Method and system for determining braking energy recovery scheme of pure electric vehicle

Abstract

The invention provides a method and a system for determining a braking energy recovery scheme of a pure electric vehicle. The method comprises the following steps: constructing a fuzzy rule base; obtaining battery electric quantity, vehicle speed information, road surface type information and braking strength information of the pure electric vehicle at a to-be-controlled moment, and screening out regenerative braking force distribution coefficients of fuzzy rules corresponding to road surface types of the pure electric vehicle at the to-be-controlled moment from the fuzzy rule base; determining the regenerative braking force distribution coefficient of the pure electric vehicle at the to-be-controlled moment according to the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type at the to-be-controlled moment and the credibility corresponding to each road surface type; and motor braking force and mechanical braking force at the to-be-controlled moment are determined based on the regenerative braking force distribution coefficient at the to-be-controlled moment, and a braking energy recovery scheme of the pure electric vehicle at the to-be-controlled moment is obtained. According to the technical scheme, the braking conversion rate, the braking recovery rate and the safety of the electric vehicle are improved.

Description

Technical Field

[0001] The present application relates to the field of energy recovery technology, and in particular to a method and system for determining a braking energy recovery scheme for a pure electric vehicle. Background Art

[0002] Currently, electric vehicles widely use kinetic energy recovery technology when braking. This technology enables the electric motor of an electric vehicle to not only provide driving force when driving the vehicle, but also become a generator when the vehicle is coasting or braking, recovering kinetic energy while providing reverse braking force. The magnitude of the reverse braking force can be changed according to the depth of the brake pedal, achieving different braking forces. However, the existing technology only considers energy recovery from a single factor, resulting in low energy recovery efficiency. Therefore, it is urgent to propose an energy recovery solution that considers multiple factors to improve energy recovery efficiency and achieve energy conservation and emission reduction. Summary of the Invention

[0003] The present application provides a method and system for determining a braking energy recovery scheme for a pure electric vehicle, so as to at least solve the technical problem that energy recovery only considers a single factor, resulting in low energy recovery efficiency.

[0004] The first embodiment of the present application provides a method for determining a braking energy recovery scheme for a pure electric vehicle, the method comprising:

[0005] Obtaining a battery power type, a vehicle speed type, a road surface type, a braking intensity type, and a regenerative braking force distribution coefficient type of the pure electric vehicle, and constructing a fuzzy rule base based on the battery power type, the vehicle speed type, the road surface type, the braking intensity type, and the regenerative braking force distribution coefficient type of the pure electric vehicle;

[0006] obtaining battery power, vehicle speed information, road surface type information, and braking intensity information of the pure electric vehicle at the time to be controlled, and selecting, from the fuzzy rule library, regenerative braking force distribution coefficients corresponding to respective road surface types of the pure electric vehicle at the time to be controlled based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled;

[0007] Determining the regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled according to the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type at the time to be controlled and the credibility corresponding to each road surface type;

[0008] determining the motor braking force and the mechanical braking force of the pure electric vehicle at the time of waiting to be controlled based on the regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting to be controlled, and obtaining a braking energy recovery scheme for the pure electric vehicle at the time of waiting to be controlled;

[0009] Among them, the road surface type information includes: road surface type and the corresponding confidence level of the road surface type.

[0010] Preferably, the battery power type includes: low power, medium power, high power;

[0011] The vehicle speed type includes: low speed, medium speed, high speed;

[0012] The road surface type includes: highway, dirt road, snow;

[0013] The braking intensity type includes: light braking, medium braking, heavy braking;

[0014] The regenerative braking force distribution coefficient type includes: low coefficient, medium coefficient, high coefficient;

[0015] Among them, the low power is from 0 to A1, the medium power is from A2 to A3, the high power is from A4 to 100%, 0 < A2 < A1 < A4 < A3 < 100%, A1 is the preset first power, A2 is the preset second power, A3 is the preset third power, and A4 is the preset fourth power;

[0016] The low speed is from 0 to V1, the medium speed is from V2 to V3, the high speed is from V4 to 100%, 0 < V2 < V1 < V4 < V3 < 180 km / h, V1 is the preset first speed, V2 is the preset second speed, V3 is the preset third speed, and V4 is the preset fourth speed;

[0017] The confidence level of the highway is C1, the confidence level of the dirt road is C2, and the confidence level of the snow is C3;

[0018] The light braking is from 0 to Z1, the medium braking is from Z2 to Z3, the heavy braking is from Z4 to 1, 0 < Z2 < Z1 < Z4 < Z3 < 1, Z1 is the preset first braking value, Z2 is the preset second braking value, Z3 is the preset third braking value, and Z4 is the preset fourth braking value;

[0019] The low coefficient is from 0 to X1, the medium coefficient is from X2 to X3, the high coefficient is from X4 to 1, 0 < X2 < X1 < X4 < X3 < 1, X1 is the preset first coefficient, X2 is the preset second coefficient, X3 is the preset third coefficient, and X4 is the preset fourth coefficient.

