A method and system for controlling braking energy recovery of electric vehicles on off-road surfaces

Abstract

The present invention provides a method and system for controlling brake energy recovery on split-road surfaces for electric vehicles, comprising the following steps: inputting vehicle speed v and braking intensity z into a braking force calculation module to obtain the required braking force, the maximum braking torque of the motor, the peak torque of the motor, and the state of charge (SOC) value of the power battery; determining the slip rate of each wheel, and inputting the slip rate and the road adhesion coefficient of each wheel into a fuzzy controller based on the force analysis of the vehicle during braking; determining the degree of similarity between the current road surface and eight standard road surfaces based on the fuzzy controller, and then calculating the peak adhesion coefficient between each wheel and the road surface in combination with the peak adhesion coefficient of the standard road surfaces; and when the motor braking conditions are met, formulating and determining a braking force distribution strategy based on the peak adhesion coefficient between each wheel and the road surface, the required braking force, and the maximum motor braking force. The brake energy recovery strategy of the present invention can effectively improve the braking stability of a vehicle during brake energy recovery on split-road surfaces.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle braking, and in particular to a method and system for controlling braking energy recovery of an electric vehicle on an off-road basis. Background Art

[0002] Electric vehicles are a type of new energy vehicle that relies on batteries to store energy and power the vehicle through an electric motor. Electric motors replace traditional internal combustion engines, while batteries replace traditional fuel tanks. As a renewable energy source, electric vehicles offer abundant energy. Their key features include zero emissions, zero pollution, low noise, simple structure, and easy maintenance. As a result, electric vehicles have seen rapid growth in China in recent years. However, limited battery capacity and a lack of widespread charging stations have limited their range.

[0003] Because battery capacity cannot be increased in the short term, regenerative braking technology has begun to gain attention. Regenerative braking occurs when an electric vehicle decelerates or brakes, or on a long downhill slope. It utilizes the motor's reversible state, operating in generator mode. The vehicle controller regulates the motor's voltage, allowing the generated electricity to flow to the voltage side, where it is recovered and stored in the battery. This maximizes battery energy utilization and increases the electric vehicle's range.

[0004] Regenerative braking is typically controlled when the brake pedal is depressed or the accelerator pedal is released from the depressed position. Braking force is generated during this separation and release process. Regenerative braking often considers the distribution of braking force between the front and rear axles. However, less research has been conducted on the distribution of braking force between the left and right wheels on the same axis and its impact on vehicle braking stability and brake energy recovery efficiency. Traditional brake energy recovery strategies often consider applying equal braking force to both wheels. This can easily increase the vehicle's yaw moment on split roads, causing the vehicle to become unbalanced and potentially leading to dangerous accidents. Summary of the Invention

[0005] In response to the shortcomings of the prior art, the present invention provides a method and system for controlling brake energy recovery on a split road surface for an electric vehicle. The brake energy recovery strategy can effectively improve the braking stability of the vehicle during brake energy recovery on a split road surface, reduce the vehicle's yaw moment, and effectively improve the brake energy recovery efficiency of the electric vehicle in terms of economy. The brake energy recovery strategy of the present invention focuses on optimal braking stability and optimal brake energy recovery efficiency. On a split road surface, the braking force of the wheel on the side with the high peak road adhesion coefficient is reduced. While ensuring that the wheel does not lock, the braking force of the wheel on the side with the low peak road adhesion coefficient is increased as much as possible, reducing the difference in ground braking force between the wheels on both sides and reducing the vehicle's yaw moment. The brake energy recovery control strategy allocates the front and rear wheel motor braking force based on the required braking force and the maximum motor braking force. Compared with traditional brake energy recovery strategies, the present invention designs a road identifier to calculate the peak adhesion coefficient between each tire and the current road surface. The brake energy recovery strategy formulated based on this can effectively improve the braking stability of the vehicle during brake energy recovery on a split road surface, reduce the vehicle's yaw moment, and effectively improve the brake energy recovery efficiency of the electric vehicle in terms of economy.

[0006] The present invention achieves the above technical objectives through the following technical means.

[0007] A method for controlling braking energy recovery of an electric vehicle on a split road comprises the following steps:

[0008] S01. Calculate the required braking force and the maximum motor braking force:

[0009] At the beginning of braking, the vehicle speed v is collected according to the vehicle speed sensor, and the braking intensity z is collected according to the brake pedal sensor. The vehicle speed v and the braking intensity z are input into the braking force calculation module to obtain the required braking force, the maximum braking torque of the motor, the peak torque of the motor, and the SOC value of the power battery;

[0010] S02. Calculate the slip rate and road adhesion coefficient of each wheel:

[0011] At the beginning of braking, the slip rate of each wheel is determined based on the vehicle speed v collected by the vehicle speed sensor and the wheel speed ω collected by the wheel speed sensor. According to the force analysis of the vehicle during braking, the ground braking force F of each wheel is used to determine the slip rate of each wheel. b and the normal load F of each wheel z Calculate the road adhesion coefficient of each wheel and input the wheel slip rate and road adhesion coefficient of each wheel into the fuzzy controller;

[0012] S03. Calculate the peak road adhesion coefficient between each wheel and the road surface:

[0013] The fuzzy controller determines the similarity between the current road surface and eight standard road surfaces, and then calculates the peak adhesion coefficient between each wheel and the road surface in combination with the peak adhesion coefficient of the standard road surface.

