Energy recovery control method and device, electronic equipment and storage medium

Abstract

The invention provides an energy recovery control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the driving habit data and road condition data of a user, the driving habit of the user being the habit of the user controlling the vehicle speed and the rotating speed, and the road condition data being used for representing the road congestion condition; calculating a target energy recovery torque according to the driving habit data and the road condition data; and controlling the motor controller to output the target energy recovery torque. In the embodiment of the invention, the target energy recovery torque is calculated according to the driving habit and the road condition data of the user. It can be understood that the self-adaptive energy recovery mode in the embodiment of the invention is more in line with the driving habits of users, and even if different users drive vehicles, different target energy recovery torques can be calculated respectively, so that the requirements of different users for energy recovery intensity are met, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of energy recovery technology, and specifically to an energy recovery control method, device, electronic device and storage medium. Background Art

[0002] With the continuous development of the new energy vehicle industry, more and more new energy vehicles have added energy recovery functions. This function can improve the vehicle's endurance to a certain extent, thereby meeting users' requirements for the endurance of new energy vehicles.

[0003] In the related art, there are a variety of energy recovery control strategies. For example, some vehicles set the energy recovery mode to three levels: weak, standard, and strong for the driver to choose, or some vehicles set the energy recovery mode to two modes: standard and large.

[0004] Obviously, the energy recovery mode in the relevant technology is a fixed mode, which is very simple under different road conditions and driving styles. It cannot meet the different needs of different drivers for energy recovery intensity, affecting the user experience.

[0005] It should be pointed out that the information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or suggestion in any form that the information constitutes prior art already known to those skilled in the art. Summary of the invention

[0006] In view of this, the present application provides an energy recovery control method, device, electronic device and storage medium, so as to solve the problem that the energy recovery mode in the prior art is a fixed mode, which appears very simple under different road conditions and driving styles, and cannot meet the different needs of different drivers for energy recovery intensity, thus affecting the user experience.

[0007] In a first aspect, an embodiment of the present application provides an energy recovery control method, comprising:

[0008] Acquiring the user's driving habit data and road condition data, wherein the user's driving habit refers to the user's habit of controlling vehicle speed and rotation speed, and the road condition data is used to characterize road congestion;

[0009] Calculating a target energy recovery torque according to the driving habit data and the road condition data;

[0010] The motor controller is controlled to output the target energy recovery torque.

[0011] In the embodiment of the present application, the target energy recovery torque is calculated according to the user's driving habits and road condition data. It can be understood that the adaptive energy recovery mode in the embodiment of the present application is more in line with the user's driving habits. Even if different users drive the vehicle, different target energy recovery torques can be calculated respectively, thereby meeting the needs of different users for energy recovery intensity and improving user experience.

[0012] In a possible implementation, the step of obtaining the user's driving habit data and road condition data includes:

[0013] Collecting the user's driving information and the vehicle's travel information, wherein the driving information is the information of the user controlling the vehicle speed and rotation speed, and the travel information is used to characterize the change of the vehicle speed;

[0014] Inputting the driving information into a driving habit recognition model to obtain driving habit data of the user;

[0015] The driving information is input into a road condition recognition model to obtain road condition data.

[0016] In the embodiment of the present application, the user's driving information is collected and input into the driving habit recognition model to obtain the user's driving habit data, and the vehicle's driving information is collected and input into the road condition recognition model to obtain the road condition data. It can be understood that the driving habit recognition model and the road condition recognition model can quickly obtain the driving habit data and the road condition data through the driving information and the vehicle's driving information, thereby improving work efficiency.

[0017] In a possible implementation, collecting the user's driving information and the vehicle's travel information includes:

[0018] Collect brake pedal opening signal, accelerator pedal opening signal, steering wheel angle signal and vehicle speed signal.

[0019] In the embodiment of the present application, the user's driving information and the vehicle's travel information are obtained by collecting the brake pedal opening signal, the accelerator pedal opening signal, the steering wheel angle signal and the vehicle speed signal. It can be understood that the user's driving information and the vehicle's travel information can be obtained more accurately through the above-mentioned multiple signals, thereby identifying more accurate driving habits and road condition data.

[0020] In a possible implementation, the step of obtaining the user's driving habit data and road condition data includes:

[0021] In response to an adaptive energy recovery instruction triggered by a user, determining whether there is a self-learning record, the self-learning record being a target energy recovery torque corresponding to the stored driving habit data and road condition data;

[0022] If the self-learning record does not exist, the user's driving habit data and road condition data are obtained.

[0023] In the embodiment of the present application, the adaptive energy recovery mode is turned on only after the user triggers the adaptive energy recovery instruction, and after the adaptive energy recovery mode is turned on, it is first determined whether there is a self-learning record. If there is no self-learning record, the user's driving habit data and road condition data are obtained. It can be understood that the user's driving habit data and road condition data are obtained only when there is no self-learning record, which can avoid repeated data acquisition, thereby saving user time.