[0020] Furthermore, constructing a fuzzy rule base according to the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle includes:

[0021] Performing permutation and combination based on the battery power type, vehicle speed type, road surface type, and braking intensity type to obtain multiple groups of initial fuzzy rules;

[0022] Determining the regenerative braking force distribution coefficient types corresponding to the multiple groups of initial fuzzy rules according to the preset regenerative braking force distribution rules;

[0023] A fuzzy rule base is generated based on the multiple groups of initial fuzzy rules and the types of regenerative braking force distribution coefficients corresponding to the multiple groups of initial fuzzy rules.

[0024] Furthermore, the regenerative braking force distribution coefficients of the fuzzy rules corresponding to the road types of the pure electric vehicle at the time to be controlled are screened out from the fuzzy rule base based on the battery power, vehicle speed information, road type information, and braking intensity information at the time to be controlled, including:

[0025] Based on the battery power, vehicle speed, road surface type, and braking intensity information at the time to be controlled, fuzzy rules corresponding to each road surface type in the road surface type information are screened from the fuzzy rule library, wherein the fuzzy rules include: battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type;

[0026] determining, according to a preset regenerative braking force distribution coefficient parameterization rule, a regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information;

[0027] The preset regenerative braking force distribution coefficient parameterization rule includes:

[0028] When the regenerative braking force distribution coefficient type is low coefficient, 0 is used as the regenerative braking force distribution coefficient;

[0029] When the regenerative braking force distribution coefficient type is a medium coefficient, 0.5 is used as the regenerative braking force distribution coefficient;

[0030] When the regenerative braking force distribution coefficient type is a high coefficient, 1 is used as the regenerative braking force distribution coefficient.

[0031] Furthermore, determining the regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled based on the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type at the time to be controlled and the credibility corresponding to each road surface type includes:

[0032] Filtering fuzzy rules corresponding to each road surface type in the road surface type information from the fuzzy rule library based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled, and then determining the membership degree of each fuzzy rule corresponding to each road surface type;

[0033] Determining the activation strength of each fuzzy rule corresponding to each road surface type according to the credibility corresponding to each road surface type and the membership degree of each fuzzy rule corresponding to each road surface type;

[0034] The regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control is determined according to the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information.

[0035] Furthermore, determining the regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control based on the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information includes:

[0036] Determine the sum of the products of the regenerative braking force distribution coefficient and the activation intensity of each fuzzy rule in each road surface type, and obtain the regenerative braking force distribution coefficient of each road surface type;

[0037] The sum of the regenerative braking force distribution coefficients of each road surface type is determined, and the sum of the regenerative braking force distribution coefficients of each road surface type is used as the regenerative braking force distribution coefficient of the pure electric vehicle at the time when the pure electric vehicle is to be controlled.

[0038] A second embodiment of the present application provides a system for determining a braking energy recovery scheme for a pure electric vehicle, comprising:

[0039] a construction module for obtaining a battery power type, a vehicle speed type, a road surface type, a braking intensity type, and a regenerative braking force distribution coefficient type of a pure electric vehicle, and constructing a fuzzy rule base according to the battery power type, the vehicle speed type, the road surface type, the braking intensity type, and the regenerative braking force distribution coefficient type of the pure electric vehicle;

[0040] a screening module, configured to obtain battery power, vehicle speed information, road surface type information, and braking intensity information of the pure electric vehicle at the time to be controlled, and to screen, from the fuzzy rule base, regenerative braking force distribution coefficients corresponding to respective road surface types of the pure electric vehicle at the time to be controlled based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled;

[0041] a first determining module, configured to determine a regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled based on the regenerative braking force distribution coefficients of the fuzzy rules corresponding to the road surface types at the time to be controlled and the credibility corresponding to the road surface types;

[0042] A second determination module, configured to determine the electric braking force and mechanical braking force of the pure electric vehicle at the moment to be controlled based on the regenerative braking force distribution coefficient of the pure electric vehicle at the moment to be controlled, so as to obtain the braking energy recovery scheme of the pure electric vehicle at the moment to be controlled;

[0043] Wherein, the road surface type information includes: road surface type and the credibility corresponding to the road surface type.

[0044] Preferably, the battery power type includes: low power, medium power, high power;

[0045] The vehicle speed type includes: low speed, medium speed, high speed;

[0046] The road surface type includes: highway, dirt road, snow;

[0047] The braking intensity type includes: light braking, medium braking, heavy braking;

[0048] The regenerative braking force distribution coefficient type includes: low coefficient, medium coefficient, high coefficient;

[0049] Wherein, the low power is from 0 to A1, the medium power is from A2 to A3, the high power is from A4 to 100%, 0 < A2 < A1 < A4 < A3 < 100%, A1 is a preset first power, A2 is a preset second power, A3 is a preset third power, and A4 is a preset fourth power;

