Smooth control method and system for switching braking modes of electric automobile

Abstract

The invention provides a smoothness control method and system for braking mode switching of an electric vehicle. The smoothness control method comprises the steps that firstly, real-time information of the vehicle running state is collected through a sensor; secondly, analyzing the corresponding data through an analysis decision module, judging the state of the vehicle, and making a corresponding ride comfort braking scheme based on a judgment result; and finally, performing negative feedback regulation type algorithm control. According to the method, firstly, a plurality of sensors are used for collecting real-time information of the vehicle running state, the collected real-time state information of the multiple vehicles is analyzed, a corresponding smooth braking scheme is formulated, robust control is used for optimizing the output speed and accuracy of a hydraulic braking system, and the control precision of the hydraulic braking system is improved. The response difference of different brake output sources is weakened through the fuzzy PID algorithm, the brake scheme is dynamically adjusted according to the real-time vehicle brake state, it is ensured that brake output and brake requirements are accurately matched in real time, and the brake smoothness during composite brake mode switching is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle braking, and in particular to a method and system for controlling the smoothness of switching braking modes of an electric vehicle. Background Art

[0002] The energy consumed by braking under typical urban conditions accounts for about half of the total driving energy, and the composite braking technology of new energy vehicles can significantly reduce the energy consumption during braking and increase the mileage. The electro-hydraulic composite braking system of pure electric vehicles includes an electro-hydraulic braking system and a regenerative braking system, which can realize multiple braking methods such as single hydraulic braking, single motor braking and electro-hydraulic composite braking. Compared with the traditional hydraulic braking system, the composite braking system adds a regenerative braking subsystem, which gives priority to regenerative braking for energy recovery during braking, and hydraulic braking begins to intervene when the braking demand cannot be met. Since the motor will exit the brake due to the influence of factors such as SOC, motor speed, and anti-lock intervention, and there is a difference in the response speed of the motor and the hydraulic system, it will cause impact when switching the braking mode, reducing ride comfort.

[0003] At present, a large number of scholars have conducted extensive and in-depth research on the economic issues of energy recovery in electric vehicles, but there are fewer studies on the smoothness issue of reducing impact when switching braking modes. Therefore, it is of great significance to improve braking smoothness by conducting research on coordinated control of composite braking systems. Summary of the invention

[0004] In view of the defects in the prior art, an object of the present invention is to provide a method and system for controlling the smoothness of switching braking modes of an electric vehicle.

[0005] The method for controlling the smoothness of switching the braking mode of an electric vehicle provided by the present invention comprises:

[0006] Information collection steps: First, collect the real-time information of the vehicle's driving status through sensors, including real-time vehicle speed, wheel speed, SOC, brake pedal opening and brake pedal opening change rate;

[0007] Analysis and decision-making steps: Analyze, calculate and make decisions on the collected data, and formulate corresponding ride comfort solutions. The state analysis is based on the vehicle's real-time speed, wheel speed, and SOC parameters. The solution decision is based on the real-time braking demand and the vehicle's real-time braking state calculated based on the brake pedal opening and brake pedal opening change rate data;

[0008] Algorithm control steps: Use robust control to optimize the output speed and accuracy of the hydraulic braking system, use fuzzy PID algorithm to weaken the response differences of different braking output sources, and dynamically adjust the braking scheme according to the real-time vehicle braking status. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms. The output signal is adjusted according to the current state and the target state to stabilize the system and achieve the desired effect. The vehicle is regarded as a system, the required braking force is set to the desired effect, and the robust control algorithm is used to adjust the hydraulic braking output to improve the hydraulic braking output followability. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake, thereby optimizing the braking smoothness during the vehicle's braking mode switching process, and adjusting the braking output in real time according to the vehicle's real-time status.

[0009] Furthermore, in the analysis and decision step, the state analysis divides the vehicle into low-speed, medium-speed or high-speed driving states based on the real-time speed of the vehicle, determines the braking demand based on the pedal input, obtains the real-time output capacity of the motor based on the real-time wheel speed combined with the motor characteristic curve, provides reference limits for the electric / hydraulic braking force distribution, determines the real-time state of the battery based on the real-time SOC value of the battery, determines whether to charge and at what power to charge in combination with preset rules for protecting the battery, and provides a basis for judging whether to switch the braking mode when the braking demand remains unchanged.

