Braking energy recovery method based on fuzzy control rule

Abstract

The invention relates to a fuzzy control rule-based brake energy recovery method, which comprises the following steps of: acquiring electric vehicle quality information and brake pedal opening information, and taking the electric vehicle quality information and the brake pedal opening information as input variables of a first-layer fuzzy controller, the target braking force serves as an output variable of a first-layer fuzzy controller; the braking strength is determined according to the target braking force; formulating a front and rear axle braking force distribution method according to the braking strength; according to a front and rear shaft braking force distribution method, the proportion of motor braking in a driving shaft is determined; the whole vehicle SOC information and the vehicle speed information are obtained, the whole vehicle SOC information, the vehicle speed information and the braking strength serve as input variables of a second-layer fuzzy controller, and the proportion of motor braking in a driving shaft serves as an output variable of the second-layer fuzzy controller; and the output torque of the motor is adjusted according to the proportion of motor braking in the driving shaft, and braking energy recovery is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of electric vehicles, and in particular to a braking energy recovery method based on fuzzy control rules. Background Art

[0002] The braking energy recovery system of electric vehicles can convert part of the mechanical energy generated by the motor into electrical energy and store it in the battery, effectively increasing the vehicle's range. Existing braking energy recovery methods include a fixed ratio method and a neural network-based braking force distribution method. The former distributes the total required braking force to the front and rear axles at a fixed ratio. This method is simple and easy to implement, but it fails to fully consider the influence of other factors, resulting in poor driver experience and low braking energy recovery efficiency. The latter outputs the braking force distribution coefficient by constructing a network model. This method can combine the influence of factors such as deceleration and speed on braking energy recovery, but due to the poor interpretability of neural networks, the large number of internal control parameters, and the high degree of dependence on data sets, it is easy to lead to poor expected results.

[0003] In summary, it is necessary to propose a new energy vehicle braking energy recovery method that is simple to calculate and can improve user experience. Summary of the invention

[0004] The purpose of the present invention is to provide a braking energy recovery method based on fuzzy control rules in view of the deficiencies in the prior art.

[0005] The present invention is achieved by adopting the following technical solutions: A braking energy recovery method based on fuzzy control rules comprises the following steps: Obtaining electric vehicle mass information and brake pedal opening information, and using the electric vehicle mass information and brake pedal opening information as input variables of the first-layer fuzzy controller, and using the target braking force as the output variable of the first-layer fuzzy controller; Determine the braking intensity according to the target braking force; Formulate a method for distributing braking force on the front and rear axles based on braking intensity; Determine the proportion of motor braking in the drive shaft according to the front and rear axle braking force distribution method; The vehicle SOC information and vehicle speed information are obtained, and the vehicle SOC information, vehicle speed information and braking intensity are used as input variables of the second-layer fuzzy controller, and the proportion of motor braking in the drive shaft is used as the output variable of the second-layer fuzzy controller; The motor output torque is adjusted according to the proportion of motor braking in the drive shaft to achieve braking energy recovery.

[0006] As a further illustration of the invention, according to the first layer fuzzy controller, the input variables and the output variables are fuzzified, the fuzzy inference rules are determined, a rule base is formed, and the output variables are defuzzified; Among them, the quality information of electric vehicles includes three situations: no-load, half-load and full-load; the membership function is used to divide the quality information of electric vehicles into fuzzy subsets, which are {E, H, F}; the brake pedal opening information includes three situations: small, moderate and large; the membership function is used to divide the brake pedal opening information into fuzzy subsets, which are {S1, M1, L1}; the target braking force includes three situations: small, moderate and large, and the membership function is used to divide the target braking force into fuzzy subsets, which are {S2, M2, L2}; thus nine rules are constructed.

[0007] As a further explanation of the invention, the braking intensity is determined according to the target braking force. The braking intensity includes three situations: small, moderate and large. The calculation formula is: ; Among them, z represents the braking intensity, F 1 represents the target braking force, m represents the mass of the electric vehicle, and g represents the acceleration due to gravity.