[0050] The low speed is from 0 to V1, the medium speed is from V2 to V3, the high speed is from V4 to 100%, 0 < V2 < V1 < V4 < V3 < 180 km / h, V1 is a preset first speed, V2 is a preset second speed, V3 is a preset third speed, and V4 is a preset fourth speed;

[0051] The credibility of the highway is C1, the credibility of the dirt road is C2, and the credibility of the snow is C3;

[0052] The light braking is from 0 to Z1, the medium braking is from Z2 to Z3, the heavy braking is from Z4 to 1, 0 < Z2 < Z1 < Z4 < Z3 < 1, Z1 is a preset first braking value, Z2 is a preset second braking value, Z3 is a preset third braking value, and Z4 is a preset fourth braking value;

[0053] The low coefficient is from 0 to X1, the medium coefficient is from X2 to X3, the high coefficient is from X4 to 1, 0 < X2 < X1 < X4 < X3 < 1, X1 is a preset first coefficient, X2 is a preset second coefficient, X3 is a preset third coefficient, and X4 is a preset fourth coefficient.

[0054] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method described in the first aspect is implemented.

[0055] A fourth embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first embodiment.

[0056] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0057] The present application proposes a method and system for determining a braking energy recovery scheme for a pure electric vehicle, the method comprising: obtaining the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle, and constructing a fuzzy rule base based on the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle; obtaining the battery power, vehicle speed information, road surface type information and braking intensity information of the pure electric vehicle at the time to be controlled, and constructing a fuzzy rule base based on the battery power, vehicle speed information, road surface type information and braking intensity information of the pure electric vehicle at the time to be controlled The regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the time when the pure electric vehicle is to be controlled are screened out; the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the time when the pure electric vehicle is to be controlled are determined based on the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the time when the pure electric vehicle is to be controlled and the credibility corresponding to each road surface type at the time when the pure electric vehicle is to be controlled are determined; the motor braking force and mechanical braking force of the pure electric vehicle at the time when the pure electric vehicle is to be controlled are determined based on the regenerative braking force distribution coefficients at the time when the pure electric vehicle is to be controlled, and a braking energy recovery scheme for the pure electric vehicle at the time when the pure electric vehicle is to be controlled is obtained; wherein the road surface type information includes: road surface type and credibility corresponding to road surface type. The technical solution proposed in this application improves the braking conversion rate, braking recovery rate, and safety of electric vehicles.

[0058] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0060] Figure 1 A flowchart of a method for determining a braking energy recovery scheme for a pure electric vehicle according to one embodiment of the present application;

[0061] Figure 2A structural diagram of a regenerative braking system provided according to one embodiment of the present application;

[0062] Figure 3 The present invention is a structural diagram of a system for determining a braking energy recovery scheme for a pure electric vehicle according to one embodiment of the present application. DETAILED DESCRIPTION

[0063] The following describes in detail embodiments of the present application, 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 application, and should not be construed as limiting the present application.

[0064] The present application proposes a method and system for determining a braking energy recovery scheme for a pure electric vehicle, the method comprising: obtaining the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle, and constructing a fuzzy rule base based on the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle; obtaining the battery power, vehicle speed information, road surface type information and braking intensity information of the pure electric vehicle at the time to be controlled, and constructing a fuzzy rule base based on the battery power, vehicle speed information, road surface type information and braking intensity information at the time to be controlled. The regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the time when the pure electric vehicle is to be controlled are screened out; the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the time when the pure electric vehicle is to be controlled are determined based on the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the time when the pure electric vehicle is to be controlled and the credibility corresponding to each road surface type at the time when the pure electric vehicle is to be controlled are determined; the motor braking force and mechanical braking force of the pure electric vehicle at the time when the pure electric vehicle is to be controlled are determined based on the regenerative braking force distribution coefficients at the time when the pure electric vehicle is to be controlled, and a braking energy recovery scheme for the pure electric vehicle at the time when the pure electric vehicle is to be controlled is obtained; wherein the road surface type information includes: road surface type and credibility corresponding to road surface type. The technical solution proposed in this application improves the braking conversion rate, braking recovery rate, and safety of electric vehicles.

[0065] The following describes a method and system for determining a braking energy recovery scheme for a pure electric vehicle according to an embodiment of the present application with reference to the accompanying drawings.

[0066] Example 1

[0067] Figure 1 This is a flow chart of a method for determining a braking energy recovery scheme for a pure electric vehicle according to one embodiment of the present application, as shown in FIG. Figure 1 As shown, the method includes:

[0068] Step 1: Obtain the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle, and construct a fuzzy rule base based on the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle.

[0069] It should be noted that the regenerative braking system of pure electric vehicles is a highly efficient energy recovery mechanism. During vehicle deceleration or braking, it converts kinetic energy into electrical energy through the reverse rotation of the electric motor and stores this electrical energy back in the battery, thereby improving energy utilization and increasing the vehicle's range. This system can significantly reduce the energy consumption of the entire vehicle, allowing pure electric vehicles to reduce energy consumption and increase their range.