[0010] Furthermore, in the analysis and decision-making step, the analysis and decision-making module integrates and calculates multiple sensor data, identifies the real-time status of the vehicle and derives the real-time braking demand, and formulates a smooth braking plan based on the vehicle status. Specifically, first, the vehicle braking status is determined based on the vehicle speed, wheel speed, and real-time SOC sensor data. Secondly, it is determined whether the braking mode needs to be switched based on the collected pedal sensor data. Finally, the above decision results are integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustments are made according to the real-time data during the braking process.

[0011] Furthermore, in the algorithm control step, robust control is used to adjust the hydraulic brake output, and its related functions and processes are: sensitivity function S(s) = (1 + G(s) K(s)) -1 , open-loop transfer function R(s) = (1 + G(s)K(s)) -1 K(s), complementary sensitivity function T(s) = (1+G(s)K(s)) -1 K(s)G(s), introduce the sensitivity weighting function W S and complementary sensitivity weighting function W T , in order to take into account both anti-interference and stability, the constraint weighting function W is introduced R , limit the control signal input to avoid actuator saturation, and use MATLAB to solve the problem of satisfying ||W S S||≤1,||W RR||≤1,||W T A stable controller with T||≤1 is then applied to the controller to improve the followability of the hydraulic brake output, where K(s) is the controller and G(s) is the controlled object.

[0012] Furthermore, in the algorithm control step, a fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake, specifically: through motor compensation, the hysteresis of the hydraulic brake is compensated with the rapid response of the motor, the difference between the target hydraulic braking force and the actual hydraulic braking force is used as the feedforward variable of the regenerative braking, and the error generated during the regenerative braking process is used as the feedback variable. A feedforward feedback fuzzy PID controller is designed to control the regenerative braking response process. On the basis of ensuring that the total braking torque output of the wheel meets the braking requirements, the braking smoothness during the vehicle braking mode switching process is optimized. The algorithm-related expression is: U(t)=Kp*e(t)+Ki*∫e(t)d(t)+Kd*de(t) / dt, where Kp, Ki, and Kd are adjustment parameters of proportional, integral, and differential control, respectively, U(t) is the controller output, and e(t) is the error signal; fuzzy control is implemented by applying the defined MATLAB built-in blank fuzzy controller module, and its input is the sum of the difference e between the expected braking torque and the output braking torque of the motor and hydraulic braking system and the rate of change of the difference e c The output is the three adjustment parameters of PID, the sum of the difference e and the rate of change of the difference e c The fuzzy values ​​of are divided into seven sections in ascending order, and the domain is [-5,5]. The fuzzy values ​​of the three adjustment parameters are divided into five sections, and the domain is [-1,1]. Based on experience, the fuzzy control rule tables of Kp, Ki, and Kd are established respectively.

[0013] The smoothness control system for switching the braking mode of an electric vehicle provided by the present invention comprises:

[0014] Information collection module: First, the real-time information of the vehicle's driving status is collected through sensors, including real-time vehicle speed, wheel speed, SOC, brake pedal opening and brake pedal opening change rate;

[0015] Analysis and decision-making module: Analyze, calculate and make decisions on the collected data, and formulate corresponding ride comfort solutions. The state analysis is based on the vehicle's real-time speed, wheel speed, and SOC parameters. The solution decision is based on the real-time braking demand and the vehicle's real-time braking state calculated based on the brake pedal opening and brake pedal opening change rate data;

[0016] Algorithm control module: Use robust control to optimize the output speed and accuracy of the hydraulic braking system, use fuzzy PID algorithm to weaken the response differences of different braking output sources, and dynamically adjust the braking scheme according to the real-time vehicle braking status. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms. The output signal is adjusted according to the current state and the target state to stabilize the system and achieve the desired effect. The vehicle is regarded as a system, the required braking force is set to the desired effect, and the robust control algorithm is used to adjust the hydraulic braking output to improve the hydraulic braking output followability. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake, thereby optimizing the braking smoothness during the vehicle's braking mode switching process, and adjusting the braking output in real time according to the vehicle's real-time status.