[0014] S04. Determine whether the conditions for motor braking are met: Determine whether the conditions for motor braking are met based on the vehicle speed v, the battery SOC value, and the braking intensity z. When the conditions for motor braking are met, formulate and determine a braking force distribution strategy based on the peak adhesion coefficient between each wheel and the road surface, the required braking force, and the maximum motor braking force.

[0015] Furthermore, the vehicle speed v and braking intensity z are input into the braking force calculation module to obtain the required braking force, the maximum braking torque of the motor, and the SOC value of the power battery, which are specifically:

[0016] Required braking force F when the car brakes need for:

[0017]

[0018] F μf +F μr =F need ,

[0019] Where: F μf is the braking force of the front axle brake; F μr is the braking force of the rear axle brake; G is the gravity of the car;

[0020] Determine the braking force relationship between the front and rear axles based on the I curve:

[0021]

[0022]

[0023] Where: G is the weight of the car; a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; z is the braking strength; h g is the height of the vehicle's center of mass;

[0024] The maximum braking torque of the motor is determined by the following formula:

[0025]

[0026] Where: T emax is the maximum braking torque of the motor; P max is the peak power of the motor; P Bmax is the maximum charging power of the battery; η b Battery charging efficiency; T max is the peak torque of the motor; n is the motor speed; n d is the rated speed of the motor;

[0027] The charging current is proportional to the motor force and can be expressed as:

[0028] Where: I m is the charging current; η m is the motor power generation efficiency; T n is the torque of a single motor; U ec is the battery terminal voltage;

[0029] The SOC value of the power battery is calculated using the ampere-hour integration method:

[0030] Where: SOC init is the initial SOC of the battery; Q cap is the battery capacity.

[0031] Furthermore, according to the vehicle speed v and wheel speed ω of each wheel, the slip rate of each wheel is determined by the following formula:

[0032]

[0033] Where: S ij is the slip rate of each wheel; ω ij is the speed of each wheel, where ij∈(fl, fr, rl, rr), fl represents the left front wheel, fr represents the right front wheel, rl represents the left rear wheel, and rr represents the right rear wheel.

[0034] Furthermore, according to the force analysis of the vehicle during braking, the ground braking force F of each wheel is b and the normal load F of each wheel z Calculate the road adhesion coefficient of each wheel as follows:

[0035] According to the force analysis of the vehicle during braking:

[0036] J ij ω ij =F b_ij ·rT μ_ij

[0037] Where: J ij is the moment of inertia of each wheel; T μ_ij is the brake torque of each wheel;

[0038] The calculation formula for the braking torque of each wheel brake is: T μ_ij =T m_ij +T e_ij ;

[0039] Where: T m_ij is the mechanical braking torque, N·m; T e_ij is the motor torque;

[0040] The braking force of the brake can be calculated by the following formula:

[0041]

[0042] The road adhesion coefficient of each wheel is:

[0043] Where: μ ij is the road adhesion coefficient of each wheel; F z_ij is the normal load from the ground on each wheel, calculated from the vehicle model.

[0044] Furthermore, the similarity between the current road surface and the eight standard road surfaces is obtained based on the fuzzy controller, which is:

[0045] Enter S ij According to the first membership function, the fuzzy sets are fuzzified into small slip rate fuzzy subsets, medium slip rate fuzzy subsets and large slip rate fuzzy subsets;

[0046] When the input S ij Located in the small slip rate fuzzy subset, under the small slip rate fuzzy subset, the input μ ij According to the second membership function, the first dry cement fuzzy subset and the first dry asphalt fuzzy subset are obtained. ij Compared with the first dry cement fuzzy subset and the first dry asphalt fuzzy subset, the input μ ij The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subset and other fuzzy subsets. The closer the distance, the more similar they are.

[0047] When the input S ij Located in the medium slip rate fuzzy subset, under the medium slip rate fuzzy subset, the input μ ij According to the third membership function, the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset are obtained; the input μ ij Compared with the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset, the input μ ij The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subsets and other fuzzy subsets. The closer the distance, the more similar they are.

[0048] When the input S ij Located in the large slip fuzzy subset, under the large slip fuzzy subset, the input μ ijAccording to the fourth membership function, the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset are obtained; the input μ ij Compared with the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset, the input μ ij The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subset and other fuzzy subsets. The closer the distance, the more similar they are.

[0049] The similarity degree is defuzzified to obtain the similarity degree between the current road surface of each round and the eight standard road surface curves.