[0070] The regenerative braking system consists of two parts: a hydraulic braking system and a self-regenerative braking system. The hydraulic braking system is responsible for establishing and regulating the mechanical friction braking force, while the electric braking system, which includes the drive motor and its controller, the power battery and management system, and the vehicle controller, is responsible for converting the drivetrain's kinetic energy into electrical energy and storing it. During braking, the relevant sensors transmit the brake pedal signal collected to the brake controller, which calculates the total braking force required and analyzes vehicle speed, battery state of charge (SOC), motor torque, and vehicle driving conditions to allocate the proportion of mechanical braking to regenerative braking.

[0071] Different braking energy recovery strategies are required for different battery levels, current vehicle speeds, road types, braking intensity and other conditions to maximize energy recovery and achieve energy conservation and emission reduction effects.

[0072] The regenerative braking system of the pure electric vehicle is composed of a battery management system controller (BMS), a vehicle controller (VCU), and an intelligent driving controller (ADCU). Figure 2 shown.

[0073] The vehicle uses a forward-looking camera to collect road surface information in front of the vehicle and uses deep learning to identify road surface types. The results are then transmitted from the ADCU to the VCU via the CAN bus. Identifiable road types include highway, dirt road, and snowy terrain.

[0074] The battery management system controller (BMS) can collect the current battery power and output it to the vehicle control unit (VCU) through the CAN bus.

[0075] The vehicle control unit (VCU) can obtain the current vehicle speed and the driver's braking intensity signal. The VCU can also intelligently allocate the ratio of electric braking and mechanical braking, which is the regenerative braking force distribution coefficient.

[0076] In the embodiments of the present disclosure, the battery power types include: Low power, Medium power, High power;

[0077] The vehicle speed types include: Low speed, Medium speed, High speed;

[0078] The road surface types include: Dry road, Mud road, Snow road;

[0079] The braking intensity types include: Light braking, Medium braking, Heavy braking;

[0080] The regenerative braking force distribution coefficient types include: Low coefficient, Medium coefficient, High coefficient;

[0081] Among them, the Low power is from 0 to A1, the Medium power is from A2 to A3, the High power is from A4 to 100%, 0 < A2 < A1 < A4 < A3 < 100%, A1 is a preset first power, A2 is a preset second power, A3 is a preset third power, and A4 is a preset fourth power;

[0082] The Low speed is from 0 to V1, the Medium speed is from V2 to V3, the High speed is from V4 to 100%, 0 < V2 < V1 < V4 < V3 < 180 km / h, V1 is a preset first speed, V2 is a preset second speed, V3 is a preset third speed, and V4 is a preset fourth speed;

[0083] The credibility of the Dry road is C1, the credibility of the Mud road is C2, and the credibility of the Snow road is C3;

[0084] The Light braking is from 0 to Z1, the Medium braking is from Z2 to Z3, the Heavy braking is from Z4 to 1, 0 < Z2 < Z1 < Z4 < Z3 < 1, Z1 is a preset first braking value, Z2 is a preset second braking value, Z3 is a preset third braking value, and Z4 is a preset fourth braking value;

[0085] The Low coefficient is from 0 to X1, the Medium coefficient is from X2 to X3, the High coefficient is from X4 to 1, 0 < X2 < X1 < X4 < X3 < 1, X1 is a preset first coefficient, X2 is a preset second coefficient, X3 is a preset third coefficient, and X4 is a preset fourth coefficient.

[0086] In the embodiments of the present disclosure, the construction of the fuzzy rule base according to the battery power type, vehicle speed type, road surface type, braking intensity type and regenerative braking force distribution coefficient type of the pure electric vehicle includes:

[0087] Based on the battery power type, vehicle speed type, road surface type, and braking intensity type, multiple groups of initial fuzzy rules are obtained by permutation and combination;

[0088] Determining the regenerative braking force distribution coefficient types corresponding to the multiple groups of initial fuzzy rules according to the preset regenerative braking force distribution rules;

[0089] A fuzzy rule base is generated based on the multiple groups of initial fuzzy rules and the types of regenerative braking force distribution coefficients corresponding to the multiple groups of initial fuzzy rules.

[0090] It should be noted that the fuzzy rule base is shown in the following table:

[0091] Fuzzy rule base table

[0092]

[0093]

[0094]

[0095] It should be noted that the fuzzy rule base is established based on expert experience and actual needs.

[0096] 1. If the SOC is low and the vehicle speed is high, the regenerative braking force distribution coefficient is low.

[0097] 2. If the SOC is high and the vehicle speed is low, the regenerative braking force distribution coefficient is high.

[0098] 3. If the road surface type is snow, the regenerative braking force distribution coefficient is low.

[0099] 4. If the braking intensity is heavy, the regenerative braking force distribution coefficient is high.

[0100] in,

[0101] Low SOC and high vehicle speed: Reduce the regenerative braking force distribution coefficient to avoid excessive vehicle deceleration due to regenerative braking.

[0102] High SOC and low vehicle speed: Increase the regenerative braking force distribution coefficient to maximize energy recovery.