[0017] Furthermore, in the analysis and decision-making module, the state analysis divides the vehicle into low-speed, medium-speed or high-speed driving states based on the real-time vehicle speed, judges the braking demand based on the pedal input, obtains the real-time output capacity of the motor based on the real-time wheel speed combined with the motor characteristic curve, provides reference limits for the electric / hydraulic braking force distribution, determines the real-time state of the battery based on the real-time SOC value of the battery, determines whether to charge and at what power to charge in combination with the preset rules for protecting the battery, and provides a basis for judging whether to switch the braking mode when the braking demand remains unchanged.

[0018] Furthermore, in the analysis and decision-making module, the analysis and decision-making module integrates and calculates multiple sensor data, identifies the real-time status of the vehicle and derives the real-time braking requirements, and formulates a smooth braking plan based on the vehicle status. Specifically, first, the vehicle braking status is determined based on the vehicle speed, wheel speed, and real-time SOC sensor data. Secondly, it is determined whether the braking mode needs to be switched based on the collected pedal sensor data. Finally, the above decision results are integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustments are made based on real-time data during the braking process.

[0019] Furthermore, in the algorithm control module, robust control is used to adjust the hydraulic brake output, and its related functions and processes are: sensitivity function S(s) = (1 + G(s) K(s)) -1 , open-loop transfer function R(s) = (1 + G(s)K(s)) -1 K(s), complementary sensitivity function T(s) = (1+G(s)K(s)) -1 K(s)G(s), introduce the sensitivity weighting function W S and complementary sensitivity weighting function W T , in order to take into account both anti-interference and stability, the constraint weighting function W is introduced R , limit the control signal input to avoid actuator saturation, and use MATLAB to solve the problem of satisfying ||W S S||≤1,||W RR||≤1,||W T A stable controller with T||≤1 is then applied to the controller to improve the followability of the hydraulic brake output, where K(s) is the controller and G(s) is the controlled object.

[0020] Furthermore, in the algorithm control module, a fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake. Specifically, the hysteresis of the hydraulic brake is compensated by the motor's rapid response through motor compensation. The difference between the target hydraulic braking force and the actual hydraulic braking force is used as the feedforward variable of the regenerative braking. The error generated during the regenerative braking process is used as the feedback variable. A feedforward feedback fuzzy PID controller is designed to control the regenerative braking response process. On the basis of ensuring that the total braking torque output of the wheel meets the braking requirements, the braking smoothness during the vehicle braking mode switching process is optimized. The algorithm-related expression is: U(t) = Kp*e(t)+Ki*∫e(t)d(t)+Kd*de(t) / dt, where Kp, Ki, and Kd are adjustment parameters for proportional, integral, and differential control, respectively. U(t) is the controller output, and e(t) is the error signal. The fuzzy control is implemented by applying the defined MATLAB built-in blank fuzzy controller module, and its input is the sum of the difference between the expected braking torque and the output braking torque of the motor and hydraulic braking system, e, and the rate of change of the difference, e. c The output is the three adjustment parameters of PID, the sum of the difference e and the rate of change of the difference e c The fuzzy values ​​of are divided into seven sections in ascending order, and the domain is [-5,5]. The fuzzy values ​​of the three adjustment parameters are divided into five sections, and the domain is [-1,1]. Based on experience, the fuzzy control rule tables of Kp, Ki, and Kd are established respectively.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] (1) The present invention uses sensors to collect real-time information on the vehicle's driving status. Sensors can be arranged at multiple locations. During the collection process, multiple sensor data of the same parameter are compared to ensure that the input information is true and valid, improve the data transmission speed of each sensor, and ensure that the decision-making analysis module can receive the data information of each sensor in real time during the vehicle braking process, so that the analysis and decision-making module can dynamically adjust the corresponding ride comfort solution in a timely manner according to the vehicle status;

[0023] (2) The present invention uses a robust control algorithm to adjust the hydraulic brake output and improve the hydraulic brake output tracking performance. It uses a fuzzy PID algorithm to adjust the output of the vehicle's motor brake and hydraulic brake, weakening the response differences of different brake output sources. The braking scheme is dynamically adjusted according to the real-time vehicle braking status to ensure that the braking output is accurately matched with the braking demand in real time, thereby improving the braking smoothness when switching the compound braking mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0025] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0027] Example 1

