorithm. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0054] The present invention provides a speed control optimization method for an electric vehicle, which specifically comprises the following steps: Figure 1 shown.

[0055] S1. Construct an electric vehicle speed control system, which includes an error calculation module, a PID controller module, an improved information acquisition optimization algorithm module, a vehicle dynamics simulation module, and a speed detection module.

[0056] Furthermore, the electric vehicle speed control system described in S1 has the following operating process: first, the target speed is set, the target speed and the actual speed detected by the speed detection module are input into the error calculation module to obtain the real-time error e(t), the real-time error e(t) is input into the PID controller module, the parameters of the PID controller module are adjusted through the information acquisition optimization algorithm, the PID controller outputs the control quantity u(t) according to the obtained optimal parameters, the control quantity u(t) is input into the simulation vehicle dynamics module to simulate the actual operation scene to obtain the speed of the electric vehicle, and the calculation formula of the control quantity u(t) is:

[0057]

[0058] In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error.

[0059] Furthermore, the simulated vehicle dynamics module in the electric vehicle speed control system described in S1 is a module that constructs a mathematical model based on the actual operating state of the electric vehicle. The specific mathematical model obtained by combining the dynamics model and the control strategy is:

[0060]

[0061] In formula (2), m represents the mass of the vehicle, V represents the speed of the vehicle, and u(t) represents the control quantity output by the PID controller. As shown in formula (1), C drepresents the air resistance coefficient, ρ represents the air density, A represents the frontal area of ​​the vehicle, C r represents the rolling resistance coefficient and g represents the acceleration due to gravity.

[0062] S2. Improve the information acquisition optimization algorithm. The specific improvement strategy is:

[0063] S21. Introduce a hyperbolic cosine adaptive weight factor ω to improve the mathematical model of the information collection phase of the information acquisition optimization algorithm. The hyperbolic cosine adaptive weight factor ω disturbs the update of the population position through the nonlinear change of the hyperbolic cosine function, and adaptively adjusts the weight according to the number of iterations.

[0064] S22. A growth spiral update strategy that integrates reverse learning is used to improve the mathematical model of the information filtering and evaluation stages of the information acquisition optimization algorithm. The growth spiral update strategy that integrates reverse learning adjusts the position update of the population by changing the spiral radius and angle increment of each iteration, and reversely calculates the current position and the optimal position obtained by the current optimization.

[0065] Furthermore, the hyperbolic cosine adaptive weight factor ω described in S21 has a specific mathematical model as follows:

[0066]

[0067] In formula (3), cosh() represents the hyperbolic cosine function, α represents the rate of change, and its value range is [1,2]. Iter represents the current number of iterations, and max_iter represents the maximum number of iterations.

[0068] The mathematical model of the information collection phase improved by the hyperbolic cosine adaptive weight factor is:

[0069]

[0070] In formula (4), represents the updated position of the i-th search agent, represents the position of the i-th search agent at the iter-th iteration, ω represents the cosine adaptive weight factor, and the calculation formula is shown in formula (3). and denotes the positions of two search agents randomly selected from the search population.

[0071] Furthermore, the mathematical model of the information filtering and evaluation stage improved by using a growth spiral updating strategy integrating reverse learning described in S22 is:

[0072]

[0073] In formula (5), represents the updated position of the i-th search agent, represents the position of the i-th search agent at the iter-th iteration, R represents the spiral radius, R = R0·e 0.5·iter , R0 represents the initial radius, iter represents the number of iterations, v1 and v2 represent two randomly generated unit vectors, θ represents the angle increment, θ=θ0+λ·iter, θ0 represents the initial angle, λ represents the angle increment change rate, β represents the weight system of reverse learning, controls the intensity of reverse learning, X best Indicates the current optimal position.

[0074] S3. Use the improved information acquisition optimization algorithm to adjust the parameters of the PID controller module of the electric vehicle speed control system, and obtain the optimal three parameters Kp, Ki, and Kd through algorithm optimization.

[0075] Furthermore, the improved information acquisition optimization algorithm described in S3 is used to perform parameter tuning on the PID controller module of the electric vehicle speed control system, and the specific steps are:

[0076] S31, setting initial parameters of the improved information acquisition optimization algorithm, including population size N, problem dimension dim, maximum number of iterations max_iter, search space upper limit ub, lower limit lb, and initializing the population position through the initial parameters;

[0077] S32, encoding the three parameters Kp, Ki, Kd of the PID controller into the population position vector of the algorithm;

[0078] S33, setting the fitness value function of the improved information acquisition optimization algorithm. The fitness value is the evaluation standard of the algorithm in the optimization process. The time-weighted integrated absolute error ITAE is used as the fitness value function. The calculation formula of ITAE is:

[0079]

[0080] In formula (6), e(t) represents the real-time error value, and T represents the total system operation time;

[0081] S34, calculate the fitness value of each position in the population according to the fitness value function in S33, and select the individual with the best fitness value as the position X of the best individual best ;

[0082] S35, by improving the mathematical model of the information acquisition optimization algorithm, the position of the search population is updated, and the position X of the optimal individual in the population is updated. best ;

[0083] S36, determine whether the number of iterations has reached the maximum number, if so, stop the optimization and output the optimal individual position in the population as the optimal solution, if not, continue to execute S35 to perform optimization.