[0008] As a further illustration of the invention, a method for distributing the braking force between the front and rear axles is formulated according to the braking intensity; the steps include: In the ideal braking force curve, considering the ECE curve and the I curve, the front axle braking force F f As the horizontal axis, the rear axle braking force F r As the vertical axis, the braking intensity is divided into 0≤z≤z A , z A <z<0.7, z≥0.7, where point O represents the zero point, and point A represents the intersection of the ECE curve and the horizontal axis, corresponding to the front axle braking force F A , corresponding to the braking intensity z A ; When 0≤z≤z A The vehicle braking intensity is small, and the front axle braking force F f Size is based on OA line, rear axle braking force F r Zero; front axle electric motor force F fm =min(F 1 , F max ), F 1 Indicates the target braking force, F max Indicates the maximum braking force that the motor can provide, the front axle friction braking force F ff =F 1 -F fm ; When z A<z<0.7, the vehicle braking intensity is moderate, divided into: When F fz ≥F max When the front axle braking force F f Size based on I curve, front axle electric motor force F fm =F max , front axle friction braking force F ff =F fz -F fm , rear axle braking force F r =F 1 -F fz ; F fz Indicates the front axle braking force F corresponding to the system intensity z in the I curve f ; When F fz <F max When the front axle electric motor force F fm =t×F fz , front axle friction braking force F ff =(1-t)×F fz , rear axle braking force F r =F 1 -F fz , t represents the proportion of motor braking in front axle braking; When z ≥ 0.7, the vehicle braking intensity is large, and the front axle braking force F f The size is based on the I curve, and the motor does not participate in the distribution; the front axle motor force F fm =0, front axle friction braking force F ff =F fz , F r =F rz , F rz Indicates the rear axle braking force corresponding to the braking intensity z in the I curve.

[0009] As a further illustration of the invention, the front axle braking force F A The calculation formula is: ; Among them, b represents the horizontal distance between the center of mass of the car and the center line of the rear axle, h represents the height of the center of mass, m represents the mass, g represents the acceleration of gravity, and L represents the wheelbase.

[0010] As a further illustration of the invention, when z A <z<0.7 and when F fz <F max hour; The vehicle SOC information and vehicle speed information are obtained, and the vehicle SOC information, vehicle speed information and braking intensity are used as input variables of the second-layer fuzzy controller, and the proportion of motor braking in the drive shaft is used as the output variable of the second-layer fuzzy controller.

[0011] As a further illustration of the invention, according to the second-layer fuzzy controller, the input variables and the output variables are fuzzified, the fuzzy inference rules are determined, a rule base is formed, and the output variables are defuzzified; Among them, the SOC information of the whole vehicle includes three cases: small, moderate and large; the membership function is used to divide the SOC information of the whole vehicle into fuzzy subsets, which are {S3, M3, L3}; the vehicle speed information includes three cases: small, moderate and large; the membership function is used to divide the vehicle speed information into fuzzy subsets, which are {S4, M4, L4}; the braking intensity includes three cases: small, moderate and large; the membership function is used to divide the braking intensity into fuzzy subsets, {S5, M5, L5}; the proportion of motor braking in the drive shaft includes five cases: extremely small, small, moderate, large and extremely large; the membership function is used to divide the proportion of motor braking in the drive shaft into fuzzy subsets, which are {VS, S6, M6, L6, VL}; thus thirty-six rules are constructed.

[0012] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention uses a double-layer fuzzy controller to comprehensively consider the influence of electric vehicle mass, brake pedal opening, vehicle speed and vehicle SOC on braking energy recovery, reduces calculation complexity, and achieves a better braking energy recovery effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of the present invention; Figure 2 It is the front and rear axle braking force distribution curve diagram of the present invention. DETAILED DESCRIPTION