[0028] like Figure 1 The present invention provides a method for controlling the smoothness of switching braking modes of an electric vehicle, and the method steps include the following:

[0029] Step 1, information collection: First, collect real-time information on the vehicle's driving status through sensors, including real-time vehicle speed, wheel speed, SOC, brake pedal opening and its change rate, etc. When collecting information, consider using dual / multiple sensors to collect the same parameter to avoid sudden abnormalities in a single sensor affecting data accuracy: When collecting information, consider the following factors: first, arrange sensors in multiple locations, and compare multiple sensor data of the same parameter during collection to ensure that the input information is true and valid; second, improve the data transmission speed of each sensor to ensure that the decision-making analysis module can receive the data information of each sensor in real time during vehicle braking, so that the analysis and decision-making module can dynamically adjust the corresponding smoothness plan in time according to the vehicle status.

[0030] Step 2, analysis and decision-making: After the information is collected, the collected data needs to be analyzed, calculated, and decided, and a corresponding smoothness plan should be formulated. The state analysis divides the vehicle into low-speed, medium-speed, or high-speed driving states based on the real-time speed of the vehicle, judges the braking demand based on the pedal input, obtains the real-time output capacity of the motor based on the real-time wheel speed combined with the motor characteristic curve, provides reference limits for the distribution of electric / hydraulic braking force, determines the real-time state of the battery based on the real-time SOC value of the battery, and determines whether to charge and at what power in combination with the corresponding rules (to protect the battery), and provides a basis for judging whether to switch the braking mode when the braking demand remains unchanged; the analysis and decision-making module integrates and calculates multiple sensor data, identifies the real-time state of the vehicle and obtains the real-time braking demand, and formulates a smoothness braking plan based on the vehicle state. First, the vehicle braking state is determined based on sensor data such as vehicle speed, wheel speed, and real-time SOC. Secondly, it is determined whether the braking mode needs to be switched based on the collected pedal sensor data. Finally, the above decision results are integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustments are made based on real-time data during braking.

[0031] Step 3, algorithm control: After the analysis and decision-making is completed, robust control is required to optimize the output speed and accuracy of the hydraulic brake system, and the fuzzy PID algorithm is used to weaken the response differences of different brake output sources. The braking scheme is dynamically adjusted according to the real-time vehicle braking state. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms. The output signal can be adjusted according to the current state and the target state to stabilize the system and achieve the desired effect. In this case, the vehicle is regarded as a system, the required braking force is set to the desired effect, and robust control is used to adjust the hydraulic brake output. The relevant function and process are: Sensitivity function S(s) = (1+G(s)K(s)) -1 , open-loop transfer function R(s) = (1 + G(s)K(s)) -1 K(s), complementary sensitivity function T(s) = (1+G(s)K(s)) -1 K(s)G(s), introduce the sensitivity weighting function W S and complementary sensitivity weighting function W T , in order to take into account both anti-interference and stability, the constraint weighting function W is introduced R , limit the control signal input to avoid actuator saturation, select a suitable weighting function, and use MATLAB to solve the problem of satisfying ||W S S||≤1, ||W R R||≤1,||W TThe stable controller K(s) with T||≤1 is then applied to the controller to improve the followability of hydraulic brake output. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake. Through motor compensation, the hysteresis of the hydraulic brake is compensated with the rapid response of the motor. The difference between the target hydraulic braking force and the actual hydraulic braking force is used as the feedforward variable of the regenerative braking. The error generated during the regenerative braking process is used as the feedback variable. The feedforward feedback fuzzy PID controller is designed to control the regenerative braking response process. On the basis of ensuring that the total braking torque output of the wheel meets the braking requirements, the braking smoothness of the vehicle during the braking mode switching process is optimized. The algorithm-related expression is: U(t)=Kp*e(t)+Ki*∫e(t)d(t)+Kd*de(t) / dt, where Kp, Ki, and Kd are the adjustment parameters of proportional, integral, and differential control respectively; fuzzy control is implemented by applying the defined MATLAB built-in blank fuzzy controller module, and its input is the sum of the difference between the expected braking torque and the output braking torque of the motor and hydraulic brake system, and the rate of change of the difference, e c The output is the three adjustment parameters of PID. The difference e and the rate of change of the difference e c The fuzzy value of is divided into seven segments in ascending order, with the domain being [-5,5]. The fuzzy values ​​of the three adjustment parameters are divided into five segments, with the domain being [-1,1]. Based on experience, fuzzy control rule tables for kp, ki, and kd are established respectively. The use of the above methods can achieve real-time adjustment of the braking output according to the real-time status of the vehicle: use robust control to optimize the output speed and accuracy of the hydraulic braking system, reduce output delay, and improve the collaborative braking effect. Use the fuzzy PID algorithm to weaken the response differences of different braking output sources. Dynamically adjust the braking scheme according to the real-time vehicle braking status to ensure that the braking output and braking demand are accurately matched in real time. Establish multi-level feedback to ensure a smooth transition during the braking mode switching process.