[0024] In a possible implementation, controlling the motor controller to output the target energy recovery torque includes:

[0025] If the self-learning record exists, the motor controller is controlled to output the target energy recovery torque corresponding to the self-learning record selected by the user.

[0026] In the embodiment of the present application, if there is a self-learning record, the motor controller is controlled to output the target energy recovery torque corresponding to the self-learning record selected by the user. It is understandable that the vehicle in the embodiment of the present application can store multiple self-learning records. When there is a self-learning record, the user is provided with a choice, and the motor controller is controlled to output the corresponding target energy recovery torque according to the self-learning record selected by the user. On the one hand, it can improve work efficiency, and on the other hand, it can make the target energy recovery torque more in line with the user's expectations.

[0027] In a possible implementation, calculating the target energy recovery torque according to the driving habit data and the road condition data includes:

[0028] Calculating a recovery torque correction coefficient according to the driving habit data and the road condition data;

[0029] The target energy recovery torque is calculated according to the basic recovery torque and the recovery torque correction coefficient.

[0030] In the embodiment of the present application, the recovery torque correction coefficient is first calculated according to the driving habit data and the road condition data, and then the target energy recovery torque is calculated according to the basic recovery torque and the recovery torque correction coefficient. On the one hand, by calculating the recovery torque correction coefficient according to the driving habit data and the road condition data, a recovery torque correction coefficient that is more in line with the user's driving habits can be obtained; on the other hand, by using the recovery torque correction coefficient as the gain of the basic recovery torque, the target energy recovery torque can be quickly calculated to improve work efficiency.

[0031] In a possible implementation, controlling the motor controller to output the target energy recovery torque includes:

[0032] Outputting first interaction information, where the first interaction information is used to prompt the user that the self-learning has been completed, and asks the user to choose whether to experience it;

[0033] If the user chooses to experience, the motor controller is controlled to output the target energy recovery torque;

[0034] If the user chooses not to experience it, the motor controller is controlled to output the preset energy recovery torque.

[0035] In the embodiment of the present application, the user is prompted that the self-learning has been completed through the first interactive information, and the user's choice is obtained. If the user chooses to experience, the motor controller is controlled to output the calculated target energy recovery torque; if the user chooses not to experience, the motor controller is controlled to output the preset energy recovery torque. It can be understood that after the self-learning is completed, a selection interface is provided for the user, which improves the interactivity and intelligence of the page and is conducive to improving the user experience.

[0036] In a possible implementation, after controlling the motor controller to output the target energy recovery torque, the method further includes:

[0037] Record the number of energy recovery times;

[0038] If the energy recovery times are greater than or equal to a preset value, a second interaction message is output, where the second interaction message is used to prompt the user that multiple energy recovery has been performed, and asks the user to confirm whether to save the current energy recovery mode;

[0039] If the user chooses to save, the energy recovery mode is stored as a new self-learning record;

[0040] If the user chooses not to save, the motor controller is controlled to output a preset energy recovery torque.

[0041] In the embodiment of the present application, when the number of energy recovery times is greater than or equal to a preset value, the second interactive information is output to prompt the user that multiple energy recovery has been performed and whether to save the current energy recovery mode. If the user chooses not to save, the motor controller is controlled to output the preset energy recovery torque. It can be understood that through the second interactive information, another selection interface is provided to the user. If the user is satisfied with the adaptive energy recovery mode, the adaptive energy recovery mode can be saved; if the user is not satisfied with the adaptive energy recovery mode, the standard energy recovery mode can continue to be used. Providing the user with another selection interface improves the interactivity and intelligence of the page, which is conducive to improving the user experience.

[0042] In a possible implementation, if the user chooses not to save, controlling the motor controller to output a preset energy recovery torque includes:

[0043] If the user chooses not to save, outputting third interaction information, wherein the third interaction information is used to prompt the user to choose whether to continue learning;

[0044] If the user chooses not to continue learning, the motor controller is controlled to output a preset energy recovery torque.

[0045] In the embodiment of the present application, when the user chooses not to save the adaptive energy recovery mode, the third interactive information is output to prompt the user to choose whether to continue learning. If the user chooses not to continue learning, the motor controller is controlled to output the preset energy recovery torque. It is understandable that the user is just dissatisfied with the learning result and does not necessarily want to end the learning process. Therefore, providing the user with the third interactive information can obtain an energy recovery mode that better meets the user's expectations.

[0046] In a second aspect, an embodiment of the present application provides an energy recovery control device, comprising:

[0047] A data acquisition module, used to acquire the user's driving habit data and road condition data, wherein the user's driving habit refers to the user's habit of controlling the vehicle speed and rotation speed, and the road condition data is used to characterize the road congestion situation;

[0048] a target energy recovery torque calculation module, used to calculate the target energy recovery torque according to the driving habit data and the road condition data;

[0049] The control module is used to control the motor controller to output the target energy recovery torque.

