Pure electric vehicle low temperature integrated thermal management control method

Abstract

The application discloses a kind of low temperature integrated thermal management control methods of pure electric vehicle, comprising: S1.current state information of vehicle is collected;S2.according to the vehicle speed under the current state of vehicle, the expected acceleration of vehicle in next time is predicted;S3.the driving power of vehicle is calculated according to expected acceleration;S4.the optimal battery heating power and optimal passenger compartment heating power are solved;S5.battery and passenger compartment are heated by optimal battery heating power and optimal passenger compartment heating power respectively.The application can reasonably distribute battery heating power and passenger compartment heating power, improve the power performance of whole vehicle and improve the comfort of passenger compartment.

Description

Technical Field

[0001] This invention relates to the field of thermal management of electric vehicles, and more specifically to a low-temperature integrated thermal management control method for pure electric vehicles. Background Technology

[0002] In recent years, the penetration rate of new energy vehicles in my country's automotive market has been increasing, while the industry is continuously transforming towards intelligent and electric vehicles. Pure electric vehicles, due to their advantages such as zero pollution and zero emissions, have become the future trend of the industry. However, the performance of pure electric vehicles degrades significantly in low-temperature environments, which greatly limits their further promotion and application.

[0003] Compared to traditional gasoline-powered vehicles, pure electric vehicles place higher demands on their thermal management systems. Currently, pure electric vehicle thermal management systems are transitioning from single-system management to integrated systems, combining battery thermal management, motor thermal management, and passenger compartment thermal management for energy management. However, existing technologies and methods struggle to rationally plan the energy of these three systems at low temperatures, resulting in poor vehicle performance and low passenger compartment comfort. Therefore, a low-temperature integrated thermal management control method for pure electric vehicles is needed to address these issues. Summary of the Invention

[0004] In view of this, the purpose of this invention is to overcome the defects in the prior art and provide a low-temperature integrated thermal management control method for pure electric vehicles, which can reasonably allocate the battery heating power and the passenger compartment heating power, thereby improving the overall vehicle power performance and passenger compartment comfort.

[0005] The low-temperature integrated thermal management control method for pure electric vehicles of the present invention includes the following steps:

[0006] S1. Collect current vehicle status information, including vehicle speed, battery temperature, remaining battery charge, motor temperature, and passenger compartment temperature;

[0007] S2. Based on the vehicle's current speed, predict the vehicle's expected acceleration at the next moment;

[0008] S3. Calculate the vehicle's driving power based on the desired acceleration;

[0009] S4. Solve for the optimal battery heating power and the optimal crew compartment heating power;

[0010] S5. Heat the battery and the passenger compartment using the optimal battery heating power and the optimal passenger compartment heating power, respectively.

[0011] Furthermore, step S2 specifically includes:

[0012] S21. Construct the acceleration transition probability matrix:

[0013] S211. Simulate vehicle driving conditions and calculate vehicle performance. Moment State The acceleration below:

[0014] ;

[0015] in, For acceleration; for The vehicle speed at any given moment; for The vehicle speed at any given moment; For time intervals;

[0016] The calculated accelerations are discretized to obtain an acceleration state set. :

[0017] ;

[0018] in, For the discrete first Acceleration in each state; ;

[0019] S212. Calculate the vehicle's... Moment State Acceleration transition probability under :

[0020] ;

[0021] in, For acceleration by Moment State Total number of transfers; For prediction of the time domain Internal acceleration is Moment State Transition to the next state The number of times;

[0022] S213. Transfer probability of acceleration These elements form the acceleration transition probability matrix.

[0023] S22. Calculate the acceleration corresponding to the vehicle speed in the current state of the vehicle. Find the acceleration from the acceleration transition probability matrix The probability of acceleration shifting at the next moment is calculated, and the acceleration corresponding to the maximum value among these probabilities is taken as the vehicle's expected acceleration.

[0024] Furthermore, in step S3, the vehicle drive power is determined according to the following formula. :

[0025] ;

[0026] in, The power consumed by driving resistance; The power consumed by the ramp resistance; The power consumed by air resistance; The power consumed to accelerate resistance; For vehicle speed; For the car's gravity; This is the rolling resistance coefficient; Road slope; This refers to the air drag coefficient; For windward area; This is the rotational mass conversion factor; For car quality; For time, The desired acceleration.

