Braking method and system of single-pedal electric automobile based on intention recognition

Abstract

The invention discloses a braking method and system of a single-pedal electric vehicle based on intention recognition, and relates to the technical field of electric vehicle braking control. According to the method, dual fuzzy control is designed for the target electric vehicle to determine the braking intensity, the braking intention of a driver is accurately reflected, then braking strategies under different working conditions are designed according to the braking intensity so as to meet the braking requirement of the driver, flexibility is high, and the use effect is good. When regenerative braking is adopted for the rear wheels, linear model predictive control is used for solving the longitudinal dynamic model of the whole vehicle, combined control is conducted on the acceleration, the jerk and the rear wheel slip rate, the requirement of a driver for the current dynamic property of the vehicle is met, it is guaranteed that the vehicle can achieve the ideal braking effect, and the driving safety is improved. Meanwhile, vertical vibration is restrained, the jolting feeling of a driver during riding is reduced, the riding comfort is improved, safety is considered, and the dangerous conditions such as wheel slipping or locking in the braking process are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle braking control, and more particularly to a braking method and system for a single-pedal electric vehicle based on intention recognition. Background Art

[0002] Compared to traditional fuel-powered vehicles, electric vehicles utilize a direct-drive motor system, enabling instantaneous torque output and a more linear and smooth acceleration experience. Due to the operating characteristics of the motor, electric vehicles do not require a complex gearbox structure, fundamentally eliminating shift jerk and making the vehicle's handling more responsive and direct. Furthermore, electric vehicles offer far greater energy conversion efficiency than fuel-powered vehicles, and their significantly simplified mechanical structure results in lower failure rates and longer maintenance cycles, significantly improving their economical and reliable operation.

[0003] It is precisely based on the above-mentioned unique design of electric vehicles that the single-pedal driving mode came into being. This driving mode makes full use of the electric vehicle's electronic control system and energy recovery technology. Through precise control of the accelerator pedal, the driver can use one pedal to complete the vehicle's starting, acceleration, deceleration and even stopping operations; when the accelerator pedal is released, the vehicle will automatically recover energy and produce a braking effect, which not only simplifies driving operations, but also effectively improves the vehicle's energy utilization efficiency and extends the cruising range.

[0004] However, the single-pedal mode currently on the market is not truly "single-pedal". For safety reasons, the vehicle still retains an independent brake pedal to deal with emergency braking situations. The existing single-pedal mode mainly integrates the acceleration and regular deceleration functions on the accelerator pedal, but there is still room for improvement in driver operation intention recognition and control logic optimization. More importantly, this driving mode conflicts with the long-established driving habits of many drivers. In traditional driving, releasing the accelerator often means that the vehicle will immediately start to glide or slightly decelerate, while the obvious braking effect in the single-pedal mode may make the driver feel uncomfortable and even create safety hazards in some cases.

[0005] Today, we are faced with a certain type of electric vehicle equipped with a single pedal. The overall structure of this electric vehicle can be considered a bilaterally symmetrical design. The two rear wheels on the rear axle are controlled uniformly and support regenerative braking (achieved by the motor equipped on the rear axle) and mechanical braking (achieved by the brake equipped on the rear axle, which is generally based on a hydraulic design). The two front wheels on the front axle are controlled uniformly and support only mechanical braking (achieved by the brake equipped on the front axle, which is generally based on a hydraulic design). Therefore, how to ensure the safety, stability, and comfort of this electric vehicle while leveraging the advantages of the single-pedal mode has become a pressing issue. Summary of the Invention

[0006] Based on this, it is necessary to provide a braking method and system for a single-pedal electric vehicle based on intention recognition to address the above two deficiencies in the prior art.

[0007] The present invention is achieved by adopting the following technical solutions:

[0008] In a first aspect, the present invention discloses a braking method for a single-pedal electric vehicle based on intention recognition, comprising:

[0009] Get the pedal opening θ[k] and pedal opening change rate of the target electric vehicle at the current time k Vehicle speed v[k];

[0010] Based on the braking intention fuzzy rule, according to θ[k] and Get the braking intention BI[k] corresponding to k;

[0011] Based on the braking intensity fuzzy rule, the braking intensity z[k] corresponding to k is obtained according to BI[k] and v[k];

[0012] Select the corresponding braking strategy according to z[k] to perform braking;

[0013] If z[k] falls within the light braking range, the rear wheels use regenerative braking and the front wheels do not participate in braking; if z[k] falls within the medium-intensity braking range or the high-intensity braking range, the front wheels use mechanical braking and the rear wheels use regenerative braking; if z[k] falls within the emergency braking range, both the front and rear wheels use mechanical braking;

[0014] When regenerative braking is used on the rear wheels, the longitudinal dynamics model of the target electric vehicle is first solved by linear model predictive control according to the acceleration a[k], jerk κ[k], and rear wheel slip rate λ[k] corresponding to k to obtain the optimal rear axle braking torque change Δu*[k] corresponding to k, and then Δu*[k] is compared with the rear axle braking torque T at the previous moment k-1. m [k-1] is added to obtain the rear axle braking torque T corresponding to k m [k].

[0015] This single-pedal electric vehicle braking method based on intention recognition implements the method or process according to an embodiment of the present disclosure.

[0016] In a second aspect, the present invention discloses a braking system for a single-pedal electric vehicle based on intention recognition, which uses the braking method for a single-pedal electric vehicle based on intention recognition disclosed in the first aspect.

[0017] The braking system of a single-pedal electric vehicle based on intention recognition includes: a vehicle state acquisition module, a braking intention fuzzy control module, a braking intensity fuzzy control module, and a braking strategy selection and application module.