[0103] Snow: Reduce the regenerative braking force distribution coefficient to avoid vehicle loss of control.

[0104] Heavy braking intensity: Increases the regenerative braking force distribution coefficient to recover more energy.

[0105] Step 2: Obtain the battery power, vehicle speed information, road surface type information, and braking intensity information of the pure electric vehicle at the time to be controlled, and based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled, filter out the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type of the pure electric vehicle at the time to be controlled in the fuzzy rule library, wherein the road surface type information includes: road surface type and the credibility corresponding to the road surface type.

[0106] In the embodiment of the present disclosure, step 2 specifically includes:

[0107] Based on the battery power, vehicle speed, road surface type, and braking intensity information at the time to be controlled, fuzzy rules corresponding to each road surface type in the road surface type information are screened from the fuzzy rule library, wherein the fuzzy rules include: battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type;

[0108] determining, according to a preset regenerative braking force distribution coefficient parameterization rule, a regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information;

[0109] The preset regenerative braking force distribution coefficient parameterization rule includes:

[0110] When the regenerative braking force distribution coefficient type is low coefficient, 0 is used as the regenerative braking force distribution coefficient;

[0111] When the regenerative braking force distribution coefficient type is a medium coefficient, 0.5 is used as the regenerative braking force distribution coefficient;

[0112] When the regenerative braking force distribution coefficient type is a high coefficient, 1 is used as the regenerative braking force distribution coefficient.

[0113] Step 3: determining the regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled based on the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type at the time to be controlled and the credibility corresponding to each road surface type;

[0114] In the embodiment of the present disclosure, step 3 specifically includes:

[0115] 3.1 Based on the battery charge, vehicle speed, road surface type, and braking intensity information at the time of control, the fuzzy rule library is filtered to identify fuzzy rules corresponding to each road surface type in the road surface type information, and then the membership degree of each fuzzy rule corresponding to each road surface type is determined;

[0116] 3.2 Determining the activation strength of each fuzzy rule corresponding to each road surface type based on the credibility corresponding to each road surface type and the membership degree of each fuzzy rule corresponding to each road surface type;

[0117] 3.3 Determine the regenerative braking force distribution coefficient of the pure electric vehicle at the time when the control is to be performed based on the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information.

[0118] It should be noted that the determination of the regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control based on the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information includes:

[0119] Determine the sum of the products of the regenerative braking force distribution coefficient and the activation intensity of each fuzzy rule in each road surface type, and obtain the regenerative braking force distribution coefficient of each road surface type;

[0120] The sum of the regenerative braking force distribution coefficients of each road surface type is determined, and the sum of the regenerative braking force distribution coefficients of each road surface type is used as the regenerative braking force distribution coefficient of the pure electric vehicle at the time when the pure electric vehicle is to be controlled.

[0121] It should be noted that at the current moment, i.e., the moment to be controlled, the SOC power is C, the vehicle speed is V, the road type is road with a credibility of C1, the credibility of muddy road is C2, the credibility of snow is C3, and the braking intensity is Z. The Mamdani reasoning method is used to perform fuzzy reasoning based on fuzzy rules and fuzzified input to obtain the fuzzy output regenerative braking force distribution coefficient X, which is as follows:

[0122] Assume that the number of road type rules that meet the conditions is R, and their membership degrees are H1, H2…HR respectively;

[0123] There are S rules for mud road types that meet the conditions, and their membership degrees are M1, M2…MS respectively;

[0124] There are T snow type rules that meet the conditions, and their membership degrees are S1, S2…ST respectively;

[0125] Among them, the H1, H2...HR are all equal to 1 / R, the M1, M2...MS are all equal to 1 / S, and the S1, S2...ST are all equal to 1 / T.

[0126] For example, the medium range of battery power is 20% to 70%, so a=(20+70) / 2=45, b=(70-20) / 2=25.

[0127] It should be noted that the regenerative braking force distribution coefficient is parameterized as Low = 0, Medium = 0.5, and High = 1. The final fuzzy output result Result is obtained by multiplying the defined value by the activation strength of each fuzzy set that meets the conditions.

[0128] Result=C1*H1*(0 / 0.5 / 1)+C1*H2*(0 / 0.5 / 1)+C1*HR*(0 / 0.5 / 1)+

[0129] C2*M1*(0 / 0.5 / 1)+C2*M2*(0 / 0.5 / 1)+C2*MS*(0 / 0.5 / 1)+C3*S1*(0 / 0.5 / 1)+

[0130] C3*S2*(0 / 0.5 / 1)+C3*ST*(0 / 0.5 / 1)

[0131] The regenerative braking force distribution coefficient obtained by querying the fuzzy rule base table is equal to 0 if the result is low, 0.5 if mid, and 1 if high. For example, the result corresponding to High\High\Dry\Medium in the table is High.

[0132] Step 4: Determine the motor braking force and mechanical braking force of the pure electric vehicle at the time of waiting for control based on the regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control, and obtain a braking energy recovery plan for the pure electric vehicle at the time of waiting for control.