[0027] Furthermore, step S4 specifically includes:

[0028] S41. Battery temperature Remaining battery power Motor temperature and crew cabin temperature As a state variable, the battery heating power crew cabin heating power As a control variable, the vehicle driving conditions are divided into Each stage; making the stage ;

[0029] S42. Traversal yields... All state variables of the stage , , , combination;

[0030] S43. Calculate based on the state transition equation. Stage state variables: , , , The state transition equation is determined according to the following formula:

[0031] ;

[0032] in, It is a state function; for System state variables at each stage; for System control variables for each stage;

[0033] S44. Judgment If the state variables of a stage meet the set constraints, then construct an integrated thermal management comprehensive index evaluation model, adjust the parameter values ​​in the integrated thermal management comprehensive index evaluation model to achieve the minimum value, and store the battery heating power and passenger compartment heating power set when the minimum value is achieved as the optimal battery heating power and optimal passenger compartment heating power, and then proceed to step S45; otherwise, filter out... The state variables of the stage are determined, and the process proceeds to step S45;

[0034] S45. Make the stage Subtract 1 to get the updated stage. Determine the updated stage Is it equal to 1? If yes, proceed to step S46; otherwise, return to step S42.

[0035] S46. Store the optimal battery heating power and optimal crew cabin heating power as tabular data; making the phase Initialize the state variables and use the initialized state variables as input state variables;

[0036] S47. From the table data, find the optimal battery heating power and the optimal passenger compartment heating power corresponding to the input state variables;

[0037] S48. Calculate based on the state transition equation. Stage state variables: , , , ; and will The state variables of the stage are used as input state variables;

[0038] S49. Make the stage Add 1 to get the updated stage. Determine the updated stage Is it equal to If yes, then end; otherwise, return to step S47.

[0039] Furthermore, the constraints set include:

[0040] ;

[0041] in, for Battery temperature during the stage, , These are the lower and upper limits of the battery temperature, respectively. for The remaining battery power at this stage , These are the lower and upper limits of the remaining battery capacity, respectively. for The temperature of the crew cabin during the phase. , These are the lower and upper limits of the crew cabin temperature, respectively. for Battery heating power at each stage , These are the lower and upper limits of the battery heating power, respectively. for The stage of crew cabin heating power, , These are the lower and upper limits of the crew cabin heating power, respectively. for Motor speed during the stage, , These are the lower and upper limits of the motor speed, respectively; for Motor temperature during the stage, , These are the lower and upper limits of the motor temperature, respectively. for The motor output power at each stage , These represent the lower and upper limits of the motor's output power, respectively.

[0042] Furthermore, an integrated thermal management comprehensive index evaluation model is constructed based on the following formula. :

[0043]

[0044] in, For the first Stage-based vehicle economic evaluation function The function after normalization; For the first Stage vehicle dynamics evaluation function The function after normalization; For the first Staged cabin comfort evaluation function The function after normalization; The values ​​are 1, 2, ... , Total number of stages; , as well as All are weighting coefficients; This is a penalty factor.

[0045] Furthermore, the first [number] is determined according to the following formula. Stage-based vehicle economic evaluation function :

[0046] ;

[0047] in, For the first The remaining battery power at this stage; For the first The remaining battery power at this stage;

[0048] The number is determined according to the following formula. Stage vehicle dynamics evaluation function :

[0049] ;

[0050] in, For the first Maximum battery discharge power during the phase; For the first The time at which the phase begins; For the first The time when the phase ends;

[0051] The number is determined according to the following formula. Staged cabin comfort evaluation function :

[0052] ;

[0053] in, The target temperature for the crew cabin. For the first Temperature in the crew cabin during the phase; For the first The time at which the phase begins; For the first The time when the phase ends.

[0054] Furthermore, the penalty factor is determined according to the following method. :

[0055] Step 1. Change the vehicle's drive power and acceleration change As the input to the fuzzy system, the penalty factor As the output of a fuzzy system;

[0056] Vehicle drive power variation Calculated using the following formula:

[0057] ;

[0058] in, , They are respectively Time and Vehicle drive power at any given time For time intervals;

[0059] Acceleration change Calculated using the following formula:

[0060] ;

[0061] in, , They are respectively Time and Vehicle acceleration at any given moment;

[0062] Step 2. Determine the universe of discourse and fuzzy subsets of the input and output quantities, design the membership functions of the input and output quantities using trigonometric functions, and generate a fuzzy control rule base;

[0063] Step 3. Based on the real-time input and the established fuzzy control rule base, perform inference calculations to obtain the penalty factor. ;

[0064] Step 4. Apply the penalty factor obtained through reasoning. The center of gravity method is used to convert it into an actual value.