[0050] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0051] processor;

[0052] Memory;

[0053] A communication unit, used for establishing a communication channel;

[0054] And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes any one of the methods described in the first aspect.

[0055] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described in the first aspect.

[0056] It is understandable that the energy recovery control device provided in the second aspect, the electronic device provided in the third aspect, and the computer-readable storage medium provided in the fourth aspect are used to execute the method provided in the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 A schematic diagram of a flow chart of an energy recovery control method provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a flow chart of a road condition recognition model provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of the corresponding relationship between driving information and weight assignment provided in an embodiment of the present application;

[0061] Figure 4 A schematic diagram of a driving habit recognition model provided in an embodiment of the present application;

[0062] Figure 5 A schematic diagram of the corresponding relationship between driving information and weight assignment provided in an embodiment of the present application;

[0063] Figure 6 A schematic diagram of a triggering interface of an adaptive energy recovery mode provided in an embodiment of the present application;

[0064] Figure 7 A schematic diagram of a process for calculating a target recovery torque provided in an embodiment of the present application;

[0065] Figure 8 A schematic diagram of another energy recovery control method provided in an embodiment of the present application;

[0066] Fig. 9 A schematic diagram of the structure of an energy recovery control device provided in an embodiment of the present application;

[0067] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0069] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0070] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0071] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0072] With the continuous development of the new energy vehicle industry, more and more new energy vehicles have added energy recovery functions. This function can improve the vehicle's endurance to a certain extent, thereby meeting users' requirements for the endurance of new energy vehicles.

[0073] In the related art, there are a variety of energy recovery control strategies. For example, some vehicles set the energy recovery mode to three levels: weak, standard, and strong for the driver to choose, or some vehicles set the energy recovery mode to two modes: standard and large.

[0074] Obviously, the energy recovery mode in the relevant technology is a fixed mode, which is very simple under different road conditions and driving styles. It cannot meet the different needs of different drivers for energy recovery intensity, affecting the user experience.

[0075] In view of the above problems, the embodiment of the present application provides an energy recovery control method, which calculates the target energy recovery torque according to the user's driving habits and road condition data. It can be understood that the adaptive energy recovery mode in the embodiment of the present application is more in line with the user's driving habits. Even if different users drive the vehicle, different target energy recovery torques can be calculated respectively, thereby meeting the needs of different users for energy recovery intensity and improving the user experience. The details are described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0076] See also Figure 1 , is a flow chart of an energy recovery control method provided in an embodiment of the present application. Figure 1 As shown, it mainly includes the following steps.

[0077] Step S101: Acquire the user's driving habit data and road condition data.

[0078] Specifically, the energy recovery control device obtains the user's driving habit data and road condition data, wherein the user's driving habit is the user's habit of controlling the vehicle speed and rotation speed, and the road condition data is used to characterize the road congestion situation.

[0079] In a possible implementation, the user's driving information and the vehicle's driving information are collected to obtain the user's driving habit data and road condition data. Specifically, the energy recovery control device collects the user's driving information and the vehicle's driving information; inputs the driving information into a driving habit recognition model to obtain the user's driving habit data; and inputs the driving information into a road condition recognition model to obtain road condition data.

[0080] In the embodiment of the present application, driving information is information about the speed and rotation speed controlled by the user, and driving information is used to characterize the speed change of the vehicle. In order to obtain driving information and driving information, the energy recovery control device collects brake pedal opening signals, accelerator pedal opening signals, steering wheel angle signals and vehicle speed signals in real time, and obtains the vehicle's driving information through the vehicle speed signal. The vehicle's driving information includes but is not limited to braking cycles, parking cycles and average vehicle speed.

[0081] To facilitate understanding, an embodiment of the present application provides a flow chart of a road condition recognition model.

[0082] See also Figure 2 , is a flow chart of a road condition recognition model provided in an embodiment of the present application. Figure 2 As shown, the energy recovery control device collects the vehicle's brake pedal signal and vehicle speed signal within a preset time T, records the number of times the brake pedal opening is ≥ 10% N, and thus calculates the braking cycle N brk =N / T; By collecting the vehicle speed signal, record the duration T1 when the vehicle speed is ≤2km / h. Because when the vehicle speed is ≤2km / h, the vehicle can be considered to be in a stopped state, so the parking period T can be calculated based on T1 stop =T1 / T; By integrating the vehicle speed signal, the mileage S can be obtained, and the average vehicle speed v can be calculated avg =S / T. For braking cycle N brk , parking period T stop and average vehicle speed v avg By performing weight calculation, the corresponding road condition data can be identified.

[0083] It should be pointed out that Figure 2The preset values ​​and calculation methods in the illustrated embodiments are exemplary descriptions and should not be regarded as limiting the scope of protection of the present application.