[0050] Furthermore, the peak adhesion coefficient between each wheel and the road surface is calculated in combination with the peak adhesion coefficient of the standard road surface, which is specifically obtained by the following formula:

[0051]

[0052] Where: μ max1 is the peak adhesion coefficient on standard ice road, μ max2 is the peak adhesion coefficient of the standard snow road, μ max3 is the peak adhesion coefficient of the standard wet cobblestone road, μ max4 is the peak adhesion coefficient of the standard wet asphalt pavement, μ max5 is the peak adhesion coefficient of the standard wet asphalt pavement, μ max6 is the peak adhesion coefficient of the standard wet asphalt pavement, μ max7 is the peak adhesion coefficient of the standard dry cement pavement, μ max8 is the peak adhesion coefficient of the standard dry asphalt pavement; μ maxij is the peak adhesion coefficient between each wheel and the road; x 1ij is the similarity between the current road surface of each round and the standard ice road surface, x 2ij is the similarity between the current road surface of each round and the standard snow road surface, x 3ij is the similarity between the current road surface and the standard wet cobblestone road surface, x 4ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 5ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 6ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 7ij is the similarity between the current road surface and the standard dry cement road surface, x 8ijIt is the similarity between the current road surface of each round and the standard dry asphalt road surface.

[0053] Furthermore, when the motor braking conditions are met, a braking force distribution strategy is formulated based on the peak adhesion coefficient between each wheel and the road surface, the required braking force, and the maximum motor braking force, specifically:

[0054] When the required braking force F need ≤2F emax When F emax The maximum braking force of a single motor;

[0055] When the required braking force F need >2F emax When F need ≤4F emax , and |μ|≤0.2, the left front wheel motor, right front wheel motor, left rear wheel motor and right rear wheel motor jointly provide braking force,

[0056]

[0057]

[0058] Where:

[0059] |μ|=|μ l -μ r |,μ l =min{μ maxlf , μ maxlr},μ r =min{μ maxrf , μ maxrr}; L is the wheelbase of the car;

[0060] F elf is the left front wheel electric motor power, F erf Right front wheel electric motor power, F elr The left rear wheel electric motor is powered, F err Right rear wheel electric motor power;

[0061] When the required braking force F need >2F emax When F need ≤4F emax , and |μ|>0.2, the braking force on the coaxial left and right wheels is distributed as follows:

[0062]

[0063]

[0064] When the required braking force Fneed >2F emax When F need >4F emax , and |μ|≤0.2, the left front wheel motor, right front wheel motor, left rear wheel motor, and right rear wheel motor provide the maximum electric braking force, and the insufficient part is compensated by the mechanical braking force, specifically:

[0065] F elf =F erf =F elr =F err =F emax

[0066]

[0067]

[0068] Where: F mlf is the mechanical braking force of the left front wheel; F mrf is the mechanical braking force of the right front wheel; F mlr Left rear wheel mechanical braking force; F mrr Provides mechanical braking force for the right rear wheel;

[0069] When the required braking force F need >2F emax When F need >4F emax , and |μ|>0.2, the braking force on the coaxial left and right wheels is distributed as follows:

[0070]

[0071]

[0072] A control system for a method of controlling braking energy recovery on an electric vehicle on a split road, comprising a vehicle speed sensor, a wheel speed sensor, a brake pedal sensor, and a controller. The controller comprises a regenerative braking start-up determination module, a required braking force estimation module, a road surface recognition module, a braking force distribution module, a motor module, and a battery module.

[0073] The regenerative braking start judgment module is used to judge whether to start the motor braking according to the vehicle speed v, SOC value and braking intensity z;

[0074] The required braking force estimation module is used to calculate the total required braking force and obtain the front and rear axle brake force distribution curve;

[0075] The road surface recognition module is used to output the peak road adhesion coefficient between each wheel and the road surface;

[0076] The braking force distribution module determines the braking force distribution strategy based on the total required braking force calculated by the required braking force estimation module and the peak adhesion coefficient between each wheel and the road surface output by the road surface recognition module, and outputs the motor braking force F of each wheel under different braking intensity and road surface adhesion conditions. e With mechanical braking force F m ;

[0077] The motor module converts the vehicle's kinetic energy during braking into electrical energy when in a power generation state;

[0078] The battery module is used to store electric energy recovered by braking, thereby realizing the energy recovery function.

[0079] The beneficial effects of the present invention are:

[0080] 1. The present invention provides a method and system for controlling the braking energy recovery of an electric vehicle on a split road. The method uses a fuzzy controller to calculate the peak adhesion coefficient between each tire and the current road surface, and uses a fuzzy control strategy to determine the similarity inputs of the slip rate and adhesion utilization coefficient of each wheel with eight types of roads. The method and system are designed to ensure optimal braking stability and braking energy recovery efficiency for electric vehicles.

[0081] 2. The method for controlling braking energy recovery on an electric vehicle on a two-way road surface described in the present invention can obtain the electric braking force and mechanical braking force of each wheel under different braking intensities and road adhesion conditions, narrow the difference in ground braking force between the left and right wheels, reduce the vehicle's yaw moment, and effectively improve the braking stability and braking energy recovery efficiency of the vehicle during braking energy recovery on a two-way road surface.