[0065] The beneficial effects of this invention are as follows: The low-temperature integrated thermal management control method for pure electric vehicles disclosed in this invention predicts the future state based on the current state of the vehicle, and rationally allocates the battery heating power and passenger compartment heating power at low temperatures, thereby solving for the optimal battery heating power and passenger compartment heating power. This not only improves the overall vehicle power performance and reduces energy consumption, but also enhances the comfort of the passenger compartment. Attached Figure Description

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0067] Figure 1 This is a schematic diagram of the thermal management control method of the present invention;

[0068] Figure 2 This is a schematic diagram of the optimal heating power solution process based on dynamic programming algorithm of the present invention. Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings, as shown in the figures:

[0070] The low-temperature integrated thermal management control method for pure electric vehicles of the present invention includes the following steps:

[0071] S1. Collect current vehicle status information, including vehicle speed, battery temperature, remaining battery charge, motor temperature, and passenger compartment temperature;

[0072] S2. Based on the vehicle's current speed, predict the vehicle's expected acceleration at the next moment;

[0073] S3. Calculate the vehicle's driving power based on the desired acceleration;

[0074] S4. Solve for the optimal battery heating power and the optimal crew compartment heating power;

[0075] S5. Heat the battery and the passenger compartment using the optimal battery heating power and the optimal passenger compartment heating power, respectively.

[0076] This invention uses a dynamic programming algorithm to solve for the optimal battery heating power and passenger compartment heating power. Battery temperature, battery SOC, motor temperature, and passenger compartment temperature are used as state variables, while battery heating power and passenger compartment heating power are used as control variables. A multi-objective evaluation index is established to control the temperature of the integrated thermal management system within its optimal operating temperature range. The integrated thermal management system includes battery thermal management, motor thermal management, and passenger compartment thermal management.

[0077] In this embodiment, step S2 specifically includes:

[0078] S21. Construct the acceleration transition probability matrix:

[0079] S211. Simulate vehicle driving conditions and calculate vehicle performance. Moment State The acceleration below:

[0080] ;

[0081] in, For acceleration; for The vehicle speed at any given moment; for The vehicle speed at any given moment; For time intervals;

[0082] The calculated accelerations are discretized, that is, the accelerations are discretized at intervals of 0.1 to obtain the acceleration state set. :

[0083] ;

[0084] in, For the discrete first Acceleration in each state; Discretizing the acceleration simplifies subsequent calculations.

[0085] S212. Calculate the vehicle's... Moment State Acceleration transition probability under :

[0086] ;

[0087] in, For acceleration by Moment State Total number of transfers; For prediction of the time domain Internal acceleration is Moment State Transition to the next state The number of times;

[0088] S213. Transfer probability of acceleration These elements form the acceleration transition probability matrix.

[0089] S22. Calculate the acceleration corresponding to the vehicle speed in the current state of the vehicle. Find the acceleration from the acceleration transition probability matrix The probability of acceleration shifting at the next moment is calculated, and the acceleration corresponding to the maximum value among these probabilities is taken as the vehicle's expected acceleration.

[0090] In this embodiment, in step S3, the vehicle driving power is determined according to the following formula. :

[0091] ;

[0092] in, The power consumed by driving resistance; The power consumed by the ramp resistance; The power consumed by air resistance; The power consumed to accelerate resistance; For vehicle speed; For the car's gravity; This is the rolling resistance coefficient; Road slope; This refers to the air drag coefficient; For windward area; This is the rotational mass conversion factor; For car quality; For time, The desired acceleration.