[0032] In summary, the present invention first uses sensors to collect real-time information on the vehicle's driving status. Sensors can be arranged in multiple locations. During the collection, multiple sensor data of the same parameter are compared to ensure that the input information is true and valid, improve the data transmission speed of each sensor, and ensure that the decision-making analysis module can receive the data information of each sensor in real time during the vehicle braking process, so that the analysis and decision-making module can dynamically adjust the corresponding smoothness plan according to the vehicle state in time, analyze, calculate, and make decisions on the collected multiple demand data information, and formulate corresponding smoothness plans. The state analysis is based on the vehicle's real-time speed, wheel speed, SOC and other parameters. The plan decision is based on the real-time braking demand and the real-time state of the vehicle calculated based on the brake pedal opening and the brake pedal opening change rate data. The decision is integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustment is performed based on the real-time data during the braking process. Dynamic adjustment, use robust control to optimize the output speed and accuracy of the hydraulic braking system, use fuzzy PID algorithm to weaken the response differences of different braking output sources, and dynamically adjust the braking scheme according to the real-time vehicle braking state. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms, which can adjust the output signal according to the current state and the target state to stabilize the system and achieve the desired effect. In this case, the vehicle is regarded as a system, the required braking force is set to the desired effect, and the robust control algorithm is used to adjust the hydraulic braking output to improve the followability of the hydraulic braking output. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake to weaken the response differences of different braking output sources. The braking scheme is dynamically adjusted according to the real-time vehicle braking state to ensure that the braking output and the braking demand are accurately matched in real time, and the braking smoothness when the compound braking mode is switched is improved.

[0033] Example 2

[0034] The present invention also provides a smoothness control system for switching braking modes of an electric vehicle, comprising:

[0035] Information collection module: First, the real-time information of the vehicle's driving status is collected through sensors, including real-time vehicle speed, wheel speed, SOC, brake pedal opening and brake pedal opening change rate;

[0036] Analysis and decision-making module: Analyze, calculate and make decisions on the collected data, and formulate corresponding ride comfort solutions. The state analysis is based on the vehicle's real-time speed, wheel speed, and SOC parameters. The solution decision is based on the real-time braking demand and the vehicle's real-time braking state calculated based on the brake pedal opening and brake pedal opening change rate data;

[0037] Algorithm control module: Use robust control to optimize the output speed and accuracy of the hydraulic braking system, use fuzzy PID algorithm to weaken the response differences of different braking output sources, and dynamically adjust the braking scheme according to the real-time vehicle braking status. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms. The output signal is adjusted according to the current state and the target state to stabilize the system and achieve the desired effect. The vehicle is regarded as a system, the required braking force is set to the desired effect, and the robust control algorithm is used to adjust the hydraulic braking output to improve the hydraulic braking output followability. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake, thereby optimizing the braking smoothness during the vehicle's braking mode switching process, and adjusting the braking output in real time according to the vehicle's real-time status.

[0038] In the analysis and decision-making module, the state analysis divides the vehicle into low-speed, medium-speed or high-speed driving states based on the vehicle's real-time speed, judges the braking demand based on the pedal input, obtains the real-time output capacity of the motor based on the real-time wheel speed combined with the motor characteristic curve, provides reference limits for the electric / hydraulic braking force distribution, determines the real-time state of the battery based on the real-time SOC value of the battery, and determines whether to charge and at what power in combination with the preset rules for protecting the battery, providing a basis for judging whether to switch the braking mode when the braking demand remains unchanged.