[0082] 3. The method for controlling braking energy recovery on a split road surface of an electric vehicle described in the present invention reasonably distributes the braking forces of the left front, right front, left rear and right rear according to the requirements of braking stability and the size of the peak adhesion coefficient of the road surface, with the main goal of ensuring braking stability. On a split road surface, the difference in ground braking forces of the coaxial wheels is reduced, the braking force of the wheel on the side with a high peak road adhesion coefficient is controlled to be reduced, and the braking force of the wheel on the side with a low peak road adhesion coefficient is increased while ensuring that the wheel is not locked. According to the requirements of economy, based on the currently required braking force, the front axle motor force is first distributed. When the front wheel motor force can meet the braking demand, the braking force is only provided by the front wheel motor. When the front wheel motor force cannot meet the braking demand, the rear wheel motor is involved in the work, which can make full use of the motor efficiency and meet the requirements of economy.

[0083] 4. The braking energy recovery strategy of the braking energy recovery control method for electric vehicles on opposite sides of the road is compared with the braking energy recovery control strategy that uses two front wheel motors to provide braking force at a fixed ratio and the braking energy recovery control strategy that uses four motors to provide braking force at a fixed ratio. The braking energy recovery control strategy proposed by the present invention can effectively improve the braking energy recovery efficiency and increase the driving range of the vehicle; the braking energy recovery strategy of the present invention is compared with the braking energy recovery control strategy that uses the same braking force on both sides of the wheels, which can effectively improve the braking stability of the vehicle during braking energy recovery on opposite sides of the road. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] 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. The drawings described below are some embodiments of the present invention. For ordinary technicians in this field, it is obvious that other drawings can be obtained based on these drawings without paying any creative work.

[0085] Figure 1 This is the principle diagram of the fuzzy controller of the present invention.

[0086] Figure 2 This is the slip rate membership curve of the fuzzy controller of the present invention.

[0087] Figure 3 The μ-s curves of eight standard road surfaces of the present invention are as follows;

[0088] Figure 4 is the adhesion coefficient membership curve corresponding to the small slip rate fuzzy subset of the present invention;

[0089] Figure 5 is the adhesion coefficient membership curve corresponding to the slip rate fuzzy subset in the present invention;

[0090] Figure 6 is the adhesion coefficient membership curve corresponding to the large slip rate fuzzy subset of the present invention;

[0091] Figure 7 is the similarity membership function curve of the present invention;

[0092] Figure 8 This is a flow chart of the braking energy recovery control strategy on split road surfaces according to the present invention.

[0093] Figure 9 It is a fuzzy logic reasoning diagram.

[0094] Figure 10 The peak adhesion coefficient diagram of each road surface in μ-s DETAILED DESCRIPTION

[0095] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0096] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0097] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "axial", "radial", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0098] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integral connection; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0099] The method for controlling braking energy recovery on a split road surface for an electric vehicle of the present invention rationally distributes the braking force on the left front, right front, left rear, and right rear wheels, thereby ensuring optimal braking stability, effectively improving braking energy recovery efficiency, increasing driving range, and improving economy. The method comprises the following steps:

[0100] S01. Calculate the required braking force and the maximum motor braking force:

[0101] At the start of braking, the vehicle speed v is collected by the vehicle speed sensor, and the braking intensity z is collected by the brake pedal sensor. The vehicle speed v and braking intensity z are input into the braking force calculation module to obtain the required braking force, the maximum braking torque of the motor, and the SOC value of the power battery. Specifically,

[0102] Required braking force F when the car brakes need for:

[0103]

[0104] F μf +F μr =F need ,

[0105] Where: F μf is the braking force of the front axle brake; F μr is the braking force of the rear axle brake; G is the gravity of the car;

[0106] Determine the braking force relationship between the front and rear axles based on the I curve:

[0107]

[0108]

[0109] Where: G is the weight of the car; a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; z is the braking strength; h g is the height of the vehicle's center of mass;

[0110] The maximum braking torque of the motor is determined by the following formula:

[0111]

[0112] Where: T emax is the maximum braking torque of the motor; P max is the peak power of the motor; P Bmax is the maximum charging power of the battery; η b Battery charging efficiency; T max is the peak torque of the motor; n is the motor speed; n d is the rated speed of the motor;

[0113] The charging current is proportional to the motor force and can be expressed as:

[0114] Where: I m is the charging current; η m is the motor power generation efficiency; T n is the torque of a single motor; U ec is the battery terminal voltage;

[0115] The SOC value of the power battery is calculated using the ampere-hour integration method:

[0116] Where: SOC init is the initial SOC of the battery; Qcap is the battery capacity.

[0117] S02. Calculate the slip rate and road adhesion coefficient of each wheel:

[0118] At the start of braking, the slip rate of each wheel is determined by the following formula based on the vehicle speed v collected by the vehicle speed sensor and the wheel speed ω collected by the wheel speed sensor:

[0119]

[0120] Where: S ij is the slip rate of each wheel; ω ij is the speed of each wheel, where ij∈(fl, fr, rl, rr), fl represents the left front wheel, fr represents the right front wheel, rl represents the left rear wheel, and rr represents the right rear wheel.