[0018] The vehicle state acquisition module is used to obtain the pedal opening θ[k] and the pedal opening change rate at the current moment k The braking intention fuzzy control module is used to calculate the braking intention fuzzy rules according to θ[k] and The braking intention BI[k] corresponding to k is obtained. The braking intensity fuzzy control module is used to obtain the braking intensity z[k] corresponding to k based on the braking intensity fuzzy rules according to BI[k] and v[k]. The braking strategy selection and application module is used to select the corresponding braking strategy for braking based on z[k].

[0019] The intention recognition-based braking system for a single-pedal electric vehicle implements a method or process according to an embodiment of the present disclosure.

[0020] In a third aspect, the present invention discloses a computer program product, comprising a computer program. When the computer program is executed by a processor, the computer program implements the steps of the single-pedal electric vehicle braking method based on intention recognition disclosed in the first aspect.

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

[0022] 1. The present invention first designs a dual fuzzy control for the target electric vehicle to determine the braking intensity and accurately reflect the driver's braking intention. Then, according to the braking intensity, the braking strategy under different working conditions is designed to meet the driver's braking needs. It has high flexibility and good use effect.

[0023] 2. The present invention provides braking strategies under different disclosures. During light braking, braking is performed by relying solely on rear wheel regenerative braking; during medium or high intensity braking, braking is performed by relying on a combination of front wheel mechanical braking and rear wheel regenerative braking; during emergency braking, braking is performed by relying on a combination of front wheel mechanical braking and rear wheel mechanical braking. This avoids the situation where the braking torque provided by the motor is insufficient when the braking intensity is high, thereby improving the overall braking efficiency of the vehicle while ensuring the energy recovery efficiency is as high as possible.

[0024] 3. When regenerative braking is applied to the rear wheels, the present invention uses linear model predictive control to solve the longitudinal dynamics model of the entire vehicle and jointly controls acceleration, jerk, and rear wheel slip rate. This not only meets the driver's current dynamic requirements for the vehicle and ensures that the vehicle can achieve the ideal braking effect, but also suppresses vertical vibration, reduces the driver's riding bumps, improves ride comfort, and takes safety into account, effectively preventing dangerous conditions such as wheel slippage or locking during braking. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 This is a brief flowchart of a braking method for a single-pedal electric vehicle based on intention recognition in Example 1 of the present invention;

[0027] Figure 2 for Figure 1 Corresponding data flow diagram;

[0028] Figure 3 for Figure 1 or Figure 2 Data flow diagram corresponding to regenerative braking of the middle and rear wheels;

[0029] Figure 4 The simulation results provided for Example 1 of the present invention Figure 1 ;

[0030] Figure 5 The simulation results provided for Example 1 of the present invention Figure 2 ;

[0031] Figure 6 The simulation results provided for Example 1 of the present invention Figure 3 ;

[0032] Figure 7 The simulation results provided for Example 1 of the present invention Figure 4 . DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component. When a component is considered to be "fixed to" another component, it may be directly fixed to the other component or there may be a central component.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0036] Example 1

[0037] See also Figures 1 and 2 , which are respectively a schematic diagram of the braking method of a single-pedal electric vehicle based on intention recognition disclosed in this embodiment 1 and a corresponding data flow diagram.

[0038] First of all, it should be noted that this braking method is designed for the target electric vehicle mentioned in the background technology (whose rear wheels support regenerative braking and mechanical braking, and the front wheels only support mechanical braking and are equipped with a single pedal).

[0039] like Figures 1 and 2 As shown, the braking method of a single-pedal electric vehicle based on intention recognition includes the following steps:

[0040] Step 1: Obtain the pedal opening θ[k] and pedal opening change rate of the target electric vehicle at the current time k Vehicle speed v[k].

[0041] It should be noted that:

[0042] 1. Pedal opening refers to the actual pedal position. This is captured in real time by a position sensor installed on the pedal, accurately reflecting the pedal's position after the driver brakes. Assuming the pedal's initial position is 0%, pressing down increases the pedal's opening angle, while pressing up decreases it.

[0043] 2. The pedal travel rate, or actual pedal speed, is calculated by taking the derivative of pedal travel with respect to time. It accurately reflects the driver's desired pedal speed and indirectly reflects the intensity of the driver's braking intent. Assuming the rate of change is 0 in the initial stationary state, a downward pedal stroke represents a positive change, which increases with increasing pressure. An upward pedal stroke represents a negative change, which increases with increasing pressure.

[0044] 3. Vehicle speed is a direct reflection of the movement of the target electric vehicle, which is collected in real time through the vehicle speed sensor.

[0045] Step 2: Based on the braking intention fuzzy rule, according to θ[k] and Get the braking intention BI[k] corresponding to k.

[0046] As mentioned above, the pedal opening and the pedal opening change rate can be used as dynamic variables to measure the driver's braking intention. Then, through fuzzy design, a braking intention fuzzy rule is formulated (which is equivalent to building a braking intention recognition controller), with θ[k], As the input variable, BI[k] is used as the output variable.

[0047] Specifically, step 2 includes:

[0048] The pedal opening is fuzzified into three fuzzy subsets, including: small S θ 、M θ , Big B θ In this embodiment 1, the pedal opening domain is [0,100], which is divided into three intervals: [0,30], [15,80], and [65,100], corresponding to S θ 、M θ 、B θ .