[0084] Furthermore, the step of updating the position of the search population by improving the mathematical model of the information acquisition optimization algorithm in S35 comprises the following specific steps:

[0085] Step 1: The algorithm enters the information collection phase to update the position. The mathematical model of this phase introduces a hyperbolic cosine adaptive weight factor to improve the information collection mathematical model, as shown in formula (4). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position.

[0086] Step 2: The algorithm enters the information filtering and evaluation stage to update the position. The mathematical model of this stage uses a growth spiral update strategy that integrates reverse learning to improve the mathematical model of the information filtering and evaluation stage, as shown in formula (5). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position.

[0087] Step 3: The algorithm enters the information analysis and organization stage to update the position. The mathematical model of this stage is shown in formula (7). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position.

[0088]

[0089] In formula (7), represents the updated position of the i-th search agent, X best represents the position of the current optimal individual, η represents the control factor value for analyzing and organizing information, rand1, rand2, rand3, and rand4 represent random numbers in the range of [0,1], represents the average value of the best individual positions generated in the previous iteration, Represents the information quality factor, which is determined by The value of is used to switch different update formulas for position update. The calculation formula is shown in formula (8);

[0090]

[0091] In formula (8), σ represents a random number in the range [0, 1], iter represents the current number of iterations, and max_iter represents the maximum number of iterations.

[0092] S4. The three optimal parameters of the PID controller obtained by improving the information acquisition optimization algorithm are used in the PID controller of the electric vehicle speed control system, thereby optimizing the control performance of the entire control system.

[0093] Furthermore, in order to verify that the speed control optimization method for electric vehicles proposed in the present invention has better performance than other methods, the present invention will conduct simulation experiments through Matlab and Simulink software, use Matlab to improve the standard information acquisition optimization algorithm, build an electric vehicle speed control system model through Simulink, and initialize the parameters of the improved information acquisition optimization algorithm in Matlab, where the population size N=100, the problem dimension dim=3, the maximum number of iterations is 25, the search upper limit is 20, and the lower limit is 0.001. The mathematical model in the simulated vehicle dynamics module is sorted out, and the second-order transfer function is generated by Laplace transform for simulation experiments. The transfer function is S is the complex frequency domain variable in Laplace transform. Run the program and simulation model to obtain the experimental result graph, as shown in Figure 2-Figure 5 shown.

[0094] Furthermore, Figure 2 This is a comparison chart of the fitness value changes during the tuning process of the improved information acquisition optimization algorithm and the standard information acquisition optimization algorithm. From the figure, it can be seen that the improved information acquisition optimization algorithm converges faster after the iteration starts and quickly reaches the optimal solution. Figure 3 and Figure 4 They are the changes of parameter values ​​Kp, Ki, and Kd during the tuning process of the improved information acquisition optimization algorithm and the standard information acquisition optimization algorithm. It can be seen from the figure that the parameter values ​​of the improved information acquisition optimization algorithm change steadily and with a small amplitude during the optimization process, while the parameter values ​​of the standard information acquisition optimization algorithm change with a large amplitude, indicating that the improved information acquisition optimization algorithm is more stable during the optimization process, reduces the impact of large parameter changes on the controlled object, and has stronger reliability. Figure 5 This is a comparison chart of the effects of optimizing PID controllers using the improved information acquisition optimization algorithm and the standard information acquisition optimization algorithm. The target value is set to 1 unit. It can be seen from the figure that the effects of PID controllers tuned by the standard information acquisition optimization algorithm and the improved information acquisition optimization algorithm are relatively stable, but the overshoot of the response curve of the PID controller optimized by the improved information acquisition optimization algorithm is lower and the control effect is better. This also shows that the parameter solution found by the improved algorithm is more accurate and the effect is better.