[0121] According to the force analysis of the vehicle during braking, the ground braking force F of each wheel is b and the normal load F of each wheel z Calculate the road adhesion coefficient of each wheel as follows:

[0122] According to the force analysis of the vehicle during braking:

[0123] J ij ω ij =F b_ij ·rT μ_ij

[0124] Where: J ij is the moment of inertia of each wheel; T μ_ij is the brake torque of each wheel;

[0125] The calculation formula for the braking torque of each wheel brake is: T μ_ij =T m_ij +T e_ij ;

[0126] Where: T m_ij is the mechanical braking torque, N·m; T e_ij is the motor torque;

[0127] When only the mechanical brake is in effect, the motor braking torque is 0. When only the motor brake is in effect, the mechanical braking torque is 0. When the mechanical braking force and the motor braking force act together, the brake braking force can be calculated by the following formula:

[0128]

[0129] The road adhesion coefficient of each wheel is:

[0130] Where: μ ijis the road adhesion coefficient of each wheel; F z_ij is the normal load from the ground on each wheel, calculated from the vehicle model.

[0131] S03. Calculate the peak road adhesion coefficient between each wheel and the road surface:

[0132] like Figure 1 As shown in the figure, the wheel slip rate and the wheel-road adhesion coefficient are input into the fuzzy controller. The fuzzy controller obtains the similarity between the current road surface and the eight standard road surfaces, and then calculates the peak adhesion coefficient between the current wheel and the road surface in combination with the peak adhesion coefficient of the standard road surface.

[0133] With:

[0134] Enter S ij According to the first membership function, the fuzzy sets are fuzzified into small slip rate fuzzy subsets, medium slip rate fuzzy subsets and large slip rate fuzzy subsets;

[0135] When the input S ij Located in the small slip rate fuzzy subset, under the small slip rate fuzzy subset, the input μ ij According to the second membership function, the first dry cement fuzzy subset and the first dry asphalt fuzzy subset are obtained. ij Compared with the first dry cement fuzzy subset and the first dry asphalt fuzzy subset, the input μ ij The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subset and other fuzzy subsets. The closer the distance, the more similar they are.

[0136] When the input S ij Located in the medium slip rate fuzzy subset, under the medium slip rate fuzzy subset, the input μ ij According to the third membership function, the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset are obtained; the input μ ij Compared with the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset, the input μ ij The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subset and other fuzzy subsets. The closer the distance, the more similar they are.

[0137] When the input S ij Located in the large slip fuzzy subset, under the large slip fuzzy subset, the input μ ijAccording to the fourth membership function, the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset are obtained; the input μ ij Compared with the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset, the input μ ij The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subsets and other fuzzy subsets. The closer the distance, the more similar they are.

[0138] The similarity degree is defuzzified to obtain the similarity degree between the current road surface of each round and the eight standard road surface curves.

[0139] Implementation example Figure 2 As shown, the input S ij According to the first membership function, it is fuzzified into small slip rate fuzzy subset, medium slip rate fuzzy subset and large slip rate fuzzy subset, and the corresponding slip rate intervals are [0, 0.08], [0.08, 0.15], and [0.15, 1] ​​respectively. The first membership function uses the triangular membership function. Figure 3 are eight standard road μ-s curves, Figure 3 The adhesion coefficients of the eight road surfaces vary differently under different slip ratio fuzzy subsets. When the adhesion coefficient range is small, some intersections are generated between the line segments, and the difference is not obvious. Only two high adhesion coefficient road surfaces are compared, and the fuzzy processing is basically a straight line. Under the medium slip ratio fuzzy subset, the adhesion coefficient increases rapidly with the increase of slip ratio, and the adhesion coefficient variation range is large. Under the large slip ratio fuzzy subset, the adhesion coefficient decreases slowly with the increase of slip ratio, and the adhesion coefficient variation range is small. That is, when the input S ij Located in the small slip rate fuzzy subset, under the small slip rate fuzzy subset, the input μ ij The first dry cement fuzzy subset and the first dry asphalt fuzzy subset are obtained by dividing according to the second membership function. ij Located in the medium slip rate fuzzy subset, under the medium slip rate fuzzy subset, the input μ ij According to the third membership function, we can obtain the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset. ij Located in the large slip fuzzy subset, under the large slip fuzzy subset, the input μ ijAccording to the fourth membership function, the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset are obtained; Figure 4 、 Figure 5 、 Figure 6 The adhesion coefficient membership functions under small slip, medium slip and high slip are respectively selected. The triangular membership function is selected, that is, the second membership function, the third membership function and the fourth membership function are all triangular membership functions. The triangular membership function type is selected, which has the advantages of high resolution and high control sensitivity. The triangular membership function is obtained through expert experience. After fuzzification, the fuzzy subset of the current slip rate and the fuzzy subset of the current adhesion coefficient are obtained. The fuzzy rules are as follows: Figure 9 As shown. Figure 7 As shown in Figure 1, the similarity is divided into five fuzzy subsets, represented by the letters {VS, S, C, D, VD}. VS indicates very similar, S indicates similar, C indicates moderately similar, D indicates dissimilar, and VD indicates completely dissimilar. The fuzzy subset in which the current adhesion coefficient is located is defined as very similar. The similarity is determined based on the distance between the fuzzy subset in which the current adhesion coefficient is located and the other fuzzy subsets. The closer the distance, the more similar. The fuzzy set is converted into a numerical value to obtain the similarity x between the current road surface and the eight standard curves. 1ij to x 8ij The membership function type in the defuzzification is selected as the Gaussian membership function, which has the effect of high control accuracy and good stability. The area centroid method is used for clarification.