[0049] The pedal opening rate of change is fuzzyized into 5 fuzzy subsets, including: negative extreme VB θ , negative big NB θ 、Negative medium NM θ 、Negative small NS θ , positive PB θ . In this embodiment 1, the domain of the pedal opening rate change is selected as [-300.300]. This is because it takes about 0.3s to 0.6s for the driver to completely complete the braking action from the beginning of contact with the pedal in an emergency braking situation. For safety reasons, it is selected that the driver can change the pedal position by 100% in 0.3s. Therefore, it is more reasonable to take the domain of the pedal opening rate change as [-300.300]. Without considering the acceleration situation, [-300.300] is divided into four negative half intervals (including:) and one positive interval (i.e. [160,300]), corresponding to VB θ NB θ 、NM θ 、NS θ PB θ .

[0050] The braking intention is fuzzified into 4 fuzzy subsets, including: mild S BI (corresponding to conditions such as intersection passing and turning obstacle avoidance, the pedal displacement and speed are both small), medium M BI_1 (corresponding to normal deceleration conditions, the driver continues to lightly press the pedal, the pedal displacement and speed are medium), heavy M BI_2 (Corresponding to the rapid deceleration condition, the driver presses the pedal with greater force, and the pedal displacement and speed continue to increase), emergency B BI(Corresponding to the emergency avoidance condition, the pedal displacement and speed change dramatically.) In this embodiment 1, the braking intention domain is [0,3], which is divided into [0,0.8], [0.5,1.6], [1.25,2.4], and [2,3], corresponding to S BI 、M BI_1 、M BI_2 、B BI .

[0051] Pedal opening and pedal opening rate of change are both important indicators for measuring the driver's operating intention. The closer the pedal position is to 100% and the faster the negative change in the pedal opening is, the stronger the driver's braking intention is. If the emergency brake is accidentally pressed, a sudden positive change in the pedal opening indicates a strong emergency braking intention. Therefore, based on the above principles, the following braking intention fuzzy rules can be formulated:

[0052] When θ[k] is S θ , NS θ 、NM θ NB θ ,VB θ PB θ , then BI[k] corresponds to M BI_1 、M BI_2 、M BI_2 、B BI 、B BI ;

[0053] When θ[k] is M θ , NS θ 、NM θ NB θ ,VB θ PB θ , then BI[k] corresponds to S BI 、M BI_1 、M BI_2 、B BI 、B BI ;

[0054] When θ[k] is B θ , NS θ 、NM θ NB θ ,VB θ PB θ , then BI[k] corresponds to S BI 、S BI 、M BI_1 、M BI_2 、B BI .

[0055] The above braking intention fuzzy rules can also be presented in table 1.

[0056] Table 1 Fuzzy rules for braking intention

[0057]

[0058] Step 3: Based on the braking intensity fuzzy rule, the braking intensity z[k] corresponding to k is obtained according to BI[k] and v[k].

[0059] Since step 2 characterizes the driver's actual braking intention, a braking intensity fuzzy rule is developed through fuzzy design (which is equivalent to building a braking intensity fuzzy controller), with BI[k] and v[k] as input variables and z[k] as the output variable - which is the braking demand transmitted by the driver through a single pedal.

[0060] Specifically, step three continues the fuzzy design of braking intention in step two, including:

[0061] The braking intention is fuzzified into 4 fuzzy subsets, including: mild S BI , Moderate M BI_1 , severe M BI_2 Emergency B BI In this embodiment 1, the braking intention domain is [0,3], which is divided into [0,0.8], [0.5,1.6], [1.25,2.4], and [2,3], corresponding to S BI 、M BI_1 、M BI_2 、B BI .

[0062] Fuzzify the vehicle speed into 5 fuzzy subsets, including: low speed VS v , medium and low speed S v , medium speed M v , medium and high speed B v , high-speed VB v In this embodiment 1, the vehicle speed domain is selected as [0,80], which is divided into [0,8], [2,38], [22,58], [42,78], and [62,89], corresponding to VS v 、S v 、M v 、B v ,VB v .

[0063] The braking intensity is fuzzified into 4 fuzzy subsets, including: small S z 、M z , Big B z , extremely large VB zIn this embodiment 1, the braking intensity domain is selected as [0,1], which is divided into [0,0.2], [0.1,0.55], [0.45,0.9], and [0.84,1], corresponding to S z 、M z 、B z ,VB z .

[0064] The braking intensity of a vehicle can well reflect the driver's braking needs, and is the braking response of the vehicle after the driver steps on the brake pedal. For braking intensity identification, it is particularly important to clarify the driver's braking intention at this time. Different degrees of braking intention correspond to corresponding braking intensity intervals, which also directly determines the strategy of single-pedal vehicle braking force distribution. However, only braking intensity as an input variable cannot well reflect the required vehicle motion state. By combining the actual vehicle speed information collected at the previous moment, the current vehicle motion state can be clearly reflected, which is used to assist the braking intention as an input variable of another braking intensity fuzzy identifier. If the currently identified driver's braking intention is stronger and the vehicle speed is greater, the expected braking intensity will also be greater. Therefore, according to the above principles, the following braking intensity fuzzy rules can be formulated:

[0065] When BI[k] is S BI , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to S z 、S z 、S z 、S z 、M z ;

[0066] When BI[k] is M BI_1 , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to S z 、M z 、M z 、M z 、M z ;

[0067] When BI[k] is M BI_2 , v[k] is VS v 、S v 、M v 、B v ,VB v, then z[k] corresponds to M z 、B z 、B z 、B z 、B z ;

[0068] When BI[k] is B BI , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to B z ,VB z ,VB z ,VB z ,VB z .

[0069] The above braking intensity fuzzy rules can also be presented in table 2.