[0014] like Figure 1-2 As shown, a braking energy recovery method based on fuzzy control rules includes the following steps: Obtaining electric vehicle mass information and brake pedal opening information, and using the electric vehicle mass information and brake pedal opening information as input variables of the first-layer fuzzy controller, and using the target braking force as the output variable of the first-layer fuzzy controller; Determine the braking intensity according to the target braking force; Formulate a method for distributing braking force on the front and rear axles based on braking intensity; Determine the proportion of motor braking in the drive shaft according to the front and rear axle braking force distribution method; The vehicle SOC information and vehicle speed information are obtained, and the vehicle SOC information, vehicle speed information and braking intensity are used as input variables of the second-layer fuzzy controller, and the proportion of motor braking in the drive shaft is used as the output variable of the second-layer fuzzy controller; The motor output torque is adjusted according to the proportion of motor braking in the drive shaft to achieve braking energy recovery.

[0015] According to the first-layer fuzzy controller, the input variables and output variables are fuzzified, the fuzzy reasoning rules are determined, a rule base is formed, and the output variables are defuzzified; Among them, the quality information of electric vehicles includes three situations: no-load, half-load and full-load; the membership function (there are many types of membership functions, preferably normal distribution functions) is used to divide the quality information of electric vehicles into fuzzy subsets, that is, the domain of electric vehicle quality is defined as [0, 12], and the corresponding fuzzy subsets are {E, H, F}; the brake pedal opening information includes three situations: small, moderate and large; the membership function is used to divide the brake pedal opening information into fuzzy subsets, that is, the domain of the brake pedal opening is defined as [0, 1] (that is, 0-100%), and the corresponding fuzzy subsets are {Small, Medium, Large}, simply expressed as {S1, M1, L1}; the target braking force includes three situations: small, moderate and large, and the membership function is used to divide the target braking force into fuzzy subsets, that is, the domain of the target braking force is defined as [0, 120], and the corresponding fuzzy subsets are {S2, M2, L2}; thus, nine rules are constructed, see the first-layer fuzzy controller rule table: The first layer fuzzy controller rule table ; The braking intensity is determined according to the target braking force. The braking intensity includes small, moderate and large conditions. The calculation formula is: ; Among them, z represents the braking intensity, F 1 represents the target braking force, m represents the mass of the electric vehicle, and g represents the acceleration due to gravity.

[0016] Formulate a method for distributing the braking force of the front and rear axles according to the braking intensity; the steps include: In the ideal braking force curve, considering the ECE curve and the I curve, the front axle braking force F f As the horizontal axis, the rear axle braking force F r As the vertical axis, the braking intensity is divided into 0≤z≤z A , z A <z<0.7, z≥0.7, where point O represents the zero point, and point A represents the intersection of the ECE curve and the horizontal axis, corresponding to the front axle braking force F A, corresponding to the braking intensity z A ; Take the example of an electric car with front-wheel drive, where the front axle serves as the drive shaft; When 0≤z≤z A The vehicle braking intensity is small (i.e. the vehicle is in a light braking state), and the front axle braking force F f Size is based on OA line, rear axle braking force F r Zero; front axle electric motor force F fm =min(F 1 , F max ), F 1 Indicates the target braking force, F max Indicates the maximum braking force that the motor can provide, the front axle friction braking force F ff =F 1 -F fm ; When z A <z<0.7, the vehicle braking intensity is moderate, divided into: When F fz ≥F max When the front axle braking force F f Size based on I curve, front axle electric motor force F fm =F max , front axle friction braking force F ff =F fz -F fm , rear axle braking force F r =F 1 -F fz ; F fz Indicates the front axle braking force F corresponding to the system intensity z in the I curve f ; When F fz <F max When the front axle electric motor force F r =F 1 -F fz , front axle friction braking force F ff =(1-t)·F fz , rear axle braking force F r = F 1 -F fz , t represents the proportion of motor braking in front axle braking; When z ≥ 0.7, the vehicle braking intensity is large (i.e. the vehicle is in emergency braking state), the front axle braking force F f The size is based on the I curve, and the motor does not participate in the distribution; the front axle motor force F fm =0, front axle friction braking force F ff =F fz , F r =Frz , F rz Indicates the rear axle braking force corresponding to the braking intensity z in the I curve.