Claims

1. A speed control optimization method for an electric vehicle, characterized in that: The specific steps are: S1. Construct an electric vehicle speed control system, wherein the control system includes an error calculation module, a PID controller module, an improved information acquisition optimization algorithm module, a vehicle dynamics simulation module, and a speed detection module; S2. Improve the information acquisition optimization algorithm. The specific improvement strategy is: S21. A hyperbolic cosine adaptive weight factor ω is introduced to improve the mathematical model of the information collection phase of the information acquisition optimization algorithm. The hyperbolic cosine adaptive weight factor ω disturbs the update of the population position through the nonlinear change of the hyperbolic cosine function, and adaptively adjusts the weight according to the number of iterations. The specific mathematical model is: In formula (3), cosh() represents the hyperbolic cosine function, α represents the rate of change, and its value range is [1,2]. Iter represents the current number of iterations, and max_iter represents the maximum number of iterations. The mathematical model of the information collection phase improved by the hyperbolic cosine adaptive weight factor is: In formula (4), represents the updated position of the i-th search agent, represents the position of the i-th search agent at the iter-th iteration, ω represents the cosine adaptive weight factor, and the calculation formula is shown in formula (3). and denotes the positions of two search agents randomly selected in the search population; S22. A growth spiral update strategy integrating reverse learning is used to improve the mathematical model of the information filtering and evaluation phase of the information acquisition optimization algorithm. The growth spiral update strategy integrating reverse learning is to adjust the position update of the population by changing the spiral radius and angle increment of each iteration, and reversely calculate the current position and the optimal position obtained by the current optimization. The specific mathematical model is: In formula (5), represents the updated position of the i-th search agent, represents the position of the i-th search agent at the iter-th iteration, R represents the spiral radius, R = R0·e 0.5·iter , R0 represents the initial radius, iter represents the number of iterations, v1 and v2 represent two randomly generated unit vectors, θ represents the angle increment, θ=θ0+λ·iter, θ0 represents the initial angle, λ represents the angle increment change rate, β represents the weight factor of reverse learning, which is used to control the intensity of reverse learning, X best Indicates the current optimal position; S3, using the improved information acquisition optimization algorithm to adjust the parameters of the PID controller module of the electric vehicle speed control system, and obtaining the optimal three parameters Kp, Ki, and Kd through algorithm optimization; S4. The three optimal parameters of the PID controller obtained by improving the information acquisition optimization algorithm are used in the PID controller of the electric vehicle speed control system, thereby optimizing the control performance of the entire control system.

2. A speed control optimization method for electric vehicles according to claim 1, characterized in that: The electric vehicle speed control system described in S1 has the following operating process: first, the target speed is set, the target speed and the actual speed detected by the speed detection module are input into the error calculation module to obtain the real-time error e(t), the real-time error e(t) is input into the PID controller module, the parameters of the PID controller module are adjusted through the information acquisition optimization algorithm, the PID controller outputs the control quantity u(t) according to the obtained optimal parameters, the control quantity u(t) is input into the simulation vehicle dynamics module, the actual operation scene is simulated to obtain the speed of the electric vehicle, and the calculation formula of the control quantity u(t) is: In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error; The simulated vehicle dynamics module in the electric vehicle speed control system described in S1 is a mathematical model constructed according to the actual operating state of the electric vehicle. The specific mathematical model obtained by combining the dynamics model and the control strategy is: In formula (2), m represents the mass of the vehicle, V represents the speed of the vehicle, and u(t) represents the control quantity output by the PID controller. As shown in formula (1), C d represents the air resistance coefficient, ρ represents the air density, A represents the frontal area of ​​the vehicle, C r represents the rolling resistance coefficient and g represents the acceleration due to gravity.

3. A speed control optimization method for electric vehicles according to claim 2, characterized in that: The improved information acquisition optimization algorithm described in S3 is used to perform parameter tuning on the PID controller module of the electric vehicle speed control system, and the specific steps are as follows: S31, setting initial parameters of the improved information acquisition optimization algorithm, including population size N, problem dimension dim, maximum number of iterations max_iter, search space upper limit ub, lower limit lb, and initializing the population position through the initial parameters; S32, encoding the three parameters Kp, Ki, Kd of the PID controller into the population position vector of the algorithm; S33, setting the fitness value function of the improved information acquisition optimization algorithm. The fitness value is the evaluation standard of the algorithm in the optimization process. The time-weighted integrated absolute error ITAE is used as the fitness value function. The calculation formula of ITAE is: In formula (6), e(t) represents the real-time error value, and T represents the total system operation time; S34, calculate the fitness value of each position in the population according to the fitness value function in S33, and select the individual with the best fitness value as the position X of the best individual best ; S35, by improving the mathematical model of the information acquisition optimization algorithm, the position of the search population is updated, and the position X of the optimal individual in the population is updated. best ; S36, determine whether the number of iterations has reached the maximum number, if so, stop the optimization and output the optimal individual position in the population as the optimal solution, if not, continue to execute S35 to perform optimization.

4. A speed control optimization method for electric vehicles according to claim 3, characterized in that: The specific steps of updating the position of the search population by improving the mathematical model of the information acquisition optimization algorithm described in S35 are as follows: Step 1: The algorithm enters the information collection phase to update the position. The mathematical model of this phase introduces a hyperbolic cosine adaptive weight factor to improve the information collection mathematical model, as shown in formula (4). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position. Step 2: The algorithm enters the information filtering and evaluation stage to update the position. The mathematical model of this stage uses a growth spiral update strategy that integrates reverse learning to improve the mathematical model of the information filtering and evaluation stage, as shown in formula (5). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position. Step 3: The algorithm enters the information analysis and organization stage to update the position. The mathematical model of this stage is shown in formula (7). The fitness value of the updated position is calculated and compared with the optimal fitness value to update the optimal position. In formula (7), represents the updated position of the i-th search agent, X best represents the position of the current optimal individual, η represents the control factor value for analyzing and organizing information, rand1, rand2, rand3, and rand4 represent random numbers in the range of [0,1], represents the average value of the best individual positions generated in the previous iteration, Represents the information quality factor, which is determined by The value of is used to switch different update formulas for position update. The calculation formula is shown in formula (8); In formula (8), σ represents a random number in the range [0, 1], iter represents the current number of iterations, and max_iter represents the maximum number of iterations.

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