[0070] Table 2 Braking intensity fuzzy rules

[0071]

[0072]

[0073] Step 4: Select the braking strategy under the corresponding working condition according to the situation of z[k] to perform braking.

[0074] Step 4 distinguishes the working conditions based on z[k] because:

[0075] Without considering steering, if we only analyze the longitudinal force during braking, we will see an increase in the front axle load and a decrease in the rear axle load, that is, the load is transferred between the front and rear axles. Then, from the vehicle force balance, we can know that:

[0076] 1. Taking the torque at the rear wheel’s ground contact point, we get:

[0077]

[0078] Where, F z1 represents the normal reaction force of the ground on the front wheels; G represents the vehicle's gravity; represents the distance from the center of mass of the vehicle to the center of the rear axle; m represents the mass of the vehicle; h ... g Indicates the height of the vehicle's center of mass; Indicates vehicle deceleration.

[0079] 2. Taking the torque at the front wheel’s contact point, we get:

[0080]

[0081] Where, F z2 represents the normal reaction force of the ground on the rear wheel; Indicates the distance from the vehicle's center of mass to the center of the rear axle.

[0082] Then we get F z1 、F z2 :

[0083]

[0084] It can be seen from the above formula that the front axle load increases and the rear axle load decreases during braking; and as the braking intensity increases, the axle load transfer also increases.

[0085] Because F z1 、F z2 It represents the vertical load, so based on the wheel adhesion coefficient (Assuming the ideal power distribution, that is, the front wheel adhesion coefficient is equal to the rear wheel adhesion coefficient) F x1 、F x2 :

[0086]

[0087] Where, F x1 Indicates the front wheel tangential force; F x2 Represents the rear wheel tangential force.

[0088] Since the vertical load and tangential force are linearly related, F x1 、F x2 It is also related to the braking intensity, so it can be divided according to z[k].

[0089] Specifically:

[0090] ① If z[k] falls within the light braking range, the rear wheels use regenerative braking and the front wheels do not participate in braking.

[0091] In this embodiment 1, the light braking range is set to (0, 0.3]. That is, when 0 < z[k] ≤ 0.3, it is considered light braking. Under this working condition, in order to maximize the energy recovery of the target electric vehicle, only regenerative braking is used for braking. Therefore, only the regenerative braking of the rear wheels is effective, and the front wheels do not participate in braking. Then, within the light braking range, the braking force distribution of the front and rear axles is:

[0092]

[0093] Where, F bf [k] represents the front axle braking force corresponding to k; F br [k] represents the rear axle braking force corresponding to k; T m[k] represents the rear axle braking torque corresponding to k; i0 represents the vehicle's main reduction ratio; η t represents the transmission efficiency; r w Indicates the wheel radius.

[0094] That is to say, within the light braking range, there is no need to brake the front wheels. br [k] is applied to the rear axle by the rear axle motor, that is, this is the motor independent braking mode.

[0095] ② If z[k] falls within the medium-intensity braking range or the high-intensity braking range, mechanical braking is used on the front wheels and regenerative braking is used on the rear wheels.

[0096] In this embodiment 1, the medium-intensity braking range is set to (0.3, 0.5], and the high-intensity braking range is set to (0.5, 0.7]. That is, when 0.3 < z[k] ≤ 0.5 or 0.5 < z[k] ≤ 0.7, it belongs to medium-intensity braking or high-intensity braking. In this working condition, in order to ensure the braking effect, a hybrid braking method is adopted: mechanical braking is used for the front wheels, and regenerative braking is used for the rear wheels. Then, in the medium-intensity braking and high-intensity braking ranges, the braking force distribution of the front and rear axles is:

[0097]

[0098] Where, F bf [k] represents the front axle braking force corresponding to k; F br [k] represents the rear axle braking force corresponding to k; F ref Indicates the required braking force, which is usually an empirical value; T m [k] represents the rear axle braking torque corresponding to k; i0 represents the vehicle's main reduction ratio; η t represents the transmission efficiency; r w Indicates the wheel radius.

[0099] That is to say, in the medium-intensity braking range or high-intensity braking range, F bf [k] The front axle mechanical brake is applied directly to the front wheels, F br [k] is applied to the rear axle by the rear axle motor, that is, it is an electro-hydraulic hybrid braking mode.

[0100] It should be noted that from the braking force distribution formulas ① and ②, if rear wheel regenerative braking is used, T m [k]——Its value is not fixed under different braking intensities, and its value affects the braking effect.

[0101] Then, see Figure 3 , this method provides T m The optimization method for the value of [k] includes the following steps:

[0102] S1, using linear model predictive control to solve the longitudinal dynamics model of the target electric vehicle based on the acceleration a[k], jerk κ[k], and rear wheel slip rate λ[k] corresponding to k to obtain the optimal rear axle braking torque change Δu*[k] corresponding to k.

[0103] It should be noted that the longitudinal dynamics model of the target electric vehicle takes into account three aspects: driving safety, energy recovery efficiency, and driving comfort. It sets three state variables (including acceleration, jerkiness, and rear wheel slip rate), one control variable (i.e., rear axle braking torque), and three output variables (including acceleration, jerkiness, and rear wheel slip rate).

[0104] Specifically, the calculation method of Δu*[k] includes:

[0105] S101 , discretizing the longitudinal dynamics model of the target electric vehicle from a continuous-time model into a discrete-time model.