[0039] In the analysis and decision-making module, the analysis and decision-making module integrates and calculates multiple sensor data, identifies the real-time status of the vehicle and derives the real-time braking requirements, and formulates a smooth braking plan based on the vehicle status. Specifically: first, the vehicle braking status is determined based on the vehicle speed, wheel speed, and real-time SOC sensor data. Secondly, it is determined whether the braking mode needs to be switched based on the collected pedal sensor data. Finally, the above decision results are integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustments are made based on real-time data during the braking process.

[0040] In the algorithm control module, robust control is used to adjust the hydraulic brake output. The relevant functions and processes are as follows: Sensitivity function S(s) = (1 + G(s) K(s)) -1 , open-loop transfer function R(s) = (1 + G(s)K(s)) -1 K(s), complementary sensitivity function T(s) = (1+G(s)K(s)) -1 K(s)G(s), introduce the sensitivity weighting function W S and complementary sensitivity weighting function W T , in order to take into account both anti-interference and stability, the constraint weighting function W is introduced R , limit the control signal input to avoid actuator saturation, and use MATLAB to solve the problem of satisfying ||W S S||≤1, ||W R R||≤1,||W TA stable controller with T||≤1 is then applied to the controller to improve the followability of the hydraulic brake output, where K(s) is the controller and G(s) is the controlled object.

[0041] In the algorithm control module, the fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake. Specifically, the hysteresis of the hydraulic brake is compensated by the motor's rapid response through motor compensation. The difference between the target hydraulic braking force and the actual hydraulic braking force is used as the feedforward variable of the regenerative braking. The error generated during the regenerative braking process is used as the feedback variable. A feedforward feedback fuzzy PID controller is designed to control the regenerative braking response process. On the basis of ensuring that the total braking torque output of the wheel meets the braking requirements, the braking smoothness during the vehicle braking mode switching process is optimized. The algorithm-related expression is: U(t) = Kp*e(t)+Ki*∫e(t)d(t)+Kd*de(t) / dt, where Kp, Ki, and Kd are the adjustment parameters of proportional, integral, and differential control, respectively. U(t) is the controller output, and e(t) is the error signal. The fuzzy control is implemented by applying the defined MATLAB built-in blank fuzzy controller module, and its input is the sum of the difference between the expected braking torque and the output braking torque of the motor and hydraulic braking system, and the rate of change of the difference, e c The output is the three adjustment parameters of PID, the sum of the difference e and the rate of change of the difference e c The fuzzy values ​​of are divided into seven sections in ascending order, and the domain is [-5,5]. The fuzzy values ​​of the three adjustment parameters are divided into five sections, and the domain is [-1,1]. Based on experience, the fuzzy control rule tables of Kp, Ki, and Kd are established respectively.

[0042] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0043] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for controlling the smoothness of braking mode switching of an electric vehicle, characterized in that: include: Information collection steps: First, collect the real-time information of the vehicle's driving status through sensors, including real-time vehicle speed, wheel speed, SOC, brake pedal opening and brake pedal opening change rate; Analysis and decision-making steps: Analyze, calculate and make decisions on the collected data, and formulate corresponding ride comfort solutions. The state analysis is based on the vehicle's real-time speed, wheel speed, and SOC parameters. The solution decision is based on the real-time braking demand and the vehicle's real-time braking state calculated based on the brake pedal opening and brake pedal opening change rate data; Algorithm control steps: Use robust control to optimize the output speed and accuracy of the hydraulic braking system, use fuzzy PID algorithm to weaken the response differences of different braking output sources, and dynamically adjust the braking scheme according to the real-time vehicle braking status. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms. The output signal is adjusted according to the current state and the target state to stabilize the system and achieve the desired effect. The vehicle is regarded as a system, the required braking force is set to the desired effect, and the robust control algorithm is used to adjust the hydraulic braking output to improve the hydraulic braking output followability. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake, thereby optimizing the braking smoothness during the vehicle's braking mode switching process, and adjusting the braking output in real time according to the vehicle's real-time status.