[0084] In the embodiment of the present application, the traffic condition data includes four types, namely: City-Crowded, CC traffic condition, City-Mid, CM traffic condition, Suburbs-Low, SL traffic condition, and Suburbs-High, SH traffic condition. brk , parking period T stop and average vehicle speed v avg By performing weight calculation, different road condition data can be determined.

[0085] In a possible implementation, the braking cycle N brk The corresponding weight is 10%, and the parking period is T stop The corresponding weight is 30%, and the average vehicle speed v avg The corresponding weight is 60%. Based on the weight, different driving information is assigned values ​​respectively, so as to obtain different weight assignments corresponding to different driving information. According to the weight assignment corresponding to the current driving information and the correspondence between different weight assignments and road condition data, the road condition data corresponding to the current driving information can be determined. For ease of understanding, the embodiment of the present application also provides a corresponding table of driving information and weight assignment.

[0086] See also Figure 3 , is a schematic diagram of the corresponding relationship between driving information and weight assignment provided in an embodiment of the present application. Figure 3 As shown, the braking cycle N brk The corresponding weight is 10%. brk ≥15, the value is 1, then N brk The weight is assigned to 0.1, when 15>N brk ≥10, the value is 2, then N brk The weight is assigned to 0.2, when 10>N brk ≥5, the value is 3, then N brk The weight is assigned to 0.3. brk When <5, the value is 4. At this time, N brk The weight of is 0.4. Parking period T stop and average vehicle speed v avg The calculation method of weight assignment and braking cycle N brk The calculation method of is similar and will not be elaborated here.

[0087] In practical applications, according to the collected braking cycle N brk, parking period T stop and average vehicle speed v avg , calculate the corresponding weight values ​​respectively, and add the three weight values ​​to obtain a road condition weight. When the road condition weight is in the interval [1,1.8], the current road condition data is determined to be the above-mentioned CC road condition; when the road condition weight is in the interval [1.9,2.7], the current road condition data is determined to be the above-mentioned CM road condition; when the road condition weight is in the interval [2.8,3.4], the current road condition data is determined to be the above-mentioned SL road condition; when the road condition weight is in the interval [3.5,4], the current road condition data is determined to be the above-mentioned SH road condition.

[0088] For example, if the current braking cycle N brk The weight is assigned to 0.1, and the parking period is T stop The weight is assigned to 0.3, and the average speed v avg The weight of is 0.6, then the current road condition weight is calculated to be 1, which is in the interval [1,1.8], and the current road condition data can be determined to be the above CC road condition. Similarly, if the current braking cycle N brk The weight is assigned to 0.3, and the parking period is T stop The weight is assigned to 0.6, and the average speed v avg The weight of is 1.2, then the current road condition weight can be calculated to be 2.1, which is in the interval [1.9, 2.7]. Therefore, the current road condition data can be determined to be the above-mentioned CM road condition.

[0089] It should be pointed out that the above-mentioned correspondence between driving information and weight assignment is only an exemplary description. In practical applications, technical personnel in this field can adaptively adjust the correspondence between driving information and weight assignment according to actual needs, and the embodiments of the present application do not impose specific restrictions on this.

[0090] To facilitate understanding, an embodiment of the present application provides a flow chart of a driving habit recognition model.

[0091] See also Figure 4 , is a flow chart of a driving habit recognition model provided in an embodiment of the present application. Figure 4 As shown, the energy recovery control device collects the vehicle's brake pedal signal, accelerator pedal signal, steering wheel angle signal and vehicle speed signal within a preset time T, and records the duration T2 when the accelerator pedal opening is ≤1% and the brake pedal opening is <10%, thereby calculating the coasting period T taxi =T2 / T, and calculate the accelerator pedal opening rate, steering wheel angle rate and vehicle speed rate by differentiation. taxi , accelerator pedal opening rate, steering wheel angle rate and vehicle speed rate are weighted to identify the corresponding driving habits.

[0092] It should be pointed out that Figure 4 The preset values ​​and calculation methods in the illustrated embodiments are exemplary descriptions and should not be regarded as limiting the scope of protection of the present application.

[0093] In the embodiment of the present application, there are three types of driving habit data, namely, Driving-Prudent (DP type for short), Driving-Steady (DS type for short), and Driving-Radical (DR type for short). taxi , steering wheel angle change rate △StrWhl_Rate, accelerator pedal opening change rate △AccPde_Rate and vehicle speed change rate △VehSpd_Rate are weighted to determine different driving habit data.

[0094] In a possible implementation, the gliding period T taxi The corresponding weight is 10%, the corresponding weight of the steering wheel angle change rate △StrWhl_Rate is 20%, the corresponding weight of the accelerator pedal opening change rate △AccPde_Rate is 30%, and the corresponding weight of the vehicle speed change rate △VehSpd_Rate is 40%. Based on the weight, different driving information is assigned respectively to obtain different weight assignments corresponding to different driving information. According to the weight assignment corresponding to the current driving information and the correspondence between different weight assignments and driving habit data, the driving habit data corresponding to the current driving information can be determined. For ease of understanding, the embodiment of the present application also provides a corresponding table of driving information and weight assignments.