[0106] The derivation process of the discrete-time model is as follows:

[0107] For the overall force analysis of the vehicle during braking (without considering the slope), the following can be obtained based on the vehicle driving equation:

[0108]

[0109] Where, T m represents the rear axle braking torque; i0 represents the vehicle's main reduction ratio; η t represents the transmission efficiency; r w Indicates wheel radius; C D represents the air resistance coefficient; A represents the frontal area of ​​the vehicle; ρ represents the air density; v represents the vehicle speed; δ represents the rotational mass conversion factor; m represents the vehicle mass; represents vehicle acceleration; g represents acceleration due to gravity; and f represents the coefficient of road friction.

[0110] In the above formula (1), Changing to the form of derivative with respect to v, we have:

[0111]

[0112] Where, represents the derivative of v.

[0113] Taking the derivative of both sides of equation (2) above, we can get:

[0114]

[0115] Where a represents the vehicle acceleration; represents the derivative of a; Indicates T m The first derivative of .

[0116] Taking the derivative of both sides of equation (3) above, we can get:

[0117]

[0118] Where, κ represents the vehicle jerk; represents the derivative of κ; Indicates T m The second derivative of .

[0119] So far, we have obtained expression.

[0120] The rear wheel slip rate can be expressed as:

[0121]

[0122] Where λ represents the rear wheel slip rate; ω2 represents the rear wheel rotation angular velocity.

[0123] By taking the derivative of both sides of the above formula (5), we can get:

[0124]

[0125] Where, represents the derivative of λ; represents the derivative of ω2.

[0126] The force analysis of the rear wheel during braking is performed separately, and the torque balance equation of the rear wheel is obtained as follows:

[0127]

[0128] Where, F x2 Represents the tangential force on the rear axle; T t Indicates the braking torque acting on the rear wheel; T t With T m satisfy: I represents the moment of inertia of the wheel.

[0129] Substituting the above formula (7) into the above formula (6), we can get:

[0130]

[0131] So far, we have obtained expression.

[0132] The continuous-time model is formed by summarizing the above equations (3), (4), and (8). In other words, the expression of the continuous-time model is:

[0133]

[0134] It should be noted that the purpose of introducing jerk and rear wheel slip is to minimize the impact of vehicle body vibration and rear wheel slip on the vehicle's braking process. In other words, to ensure the target electric vehicle has good stability and safety, the values ​​of jerk and rear wheel slip are set as small as possible. In this embodiment 1, the target values ​​of jerk and rear wheel slip are set to 0 to eliminate their impact on the vehicle's braking process.

[0135] The control variable space equation is constructed based on the continuous time model, which is expressed as:

[0136]

[0137] Where x(t) represents the state variable in the continuous state; is the time derivative of x(t), which is used to express the rate of change of state over time; A c is the state matrix in the continuous state, which is used to describe the dynamic relationship between state variables; B cu is the input matrix under continuous state, which is used to describe the influence of input on state; u(t) represents the input variable under continuous state; B cd is the interference matrix under continuous state, which is used to describe the influence of interference on the state;

[0138] d(t) is the disturbance variable in the continuous state, which is used to represent external disturbance or uncertainty; C c is the output matrix, which is used to describe how the state variables are mapped to the output; c (t) is the output vector in the continuous state, which is used to represent the measurable output of the system.

[0139] Discretizing the above formula (9), we can get:

[0140]

[0141] Where x[k+1] is the state variable corresponding to the next moment k+1 in the discrete state; x[k] is the state variable corresponding to k in the discrete state; A u is the state matrix in the discrete state; x[k] is the state variable corresponding to k in the discrete state; B u is the input matrix in the discrete state; u[k] is the input variable corresponding to k in the discrete state (control quantity); B d is the interference matrix under discrete state; d[k] is the interference variable corresponding to k under discrete state; C c is the output matrix; y c (k) represents the output variable corresponding to k in the discrete state (controlled quantity);

[0142] Among them, Au =A c T s +1; B u =B cu T s ; B d =B cd T s ;T s is the sampling time interval.

[0143] Since three state variables (including acceleration, jerk, and rear wheel slip), one control variable (i.e., rear axle braking torque), and three output variables (including acceleration, jerk, and rear wheel slip) are set, that is, X = [x1, x2, x3] T =[a,κ,λ] T ; u=T m ; Y = [y1,y2,y3] T =[a,κ,λ] T ; In the formula, X represents the state variable group; x1, x2, x3 represent the state variables, corresponding to a, κ, λ respectively; u represents the control variable, corresponding to T m ; Y represents the output variable group; y1, y2, y3 represent output variables, corresponding to a, κ, λ respectively.

[0144] Then, the above formula (10) can be rewritten as:

[0145]

[0146] In the formula, x1[k+1] represents the value of x1 at the next time k+1; x1[k] represents the value of x1 at time k; x2[k+1] represents the value of x2 at time k+1; x2[k] represents the value of x2 at time k; x3[k+1] represents the value of x3 at time k+1; x3[k] represents the value of x3 at time k; u[k] represents the value of u at time k;

[0147] 1-b1 and 1+b1 represent the state variable coefficients; T s Indicates the sampling time interval;

[0148] c1, c2, and c3 represent the coefficients of control variables;

[0149] d1, d2, d3 represent constant terms; d1 = -c1T m [k-1]; Indicates T m The derivative of [k-1].

[0150] That is, the discrete-time model can be expressed as:

[0151]

[0152] S102 , establishing a cost function J(Δu[k]) of the linear model predictive control corresponding to k based on the discrete time model.