[0133] In summary, the present embodiment proposes a method for determining a braking energy recovery scheme for a pure electric vehicle, which combines related factors such as battery power, current vehicle speed, road type, and braking intensity, and adopts a fuzzy control strategy to perform energy recovery for electric vehicles. This method can improve the braking conversion rate and braking recovery rate of electric vehicles, and achieve better energy-saving and emission reduction effects. At the same time, for different vehicle speeds, road types, and braking intensities, the most appropriate braking strategy is adopted to avoid situations such as vehicle loss of control due to excessive braking intensity, or insufficient braking force due to insufficient braking intensity, thereby improving system safety.

[0134] Example 2

[0135] Figure 3 This is a structural diagram of a system for determining a braking energy recovery scheme for a pure electric vehicle according to one embodiment of the present application, such as Figure 3 As shown, the system includes:

[0136] A building block 100 is configured to obtain the battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type of a pure electric vehicle, and construct a fuzzy rule base according to the battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type of the pure electric vehicle.

[0137] It should be noted that the battery power type includes: low power, medium power, and high power.

[0138] The vehicle speed type includes: low speed, medium speed, and high speed.

[0139] The road surface type includes: highway, muddy road, and snow.

[0140] The braking intensity type includes: light braking, medium braking, and heavy braking.

[0141] The regenerative braking force distribution coefficient type includes: low coefficient, medium coefficient, and high coefficient.

[0142] Among them, the low power is from 0 to A1, the medium power is from A2 to A3, the high power is from A4 to 100%, 0 < A2 < A1 < A4 < A3 < 100%, A1 is a preset first power, A2 is a preset second power, A3 is a preset third power, and A4 is a preset fourth power.

[0143] The low speed is from 0 to V1, the medium speed is from V2 to V3, the high speed is from V4 to 10%, 0 < V2 < V1 < V4 < V3 < 180 km / h, V1 is a preset first speed, V2 is a preset second speed, V3 is a preset third speed, and V4 is a preset fourth speed.

[0144] The credibility of the highway is C1, the credibility of the muddy road is C2, and the credibility of the snow is C3.

[0145] The light braking is from at 0 to Z1, the medium braking is from Z2 to Z3, the heavy braking is from Z4 to 1, 0 < Z2 < Z1 < Z4 < Z3 < 1, Z1 is a preset first braking value, Z2 is a preset second braking value, Z is a preset third braking value, and Z4 is a preset fourth braking value.

[0146] The low coefficient is from 0 to X1, the medium coefficient is from X2 to X3, the high coefficient is from X4 to 1, 0 < X2 < X1 < X4 < X3 < 1, X1 is a preset first coefficient, X2 is a preset second coefficient, X3 is a preset third coefficient, and X4 is a preset fourth coefficient.

[0147] The screening module 200 is used to obtain the battery power, vehicle speed information, road surface type information, and braking intensity information of the pure electric vehicle at the time to be controlled, and based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled, screen out the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type of the pure electric vehicle at the time to be controlled in the fuzzy rule library; wherein the road surface type information includes: road surface type and the credibility corresponding to the road surface type.

[0148] A first determining module 300 is configured to determine the regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled based on the regenerative braking force distribution coefficients of the fuzzy rules corresponding to the road surface types at the time to be controlled and the credibility of the road surface types;

[0149] The second determining module 400 is configured to determine the motor braking force and the mechanical braking force of the pure electric vehicle at the time to be controlled based on the regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled, and obtain a braking energy recovery scheme for the pure electric vehicle at the time to be controlled;

[0150] In the embodiment of the present disclosure, the building module 100 is further used to:

[0151] Based on the battery power type, vehicle speed type, road surface type, and braking intensity type, multiple groups of initial fuzzy rules are obtained by permutation and combination;

[0152] Determining the regenerative braking force distribution coefficient types corresponding to the multiple groups of initial fuzzy rules according to the preset regenerative braking force distribution rules;

[0153] A fuzzy rule base is generated based on the multiple groups of initial fuzzy rules and the types of regenerative braking force distribution coefficients corresponding to the multiple groups of initial fuzzy rules.

[0154] In the embodiment of the present disclosure, the screening module 200 is further configured to:

[0155] Based on the battery power, vehicle speed, road surface type, and braking intensity information at the time to be controlled, fuzzy rules corresponding to each road surface type in the road surface type information are screened from the fuzzy rule library, wherein the fuzzy rules include: battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type;

[0156] determining, according to a preset regenerative braking force distribution coefficient parameterization rule, a regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information;

[0157] The preset regenerative braking force distribution coefficient parameterization rule includes:

[0158] When the regenerative braking force distribution coefficient type is low coefficient, 0 is used as the regenerative braking force distribution coefficient;

[0159] When the regenerative braking force distribution coefficient type is a medium coefficient, 0.5 is used as the regenerative braking force distribution coefficient;

[0160] When the regenerative braking force distribution coefficient type is a high coefficient, 1 is used as the regenerative braking force distribution coefficient.