[0095] See also Figure 5 , is a schematic diagram of the corresponding relationship between driving information and weight assignment provided in an embodiment of the present application. Figure 5 As shown, the gliding period T taxi The corresponding weight is 10%. When 3%≤Ttaxi≤10%, the value is 1. At this time, the taxiing period T taxi The weight is assigned to 0.1. When Ttaxi>10%, it is assigned to 2. At this time, the taxiing period T taxi The weight is assigned to 0.2. When Ttaxi<3%, it is assigned to 3. At this time, the taxiing period T taxi The weight assignment is 0.3. The calculation method of the weight assignment of the steering wheel angle change rate △StrWhl_Rate, the accelerator pedal opening change rate △AccPde_Rate and the vehicle speed change rate △VehSpd_Rate is the same as the coasting period T taxi The calculation method of is similar and will not be elaborated here.

[0096] In practical applications, according to the collected sliding period T taxi , steering wheel angle change rate △StrWhl_Rate, accelerator pedal opening change rate △AccPde_Rate and vehicle speed change rate △VehSpd_Rate, calculate the corresponding weight assignments respectively, and add the four weight assignments to obtain a driving habit weight. When the driving habit weight is in the interval [1,1.6], it is determined that the current driving habit data is the above-mentioned DP type; when the driving habit weight is in the interval [1.7,2.6], it is determined that the current driving habit data is the above-mentioned DS type; when the driving habit weight is in the interval [2.7,3.0], it is determined that the current driving habit data is the above-mentioned DR type.

[0097] For example, if the current gliding period T taxi The weight of is 0.1, the weight of the steering wheel angle change rate △StrWhl_Rate is 0.2, the weight of the accelerator pedal opening change rate △AccPde_Rate is 0.3, and the weight of the vehicle speed change rate △VehSpd_Rate is 0.4. It can be calculated that the current driving habit weight is 1, which is in the interval [1,1.6]. It can be determined that the current driving habit data is the above DP type. Similarly, if the current coasting period T taxi The weight of is 0.2, the weight of the steering wheel angle change rate △StrWhl_Rate is 0.4, the weight of the accelerator pedal opening change rate △AccPde_Rate is 0.6, and the weight of the vehicle speed change rate △VehSpd_Rate is 0.8. It can be calculated that the current driving habit weight is 2, which is in the interval [1.7,2.6]. It can be determined that the current driving habit data is the above-mentioned DS type.

[0098] It should be pointed out that the above-mentioned correspondence between driving information and weight assignment is only an exemplary description. In practical applications, technical personnel in this field can adaptively adjust the correspondence between driving information and weight assignment according to actual needs, and the embodiments of the present application do not impose specific restrictions on this.

[0099] The method provided in the above embodiment can obtain the user's driving habit data and road condition data. However, in actual application, the user does not necessarily need the vehicle to automatically turn on the adaptive energy recovery mode, and the vehicle may have stored the driving habit data corresponding to the user.

[0100] Therefore, in one possible implementation, in response to an adaptive energy recovery command triggered by the user, it is determined whether there is a self-learning record, where the self-learning record is the target energy recovery torque corresponding to the stored driving habit data and road condition data; if there is no self-learning record, the user's driving habit data and road condition data are obtained.

[0101] That is to say, the adaptive energy recovery mode is turned on only after the user triggers the adaptive energy recovery command, and after the adaptive energy recovery mode is turned on, it is first determined whether there is a self-learning record. If there is no self-learning record, the user's driving habit data and road condition data are obtained. It can be understood that the user's driving habit data and road condition data are obtained only when there is no self-learning record, which can avoid repeated data acquisition and save user time.

[0102] For ease of understanding, an embodiment of the present application also provides a schematic diagram of a triggering interface for an adaptive energy recovery mode.

[0103] See also Figure 6 , is a schematic diagram of a triggering interface of an adaptive energy recovery mode provided in an embodiment of the present application. Figure 6 As shown, the adaptive energy recovery mode is currently turned on, and three self-learning records have been stored, namely self-learning record 1, self-learning record 2 and self-learning record 3. Users can edit multiple self-learning records through the "Edit" menu.

[0104] Moreover, if there is a self-learning record, the motor controller is controlled to output the target energy recovery torque corresponding to the self-learning record selected by the user. It is understandable that the vehicle in the embodiment of the present application can store multiple self-learning records. When there is a self-learning record, the user is provided with a choice, and the motor controller is controlled to output the corresponding target energy recovery torque according to the self-learning record selected by the user. On the one hand, it can improve work efficiency, and on the other hand, it can make the target energy recovery torque more in line with the user's expectations.