[0153] Specifically, the expression of J(Δu[k]) is:

[0154]

[0155] Where q1, q2, q3, and r represent weighting coefficients; N p Indicates the prediction step size; N c represents the control step size; y1[k+i|k] represents the predicted acceleration value at the future time k+i corresponding to k; y r1 [k+i] represents the expected acceleration reference value at k+i; y2[k+i|k] represents the predicted jerk value of k+i corresponding to k; y r2 [k+i] represents the jerk reference value expected by k+i; y3[k+i|k] represents the rear wheel slip rate prediction value of k+i corresponding to k; y r3 [k+i] represents the desired rear wheel slip ratio reference value of k+i; Δu[k+i] represents the change in rear axle braking torque corresponding to k+i.

[0156] S103, solve the Δu[k] corresponding to the minimum of J(Δu[k]) and use it as Δu*[k].

[0157] Let J(Δu[k]) be minimized and solve the control variable increment set ΔU[k] corresponding to k.

[0158] The expression of ΔU[k] is:

[0159]

[0160] In the formula, Δu[k], Δu[k+1], Δu[k+N c -1] are respectively represented as k, k+1, k+N c The increment of the control variable at time -1.

[0161] In order to ensure the calculation effect, N is set in S102. c Take 1, N p Take 1 to make ΔU[k]=Δu[k].

[0162] Then, the expression of Δu*[k] is:

[0163]

[0164] Where R1, R2, and R3 are the reference values ​​corresponding to x1, x2, and x3, respectively; u[k-1] represents the value of u at k-1.

[0165] S2, compare Δu*[k] with the rear axle braking torque T at the previous moment k-1 m [k-1] is added to obtain the rear axle braking torque T corresponding to k m [k].

[0166] Since S1 only calculates the difference, it is necessary to use T m [k-1] is used as the basis to get T m [k].

[0167] T calculated using the above process m [k] is actually based on the joint control of acceleration, jerkiness and rear wheel slip rate. It not only meets the driver's current requirements for the vehicle's dynamics and ensures that the vehicle can achieve the ideal braking effect, but also suppresses vertical vibration, reduces the driver's riding bumpiness, improves ride comfort, and takes safety into account, effectively preventing dangerous conditions such as wheel slippage or locking during braking.

[0168] 3. If z[k] falls within the emergency braking range, mechanical braking is applied to both the front and rear wheels.

[0169] In this embodiment 1, the emergency braking range is set to (0.7, 1). That is, when 0.7 < z[k] ≤ 1, it is an emergency braking. In this working condition, in order to ensure braking stability, regenerative braking is not used, and mechanical braking is used for both the front and rear wheels. Then, within the emergency braking range, the braking force distribution of the front and rear axles is:

[0170]

[0171] Where, F bf [k] represents the front axle braking force corresponding to k; F br [k] represents the rear axle braking force corresponding to k.

[0172] That is to say, within the emergency braking range, F bf [k] The mechanical brake of the front axle is applied to the front wheels, F br [k] The mechanical brake of the rear axle is applied to the rear wheels, that is, it is hydraulic independent braking mode.

[0173] The present invention adopts different control strategies for different braking intensities, avoiding the situation where the motor provides insufficient braking torque when the braking intensity is high, thereby improving the overall braking efficiency of the vehicle while ensuring the energy recovery efficiency is as high as possible.

[0174] Simulation Verification

[0175] In order to illustrate the application effect of the present invention, a target electric vehicle (its normal driving speed was set at 60 km / h) was constructed on a simulation platform, and different operating conditions were simulated. The results showed that the braking method of the present invention can achieve relatively ideal control results regardless of whether it is light braking, medium-intensity braking, high-intensity braking, or emergency braking.

[0176] Here we take light braking as an example:

[0177] Simulating the driver lightly pressing the brake pedal, the above method obtains z[k] as 0.2, which is a light braking, so the motor independent braking mode is used. The simulation results of the rear axle braking torque, acceleration, jerk, and rear wheel slip rate are as follows: Figures 4 to 7 As shown, it shows how the corresponding variables change over time.

[0178] Depend on Figure 4 It can be seen that before 0.2 seconds, the rear axle braking torque starts to increase from the initial value of 0 (due to the braking condition, its value is negative); around 0.2 seconds, the rear axle braking torque gradually converges to the optimal value (-220N·m), with an upper and lower fluctuation range of about 10N·m and an average error of 4.55%, indicating that the overall control effect is good. Figures 5 to 7 It can be seen that the acceleration, jerkiness, and rear wheel slip rate all approach stable values ​​rapidly in the initial stage (among which, the acceleration approaches the stable value of -1.962m / s 2 , jerk approaches stable value 0, rear wheel slip rate approaches stable value 0), and the fluctuation is small after stabilization (among which, the acceleration is -2.001m / s 2 ~-1.998m / s 2 Fluctuation and jerk fluctuation range is 0.02m / s 3 The phenomenon that the left and right wheel and rear wheel slip rates fluctuate between 0.028 and 0.03 indicates that the control has good performance, fast control response and high control accuracy.

[0179] Example 2

[0180] This embodiment 2 discloses a braking system for a single-pedal electric vehicle based on intention recognition, which uses the braking method for a single-pedal electric vehicle based on intention recognition of embodiment 1.

[0181] The braking system of a single-pedal electric vehicle based on intention recognition includes: a vehicle state acquisition module, a braking intention fuzzy control module, a braking intensity fuzzy control module, and a braking strategy selection and application module.

[0182] The vehicle state acquisition module is used to obtain the pedal opening θ[k] and the pedal opening change rate at the current moment k The braking intention fuzzy control module is used to calculate the braking intention fuzzy rules according to θ[k] and The braking intention BI[k] corresponding to k is obtained. The braking intensity fuzzy control module is used to obtain the braking intensity z[k] corresponding to k based on the braking intensity fuzzy rules according to BI[k] and v[k]. The braking strategy selection and application module is used to select the corresponding braking strategy for braking based on z[k].