2. The method for controlling the smoothness of switching the braking mode of an electric vehicle according to claim 1, characterized in that: In the analysis and decision-making step, the state analysis divides the vehicle into low-speed, medium-speed or high-speed driving states based on the vehicle's real-time speed, determines the braking demand based on the pedal input, obtains the real-time output capacity of the motor based on the real-time wheel speed combined with the motor characteristic curve, provides reference limits for the electric / hydraulic braking force distribution, determines the real-time state of the battery based on the real-time SOC value of the battery, and determines whether to charge and at what power in combination with the preset rules for protecting the battery, providing a basis for judging whether to switch the braking mode when the braking demand remains unchanged.

3. The method for controlling the smoothness of switching the braking mode of an electric vehicle according to claim 1, characterized in that: In the analysis and decision-making step, the analysis and decision-making module integrates and calculates multiple sensor data, identifies the real-time status of the vehicle and derives the real-time braking demand, and formulates a smooth braking plan based on the vehicle status. Specifically, first, the vehicle braking status is determined based on the vehicle speed, wheel speed, and real-time SOC sensor data. Secondly, it determines whether the braking mode needs to be switched based on the collected pedal sensor data. Finally, the above decision results are integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustments are made during the braking process based on real-time data.

4. The method for controlling the smoothness of switching the braking mode of an electric vehicle according to claim 1, characterized in that: In the algorithm control step, robust control is used to adjust the hydraulic brake output, and its related functions and processes are: sensitivity function S(s) = (1 + G(s) K(s)) -1 , open-loop transfer function R(s) = (1 + G(s)K(s)) -1 K(s), complementary sensitivity function T(s) = (1+G(s)K(s)) -1 K(s)G(s), introduce the sensitivity weighting function W S and complementary sensitivity weighting function W T , in order to take into account both anti-interference and stability, the constraint weighting function W is introduced R , limit the control signal input to avoid actuator saturation, and use MATLAB to solve the problem of satisfying ||W S S||≤1,||W R R||≤1,||W T A stable controller with T||≤1 is then applied to the controller to improve the followability of the hydraulic brake output, where K(s) is the controller and G(s) is the controlled object.

5. The method for controlling the smoothness of switching the braking mode of an electric vehicle according to claim 1, characterized in that: In the algorithm control step, the fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake. Specifically, the hysteresis of the hydraulic brake is compensated by the motor's rapid response through motor compensation. The difference between the target hydraulic braking force and the actual hydraulic braking force is used as the feedforward variable of the regenerative braking. The error generated during the regenerative braking process is used as the feedback variable. A feedforward feedback fuzzy PID controller is designed to control the regenerative braking response process. On the basis of ensuring that the total braking torque output of the wheel meets the braking requirements, the braking smoothness during the vehicle braking mode switching process is optimized. The algorithm-related expression is: U(t) = Kp*e(t)+Ki*∫e(t)d(t)+Kd*de(t) / dt, where Kp, Ki, and Kd are the adjustment parameters of proportional, integral, and differential control, respectively. U(t) is the controller output, and e(t) is the error signal. The fuzzy control is implemented by applying the defined MATLAB built-in blank fuzzy controller module, and its input is the sum of the difference between the expected braking torque and the output braking torque of the motor and hydraulic braking system, and the rate of change of the difference, e c The output is the three adjustment parameters of PID, the sum of the difference e and the rate of change of the difference e c The fuzzy values ​​of are divided into seven sections in ascending order, and the domain is [-5,5]. The fuzzy values ​​of the three adjustment parameters are divided into five sections, and the domain is [-1,1]. Based on experience, the fuzzy control rule tables of Kp, Ki, and Kd are established respectively.

6. A smoothness control system for switching braking modes of an electric vehicle, characterized in that: include: Information collection module: First, the real-time information of the vehicle's driving status is collected through sensors, including real-time vehicle speed, wheel speed, SOC, brake pedal opening and brake pedal opening change rate; Analysis and decision-making module: Analyze, calculate and make decisions on the collected data, and formulate corresponding ride comfort solutions. The state analysis is based on the vehicle's real-time speed, wheel speed, and SOC parameters. The solution decision is based on the real-time braking demand and the vehicle's real-time braking state calculated based on the brake pedal opening and brake pedal opening change rate data; Algorithm control module: Use robust control to optimize the output speed and accuracy of the hydraulic braking system, use fuzzy PID algorithm to weaken the response differences of different braking output sources, and dynamically adjust the braking scheme according to the real-time vehicle braking status. Both the robust control algorithm and the fuzzy PID algorithm are control algorithms. The output signal is adjusted according to the current state and the target state to stabilize the system and achieve the desired effect. The vehicle is regarded as a system, the required braking force is set to the desired effect, and the robust control algorithm is used to adjust the hydraulic braking output to improve the hydraulic braking output followability. The fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake, thereby optimizing the braking smoothness during the vehicle's braking mode switching process, and adjusting the braking output in real time according to the vehicle's real-time status.