[0105] Step S102: Calculate the target energy recovery torque according to the driving habit data and the road condition data.

[0106] Specifically, the energy recovery control device calculates the target energy recovery torque based on the obtained driving habit data and road condition data, so as to control the vehicle to recover the corresponding energy, thereby improving the vehicle's endurance.

[0107] In a possible implementation, the recovery torque correction coefficient is calculated based on driving habit data and road condition data, and the target energy recovery torque is calculated based on the basic recovery torque and the recovery torque correction coefficient. The basic recovery torque is the energy recovery torque preset by the vehicle, that is, the energy recovery torque corresponding to the standard energy recovery mode.

[0108] In a possible implementation, the recovery torque correction factor includes a road condition correction factor and a driving habit correction factor. Figure 3 In the illustrated embodiment, each road condition corresponds to a road condition correction coefficient Rf, the CC road condition corresponds to a road condition correction coefficient Rf1=0.8, the CM road condition corresponds to a road condition correction coefficient Rf2=1.0, the SL road condition corresponds to a road condition correction coefficient Rf3=1.2, and the SH road condition corresponds to a road condition correction coefficient Rf4=1.3.

[0109] Similarly, in Figure 5 In the illustrated embodiment, each driving habit corresponds to a driving habit correction coefficient Df, the driving habit correction coefficient Df1 corresponding to the DP type driving habit is 1.4, the driving habit correction coefficient Df2 corresponding to the DS type driving habit is 1.1, and the driving habit correction coefficient Df3 corresponding to the DR type driving habit is 0.8.

[0110] After obtaining the road condition correction coefficient and the driving habit correction coefficient, the target energy recovery torque is calculated according to the basic recovery torque, the road condition correction coefficient and the driving habit correction coefficient. For ease of understanding, the present application embodiment also provides a flow chart of calculating the target recovery torque.

[0111] See also Figure 7 , is a flow chart of calculating the target recovery torque provided by an embodiment of the present application. When the user selects the adaptive energy recovery mode through the vehicle interface, the energy recovery control device makes a road condition judgment and a driving habit judgment, obtains the corresponding road condition correction coefficient and the driving habit correction coefficient, and multiplies the basic recovery torque with the road condition correction coefficient and the driving habit correction coefficient to obtain the target energy recovery torque. Figure 7 As shown, when the road condition is CC and the driving habit is DP, the target energy recovery torque is basic recovery torque*Rf1*Df1; when the road condition is CM and the driving habit is DP, the target energy recovery torque is basic recovery torque*Rf2*Df1; when the road condition is SL and the driving habit is DP, the target energy recovery torque is basic recovery torque*Rf3*Df1; when the road condition is SH and the driving habit is DP, the target energy recovery torque is basic recovery torque*Rf4*Df1. Other target energy recovery torque calculation methods are similar to this, and for the sake of brevity, they are not repeated here.

[0112] Through the method provided in the above embodiment, on the one hand, the recovery torque correction coefficient is calculated according to the driving habit data and the road condition data, so that a recovery torque correction coefficient that is more in line with the user's driving habits can be obtained; on the other hand, the recovery torque correction coefficient is used as the gain of the basic recovery torque, so that the target energy recovery torque can be quickly calculated to improve work efficiency.

[0113] Step S103: Control the motor controller to output the target energy recovery torque.

[0114] Specifically, after the energy recovery control device calculates the target energy recovery torque, it inputs it to the vehicle controller (VCU / PCU), and the motor controller (MCU) outputs the specific target energy torque.

[0115] In a possible implementation, after the target energy recovery torque is calculated, the first interactive information is output first, and the first interactive information is used to prompt the user that the self-learning has been completed, and ask whether to experience it; if the user chooses to experience it, the motor controller is controlled to output the target energy recovery torque; if the user chooses not to experience it, the motor controller is controlled to output the preset energy recovery torque. It can be understood that after the self-learning is completed, a selection interface is provided for the user to improve the interactivity and intelligence of the page, which is conducive to improving the user experience.

[0116] In addition, in order to ensure the user experience, when the number of energy recovery times reaches a preset value, the user is confirmed whether the current energy recovery mode meets the user's expectations. Specifically, after controlling the motor controller to output the target energy recovery torque, the number of energy recovery times is recorded; if the number of energy recovery times is greater than or equal to the preset value, the second interactive information is output. The second interactive information is used to prompt the user that multiple energy recoveries have been performed, please confirm whether to save the current energy recovery mode; if the user chooses to save, the energy recovery mode is stored as a new self-learning record; if the user chooses not to save, the motor controller is controlled to output the preset energy recovery torque. Among them, the energy recovery mode is the target energy recovery torque corresponding to the driving habit data and road condition data. In one possible implementation, the preset value is 20 times. Of course, those skilled in the art can set the preset value to any number according to actual needs.