[0017] Front axle braking force F A The calculation formula is: ; Among them, b represents the horizontal distance between the center of mass of the car and the center line of the rear axle, h represents the height of the center of mass, m represents the mass, g represents the acceleration of gravity, and L represents the wheelbase.

[0018] When z A <z<0.7 and when F fz <F max hour, The vehicle SOC information and vehicle speed information are obtained, and the vehicle SOC information, vehicle speed information and braking intensity are used as input variables of the second-layer fuzzy controller, and the proportion of motor braking in the drive shaft is used as the output variable of the second-layer fuzzy controller.

[0019] According to the second-layer fuzzy controller, the input variables and output variables are fuzzified, the fuzzy reasoning rules are determined, a rule base is formed, and the output variables are defuzzified; Among them, the vehicle SOC information includes three cases: small, moderate and large; the vehicle SOC is divided into fuzzy subsets by using the membership function, that is, the domain of the vehicle SOC information is defined as [10, 95], and the fuzzy subset is expressed as {S3, M3, L3}; the vehicle speed information includes three cases: small, moderate and large; the vehicle speed information is divided into fuzzy subsets by using the membership function, that is, the domain of the vehicle speed is defined as [0, 100], and the fuzzy subset is expressed as {S4, M4, L4}; the braking intensity ... vehicle speed information is divided into fuzzy subsets by using the membership function, that is, the domain of the vehicle speed is defined as [0, 100], and the fuzzy subset is expressed as {S4, M4, L4}; the vehicle speed information is divided into fuzzy subsets by using the membership function, that is, the domain of the vehicle speed is defined as [0, 100], and the fuzzy subset is expressed as {S4, M4, L4}; the vehicle speed information is divided into fuzzy The braking intensity is divided into fuzzy subsets, that is, the domain of braking intensity is defined as [0, 0.7], and the fuzzy subset is expressed as {S5, M5, L5}; the proportion of motor braking in the drive shaft includes five cases: extremely small, small, moderate, large, and extremely large. The membership function is used to divide the proportion of motor braking in the drive shaft into fuzzy subsets, and the domain of motor braking proportion is defined as [0, 1], and the fuzzy subset is expressed as {VS, S6, M6, L6, VL}; thus, thirty-six rules are constructed, see the second-layer fuzzy controller rule table: The second layer fuzzy controller rule table ; In the above, the computational complexity is effectively reduced through the double-layer fuzzy controller.

Claims

1. A braking energy recovery method based on fuzzy control rules, characterized in that: The following steps are involved: Obtaining electric vehicle mass information and brake pedal opening information, and using the electric vehicle mass information and brake pedal opening information as input variables of the first-layer fuzzy controller, and using the target braking force as the output variable of the first-layer fuzzy controller; Determine the braking intensity according to the target braking force; Formulate a method for distributing braking force on the front and rear axles based on braking intensity; Determine the proportion of motor braking in the drive shaft according to the front and rear axle braking force distribution method; The vehicle SOC information and vehicle speed information are obtained, and the vehicle SOC information, vehicle speed information and braking intensity are used as input variables of the second-layer fuzzy controller, and the proportion of motor braking in the drive shaft is used as the output variable of the second-layer fuzzy controller; The motor output torque is adjusted according to the proportion of motor braking in the drive shaft to achieve braking energy recovery.

2. The braking energy recovery method based on fuzzy control rules according to claim 1, characterized in that: According to the first-layer fuzzy controller, the input variables and output variables are fuzzified, the fuzzy reasoning rules are determined, a rule base is formed, and the output variables are defuzzified; Among them, the quality information of electric vehicles includes three situations: no-load, half-load and full-load; the membership function is used to divide the quality information of electric vehicles into fuzzy subsets, which are {E, H, F}; the brake pedal opening information includes three situations: small, moderate and large; the membership function is used to divide the brake pedal opening information into fuzzy subsets, which are {S1, M1, L1}; the target braking force includes three situations: small, moderate and large, and the membership function is used to divide the target braking force into fuzzy subsets, which are {S2, M2, L2}; thus nine rules are constructed.