[0183] Since this system uses the braking method of a single-pedal electric vehicle based on intention recognition in Example 1, it also has the same effect and will not be repeated here.

[0184] This embodiment 2 also simultaneously discloses an electric vehicle, which uses the single-pedal electric vehicle braking method based on intention recognition of embodiment 1.

[0185] Since this electric vehicle uses the braking method of the single-pedal electric vehicle based on intention recognition in Example 1, it also has the same effect, which will not be repeated here.

[0186] Example 3

[0187] This embodiment 3 discloses a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the single-pedal electric vehicle braking method based on intention recognition disclosed in embodiment 1 are implemented.

[0188] Computer devices may include: mobile terminals and fixed terminals. Examples of the former include mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (such as in-vehicle navigation terminals); examples of the latter include digital TVs and desktop computers.

[0189] This embodiment 3 also discloses a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the steps of the single-pedal electric vehicle braking method based on intention recognition disclosed in embodiment 1 are executed.

[0190] Among them, the readable storage medium may include, but is not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0191] This embodiment 3 further discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the single-pedal electric vehicle braking method based on intention recognition disclosed in embodiment 1 are implemented.

[0192] It should be noted that the computer program for executing the above-mentioned program can be written in one or more programming languages ​​or a combination thereof. Among them, the programming language includes object-oriented programming languages ​​such as Java, Smalltalk, C++, and also includes conventional procedural programming languages ​​such as "C" language or similar programming languages. The above-mentioned computer program can be executed completely on the user's computer, or partially on the user's computer, or partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0193] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A braking method for a single-pedal electric vehicle based on intention recognition, characterized in that: It includes: Get the pedal opening θ[k] and pedal opening change rate of the target electric vehicle at the current time k Vehicle speed v[k]; Based on the braking intention fuzzy rule, according to θ[k] and Get the braking intention BI[k] corresponding to k; Based on the braking intensity fuzzy rule, the braking intensity z[k] corresponding to k is obtained according to BI[k] and v[k]; According to the situation of z[k], select the braking strategy under the corresponding working condition to perform braking; If z[k] falls within the light braking range, the rear wheels use regenerative braking and the front wheels do not participate in braking; if z[k] falls within the medium-intensity braking range or the high-intensity braking range, the front wheels use mechanical braking and the rear wheels use regenerative braking; if z[k] falls within the emergency braking range, both the front and rear wheels use mechanical braking; When regenerative braking is used on the rear wheels, the longitudinal dynamics model of the target electric vehicle is first solved by linear model predictive control according to the acceleration a[k], jerk κ[k], and rear wheel slip rate λ[k] corresponding to k to obtain the optimal rear axle braking torque change Δu*[k] corresponding to k, and then Δu*[k] is compared with the rear axle braking torque T at the previous moment k-1. m [k-1] is added to obtain the rear axle braking torque T corresponding to k m [k].

2. The braking method of a single-pedal electric vehicle based on intention recognition according to claim 1, characterized in that: Based on the braking intention fuzzy rule, according to θ[k] and The braking intention BI[k] corresponding to k is obtained as follows: The pedal opening is fuzzified into three fuzzy subsets, including: small S θ 、M θ , Big B θ ; The pedal opening rate of change is fuzzyized into 5 fuzzy subsets, including: negative extreme VB θ , negative big NB θ 、Negative medium NM θ 、Negative small NS θ , positive PB θ ; The braking intention is fuzzified into 4 fuzzy subsets, including: mild S BI , Moderate M BI_1 , severe M BI_2 Emergency B BI ; When θ[k] is S θ , NS θ 、NM θ NB θ ,VB θ PB θ , then BI[k] corresponds to M BI_1 、M BI_2 、M BI_2 、B BI 、B BI ; When θ[k] is M θ , NS θ 、NM θ NB θ ,VB θ PB θ , then BI[k] corresponds to S BI 、M BI_1 、M BI_2 、B BI 、B BI ; When θ[k] is B θ , NS θ 、NM θ NB θ ,VB θ PB θ , then BI[k] corresponds to S BI 、S BI 、M BI_1 、M BI_2 、B BI .

3. The braking method of a single-pedal electric vehicle based on intention recognition according to claim 2, characterized in that: Based on the braking intensity fuzzy rule, the braking intensity z[k] corresponding to k is obtained according to BI[k] and v[k], including: Fuzzify the vehicle speed into 5 fuzzy subsets, including: low speed VS v , medium and low speed S v , medium speed M v , medium and high speed B v , high-speed VB v ; The braking intensity is fuzzified into 4 fuzzy subsets, including: small S z 、M z , Big B z , extremely large VB z ; When BI[k] is S BI , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to S z 、S z 、S z 、S z 、M z ; When BI[k] is M BI_1 , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to S z 、M z 、M z 、M z 、M z ; When BI[k] is M BI_2 , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to M z 、B z 、B z 、B z 、B z ; When BI[k] is B BI , v[k] is VS v 、S v 、M v 、B v ,VB v , then z[k] corresponds to B z ,VB z ,VB z ,VB z ,VB z .

4. The braking method of a single-pedal electric vehicle based on intention recognition according to claim 3, characterized in that: If z[k] belongs to the light braking range, then: k corresponding to the rear axle braking force Where i0 represents the vehicle's main reduction ratio; η t represents the transmission efficiency; r w Indicates the wheel radius; k corresponds to the front axle braking force F bf [k] is 0.