[0117] It can be understood that through the second interactive information, another selection interface is provided for the user. If the user is satisfied with the adaptive energy recovery mode, the adaptive energy recovery mode can be saved; if the user is not satisfied with the adaptive energy recovery mode, the standard energy recovery mode can be continued. Providing another selection interface for the user improves the interactivity and intelligence of the page, which is conducive to improving the user experience.

[0118] However, when the user chooses not to save the current energy recovery mode, he does not necessarily want to end the learning process. Therefore, in the embodiment of the present application, when the user chooses not to save the adaptive energy recovery mode, the third interactive information is output, and the third interactive information is used to prompt the user to choose whether to continue learning; if the user chooses not to continue learning, the motor controller is controlled to output the preset energy recovery torque. It is understandable that the user is just dissatisfied with the learning result once, and does not necessarily want to end the learning process. Therefore, providing the user with the third interactive information can obtain an energy recovery mode that better meets the user's expectations.

[0119] The energy recovery control method provided by the above embodiment does not rely on additional hardware equipment, but only relies on CAN signals and an adaptive energy recovery control strategy model to achieve an energy recovery function that is adaptive to road conditions and driving habits. In addition, by collecting data on road conditions and driving habits, it is possible to output energy recovery torque that meets the user's driving habits under different road conditions, thereby improving the user's driving experience.

[0120] Corresponding to the above embodiment, the present application also provides another energy recovery control method.

[0121] See also Figure 8 , is a schematic diagram of another energy recovery control method provided in an embodiment of the present application. Figure 8 As shown, it mainly includes the following steps.

[0122] Step S801: The user selects the adaptive energy recovery mode.

[0123] Specifically, the energy recovery control device receives an adaptive energy recovery control instruction triggered by a user.

[0124] Step S802: The vehicle computer determines whether there is any self-learning record.

[0125] If yes, execute step S803, if no, execute step S804.

[0126] Step S803: Execute the self-learning record selected by the user.

[0127] Step S804: Determine whether to start collecting driving and road condition data according to the user's instruction.

[0128] If yes, execute step S805, if no, execute step S806.

[0129] Step S805: Create a new self-learning record.

[0130] Step S806: Return to the fixed energy recovery mode (default standard intensity).

[0131] Specifically, the fixed energy recovery mode is the standard energy recovery mode mentioned above, that is, outputting a fixed energy recovery torque.

[0132] Step S807: Collect data: accelerator pedal signal, brake pedal signal, steering wheel angle signal, vehicle speed, average vehicle speed, parking cycle, average parking time.

[0133] Step S808: Identify the driving style and determine the road condition, and calculate the recovery torque correction coefficient.

[0134] Step S809: Text reminder: Self-learning has been completed, do you want to experience it?

[0135] Specifically, the text reminder in this step is the first interaction information described above. If the user selects yes, step S810 is executed; if the user selects no, step S806 is executed.

[0136] Step S810: Multiply the basic torque by the correction coefficient to obtain the adaptive energy recovery torque, input the torque to the vehicle controller, and request the motor controller to output the corresponding torque.

[0137] Step S811: Collect the motor torque signal and record the motor torque value that triggers 20 energy recovery times.

[0138] Step S812: Text reminder: whether the current energy recovery mode is in line with your habits.

[0139] Specifically, the text reminder in this step is the second interactive information mentioned above. If the user selects yes, step S813 is executed, and if the user selects no, step S814 is executed.

[0140] Step S813: Save and record the self-learning data, and the user can name it himself.

[0141] Specifically, users can Figure 6 Click the "Edit" option shown in the figure to name the self-learning record.

[0142] Step S814: Determine whether to continue learning based on user instructions.

[0143] Specifically, the user instruction in this step is the user instruction obtained through the third interaction information. If the user selects yes, step S807 is executed, and if the user selects no, step S806 is executed.

[0144] For specific contents involved in the embodiments of this application, please refer to the above Figures 1 to 7 For the sake of brevity, the description in the illustrated embodiment will not be repeated here.

[0145] Corresponding to the above embodiments, the present application also provides an energy recovery control device.

[0146] See also Fig. 9 , is a schematic diagram of the structure of an energy recovery control device provided in an embodiment of the present application. Fig. 9 As shown, the energy recovery control device includes a data acquisition module 901, a target energy recovery torque calculation module 902, and a control module 903. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of the present application. It can be a bus structure or a star structure, and can also include more or less components than shown in the figure, or combine certain components, or arrange components differently.

[0147] The data acquisition module 901 is used to acquire the user's driving habit data and road condition data, wherein the user's driving habit is the user's habit of controlling the vehicle speed and rotation speed, and the road condition data is used to characterize the road congestion situation;

[0148] A target energy recovery torque calculation module 902, configured to calculate a target energy recovery torque according to the driving habit data and the road condition data;

[0149] The control module 903 is used to control the motor controller to output the target energy recovery torque.