3. The braking energy recovery method based on fuzzy control rules as claimed in claim 2, characterized in that: The braking intensity is determined according to the target braking force. The braking intensity includes small, moderate and large conditions. The calculation formula is: ； Among them, z represents the braking intensity, F1 represents the target braking force, m represents the mass of the electric vehicle, and g represents the acceleration of gravity.

4. The braking energy recovery method based on fuzzy control rules as claimed in claim 3, characterized in that: Formulate a method for distributing the braking force of the front and rear axles according to the braking intensity; the steps include: In the ideal braking force curve, considering the ECE curve and the I curve, the front axle braking force F f As the horizontal axis, the rear axle braking force F r As the vertical axis, the braking intensity is divided into 0≤z≤z A , z A <z<0.7, z≥0.7, where point O represents the zero point, and point A represents the intersection of the ECE curve and the horizontal axis, corresponding to the front axle braking force F A , corresponding to the braking intensity z A ; When 0≤z≤z A The vehicle braking intensity is small, and the front axle braking force F f Size is based on OA line, rear axle braking force F r Zero; front axle electric motor force F fm =min(F1,F max ), F1 represents the target braking force, F max Indicates the maximum braking force that the motor can provide, the front axle friction braking force F ff =F1-F fm ; When z A <z<0.7, the vehicle braking intensity is moderate, divided into: When F fz ≥F max When the front axle braking force F f Size based on I curve, front axle electric motor force F fm =F max , front axle friction braking force F ff =F fz -F fm , rear axle braking force F r =F1-F fz ; F fz Indicates the front axle braking force F corresponding to the system intensity z in the I curve f ; When F fz <F max When the front axle electric motor force F fm =t·F fz , front axle friction braking force F ff =(1-t)·F fz , rear axle braking force F r = F1-F fz , t represents the proportion of motor braking in front axle braking; When z ≥ 0.7, the vehicle braking intensity is large, and the front axle braking force F f The size is based on the I curve, and the motor does not participate in the distribution; the front axle motor force F fm =0, front axle friction braking force F ff =F fz , F r =F rz , F rz Indicates the rear axle braking force corresponding to the braking intensity z in the I curve.

5. The braking energy recovery method based on fuzzy control rules as claimed in claim 4, characterized in that: Front axle braking force F A The calculation formula is: ； Among them, b represents the horizontal distance between the center of mass of the car and the center line of the rear axle, h represents the height of the center of mass, m represents the mass, g represents the acceleration of gravity, and L represents the wheelbase.

6. The braking energy recovery method based on fuzzy control rules as claimed in claim 5, characterized in that: When z A <z<0.7 and when F fz <F max hour; The vehicle SOC information and vehicle speed information are obtained, and the vehicle SOC information, vehicle speed information and braking intensity are used as input variables of the second-layer fuzzy controller, and the proportion of motor braking in the drive shaft is used as the output variable of the second-layer fuzzy controller.

7. The braking energy recovery method based on fuzzy control rules as claimed in claim 6, characterized in that: According to the second-layer fuzzy controller, the input variables and output variables are fuzzified, the fuzzy reasoning rules are determined, a rule base is formed, and the output variables are defuzzified; Among them, the SOC information of the whole vehicle includes three cases: small, moderate and large; the membership function is used to divide the SOC information of the whole vehicle into fuzzy subsets, which are {S3, M3, L3}; the vehicle speed information includes three cases: small, moderate and large; the membership function is used to divide the vehicle speed information into fuzzy subsets, which are {S4, M4, L4}; the braking intensity includes three cases: small, moderate and large; the membership function is used to divide the braking intensity into fuzzy subsets, {S5, M5, L5}; the proportion of motor braking in the drive shaft includes five cases: extremely small, small, moderate, large and extremely large; the membership function is used to divide the proportion of motor braking in the drive shaft into fuzzy subsets, which are {VS, S6, M6, L6, VL}; thus thirty-six rules are constructed.

Images