[0161] In the embodiment of the present disclosure, the first determining module 300 is further configured to:

[0162] Filtering fuzzy rules corresponding to each road surface type in the road surface type information from the fuzzy rule library based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled, and then determining the membership degree of each fuzzy rule corresponding to each road surface type;

[0163] Determining the activation strength of each fuzzy rule corresponding to each road surface type according to the credibility corresponding to each road surface type and the membership degree of each fuzzy rule corresponding to each road surface type;

[0164] The regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control is determined according to the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information.

[0165] Furthermore, the first determining module 300 is further configured to:

[0166] Determine the sum of the products of the regenerative braking force distribution coefficient and the activation intensity of each fuzzy rule in each road surface type, and obtain the regenerative braking force distribution coefficient of each road surface type;

[0167] The sum of the regenerative braking force distribution coefficients of each road surface type is determined, and the sum of the regenerative braking force distribution coefficients of each road surface type is used as the regenerative braking force distribution coefficient of the pure electric vehicle at the time when the pure electric vehicle is to be controlled.

[0168] In summary, the system for determining a braking energy recovery scheme for a pure electric vehicle proposed in this embodiment improves the braking conversion rate, braking recovery rate, and safety of the electric vehicle.

[0169] Example 3

[0170] To implement the above embodiments, the present disclosure further proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first embodiment is implemented.

[0171] Example 4

[0172] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0173] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0174] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0175] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for determining a braking energy recovery scheme for a pure electric vehicle, characterized in that: The method includes: Obtaining the battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type of a pure electric vehicle, and constructing a fuzzy rule base according to the battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type of the pure electric vehicle; Obtaining the battery power, vehicle speed information, road surface type information, and braking intensity information of the pure electric vehicle at the moment to be controlled, and screening out the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type of the pure electric vehicle at the moment to be controlled in the fuzzy rule base based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the moment to be controlled; Determining the regenerative braking force distribution coefficient of the pure electric vehicle at the moment to be controlled according to the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the moment to be controlled and the credibility corresponding to each road surface type; Determining the electric braking force and mechanical braking force of the pure electric vehicle at the moment to be controlled based on the regenerative braking force distribution coefficient of the pure electric vehicle at the moment to be controlled, and obtaining the braking energy recovery scheme of the pure electric vehicle at the moment to be controlled; Wherein, the road surface type information includes: road surface type and the credibility corresponding to the road surface type.

2. The method according to claim 1, wherein The battery power type includes: low power, medium power, high power; The vehicle speed type includes: low speed, medium speed, high speed; The road surface type includes: highway, muddy road, snow; The braking intensity type includes: light braking, medium braking, heavy braking; The regenerative braking force distribution coefficient type includes: low coefficient, medium coefficient, high coefficient; Wherein, the low power is from 0 to A1, the medium power is from A2 to A3, the high power is from A4 to 100%, 0 < A2 < A1 < A4 < A3 < 100%, A1 is a preset first power, A2 is a preset second power, A3 is a preset third power, and A4 is a preset fourth power; The low speed is from 0 to V1, the medium speed is from V2 to V3, the high speed is from V4 to 100%, 0 < V2 < V1 < V4 < V3 < 180 km / h, V1 is a preset first speed, V2 is a preset second speed, V3 is a preset third speed, and V4 is a preset fourth speed; The credibility of the highway is C1, the credibility of the muddy road is C2, and the credibility of the snow is C3; The light braking is from 0 to Z1, the medium braking is from Z2 to Z3, the heavy braking is from Z4 to 1, 0 < Z2 < Z1 < Z4 < Z3 < 1, Z1 is a preset first braking value, Z2 is a preset second braking value, Z3 is a preset third braking value, and Z4 is a preset fourth braking value; The low coefficient is from 0 to X1, the medium coefficient is from X2 to X3, the high coefficient is from X4 to 1, 0 < X2 < X1 < X4 < X3 < 1, X1 is a preset first coefficient, X2 is a preset second coefficient, X3 is a preset third coefficient, and X4 is a preset fourth coefficient.

3. The method according to claim 2, wherein [[ID= Based on the battery power type, vehicle speed type, road surface type, and braking intensity type, multiple groups of initial fuzzy rules are obtained by permutation and combination; Determining the regenerative braking force distribution coefficient types corresponding to the multiple groups of initial fuzzy rules according to the preset regenerative braking force distribution rules; A fuzzy rule base is generated based on the multiple groups of initial fuzzy rules and the types of regenerative braking force distribution coefficients corresponding to the multiple groups of initial fuzzy rules.