[0140] The similarity between the fuzzy controller output and the μ-s curves of eight road surfaces is obtained according to Figure 10 The peak adhesion coefficient of each standard road surface can be calculated by the following formula:

[0141]

[0142] Where: μ max1 is the peak adhesion coefficient on standard ice road, μ max2 is the peak adhesion coefficient of the standard snow road, μ max3 is the peak adhesion coefficient of the standard wet cobblestone road, μ max4 is the peak adhesion coefficient of the standard wet asphalt pavement, μ max5 is the peak adhesion coefficient of the standard wet asphalt pavement, μ max6 is the peak adhesion coefficient of the standard wet asphalt pavement, μ max7 is the peak adhesion coefficient of the standard dry cement pavement, μ max8 is the peak adhesion coefficient of the standard dry asphalt pavement; μmaxij is the peak adhesion coefficient between each wheel and the road; x 1ij is the similarity between the current road surface of each round and the standard ice road surface, x 2ij is the similarity between the current road surface of each round and the standard snow road surface, x 3ij is the similarity between the current road surface and the standard wet cobblestone road surface, x 4ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 5ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 6ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 7ij is the similarity between the current road surface and the standard dry cement road surface, x 8ij It is the similarity between the current road surface of each round and the standard dry asphalt road surface.

[0143] S04. Determine whether the conditions for motor braking are met: when the vehicle speed is less than 5 km / h, the motor's power generation efficiency is low; when the SOC is less than 0.9, the battery charging efficiency is low and overcharging should be avoided; when z is less than 0.8, this intensity indicates the driver's intention to perform emergency braking; under these three conditions, only mechanical braking is used.

[0144] When the conditions for motor braking are met, i.e., the vehicle speed is greater than 5 km / h, the SOC is greater than 0.9, and z is greater than 0.8, the braking force distribution strategy is determined based on the peak adhesion coefficient between each wheel and the road surface, the required braking force, and the maximum motor braking force, such as Figure 8 shown.

[0145] To improve brake energy recovery efficiency, when the two front wheel electric motors can meet the braking demand, only the two front wheel motors provide braking force. If the braking force is insufficient, the four front and rear wheel motors provide braking force together. If the sum of the four maximum electric motors cannot meet the total braking force demand, mechanical braking force is allocated to compensate. If the difference in peak road adhesion coefficient between the two wheels (as determined in step S3) is greater than 0.2, applying the same braking force to both wheels will result in a significant difference in ground braking force between the coaxial wheels, resulting in a large yaw moment for the vehicle and, in turn, affecting braking stability. To improve braking stability on split roads, the braking force on the wheel with the higher peak road adhesion coefficient is reduced, while the braking force on the wheel with the lower peak road adhesion coefficient is increased while ensuring the wheel does not lock, thereby narrowing the difference in ground braking force between the two wheels.

[0146] When the required braking force F need ≤2F emax When F emax The maximum braking force of a single motor;

[0147] When the required braking force Fneed >2F emax When F need ≤4F emax , and |μ|≤0.2, the left front wheel motor, right front wheel motor, left rear wheel motor and right rear wheel motor jointly provide braking force,

[0148]

[0149]

[0150] Where:

[0151] |μ|=|μ l -μ r |,μ l =min{μ maxlf , μ maxlr},μ r =min{μ maxrf , μ maxrr}; L is the wheelbase of the car;

[0152] F elf is the left front wheel electric motor power, F erf Right front wheel electric motor power, F elr The left rear wheel electric motor is powered, F err Right rear wheel electric motor power;

[0153] When the required braking force F need >2F emax When F need ≤4F emax , and |μ|>0.2, the braking force on the coaxial left and right wheels is distributed as follows:

[0154]

[0155]

[0156] When the required braking force F need >2F emax When F need >4F emax , and |μ|≤0.2, the left front wheel motor, right front wheel motor, left rear wheel motor, and right rear wheel motor provide the maximum electric braking force, and the insufficient part is compensated by the mechanical braking force, specifically:

[0157] F elf =F erf =F elr =F err =F emax

[0158]

[0159]

[0160] Where: F mlf is the mechanical braking force of the left front wheel; F mrf is the mechanical braking force of the right front wheel; F mlr Left rear wheel mechanical braking force; F mrr Provides mechanical braking force for the right rear wheel;

[0161] When the required braking force F need >2F emax When F need >4F emax , and |μ|>0.2, the braking force on the coaxial left and right wheels is distributed as follows:

[0162]

[0163]

[0164] A control system for a method of controlling braking energy recovery on an electric vehicle on a split road, comprising a vehicle speed sensor, a wheel speed sensor, a brake pedal sensor, and a controller. The controller comprises a regenerative braking start-up determination module, a required braking force estimation module, a road surface recognition module, a braking force distribution module, a motor module, and a battery module.