5. The braking method of a single-pedal electric vehicle based on intention recognition according to claim 3, characterized in that: If z[k] belongs to the medium-intensity braking range or the high-intensity braking range, then: k corresponding to the rear axle braking force Where i0 represents the vehicle's main reduction ratio; η t represents the transmission efficiency; r w Indicates the wheel radius; k corresponds to the front axle braking force F bf [k]=F ref -F br [k]; F ref Indicates the required braking force.

6. The braking method of a single-pedal electric vehicle based on intention recognition according to claim 3, characterized in that: If z[k] belongs to the emergency braking range, then: k corresponding to the rear axle braking force k corresponds to the front axle braking force Where, F x1 Indicates the front axle tangential force; F x2 Represents the tangential force on the rear axle; G represents the vehicle's gravity; Indicates the distance from the vehicle's center of mass to the center of the rear axle; represents the distance from the center of mass of the vehicle to the center of the front axle; m represents the mass of the vehicle; h ... g Indicates the height of the vehicle's center of mass; Indicates vehicle deceleration; represents the adhesion coefficient.

7. The braking method of a single-pedal electric vehicle based on intention recognition according to any one of claims 1 to 6, characterized in that: The calculation method of Δu*[k] includes: Discretize the longitudinal dynamics model of the target electric vehicle from a continuous-time model to a discrete-time model; According to the discrete time model, the cost function J(Δu[k]) of the linear model predictive control corresponding to k is established; Solve for the Δu[k] corresponding to the minimum of J(Δu[k]) and use it as Δu*[k]; The state variables of the longitudinal dynamics model of the target electric vehicle include acceleration, jerk, and rear wheel slip rate, the control variable is the rear axle braking torque, and the output variables include acceleration, jerk, and rear wheel slip rate.

8. The braking method of a single-pedal electric vehicle based on intention recognition according to claim 7, characterized in that: The expression of the continuous-time model is: Where, v represents the vehicle speed; represents the derivative of v; C D represents the air resistance coefficient; A represents the frontal area of ​​the vehicle; ρ represents the air density; i0 represents the vehicle's final reduction ratio; η t represents the transmission efficiency; m represents the vehicle mass; δ represents the rotational mass conversion coefficient; r w represents the wheel radius; a represents the vehicle acceleration; represents the derivative of a; κ represents the vehicle jerk; represents the derivative of κ; λ represents the rear wheel slip rate; represents the derivative of λ; ω2 represents the angular velocity of the rear wheel; I represents the moment of inertia of the wheel; T m Represents the rear axle braking torque; Indicates T m The first derivative of ; Indicates T m The second derivative of g represents the acceleration due to gravity; f represents the friction coefficient of the road surface; F x2 represents the tangential force on the rear axle; The expression of the discrete-time model is: Where X represents the state variable group; x1, x2, x3 represent the state variables, corresponding to a, κ, λ respectively; u represents the control variable, corresponding to T m ; Y represents the output variable group; y1, y2, and y3 represent output variables, corresponding to a, κ, and λ respectively; x1[k+1] represents the value of x1 at the next moment k+1; x1[k] represents the value of x1 at time k; x2[k+1] represents the value of x2 at time k+1; x2[k] represents the value of x2 at time k; x3[k+1] represents the value of x3 at time k+1; x3[k] represents the value of x3 at time k; u[k] represents the value of u at time k; 1-b1 and 1+b1 represent the state variable coefficients; T s Indicates the sampling time interval; c1, c2, and c3 represent the coefficients of control variables; d1, d2, d3 represent constant terms; d1 = -c1T m [k-1]; Indicates T m The derivative of [k-1]; The expression of J(Δu[k]) is: Where q1, q2, q3, and r represent weighting coefficients; N p Indicates the prediction step size, which is 1; N c represents the control step size, which is 1; y1[k+i|k] represents the predicted acceleration value at the future time k+i corresponding to k; y r1 [k+i] represents the expected acceleration reference value at k+i; y2[k+i|k] represents the predicted jerk value of k+i corresponding to k; y r2 [k+i] represents the jerk reference value expected by k+i; y3[k+i|k] represents the rear wheel slip rate prediction value of k+i corresponding to k; y r3 [k+i] represents the k+i desired rear wheel slip ratio reference value; Δu[k+i] represents the change in rear axle braking torque corresponding to k+i; The expression of Δu*[k] is: Where R1, R2, and R3 are the reference values ​​corresponding to x1, x2, and x3, respectively; u[k-1] represents the value of u at k-1.

9. A single-pedal electric vehicle braking system based on intention recognition, characterized in that: It uses the braking method of a single-pedal electric vehicle based on intention recognition as described in any one of claims 1 to 8; The braking system of the single-pedal electric vehicle based on intention recognition includes: The vehicle state acquisition module is used to obtain the pedal opening θ[k] and pedal opening change rate of the target electric vehicle at the current time k. Vehicle speed v[k]; The braking intention fuzzy control module is used to control the braking intention based on the fuzzy rules of θ[k] and Get the braking intention BI[k] corresponding to k; A braking intensity fuzzy control module is used to obtain the braking intensity z[k] corresponding to k according to BI[k] and v[k] based on the braking intensity fuzzy rule; and The braking strategy selection and application module is used to select the braking strategy under the corresponding working conditions for braking according to the situation of z[k].

10. A computer program product, characterized in that It includes a computer program; when the computer program is executed by a processor, the steps of the braking method of a single-pedal electric vehicle based on intention recognition according to any one of claims 1 to 8 are implemented.

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