[0093] In this embodiment, as Figure 2 As shown, step S4 specifically includes:

[0094] S41. Battery temperature Remaining battery power Motor temperature and crew cabin temperature As a state variable, the battery heating power crew cabin heating power As a control variable, the vehicle driving conditions are divided into Each stage; making the stage ;

[0095] S42. Traversal yields... All state variables of the stage , , , combination;

[0096] S43. Calculate based on the state transition equation. Stage state variables: , , , The state transition equation is determined according to the following formula:

[0097] ;

[0098] in, It is a state function; for System state variables at each stage; for System control variables for each stage;

[0099] S44. Judgment If the state variables of a stage meet the set constraints, then construct an integrated thermal management comprehensive index evaluation model, adjust the parameter values ​​in the integrated thermal management comprehensive index evaluation model to achieve the minimum value, and store the battery heating power and passenger compartment heating power set when the minimum value is achieved as the optimal battery heating power and optimal passenger compartment heating power, and then proceed to step S45; otherwise, filter out... The state variables of the stage are determined, and the process proceeds to step S45;

[0100] S45. Make the stage Subtract 1 to get the updated stage. Determine the updated stage Is it equal to 1? If yes, proceed to step S46; otherwise, return to step S42.

[0101] S46. Store the optimal battery heating power and optimal crew cabin heating power as tabular data; making the phase Initialize the state variables and use the initialized state variables as input state variables;

[0102] S47. From the table data, find the optimal battery heating power and the optimal passenger compartment heating power corresponding to the input state variables;

[0103] S48. Calculate based on the state transition equation. Stage state variables: , , , ; and will The state variables of each stage are used as input state variables; the state transition equation is: ;

[0104] S49. Make the stage Add 1 to get the updated stage. Determine the updated stage Is it equal to If yes, then end; otherwise, return to step S47.

[0105] In this embodiment, the constraints include:

[0106] ;

[0107] in, for Battery temperature during the stage, , These are the lower and upper limits of the battery temperature, respectively. for The remaining battery power at this stage , These are the lower and upper limits of the remaining battery capacity, respectively. for The temperature of the crew cabin during the phase. , These are the lower and upper limits of the crew cabin temperature, respectively. for Battery heating power at each stage , These are the lower and upper limits of the battery heating power, respectively. for The stage of crew cabin heating power, , These are the lower and upper limits of the crew cabin heating power, respectively. for Motor speed during the stage, , These are the lower and upper limits of the motor speed, respectively; for Motor temperature during the stage, , These are the lower and upper limits of the motor temperature, respectively. for The motor output power at each stage , These represent the lower and upper limits of the motor's output power, respectively.

[0108] In this embodiment, an integrated thermal management comprehensive index evaluation model is constructed based on the following formula. :

[0109]

[0110] in, For the first Stage-based vehicle economic evaluation function The function after normalization; For the first Stage vehicle dynamics evaluation function The function after normalization; For the first Staged cabin comfort evaluation function The function after normalization; The values ​​are 1, 2, ... , Total number of stages; , as well as All are weighting coefficients; This is a penalty factor. By setting a penalty factor, the impact of drive power on the overall vehicle performance can be quantified; the normalization process uses existing normalization methods, which will not be elaborated here. Weighting coefficients The value is 1 / 3, and the weighting coefficient is... , for: ;

[0111] The number is determined according to the following formula. Stage-based vehicle economic evaluation function :

[0112] ;

[0113] in, For the first The remaining battery power at this stage; For the first The remaining battery power at this stage;

[0114] The number is determined according to the following formula. Stage vehicle dynamics evaluation function :

[0115] ;

[0116] in, For the first Maximum battery discharge power during the phase; For the first The time at which the phase begins; For the first The time when the phase ends;

[0117] The number is determined according to the following formula. Staged cabin comfort evaluation function :

[0118] ;

[0119] in, The target temperature for the crew cabin. For the first Temperature in the crew cabin during the phase; For the first The time at which the phase begins; For the first The time when the phase ends.

[0120] In this embodiment, the total power required by the vehicle Including vehicle drive power Power requirements for integrated thermal management systems Power consumed by accessories :

[0121] ;

[0122] For pure electric vehicles, the total power required by the vehicle is provided by the output of the battery, which is calculated using the following formula:

[0123] ;

[0124] in, The overall mechanical efficiency of the vehicle.

[0125] The battery's output power is affected by the drive power, and battery performance is significantly impacted by temperature at low temperatures. Large variations in drive power can sometimes prevent the battery's output power from meeting the vehicle's power requirements. Therefore, a penalty factor will be applied based on fuzzy control rules. Settings are configured to ensure the power performance of pure electric vehicles at low temperatures.