[0165] The regenerative braking start judgment module is used to judge whether to start the motor braking according to the vehicle speed v, SOC value and braking intensity z;

[0166] The required braking force estimation module is used to calculate the total required braking force and obtain the braking force distribution curve of the front and rear axle brakes;

[0167] The road surface recognition module is used to output the peak road adhesion coefficient between each wheel and the road surface;

[0168] The braking force distribution module determines the braking force distribution strategy based on the total required braking force calculated by the required braking force estimation module and the peak adhesion coefficient between each wheel and the road surface output by the road surface recognition module, and outputs the motor braking force F of each wheel under different braking intensity and road surface adhesion conditions. e With mechanical braking force F n ;

[0169] The motor module converts the vehicle's kinetic energy during braking into electrical energy when in a power generation state;

[0170] The battery module is used to store electric energy recovered by braking, thereby realizing the energy recovery function.

[0171] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0172] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent embodiments or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling braking energy recovery of an electric vehicle on a split road, characterized in that: The steps include: S01. Calculate the required braking force and the maximum motor braking force: At the beginning of braking, the vehicle speed v is collected according to the vehicle speed sensor, and the braking intensity z is collected according to the brake pedal sensor. The vehicle speed v and the braking intensity z are input into the braking force calculation module to obtain the required braking force, the maximum braking torque of the motor, the peak torque of the motor, and the SOC value of the power battery; S02. Calculate the slip rate and road adhesion coefficient of each wheel: At the beginning of braking, the slip rate of each wheel is determined based on the vehicle speed v collected by the vehicle speed sensor and the wheel speed ω collected by the wheel speed sensor. According to the force analysis of the vehicle during braking, the ground braking force F of each wheel is used to determine the slip rate of each wheel. b and the normal load F of each wheel z Calculate the road adhesion coefficient of each wheel and input the wheel slip rate and road adhesion coefficient of each wheel into the fuzzy controller; S03. Calculate the peak road adhesion coefficient between each wheel and the road surface: The fuzzy controller determines the similarity between the current road surface and eight standard road surfaces, and then calculates the peak adhesion coefficient between each wheel and the road surface in combination with the peak adhesion coefficient of the standard road surface. S04. Determine whether the conditions for motor braking are met: Determine whether the conditions for motor braking are met based on the vehicle speed v, the battery SOC value, and the braking intensity z. When the conditions for motor braking are met, formulate and determine a braking force distribution strategy based on the peak adhesion coefficient between each wheel and the road surface, the required braking force, and the maximum motor braking force. Specifically, the strategy is as follows: When the required braking force F need ≤2F emax When F emax The maximum braking force of a single motor; When the required braking force F need >2F emax When F need ≤4F emax ,and When the left front wheel motor, right front wheel motor, left rear wheel motor and right rear wheel motor jointly provide braking force, ； ； Where: , , ; L is the wheelbase of the car; The left front wheel motor is powered. Right front wheel electric motor power, The electric motor of the left rear wheel is powered. Right rear wheel electric motor power; When the required braking force F need >2F emax When F need ≤4F emax ,and When , the braking force on the coaxial left and right wheels is distributed as follows: ， ； When the required braking force F need >2F emax When F need >4F emax ,and When the left front wheel motor, right front wheel motor, left rear wheel motor and right rear wheel motor provide the maximum electric braking force, and the insufficient part is compensated by the mechanical braking force, specifically: ， ， ； Where: It is the mechanical braking force of the left front wheel; It is the mechanical braking force of the right front wheel; Left rear wheel mechanical braking force; Provides mechanical braking force for the right rear wheel; When the required braking force F need >2F emax When F need >4F emax ,and When , the braking force on the coaxial left and right wheels is distributed as follows: ； 。 2. The method for controlling braking energy recovery of an electric vehicle on a split road according to claim 1, characterized in that: The vehicle speed v and braking intensity z are input into the braking force calculation module to obtain the required braking force, the maximum braking torque of the motor, and the SOC value of the power battery, which are specifically: Required braking force F when the car brakes need for: ， ， Where: Provides braking force for the front axle brakes; is the braking force of the rear axle brake; G is the gravity of the car; Determine the braking force relationship between the front and rear axles based on the I curve: ， ， Where: G is the weight of the car; a is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; z is the braking strength; is the height of the vehicle's center of mass; The maximum braking torque of the motor is determined by the following formula: ， Where: T emax is the maximum braking torque of the motor; P max is the peak power of the motor; P Bmax The maximum charging power of the battery; Battery charging efficiency; T max is the peak torque of the motor; n is the motor speed; n d is the rated speed of the motor; The charging current is proportional to the motor force and is expressed as: , Where: is the charging current; is the motor power generation efficiency; is the torque of a single motor; is the battery terminal voltage; The SOC value of the power battery is calculated using the ampere-hour integration method: , Where: is the initial SOC of the battery; is the battery capacity.

3. The method for controlling braking energy recovery of an electric vehicle on a split road according to claim 1, characterized in that: According to the vehicle speed v and wheel speed ω of each wheel, the slip rate of each wheel is determined by the following formula: ， Where: is the slip rate of each wheel; is the speed of each wheel, where , fl represents the left front wheel, fr represents the right front wheel, rl represents the left rear wheel, and rr represents the right rear wheel.