[0150] Corresponding to the above embodiments, the present application also provides an electronic device.

[0151] See also Fig.10 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.10 As shown, the electronic device 1000 may include: a processor 1001, a memory 1002 and a communication unit 1003. These components communicate via one or more buses. Those skilled in the art may understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0152] The communication unit 1003 is used to establish a communication channel so that the electronic device can communicate with other devices, receive user data sent by other devices or send user data to other devices.

[0153] The processor 1001 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs, instructions, and / or modules stored in the memory 1002, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 1001 can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0154] The memory 1002 is used to store the execution instructions of the processor 1001. The memory 1002 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0155] When the execution instructions in the memory 1002 are executed by the processor 1001, the electronic device 1000 can execute Figure 1 Some or all of the steps in the illustrated embodiments.

[0156] In a specific implementation, the embodiment of the present application further provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment of the simulation scene generation method provided in the embodiment of the present application. The storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0157] In a specific implementation, an embodiment of the present application also provides a computer program product, wherein the computer program product includes executable instructions, and when the executable instructions are executed on a computer, the computer executes part or all of the steps in each embodiment of the simulation scene generation method provided in the embodiment of the present application.

[0158] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0159] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0162] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

Claims

1. An energy recovery control method, characterized in that: include: Acquiring the user's driving habit data and road condition data, wherein the user's driving habit refers to the user's habit of controlling vehicle speed and rotation speed, and the road condition data is used to characterize road congestion; Calculating a target energy recovery torque according to the driving habit data and the road condition data; The motor controller is controlled to output the target energy recovery torque.

2. The method according to claim 1, characterized in that: The obtaining of the user's driving habit data and road condition data includes: Collecting the user's driving information and the vehicle's travel information, wherein the driving information is the information of the user controlling the vehicle speed and rotation speed, and the travel information is used to characterize the change of the vehicle speed; Inputting the driving information into a driving habit recognition model to obtain driving habit data of the user; The driving information is input into a road condition recognition model to obtain road condition data.

3. The method according to claim 2, characterized in that The collecting of the user's driving information and the vehicle's travel information includes: Collect brake pedal opening signal, accelerator pedal opening signal, steering wheel angle signal and vehicle speed signal.

4. The method according to claim 1, characterized in that The obtaining of the user's driving habit data and road condition data includes: In response to an adaptive energy recovery instruction triggered by a user, determining whether there is a self-learning record, the self-learning record being a target energy recovery torque corresponding to the stored driving habit data and road condition data; If the self-learning record does not exist, the user's driving habit data and road condition data are obtained.

5. The method according to claim 4, characterized in that The controlling the motor controller to output the target energy recovery torque comprises: If the self-learning record exists, the motor controller is controlled to output the target energy recovery torque corresponding to the self-learning record selected by the user.

6. The method according to claim 1, characterized in that The calculating the target energy recovery torque according to the driving habit data and the road condition data includes: Calculating a recovery torque correction coefficient according to the driving habit data and the road condition data; The target energy recovery torque is calculated according to the basic recovery torque and the recovery torque correction coefficient.

7. The method according to claim 1, characterized in that The controlling the motor controller to output the target energy recovery torque comprises: Outputting first interaction information, where the first interaction information is used to prompt the user that the self-learning has been completed, and asks the user to choose whether to experience it; If the user chooses to experience, the motor controller is controlled to output the target energy recovery torque; If the user chooses not to experience it, the motor controller is controlled to output the preset energy recovery torque.

8. The method according to claim 1, characterized in that After controlling the motor controller to output the target energy recovery torque, the method further includes: Record the number of energy recovery times; If the energy recovery times are greater than or equal to a preset value, a second interaction message is output, where the second interaction message is used to prompt the user that multiple energy recovery has been performed, and asks the user to confirm whether to save the current energy recovery mode; If the user chooses to save, the energy recovery mode is stored as a new self-learning record; If the user chooses not to save, the motor controller is controlled to output a preset energy recovery torque.

9. The method according to claim 8, characterized in that If the user chooses not to save, controlling the motor controller to output a preset energy recovery torque includes: If the user chooses not to save, outputting third interaction information, wherein the third interaction information is used to prompt the user to choose whether to continue learning; If the user chooses not to continue learning, the motor controller is controlled to output a preset energy recovery torque.

10. An energy recovery control device, characterized in that: include: A data acquisition module, used to acquire the user's driving habit data and road condition data, wherein the user's driving habit refers to the user's habit of controlling the vehicle speed and rotation speed, and the road condition data is used to characterize the road congestion situation; a target energy recovery torque calculation module, used to calculate the target energy recovery torque according to the driving habit data and the road condition data; The control module is used to control the motor controller to output the target energy recovery torque.

11. An electronic device, characterized in that: include: processor; Memory; A communication unit, used for establishing a communication channel; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 9.

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