7. The electric vehicle braking mode switching smoothness control system according to claim 6, characterized in that: In the analysis and decision-making module, the state analysis divides the vehicle into low-speed, medium-speed or high-speed driving states based on the vehicle's real-time speed, judges the braking demand based on the pedal input, obtains the real-time output capacity of the motor based on the real-time wheel speed combined with the motor characteristic curve, provides reference limits for the electric / hydraulic braking force distribution, determines the real-time state of the battery based on the real-time SOC value of the battery, and determines whether to charge and at what power in combination with the preset rules for protecting the battery, providing a basis for judging whether to switch the braking mode when the braking demand remains unchanged.

8. The electric vehicle braking mode switching smoothness control system according to claim 6, characterized in that: In the analysis and decision-making module, the analysis and decision-making module integrates and calculates multiple sensor data, identifies the real-time status of the vehicle and derives the real-time braking requirements, and formulates a smooth braking plan based on the vehicle status. Specifically: first, the vehicle braking status is determined based on the vehicle speed, wheel speed, and real-time SOC sensor data. Secondly, it is determined whether the braking mode needs to be switched based on the collected pedal sensor data. Finally, the above decision results are integrated to formulate a corresponding smoothness plan, and real-time dynamic adjustments are made based on real-time data during the braking process.

9. The electric vehicle braking mode switching smoothness control system according to claim 6, characterized in that: In the algorithm control module, robust control is used to adjust the hydraulic brake output. The relevant functions and processes are as follows: Sensitivity function S(s) = (1 + G(s) K(s)) -1 , open-loop transfer function R(s) = (1 + G(s)K(s)) -1 K(s), complementary sensitivity function T(s) = (1+G(s)K(s)) -1 K(s)G(s), introduce the sensitivity weighting function W S and complementary sensitivity weighting function W T , in order to take into account both anti-interference and stability, the constraint weighting function W is introduced R , limit the control signal input to avoid actuator saturation, and use MATLAB to solve the problem of satisfying ||W S S||≤1,||W R R||≤1,||W T A stable controller with T||≤1 is then applied to the controller to improve the followability of the hydraulic brake output, where K(s) is the controller and G(s) is the controlled object.

10. The electric vehicle braking mode switching smoothness control system according to claim 6, characterized in that: In the algorithm control module, the fuzzy PID algorithm is used to adjust the output of the vehicle's motor brake and hydraulic brake. Specifically, the hysteresis of the hydraulic brake is compensated by the motor's rapid response through motor compensation. The difference between the target hydraulic braking force and the actual hydraulic braking force is used as the feedforward variable of the regenerative braking. The error generated during the regenerative braking process is used as the feedback variable. A feedforward feedback fuzzy PID controller is designed to control the regenerative braking response process. On the basis of ensuring that the total braking torque output of the wheel meets the braking requirements, the braking smoothness during the vehicle braking mode switching process is optimized. The algorithm-related expression is: U(t) = Kp*e(t)+Ki*∫e(t)d(t)+Kd*de(t) / dt, where Kp, Ki, and Kd are the adjustment parameters of proportional, integral, and differential control, respectively. U(t) is the controller output, and e(t) is the error signal. The fuzzy control is implemented by applying the defined MATLAB built-in blank fuzzy controller module, and its input is the sum of the difference between the expected braking torque and the output braking torque of the motor and hydraulic braking system, and the rate of change of the difference, e c The output is the three adjustment parameters of PID, the sum of the difference e and the rate of change of the difference e c The fuzzy values ​​of are divided into seven sections in ascending order, and the domain is [-5,5]. The fuzzy values ​​of the three adjustment parameters are divided into five sections, and the domain is [-1,1]. Based on experience, the fuzzy control rule tables of Kp, Ki, and Kd are established respectively.

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