[0126] The penalty factor is determined using the following method. :

[0127] Step 1. Determine the fuzzy variables: Include the vehicle drive power variation. and acceleration change As the input to the fuzzy system, the penalty factor As the output of a fuzzy system;

[0128] Vehicle drive power variation Calculated using the following formula:

[0129] ;

[0130] in, , They are respectively Time and Vehicle drive power at any given time For time intervals;

[0131] Acceleration change Calculated using the following formula:

[0132] ;

[0133] in, , They are respectively Time and Vehicle acceleration at any given moment;

[0134] Step 2. Determine the universe of discourse and fuzzy subsets of the input and output quantities, design the membership functions of the input and output quantities using trigonometric functions, and generate a fuzzy control rule base;

[0135] Step 3. Output fuzzy variables: Based on the real-time input and the established fuzzy control rule base, perform inference calculations to obtain the penalty factor. ;

[0136] Step 4. Defuzzification: The penalty factor obtained from the reasoning... The center of gravity method is used to convert it into an actual value.

[0137] Among them, the change in vehicle drive power The universe of discourse is [-20, 20], and the fuzzy subset is [NB, NS, ZO, PS, PB], where NB represents a negative and large change in vehicle driving power, NS represents a negative and small change in vehicle driving power, ZO represents a zero change in vehicle driving power, PS represents a positive and small change in vehicle driving power, and PB represents a positive and large change in vehicle driving power; acceleration change The universe of discourse is [-10, 10], and the fuzzy subset is [NB, NS, ZO, PS, PB], where NB represents a negative and large change in acceleration, NS represents a negative and small change in acceleration, ZO represents a zero change in acceleration, PS represents a positive and small change in acceleration, and PB represents a positive and large change in acceleration; penalty factor The universe of discourse is [0,1], and the fuzzy subset is [S,M,B], where S represents a small penalty factor, M represents a medium penalty factor, and B represents a large penalty factor.

[0138] The fuzzy control rule base is shown in Table 1:

[0139] Table 1

[0140]

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low-temperature integrated thermal management control method for pure electric vehicles, characterized in that: Includes the following steps: S1. Collect current vehicle status information, including vehicle speed, battery temperature, remaining battery charge, motor temperature, and passenger compartment temperature; S2. Based on the vehicle's current speed, predict the vehicle's expected acceleration at the next moment; S3. Calculate the vehicle's driving power based on the desired acceleration; S4. Solve for the optimal battery heating power and the optimal crew compartment heating power; Step S4 specifically includes: S41. Battery temperature Remaining battery power Motor temperature and crew cabin temperature As a state variable, the battery heating power crew cabin heating power As a control variable, the vehicle driving conditions are divided into Each stage; making the stage ; S42. Traversal yields... All state variables of the stage , , , combination; S43. Calculate based on the state transition equation. Stage state variables: , , , The state transition equation is determined according to the following formula: ； in, It is a state function; for System state variables at each stage; for System control variables for each stage; S44. Judgment If the state variables of a stage meet the set constraints, then construct an integrated thermal management comprehensive index evaluation model, adjust the parameter values ​​in the integrated thermal management comprehensive index evaluation model to achieve the minimum value, and store the battery heating power and passenger compartment heating power set when the minimum value is achieved as the optimal battery heating power and optimal passenger compartment heating power, and then proceed to step S45; otherwise, filter out... The state variables of the stage are determined, and the process proceeds to step S45; S45. Make the stage Subtract 1 to get the updated stage. Determine the updated stage Is it equal to 1? If yes, proceed to step S46; otherwise, return to step S42. S46. Store the optimal battery heating power and optimal crew cabin heating power as tabular data; making the phase Initialize the state variables and use the initialized state variables as input state variables; S47. From the table data, find the optimal battery heating power and the optimal passenger compartment heating power corresponding to the input state variables; S48. Calculate based on the state transition equation. Stage state variables: , , , ; and will The state variables of the stage are used as input state variables; S49. Make the stage Add 1 to get the updated stage. Determine the updated stage Is it equal to If yes, then end; otherwise, return to step S47. The integrated thermal management comprehensive index evaluation model is constructed based on the following formula. : ； in, For the first Stage-based vehicle economic evaluation function The function after normalization; For the first Stage vehicle dynamics evaluation function The function after normalization; For the first Staged cabin comfort evaluation function The function after normalization; The values ​​are 1, 2, ... , Total number of stages; , as well as All are weighting coefficients; As a penalty factor; S5. Heat the battery and the passenger compartment using the optimal battery heating power and the optimal passenger compartment heating power, respectively.