4. The method according to claim 3, wherein The regenerative braking force distribution coefficients of the fuzzy rules corresponding to the road types of the pure electric vehicle at the time to be controlled are screened out from the fuzzy rule base based on the battery power, vehicle speed information, road type information, and braking intensity information at the time to be controlled, including: Based on the battery power, vehicle speed, road surface type, and braking intensity information at the time to be controlled, fuzzy rules corresponding to each road surface type in the road surface type information are screened from the fuzzy rule library, wherein the fuzzy rules include: battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type; determining, according to a preset regenerative braking force distribution coefficient parameterization rule, a regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information; The preset regenerative braking force distribution coefficient parameterization rule includes: When the regenerative braking force distribution coefficient type is low coefficient, 0 is used as the regenerative braking force distribution coefficient; When the regenerative braking force distribution coefficient type is a medium coefficient, 0.5 is used as the regenerative braking force distribution coefficient; When the regenerative braking force distribution coefficient type is a high coefficient, 1 is used as the regenerative braking force distribution coefficient.

5. The method according to claim 4, wherein The step of determining the regenerative braking force distribution coefficient of the pure electric vehicle at the time to be controlled based on the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type at the time to be controlled and the credibility corresponding to each road surface type includes: Filtering fuzzy rules corresponding to each road surface type in the road surface type information from the fuzzy rule library based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the time to be controlled, and then determining the membership degree of each fuzzy rule corresponding to each road surface type; Determining the activation strength of each fuzzy rule corresponding to each road surface type according to the credibility corresponding to each road surface type and the membership degree of each fuzzy rule corresponding to each road surface type; The regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control is determined according to the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information.

6. The method according to claim 5, wherein The determining of the regenerative braking force distribution coefficient of the pure electric vehicle at the time of waiting for control based on the activation strength of each fuzzy rule corresponding to each road surface type and the regenerative braking force distribution coefficient of each fuzzy rule corresponding to each road surface type in the road surface type information includes: Determine the sum of the products of the regenerative braking force distribution coefficient and the activation intensity of each fuzzy rule in each road surface type, and obtain the regenerative braking force distribution coefficient of each road surface type; Determine the sum of the regenerative braking force distribution coefficients for each road surface type, and use this sum as the regenerative braking force distribution coefficient for the pure electric vehicle at the moment to be controlled.

7. A system for determining a braking energy recovery scheme for a pure electric vehicle, characterized in that: The system includes: A construction module, configured to obtain the battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type of a pure electric vehicle, and construct a fuzzy rule base according to the battery power type, vehicle speed type, road surface type, braking intensity type, and regenerative braking force distribution coefficient type of the pure electric vehicle; A screening module, configured to obtain the battery power, vehicle speed information, road surface type information, and braking intensity information of the pure electric vehicle at the moment to be controlled, and screen out the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type of the pure electric vehicle at the moment to be controlled in the fuzzy rule base based on the battery power, vehicle speed information, road surface type information, and braking intensity information at the moment to be controlled; A first determination module, configured to determine the regenerative braking force distribution coefficient of the pure electric vehicle at the moment to be controlled according to the regenerative braking force distribution coefficients of each fuzzy rule corresponding to each road surface type at the moment to be controlled and the credibility corresponding to each road surface type; A second determination module, configured to determine the electric motor braking force and mechanical braking force of the pure electric vehicle at the moment to be controlled based on the regenerative braking force distribution coefficient of the pure electric vehicle at the moment to be controlled, and obtain the braking energy recovery scheme of the pure electric vehicle at the moment to be controlled; Wherein, the road surface type information includes: the road surface type and the credibility corresponding to the road surface type.

8. The system according to claim 7, wherein: The battery power type includes: low power, medium power, high power; The vehicle speed type includes: low speed, medium speed, high speed; The road surface type includes: highway, dirt road, snow; The braking intensity type includes: light braking, medium braking, heavy braking; The regenerative braking force distribution coefficient type includes: low coefficient, medium coefficient, high coefficient; Wherein, the low power is from 0 to A1, the medium power is from A2 to A3, the high power is from A4 to 100%, 0 < A2 < A1 < A4 < A3 < 100%, A1 is a preset first power, A2 is a preset second power, A3 is a preset third power, and A4 is a preset fourth power; The low speed is from 0 to V1, the medium speed is from V2 to V3, the high speed is from V4 to 100%, 0 < V2 < V1 < V4 < V3 < 180 km / h, V1 is a preset first speed, V2 is a preset second speed, V3 is a preset third speed, and V4 is a preset fourth speed; The credibility of the highway is C1, the credibility of the dirt road is C2, and the credibility of the snow is C3; The light braking is from 0 to Z1, the medium braking is from Z2 to Z3, the heavy braking is from Z4 to 1, 0 < Z2 < Z1 < Z4 < Z3 < 1, Z1 is a preset first braking value, Z2 is a preset second braking value, Z3 is a preset third braking value, and Z4 is a preset fourth braking value; The low coefficient ranges from 0 to X1, the medium coefficient ranges from X2 to X3, and the high coefficient ranges from X4 to 1, where 0 < X2 < X1 < X4 < X3 < 1, X1 is a preset first coefficient, X2 is a preset second coefficient, X3 is a preset third coefficient, and X4 is a preset fourth coefficient.

9. An electronic device, characterized in that: Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1-6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the method according to any one of claims 1-6 is implemented.

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