4. The method for controlling braking energy recovery of an electric vehicle on a split road according to claim 3, characterized in that: According to the force analysis of the vehicle during braking, the ground braking force F of each wheel is b and the normal load F of each wheel z Calculate the road adhesion coefficient of each wheel as follows: According to the force analysis of the vehicle during braking: ， Where: is the moment of inertia of each wheel; is the brake torque of each wheel; The calculation formula for the braking torque of each wheel brake is: ; Where: is the mechanical braking torque, ; is the motor torque; The brake force is calculated as follows: ； The road adhesion coefficient of each wheel is: , Where: is the road adhesion coefficient of each wheel; is the normal load from the ground on each wheel, calculated from the vehicle model.

5. The method for controlling braking energy recovery of an electric vehicle on a split road according to claim 3, characterized in that: The similarity between the current road surface and the eight standard road surfaces is obtained based on the fuzzy controller, which is: The input According to the first membership function, the fuzzy sets are fuzzified into small slip rate fuzzy subsets, medium slip rate fuzzy subsets and large slip rate fuzzy subsets; When input Located in the small slip fuzzy subset, under the small slip fuzzy subset, the input According to the second membership function, the first dry cement fuzzy subset and the first dry asphalt fuzzy subset are obtained. Compared with the first dry cement fuzzy subset and the first dry asphalt fuzzy subset, the input The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subset and other fuzzy subsets. The closer the distance, the more similar they are. When input Located in the medium slip rate fuzzy subset, under the medium slip rate fuzzy subset, the input According to the third membership function, the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset are obtained; the input Compared with the second ice fuzzy subset, the second snow fuzzy subset, the second wet pebble fuzzy subset, the second wet asphalt small fuzzy subset, the second wet asphalt medium fuzzy subset, the second wet asphalt large fuzzy subset, the second dry cement fuzzy subset and the second dry asphalt fuzzy subset, the input The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subsets and other fuzzy subsets. The closer the distance, the more similar they are. When input Located in the large slip fuzzy subset, under the large slip fuzzy subset, the input According to the fourth membership function, the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset are obtained; the input Compared with the third ice fuzzy subset, the third snow fuzzy subset, the third wet pebble fuzzy subset, the third wet asphalt small fuzzy subset, the third wet asphalt medium fuzzy subset, the third wet asphalt large fuzzy subset, the third dry cement fuzzy subset and the third dry asphalt fuzzy subset, the input The fuzzy subsets they are in are defined as very similar, and the degree of similarity is judged based on the distance between the very similar fuzzy subsets and other fuzzy subsets. The closer the distance, the more similar they are. The similarity degree is defuzzified to obtain the similarity degree between the current road surface of each round and the eight standard road surface curves.

6. The method for controlling braking energy recovery of an electric vehicle on a split road according to claim 5, characterized in that: The peak adhesion coefficient between each wheel and the road surface is calculated based on the peak adhesion coefficient of the standard road surface, which can be obtained by the following formula: ， in: is the peak adhesion coefficient on standard ice road, is the peak adhesion coefficient on standard snow road, is the peak adhesion coefficient of the standard wet cobblestone road, is the peak adhesion coefficient of the standard wet asphalt pavement, is the peak adhesion coefficient of the standard wet asphalt pavement, is the peak adhesion coefficient of the standard wet asphalt pavement, is the peak adhesion coefficient of the standard dry cement pavement, is the peak adhesion coefficient of the standard dry asphalt pavement; is the peak adhesion coefficient between each wheel and the road; x 1ij is the similarity between the current road surface of each round and the standard ice road surface, x 2ij is the similarity between the current road surface of each round and the standard snow road surface, x 3ij is the similarity between the current road surface and the standard wet cobblestone road surface, x 4ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 5ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 6ij is the similarity between the current road surface of each round and the standard wet asphalt road surface, x 7ij is the similarity between the current road surface and the standard dry cement road surface, x 8ij It is the similarity between the current road surface of each round and the standard dry asphalt road surface.

7. A control system for an electric vehicle's off-road braking energy recovery control method according to any one of claims 1 to 6, characterized in that: It includes a vehicle speed sensor, a wheel speed sensor, a brake pedal sensor and a controller. The controller includes a regenerative braking start judgment module, a required braking force estimation module, a road surface recognition module, a braking force distribution module, a motor module and a battery module. The regenerative braking start judgment module is used to judge whether to start the motor braking according to the vehicle speed v, SOC value and braking intensity z; The required braking force estimation module is used to calculate the total required braking force and obtain the front and rear axle brake force distribution curve; The road surface recognition module is used to output the peak road adhesion coefficient between each wheel and the road surface; The braking force distribution module determines the braking force distribution strategy based on the total required braking force calculated by the required braking force estimation module and the peak adhesion coefficient between each wheel and the road surface output by the road surface recognition module, and outputs the motor braking force of each wheel under different braking intensity and road adhesion conditions. With mechanical braking force ; The motor module converts the vehicle's kinetic energy during braking into electrical energy when in a power generation state; The battery module is used to store electric energy recovered by braking, thereby realizing the energy recovery function.

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