2. The low-temperature integrated thermal management control method for pure electric vehicles according to claim 1, characterized in that: Step S2 specifically includes: S21. Construct the acceleration transition probability matrix: S211. Simulate vehicle driving conditions and calculate vehicle performance. Moment State The acceleration below: ； in, For acceleration; for The vehicle speed at any given moment; for The vehicle speed at any given moment; For time intervals; The calculated accelerations are discretized to obtain an acceleration state set. : ； in, For the discrete first Acceleration in each state; ; S212. Calculate the vehicle's... Moment State Acceleration transition probability under : ； in, For acceleration by Moment State Total number of transfers; For prediction of the time domain Internal acceleration is Moment State Transition to the next state The number of times; S213. Transfer probability of acceleration These elements form the acceleration transition probability matrix. S22. Calculate the acceleration corresponding to the vehicle speed in the current state of the vehicle. Find the acceleration from the acceleration transition probability matrix The probability of acceleration shifting at the next moment is calculated, and the acceleration corresponding to the maximum value among these probabilities is taken as the vehicle's expected acceleration.

3. The low-temperature integrated thermal management control method for pure electric vehicles according to claim 1, characterized in that: In step S3, the vehicle drive power is determined according to the following formula. : ； in, The power consumed by driving resistance; The power consumed by the ramp resistance; The power consumed by air resistance; The power consumed to accelerate resistance; For vehicle speed; For the car's gravity; This is the rolling resistance coefficient; Road slope; This refers to the air drag coefficient; For windward area; This is the rotational mass conversion factor; For car quality; For time, The desired acceleration.

4. The low-temperature integrated thermal management control method for pure electric vehicles according to claim 1, characterized in that: The constraints set include: ； in, for Battery temperature during the stage, , These are the lower and upper limits of the battery temperature, respectively. for The remaining battery power at this stage , These are the lower and upper limits of the remaining battery capacity, respectively. for The temperature of the crew cabin during the phase. , These are the lower and upper limits of the crew cabin temperature, respectively. for Battery heating power at each stage , These are the lower and upper limits of the battery heating power, respectively. for The stage of crew cabin heating power, , These are the lower and upper limits of the crew cabin heating power, respectively. for Motor speed during the stage, , These are the lower and upper limits of the motor speed, respectively; for Motor temperature during the stage, , These are the lower and upper limits of the motor temperature, respectively. for The motor output power at each stage , These represent the lower and upper limits of the motor's output power, respectively.

5. The low-temperature integrated thermal management control method for pure electric vehicles according to claim 1, characterized in that: The number is determined according to the following formula. Stage-based vehicle economic evaluation function : ； in, For the first The remaining battery power at this stage; For the first The remaining battery power at this stage; The number is determined according to the following formula. Stage vehicle dynamics evaluation function : ； in, For the first Maximum battery discharge power during the phase; For the first The time at which the phase begins; For the first The time when the phase ends; The number is determined according to the following formula. Staged cabin comfort evaluation function : ； in, The target temperature for the crew cabin. For the first Temperature in the crew cabin during the phase; For the first The time at which the phase begins; For the first The time when the phase ends.

6. The low-temperature integrated thermal management control method for pure electric vehicles according to claim 1, characterized in that: The penalty factor is determined using the following method. : Step 1. Change the vehicle's drive power and acceleration change As the input to the fuzzy system, the penalty factor As the output of a fuzzy system; Vehicle drive power variation Calculated using the following formula: ； in, , They are respectively Time and Vehicle drive power at any given time For time intervals; Acceleration change Calculated using the following formula: ； in, , They are respectively Time and Vehicle acceleration at any given moment; Step 2. Determine the universe of discourse and fuzzy subsets of the input and output quantities, design the membership functions of the input and output quantities using trigonometric functions, and generate a fuzzy control rule base; Step 3. Based on the real-time input and the established fuzzy control rule base, perform inference calculations to obtain the penalty factor. ; Step 4. Apply the penalty factor obtained through reasoning. The center of gravity method is used to convert it into an actual value.

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